BEGIN:VCALENDAR
VERSION:2.0
X-WR-CALNAME:11tict4sd
X-WR-CALDESC:Event Calendar
METHOD:PUBLISH
CALSCALE:GREGORIAN
PRODID:-//Sched.com 11th International Conference on ICT for Sustainable Development//EN
X-WR-TIMEZONE:UTC
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T033000Z
DTEND:20260825T044500Z
SUMMARY:Registration with Networking Tea / Coffee and Cookies
DESCRIPTION:
CATEGORIES:INAUGURAL SESSION
LOCATION:Assembleia 1\, Goa\, India
SEQUENCE:0
UID:e83ee45d5059ad7b18d7c7f755b71ab7
URL:http://11tict4sd.sched.com/event/e83ee45d5059ad7b18d7c7f755b71ab7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T035800Z
DTEND:20260825T040000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 4A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:48f9771ea7a0627515c311e6c5281b49
URL:http://11tict4sd.sched.com/event/48f9771ea7a0627515c311e6c5281b49
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T035800Z
DTEND:20260825T040000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 4B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:330cad732496157062ac10d9f83025d1
URL:http://11tict4sd.sched.com/event/330cad732496157062ac10d9f83025d1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T035800Z
DTEND:20260825T040000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 4C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:2a261ca99339ee317fe6e242a57ceb58
URL:http://11tict4sd.sched.com/event/2a261ca99339ee317fe6e242a57ceb58
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T035800Z
DTEND:20260825T040000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 4D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:b37532efcd0f3b70fed553b95854cc1d
URL:http://11tict4sd.sched.com/event/b37532efcd0f3b70fed553b95854cc1d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T035800Z
DTEND:20260825T040000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 4E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:3a02070672dbe424e62ddbc43b522002
URL:http://11tict4sd.sched.com/event/3a02070672dbe424e62ddbc43b522002
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:A Comparative Study of Deep Learning Models for Food Freshness Detection Using Transform Learning
DESCRIPTION:Authors - Aakanksha Jain\, Tejaskumar Bhatt\, Darshita kalyani\, Jatin Modh\, Abhishek Jain Abstract - The effectiveness of the deep learning models ResNet50\, MobileNetV2\, VGG16\, InceptionV3\, and EfficientNetB0 in identifying the freshness of food is evaluated in this study using visual analysis. In resource-constrained situations\, these designs are ideal for automated food quality inspection and real-time freshness monitoring as they offer higher accuracy or computational efficiency. Transform learning are used to develop binary classifiers\, which were then trained on a dataset of annotated food photos and assessed for efficiency and accuracy. After undergoing standardized preprocessing\, the models' capacity to differentiate between fresh and stale food in a variety of test photos was evaluated. The results show the advantages and disadvantages of each model. The current stream of research frequently concentrates on generic picture classification tasks instead of the particular difficulties of distinguishing subtle visual differences in food freshness\, leaving a gap in knowledge of model robustness under real-world conditions. The study advances deep learning applications in food quality evaluation by offering useful insights for model selection based on operational requirements.
CATEGORIES:VIRTUAL ROOM 4A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:207787f042f5da2aa1f35692b1419800
URL:http://11tict4sd.sched.com/event/207787f042f5da2aa1f35692b1419800
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:A Hardware Security Review of RISC-V
DESCRIPTION:Authors - Jyotiprakash Mishra\, Sanjay K. Sahay\, Aman Pathak Abstract - RISC-V is an open-source Instruction Set Architecture (ISA) designed with a modular and extensible structure\, allowing for customizable implementations. Its simplified base ISA\, combined with optional standard and custom extensions\, provides flexibility for a wide range of computing applications\, from embedded systems to high-performance computing. Its open design accelerates innovation and customization but also introduces security challenges by exposing the architecture to potential attacks. While RISC-V offers significant advantages\, its lack of standardized security features compared to proprietary ISAs like ARM and x86 highlights persistent risks\, particularly in security-critical applications. For this reason\, scrutiny of RISC-V’s security is crucial due to its widespread use in academia and its adoption by countries like India and China\, who are looking to benefit from its open nature. We review the current security challenges in RISC-V\, examining key vulnerabilities that exist in areas such as the microarchitecture\, trusted execution environments\, secure enclaves\, secure boot\, cryptographic instruction set architectures\, memory encryption\, and electromagnetic fault injection attacks. This review aims to cater to the needs of modern researchers for the development and implementation of the RISC-V ISA in a secure manner.
CATEGORIES:VIRTUAL ROOM 4A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:4b042774f528fd3d404994fd1ba7f8c6
URL:http://11tict4sd.sched.com/event/4b042774f528fd3d404994fd1ba7f8c6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:AI-Driven Load Balancer for Cloud Computing Environments
DESCRIPTION:Authors - Nithin Kandi\, Murari Nallamalli\, Dorai Sai Charan M\, Vijay G\, Beena B. M. Abstract - Dynamic load balancing in distributed computing environments\, especially with heterogeneous nodes\, remains a significant challenge due to the fluctuating nature of workloads and resource availability. This paper presents a novel approach leveraging Deep Deterministic Policy Gradient (DDPG)\, a reinforcement learning algorithm\, for optimal workload allocation in real-time systems. The system aims to minimize latency and maximize resource utilization by dynamically adapting to varying node metrics\, including CPU usage\, memory load\, and latency. The DDPG model is trained on simulated state data\, and real-time inference is performed through an API Gateway\, enabling seamless integration with a five-node cluster. Results demonstrate that the proposed system outperforms traditional static and heuristic approaches in balancing workloads\, optimizing resource utilization\, and reducing latency. The approach is scalable\, robust\, and easily adaptable for edge and hybrid cloud architectures\, providing a cost-effective solution for dynamic load balancing in distributed systems. This work bridges the gap between traditional cloud infrastructure and edge computing\, ensuring efficient resource management in real-time systems.
CATEGORIES:VIRTUAL ROOM 4A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:de5679f432557122432f739e01769f94
URL:http://11tict4sd.sched.com/event/de5679f432557122432f739e01769f94
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:Cache and Speculative Side Channel Attacks: A Comprehensive Review
DESCRIPTION:Authors - Jyotiprakash Mishra\, Sanjay K. Sahay\, Aman Pathak Abstract - Modern processors have achieved significant performance enhancements through the implementation of speculative execution. These enhancements stem from hardware optimizations that not only improve performance but also introduce side channels\, which are exploited to undermine the system’s security model. Following the discovery of Spectre\, which revealed that speculative execution pipelines could bypass security boundaries\, nearly all microarchitectural structures and hardware optimizations have become targets for exploitation. In the wake of these attacks\, the immediate response from organizations releasing mitigation patches led to noticeable performance degradation overnight\, without fully addressing the underlying issues. This paper provides a comprehensive review of architecture-agnostic attacks on modern computing systems\, tracing their evolution from the initial emergence of Spectre to contemporary attacks targeting Apple Silicon. We detail the mechanisms of these attacks\, the environments in which they are exploited\, and their broader security implications. Furthermore\, we analyze various mitigation strategies that have been proposed\, acknowledging that these strategies often fail to fully resolve the issues and typically incur a performance cost. These mitigation patterns include proposed changes in operating systems\, hardware\, and compilers. This review aims to provide researchers and architects with the foundational knowledge needed to develop more effective mitigation strategies that address the vulnerabilities while minimizing performance overhead.
CATEGORIES:VIRTUAL ROOM 4A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:80b130063199f17ddbb833ccdc39d5b6
URL:http://11tict4sd.sched.com/event/80b130063199f17ddbb833ccdc39d5b6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:Classification of SSVEP Brain Computer Interface using CCA-CWT CNN
DESCRIPTION:Authors - Ankit Agarwal\, Ankur Pandey\, Ashlesh Kumar\, Dhanush D\, Swetha G Abstract - Steady-State Visual Evoked Potential (SSVEP)-based Brain Computer Interfaces (BCIs) are a promising tool for non-invasive neural communication and control\, particularly for individuals with severe physical or medical conditions that limit conventional interaction. However\, accurately detecting and classifying SSVEP signals remains challenging due to noise and inter-subject variability. This study evaluates the performance of established classification methods\, including Canonical Correlation Analysis (CCA)\, Filter Bank CCA (FBCCA)\, and transfer learning models such as EEGNet\, DeepConvNet\, and ShallowConvNet. To address the limitations of existing methods\, we propose a novel hybrid approach combining CCA\, Continuous Wavelet Transform (CWT)\, and Convolutional Neural Networks (CNN). This method aims to enhance feature extraction and classification accuracy. The models were evaluated on the benchmark SSVEP dataset from Tsinghua University\, with preprocessing steps involving independent component analysis (ICA) and band-pass filtering. FBCCA achieved the highest accuracy of 97.5%\, followed by CCA (93%) and DeepConvNet (86.95%). Our proposed method attained an accuracy of 77.52%\, demonstrating its potential for robust SSVEP classification. These results underline the value of advanced algorithms and preprocessing strategies in improving SSVEP-based BCI performance\, paving the way for more effective assistive technologies.
CATEGORIES:VIRTUAL ROOM 4A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:07d75447bb8b555a387bae1e53e2711c
URL:http://11tict4sd.sched.com/event/07d75447bb8b555a387bae1e53e2711c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:Legal Case Search: An AI-Powered Legal Search Engine
DESCRIPTION:Authors - Radhika V. Kulkarni\, Avish Agrawal\, Aryan Vimal\, Rohan Barde\, Raghav Bajaj\, Khursheed Gaddi Abstract - The Indian judicial system heavily relies on precedents for legal interpretations and decision making\, providing access to relevant case law a critical yet time consuming task for legal professionals and researchers. This paper presents an AI-powered Legal Case Search Engine designed to transform legal re-search by leveraging advancements in Natural Language Processing (NLP) and Large Language Models (LLMs). The system enables efficient retrieval of contextually relevant legal precedents from the Supreme Court of India’s judgments\, utilizing techniques like vector embeddings\, cosine similarity\, and semantic search. It offers concise case summaries and metadata insights to streamline decision making and improve accessibility to legal data. Built on open-source technology\, the platform emphasizes scalability\, cost efficiency\, and user centric design\, ensuring adaptability for future enhancements like multilingual support. By democratizing access to legal knowledge\, this research aims to bridge the gap between complex legal texts and their practical application\, fostering innovation in legal workflows and enhancing the rule of law.
CATEGORIES:VIRTUAL ROOM 4A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:bf8722157228c3b1b87d43671ac30868
URL:http://11tict4sd.sched.com/event/bf8722157228c3b1b87d43671ac30868
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:Sentiment Analysis on Consumer Opinion Regarding Electric Bikes in India: A Machine Learning Approach
DESCRIPTION:Authors - Prajwal S\, Praveen M P\, Dhanya M Abstract - The global transition toward electric mobility is crucial in mitigating climate change\, reducing air pollution\, and promoting sustainable urban transportation. India\, one of the fastest-growing markets for electric vehicles (EVs)\, has witnessed a surge in electric two-wheeler (E2W) adoption. However\, concerns regarding battery longevity\, charging infrastructure\, and affordability remain key barriers to widespread adoption. This study applies sentiment analysis techniques to assess consumer perceptions of electric bikes using machine learning models for sentiment classification. A dataset comprising 3\,395 consumer reviews was collected from leading automotive platforms\, including BikeWale\, BikeDekho\, OneDrive\, and ZigWheels\, using web scraping techniques. The data was analyzed using VADER\, TextBlob\, Naïve Bayes\, Logistic Regression\, and Support Vector Machines (SVM) to classify sentiment and identify key consumer concerns. The results indicate a predominantly positive sentiment towards electric bikes\, driven by environmental benefits and cost savings. However\, consumers expressed concerns over battery efficiency\, charging station availability\, and high initial costs. Among the models tested\, SVM achieved the highest accuracy\, making it the most effective in sentiment classification. This study contributes to the limited academic research on electric bikes\, offering data-driven insights into consumer perceptions. By utilizing real-world consumer data from widely used automotive platforms\, the research provides valuable information for policymakers\, manufacturers\, and industry stakeholders. The findings aim to assist in developing strategies to address consumer concerns and enhance electric bike adoption in India.
CATEGORIES:VIRTUAL ROOM 4A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:197361df985b41ff5e823e4ecf450077
URL:http://11tict4sd.sched.com/event/197361df985b41ff5e823e4ecf450077
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:Student Performance Predictor
DESCRIPTION:Authors - Vani E S\, Akshay Sinha\, Rahul Singh Rajput\, Pranjal Krishna Gupta\, Chiranth K M Abstract - The primary goal of any educational institution is to provide students with a high-quality learning experience and comprehensive knowledge. Identifying students who need additional support and implementing effective strategies to enhance their academic performance is critical to achieving this objective. This study applies three machine learning techniques to develop a predictive model for assessing student performance across various academic disciplines and institutions. The techniques include Logistic Regression\, k-Nearest Neighbours (KNN)\, and Support Vector Machine (SVM). The models were evaluated using metrics such as the Receiver Operating Characteristic (ROC) index and classification accuracy. Additional performance indicators\, including classification error\, precision\, recall\, and the F1-score were also computed. The dataset\, which consists of data from a student survey and academic records\, included information from a total of 700+ students. Among the models tested\, the SVM model outperformed the others\, achieving an ROC index of 0.82 and a classification accuracy of 84.04%.
CATEGORIES:VIRTUAL ROOM 4A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:0fc2208121c87771107aa9e8d307734e
URL:http://11tict4sd.sched.com/event/0fc2208121c87771107aa9e8d307734e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:Synergizing Fire Detection and Emergency Response: A Multi-Layered Safety System for Residential Communities
DESCRIPTION:Authors - Bhadouriya Khushi Mukeshsingh\, Rajput Adityasingh Shashikantsingh\, Parmar Smit Dharmeshkumar\, Tiwari Prashant Dineshkumar\, Nirav D. Mehta\, Anwarul.M.Haque Abstract - Fire emergencies pose significant risks\, with conventional alarms often lacking rapid response mechanisms. Delays in manual intervention can lead to severe consequences in residential and industrial settings. This study presents the Domestic Emergency System (DES)\, an IoT-integrated\, multi-layered fire detection and emergency response framework. DES utilizes flame and smoke sensors\, GSM-based emergency dialing\, and RF communication modules to enable real-time hazard detection\, structured evacuation\, and automated alerts. An adaptive thresholding algorithm minimizes false alarms while ensuring precise fire identification. Experimental validation demonstrated fire detection within 0.8 seconds\, RF signal propagation up to 25 meters\, and emergency call execution within 5 seconds\, improving response time by 70% compared to traditional alarms. The multi-tier alert mechanism and mobile notifications enhances situational awareness and security coordination. The DES framework outperforms conventional fire alarm systems by integrating automated emergency calling\, RF-enabled intra-building communication\, and a resilient multi-modal alert system. Its scalability and adaptability to smart city infrastructure make it a transformative solution for modern safety applications.
CATEGORIES:VIRTUAL ROOM 4A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:90d86afeb7e82080e6631f7460fac047
URL:http://11tict4sd.sched.com/event/90d86afeb7e82080e6631f7460fac047
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:The Impact of Financial Literacy on Thrift Behaviors: A Study Among College Students
DESCRIPTION:Authors - Samyuktha D\, Chandrathara P T\, Gowri Gireesh\, Rojalin Patri Abstract - Financial literacy affects individuals' financial conduct\, especially their capacity to save\, consume well\, and engage in thrift behaviors. In this study\, the connection among financial literacy and various thrift behaviors—Careful Thrift\, Spending Thrift\, Saving Thrift\, and Lifestyle Thrift—amongst college students is examined. Utilizing a survey quantitative methodology\, statistical analysis is conducted to assess how financial literacy can predict such thrift behaviors. It also explores if demographic variables of gender and financial support sources are moderators of such relationships. Results show that financial literacy positively influences cautious thrift\, lessens irresponsible spending\, and increases saving and lifestyle thrift. The paper finally ends with suggested recommendations for financial literacy programs used to enhance financial decision-making skill among students.
CATEGORIES:VIRTUAL ROOM 4A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:1981b5a873fa22eac119db123d1bbe8e
URL:http://11tict4sd.sched.com/event/1981b5a873fa22eac119db123d1bbe8e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:An Integrated Machine Learning Model for Automated Drip Irrigation and Crop Disease Management Using Robotics
DESCRIPTION:Authors - Uttam Patole\, Manish Shrivastava\, Archana Ugale\, Abhishek Deshmukh Abstract - Poor water use and late illness detection force farmers to struggle\, which hurts their crops and squanders resources. This study presents a computerized precision farming system combining machine learning\, humidity sensors\, and robot automation to improve irrigation and disease control. The technology operates in two phases: first\, humidity sensors calculate the ideal watering amount by assessing soil moisture and applying a Random Forest model. Second\, disease detection sensors identify agricultural diseases and forecast disease outbreaks using a Gradient Boosting Regressor (GBR)\, thereby guiding robot pesticide spraying. By integrating machine learning with real-time environmental monitoring\, the technology minimizes human interaction while assuring exact irrigation and tailored pesticide administration. This strategy avoids the usage of pesticides and excessive water consumption while enhancing resource efficiency and crop health. The proposed system is scalable and adaptive to varied farming situations\, making it a good alternative for modern precision agriculture. By combining predictive analytics and automation\, the model enables data-driven decision-making in farming\, supporting sustainable agricultural practices and boosting overall output.
CATEGORIES:VIRTUAL ROOM 4B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:4c2d98e6470a5f1aa7664c490b366bf1
URL:http://11tict4sd.sched.com/event/4c2d98e6470a5f1aa7664c490b366bf1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:Artificial Intelligence for Enhanced Logistics Tracking in Hospitals
DESCRIPTION:Authors - Rahul Dhaigude\, Ruby Chanda\, Shrikant Ghadge Abstract - The healthcare industry is a complex and vital sector that plays a fundamental role in society\, focusing on the well-being and health of individuals. In recent years\, the integration of Artificial Intelligence (AI) into hospital logistics has emerged as a transformative force in the healthcare industry. This study investigates the application of AI in hospital logistics to enhance tracking and overall operational efficiency. A questionnaire was designed to gather quantitative and qualitative data on various aspects of hospital logistics\, AI integration\, challenges\, and potential improvements. Qualitative data from open-ended questions and interviews were analyzed thematically to extract key themes and insights. The results show that AI has become an essential tool for optimizing logistics processes\, improving patient outcomes\, and enhancing the overall patient experience. However\, careful planning\, implementation\, and consideration of data privacy and staff readiness are critical for successful AI adoption in hospital logistic.
CATEGORIES:VIRTUAL ROOM 4B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:4b0374396cdb81aaedb04686a72f35d7
URL:http://11tict4sd.sched.com/event/4b0374396cdb81aaedb04686a72f35d7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:Deep Learning Neural Networks for Health Care Applications
DESCRIPTION:Authors - T. Sruthi\, Sheshikala Martha Abstract - Extracting meaningful knowledge & actionable comprehensions from complex\, multi-dimensional\, and heterogeneous biomedical data residues a significant challenge in healthcare. Modern health care systems generate various types of medical data that are often intricate\, diverse\, and typically unstructured. These large datasets are often difficult to interpret and process. Traditionally\, data mining techniques have been employed to extract features from such data\, with prediction or clustering models built on top of those features. However\, this approach faces numerous challenges\, particularly when dealing with complex data and limited domain expertise. Recent advancements in deep learning\, however\, have introduced new and effective methods for building learning models from these complex datasets. In this paper\, we discuss various clinical data types and their relevant features that can serve as inputs to deep learning net-works\, contributing to the creation of a more reliable and sustainable healthcare system.
CATEGORIES:VIRTUAL ROOM 4B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:e865e2fa3ec27a2866c356d5bf971319
URL:http://11tict4sd.sched.com/event/e865e2fa3ec27a2866c356d5bf971319
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:Efficient Real-Time Dynamic Network Slicing for 5G to Meet Diverse QoS Demands
DESCRIPTION:Authors - T. Aruna\, Dhanya Kulkarni\, Riya Javali\, Rakshan Kulkarni\, Suneeta V. Budihal\, Shamshuddin K Abstract - In 5G networks\, dynamic slicing is a major improvement\, which makes it possible to allocate specific resources to satisfy the various quality-of-service (QoS) requirements of applications including ultra-reliable low-latency communications\, enormous IoT\, and enhanced mobile broadband. However\, managing these slices in response to dynamic and varied traffic patterns requires real-time flexibility\, which poses considerable hurdles. The methods for effective dynamic network slicing are examined in this research\, with an emphasis on resource allocation optimization\, QoS adherence\, resource waste reduction\, and network stability. To accommodate upcoming developments\, the suggested solutions seek to improve 5G network performance\, scalability\, and adaptability.
CATEGORIES:VIRTUAL ROOM 4B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:7665f3935dab7efc329b2908eed99078
URL:http://11tict4sd.sched.com/event/7665f3935dab7efc329b2908eed99078
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:Empowering Farmers Through Technology: A Java-Based Mobile Marketplace for Agricultural Trade
DESCRIPTION:Authors - Ajay Talele\, Madhav Jagtap\, Radhika Gadewar\, Akanksha Katore\, Sufiyan Sajan\, Deepika Sidral\, Yash Shinde\, Gaurav Desale\, Saburi Nikam\, Shubham Landge\, Chetan Channa Abstract - Farm trade is a key component of economic viability\, but farmers usually struggle with issues of market access restriction\, price volatility\, and dependency on middlemen. This article introduces a Java-based mobile market that is capable of empowering farmers by creating direct links to customers\, thus ensuring transparency and profitability. The new platform incorporates real-time price feeds\, demand forecasts\, and electronic secure transactions to form a highly efficient and consumer-friendly trading system. Further\, the system incorporates IoT-based weather updates and agricultural advisory services to enable farmers to make appropriate decisions. The system utilizes a recommendation engine employing machine learning to maximize prices and predict demand trends. The system is highly secure because transactions are encrypted and involves a strong mechanism for user verification. Fair trade is promoted while minimizing post-harvest losses\; the marketplace helps farmers make electronic payments\, which enhances financial inclusion and sustainable agriculture. System analyses and case studies attest to its capability to revolutionize agricultural commerce\, close the urban-rural digital divide\, and enhance farmers’ economic performance.
CATEGORIES:VIRTUAL ROOM 4B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:f1360dc17be44f0cb1a2fe10670a1ee0
URL:http://11tict4sd.sched.com/event/f1360dc17be44f0cb1a2fe10670a1ee0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:Enhancing Tuberculosis Detection with HPC-Driven GAN-CNN Integration and Model Parallelism for Synthetic Image Generation
DESCRIPTION:Authors - Hemender Sai\, Bipin Sai Bhaskar\, P.Saranya Abstract - Tuberculosis continues to be a major global health concern\, particularly in remote regions with limited access to healthcare. Early and precise diagnosis is essential to prevent its spread. This project utilizes high-performance computing (HPC) to tackle two major challenges: handling inconsistent medical data and enhancing TB detection. Due to the disproportion between healthy and TB-positive samples\, Generative Adversarial Networks (GANs) are employed to create synthetic images\, expanding the dataset and improving its diversity. This enriched dataset is then used to train Convolutional Neural Networks\, which are highly effective in medical image processing. By leveraging HPC\, we accelerate the CNN training process on large-scale\, augmented datasets\, significantly cutting down computation time while preserving accuracy. This approach enhances TB detection by integrating GAN-based data augmentation with CNN models\, ensuring a quicker and more reliable diagnosis.
CATEGORIES:VIRTUAL ROOM 4B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:eb2cbbe456d1734b951e59750b3c8b71
URL:http://11tict4sd.sched.com/event/eb2cbbe456d1734b951e59750b3c8b71
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:ICT Policy and E-Governance: Navigating Inter-Governmental Issues in the Digital Era
DESCRIPTION:Authors - Shankar Lingam. M\, Raghavendra GS\, Sakthi Kamal Nathan Sambasivam Abstract - In the digital era\, the integration of ICT policies and e-Governance has emerged as a critical driver for public sector modernization. ICT policy sets the groundwork for effective e-Governance systems by enabling the digital transformation of government functions and services. However\, the realization of e-Governance goals faces significant challenges\, particularly in fostering inter-governmental cooperation. These challenges arise from varying policy frameworks\, technological disparities\, and governance structures across different levels of government. In this context\, navigating the intricacies of inter-governmental relationships is essential to ensure seamless information exchange and collaborative governance. The evolving digital landscape introduces both opportunities and complexities\, particularly in terms of data privacy\, cybersecurity\, and digital inclusion. This paper presents a comprehensive review of ICT policies and e-Governance frameworks\, with a focus on overcoming inter-governmental challenges in the digital era. Our methodology includes a scoping review of key studies and case analyses\, such as the work of Obi (2007) on global perspectives of e-Governance [Obi\, T. (2007). E-Governance: A Global Perspective on a New Paradigm]\, Prasad (2012) on India’s ICT policy for digital democracy [Prasad\, K. (2012). E-Governance Policy for Modernizing Government through Digital Democracy in India]\, and Manda (2017) on South Africa’s smart governance approach [Manda\, M. I. (2017). Towards "Smart Governance" through a Multidisciplinary Approach to E-Government Integration]. The findings highlight the importance of fostering interoperable and inclusive e-Governance systems to navigate the inter-governmental challenges posed by ICT adoption. Key implications for policy include the need for harmonized digital policies\, the development of interoperable infrastructure\, and the emphasis on inclusivity to bridge the digital divide. The results underscore the need for collaborative frameworks that enable effective governance across multiple levels of government\, thus ensuring that the digital transformation of public services benefits all stakeholders.
CATEGORIES:VIRTUAL ROOM 4B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:c8ddcffed8b5f4a80ae87b1bd793e947
URL:http://11tict4sd.sched.com/event/c8ddcffed8b5f4a80ae87b1bd793e947
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:Implementation of Predicting Space Weather Impacts Using Machine Learning Techniques for Aviation and Telecommunications
DESCRIPTION:Authors - Manisha Mane\, Gargee Nitin Rangnekar\, Gayatri Kishore Kshirsagar\, Adarsh Suresh Nikam Abstract - Monitoring and prediction of space weather have gained tremendous significance with the increasing reliance of the telecommunication and aviation sectors on satellite communication and navigation systems. The two sectors are very vulnerable to space weather occurrences because they can always interfere with the functioning of satellites\, high-frequency radio communication\, and GPS accuracy. To mitigate these exposures\, we recommend that telecommunication and aviation companies utilize a machine learning Space Weather Dashboard to facilitate real-time data visualization and predictive analytics assistance. The proposed architecture employs Azure Workspace for data storage and management\, Unreal Engine 5 for the production of high-fidelity graphics\, and machine learning models developed in Python. Our approach is based on the utilization of Long Short-Term Memory networks (LSTMs) for historical space data\, Convolutional Neural Networks (CNNs)\, and Recurrent Neural Networks (RNNs). Various types of weather data points like solar X-ray flux\, solar wind speed\, coronal mass ejections\, interplanetary magnetic field measurements\, solar energetic particles\, ionospheric data\, and auroral data are utilized to enhance prediction precision. The dashboard allows for actionable insights to be built for industry professionals and real-time monitoring of critical space weather parameters. Additionally\, in consideration of how it can improve their contribution to operation safety\, the research here addresses the forecasting power of some of the machine learning architectures used for space weather. Our comparative research affirms that improved forecasting results in effective warning and risk assessment. This is a wonderful benchmark for the aviation and telecommunications industries\, improving situation awareness and round-the-clock operating continuity.
CATEGORIES:VIRTUAL ROOM 4B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:a68f8483b5a950753438ebd4eb30a7cc
URL:http://11tict4sd.sched.com/event/a68f8483b5a950753438ebd4eb30a7cc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:Representation of Sensitive Issues in Media using Generative AI
DESCRIPTION:Authors - Surya K\, Rohit Kumar Abstract - The work deals with conceptual representation of sensitive issues in society as images in media using generative AI. Conceptual representation of sensitive information creates an overall societal impact. Conceptual processing involves understanding the represented information and the way how people respond and interact to the displayed information. Some of the issues like menstrual cycle representation\, sex education\, domestic violence\, sex abuse and mental health awareness are difficult to represent conceptually and respectfully in the media. The goal of this paper is to use generative AI and represent these issues clearly to the society using media. Our work aims at understanding the challenges in representation of these images conceptually as well as providing an overview of the generative AI tools that can be used for implementing the solutions. A case study of a generative AI tool is used to understand the underlying problem of conceptual and respectful image generation for representing it in media. There are many generative AI tools for image generation\, and we have chosen the Google Gemini AI as it gives more creative images compared to other tools [13]. In this paper\, some of the sensitive issues are taken into consideration for representation in media in a conceptual and respectful manner.
CATEGORIES:VIRTUAL ROOM 4B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:46dd9213226f275f0b13c64c2b05088a
URL:http://11tict4sd.sched.com/event/46dd9213226f275f0b13c64c2b05088a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:Spotlight on Rural Entrepreneurship: A Bibliometric Journey
DESCRIPTION:Authors - T. A. Alka\, M. Suresh\, Aswathy Sreenivasan Abstract - This study aims to explore recent trends in rural entrepreneurship (RE). 439 documents available in the Scopus database are used for the trend analysis by using the R programming Biblioshiny package for bibliometric analysis. The result shows that rural entrepreneurship has been trending since the 1970s\, and there is scope for further research. Rural entrepreneurs contribute to the society's upliftment\, and development and ultimately result in the growth of the nation. The study is focused on the Scopus database\; other databases are not considered. The study identified major themes\, such as the rural agriculture entrepreneurs' contribution to developing the rural economy and the need for rural entrepreneurship education among college students in developing countries. Trend topics in this field highlight the rural entrepreneurship contribution to the development of the rural economy\, agriculture\, rural development\, fostering innovation\, promoting sustainable development\, developing a sound entrepreneurial ecosystem\, the role of rural entrepreneurship in the growth of developing countries\, etc. The study identified North America and Asia connection\, intra-European and transatlantic collaboration\, Europe-Middle East\, and Asia-North America regional collaboration. The study is relevant even when comparing recently published papers to map the trends\; the themes used in research-related articles are always changing. This study gives insights to policymakers to help them with planning\, policy formulation\, program support\, etc.\, to the development of rural entrepreneurs and also offers future research direction through thematic analysis.
CATEGORIES:VIRTUAL ROOM 4B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:f2ac5f796a2dbbd913e351a5bec2092d
URL:http://11tict4sd.sched.com/event/f2ac5f796a2dbbd913e351a5bec2092d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:An Artificially Intelligent System to Strategize and Predict Employee Attrition and Retention in HR Management
DESCRIPTION:Authors - Reena (Mahapatra) Lenka\, Jaya Chitranshi\, Vanishree Pabalkar Abstract - Artificial Intelligence (AI) has undoubtedly emerged as an extremely dynamic and powerful tool in the area of IT. It is currently handling complex-work in data-analysis\, working through predictive modeling\, showcasing high level of capabilities and supporting the function of strategic decision-making. AI is dependent on real-time data to identify patterns of attrition\, understand dissatisfaction and predict future exits. An AI system in the area of HRM\, would thus help the organization in filtering staff-retentions and impending attrition. Meaningful insights can be developed with the use of AI that will help organizations work on reducing employee-turnover on one hand and engaging with their workforce in the long-run\, on the other. The innovation elaborated in the study\, connects with forecasting the employee-attrition and retention strategies in human resource management. It makes use of AI (Artificial Intelligence) through which HR data sources are incorporated to analyze performance evaluations\, engagement surveys\, attendance records and demographics in real time and historical context. The machine learning embedded in the system will detect patterns that show evidence of turnover and associate attrition scores to each employee\, then recommend targeted retention strategies through personalized career guidance\, modification of the workload\, and incentives. The system incorporates attrition feedback loops to heighten prediction accuracy and refine strategies over time\, enabling the organization to control and reduce turnover rates\, boost workforce stability\, and achieve cost savings. It is scalable across verticals like corporate HR\, healthcare\, education\, and retail\, where talent retention is imperative.
CATEGORIES:VIRTUAL ROOM 4C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:271042e391b31b79ab3602069a7d7949
URL:http://11tict4sd.sched.com/event/271042e391b31b79ab3602069a7d7949
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:An Individual Perception and Consumer Behaviour on Mutual Funds
DESCRIPTION:Authors - Reena (Mahapatra) Lenka\, Jaya Chitranshi\, Vanishree Pabalkar Abstract - A person's ‘perception’ and ‘consumer behaviour’ regarding mutual funds both are influenced by a variety of factors\, including socioeconomic characteristics of a given society\, financial awareness\, risk tolerance\, and prior investing experiences. As a professionally managed investment choice and a convenient investing tool\, mutual funds are particularly well-liked among middle-class and urban individuals. In contrast to traditional savings instruments\, investors typically connect mutual funds with advantages such as access to liquidity\, diversification opportunities\, and the potential for higher returns. Additionally\, perceived dangers\, market volatility\, and a lack of thorough understanding of financial instruments all have a significant impact on consumer behaviour. A mutual fund is a professional system that collects funds from different investors for investment and protection. Since shared reserves have no legal meaning\, the term applies as ambiguously as possible to aggregate speculation that is controlled\, accessible\, and open to investors. Mutual funds enjoy strengths and weaknesses instead of putting resources directly into personal protection. Today\, they represent a significant portion of household budgets. Therefore\, the current review focuses on general asset-related buyer behavior and preeminent mutual fund companies. Information was gathered from important resource sources. Important information was collected through systematic research. An opportunity-sampling method was used to collect responses\, and the process was targeted toward major Indian cities. This review provides information on donor mindfulness of communal property\, donor knowledge\, donor propensity\, and communal property sufficiency. Ideas were also developed to enhance mindfulness of joint assets and measures to select appropriate common assets to increase profit.
CATEGORIES:VIRTUAL ROOM 4C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:d005909ea8ae502cfbcdb12bd6583f32
URL:http://11tict4sd.sched.com/event/d005909ea8ae502cfbcdb12bd6583f32
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:Blockchain Technology: Scalability and Performance
DESCRIPTION:Authors - Jeevesh Sharma Abstract - Blockchain technology is largely used in banking\, but it also has applications in gaming\, real estate\, supply chain management\, and healthcare. By 2023\, digital money will be the most widely discussed blockchain application. The potential uses of blockchain technology concerning various facets of any sector\, market\, agency\, or governmental organization have gained attention in recent years. Blockchain scalability analyzes the effects on the security of scaling blockchain networks to support more transactions per second. This innovative distributed peer-to-peer architecture drew the interest of companies and communities outside and inside the financial sector. Furthermore\, the system it operates in has been created around numerous scenarios that address the trust issue in open networks without the requirement for a trustworthy third party. Even though its decentralized structure allows for a wide range of potential applications\, scalability remains a hurdle. Function extension\, excessive delay in confirmation\, and performance inefficiency are three important areas where blockchain scalability has been hindered. This research paper provides a thorough summary of previous research on scalable blockchain systems.
CATEGORIES:VIRTUAL ROOM 4C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:3aa9580d24c389ff3b6dc555a7f72b24
URL:http://11tict4sd.sched.com/event/3aa9580d24c389ff3b6dc555a7f72b24
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:Climate Resilience and Sustainability in Rural Agriculture: A Systematic Literature Review
DESCRIPTION:Authors - Ajidhashini Thulasidass\, M. Suresh Abstract - Climate change is making agriculture more challenging in many parts of the country. This is notably true in developing nations where small farmers depend on systems that obtain water from rain to keep their land open. Numerous studies were conducted from 2015 to 2025 to discover how climate change is transforming farming systems in rural regions\, how people are responding\, and what policies are in place to make these systems safer and more resilient. It looks at significant challenges\, including rising temperatures\, unclear rain\, soil depletion\, and more pests that consume food. There is less food\, which contributes to reduced food production and declining market stability. One method to make things better is to employ local expertise. Another is to employ agroforestry. Most individuals have problems agreeing because they don't have enough money\, technology\, or aid from the government. There are tips for extra reading at the end of the essay. To enhance farming\, we may employ both new and ancient equipment and processes\, as well as long-term research and approaches that engage both men and women.
CATEGORIES:VIRTUAL ROOM 4C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:a2ac868d4e0802ab6c43bcf3387d3576
URL:http://11tict4sd.sched.com/event/a2ac868d4e0802ab6c43bcf3387d3576
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:Ensuring Privacy and Data Integrity in Payroll Systems Using Blockchain and IPFS
DESCRIPTION:Authors - Reena (Mahapatra) Lenka\, Ronak Gupta\, Vanishree Pabalkar\, JayaChitranshi Abstract - Organizations in the contemporary global economy encounter substantial challenges in managing payroll operations\, particularly concerning data security\, transparency\, and compliance with diverse regulatory standards. Traditional payroll systems rely on centralized infrastructures and are vulnerable to fraud\, inefficiencies\, and elevated operational costs. These centralized systems pose significant risks\, including unauthorized data access and single points of failure\, leading to potential data breaches and financial losses. Organizations handling payroll operations in today's international market confront several obstacles\, such as protecting data\, upholding openness\, and adhering to various legal standards. Because they frequently rely on centralized infrastructures\, traditional payroll systems are vulnerable to fraud\, inefficiency\, and excessive operating expenses. In order to solve these problems\, this article investigates the integration of blockchain technology into payroll management. We suggest a multi-layered architecture that consists of (1) an off-chain Human Resources (HR) system for payroll and employee management\, (2) a distributed storage layer that uses technologies such as the Interplanetary File System (IPFS) for safe data storage\, and (3) an on-chain blockchain layer that uses smart contracts to guarantee immutable transaction records and automate payroll processing. This decentralized approach enhances transparency\, bolsters security through encryption and consensus mechanisms\, and streamlines payroll operations by reducing manual dependencies. Furthermore\, the system facilitates real-time cross-border payments and integrates with Decentralized Finance (DeFi) platforms\, offering employees innovative financial services. By leveraging blockchain for payroll\, organizations can enhance trust\, reduce operational costs\, and eliminate redundancies\, making it a promising use case for HR departments worldwide. This framework demonstrates how blockchain technology can revolutionize payroll management\, increasing organizations' efficiency\, cost-effectiveness\, and trust.
CATEGORIES:VIRTUAL ROOM 4C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:e40ec5fbb622f956521ecc33fc0ad6e3
URL:http://11tict4sd.sched.com/event/e40ec5fbb622f956521ecc33fc0ad6e3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:On-Device AI for Chat Applications: Enhancing Privacy and Productivity through Tonality-Driven Paraphrasing
DESCRIPTION:Authors - Shripada Rao\, Aadithya Mahesh\, Navya Jaideep\, Rajeshwari Hegde\, Vinay Rao\, Saurabh Suman Choudhuri Abstract - This paper introduces a novel approach to integrate LLM capabilities directly on mobile devices to enhance chat applications. By implementing a tonality-driven paraphrasing feature\, our system can rephrase poorly written messages into clear\, professional text while preserving the intended tone. Unlike conventional server-side AI solutions that raise privacy concerns\, our approach processes data locally using fine-tuned models (TinyLlama Instruct 1.1B and Qwen2 0.5B) with parameter-efficient techniques such as LoRA and QLoRA. Experimental evaluations demonstrate competitive paraphrasing quality\, improved inference speed\, and reduced resource consumption on mobile devices\, making this work a promising step toward privacy-preserving on-device conversational assistance.
CATEGORIES:VIRTUAL ROOM 4C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:a2744452f7cee1baec28c46b11d7cdff
URL:http://11tict4sd.sched.com/event/a2744452f7cee1baec28c46b11d7cdff
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:Real-Time Urban Traffic Monitoring Using YOLOv5
DESCRIPTION:Authors - Anusha.S.Pujar\, Gourishankari.S.P\, Saniya.G\, Pooja.B.L\, Sanchit.H\, Amit.N\, Suneetha.V.B Abstract - For the purpose of controlling traffic flow\, identifying congestion\, and averting accidents\, contemporary urban traffic monitoring is essential. An enhanced YOLOv5 model is presented in this study for precise vehicle tracking and identification under a variety of circumstances\, including day and night. A multi-scale feature detection layer for seeing cars of all sizes in congested regions and an improved pixel-to-real-world distance calibration for accurate speed and distance estimation are two important improvements. Real-time traffic management is improved by integrated collision warning and congestion identification algorithms. Experimental results demonstrate improved detection reliability and mean Average Precision (mAP)\, making this approach suitable for scalable urban traffic control systems.
CATEGORIES:VIRTUAL ROOM 4C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:212683bc2c2475c115140237afd9b7e0
URL:http://11tict4sd.sched.com/event/212683bc2c2475c115140237afd9b7e0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:Smart screening-Basic ML models for cardiovascular diseases prediction
DESCRIPTION:Authors - Pratiksha Kulkarni\, Rakshita Patil\, Veena S Kulkarni\, Satish Chikkamath\, Suneeta V Budihal\, Sujatha Kotabagi Abstract - The major reason for deaths across is due to alarming increase in Heart Disease. There are many factors which elevates the risk of cardiovascular diseases which includes high blood pressure\, obesity\, cholestrol\, smoking habits \, lack of physical activities and heavy work pressure. Diagonising and identifying the Heart disease in prior is a challenging task.Which can be overcome by Machine learning methods based on huge dataset of patient traits and medical indicators that help in prediction of heart diseases.
CATEGORIES:VIRTUAL ROOM 4C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:2ce03cde293d9356dfc421878ca28aec
URL:http://11tict4sd.sched.com/event/2ce03cde293d9356dfc421878ca28aec
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:Solar Powered Multipurpose Agricultural Robot
DESCRIPTION:Authors - Dipti Varpe\, Gouri Kulkarni\, Nishant Thakare\, Mitesh More\, Suyog Shinde\, Navanath Patil Abstract - Traditional farming methods often require extensive manual labour\, leading to inefficiencies and increased costs. Recent advancements in agricultural robotics provide innovative solutions to automate essential tasks. The Node-MCU based Solar Powered Multipurpose Farming Robot is an autonomous system designed to enhance farming operations\, including ploughing\, weeding\, and harvesting. Powered by solar energy\, it offers a sustainable and energy-efficient alternative to conventional farming practices. The robot operates using a Node-MCU microcontroller\, ensuring precise navigation and task execution. Integrated soil moisture and temperature sensors enable real-time environmental monitoring\, optimizing farming decisions. Programmed via Node-MCU IDE\, the robot is customizable and supports various smart farming features. With its modular design\, multiple farming tools can be attached\, reducing labour demands and improving efficiency. Additionally\, IoT connectivity enables remote monitoring and control through cloud-based platforms. By integrating renewable energy\, automation\, and IoT-driven sensing technologies\, this project enhances agricultural productivity while promoting sustainability. The system lays the groundwork for intelligent robotic solutions in modern farming.
CATEGORIES:VIRTUAL ROOM 4C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:e8620c89c942dd6e2dbd5f4b8d811141
URL:http://11tict4sd.sched.com/event/e8620c89c942dd6e2dbd5f4b8d811141
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:The Role of AI Coaching and Chatbots in Enhancing Employee Engagement
DESCRIPTION:Authors - Harie Sanker V\, Gayathri E \, Akshay R\, Vandana Madhavan Abstract - The human resource management practices have developed alongside technological advancements. AI-based coaching and engagement chatbots are new tools with a great potential to improve employee engagement. Conventional coaching processes have certainly proven their effectiveness but could never attain the scalability or cost-efficiency needed for widespread implementation. Machine learning- and natural language processing-adapted AI solutions can assist by providing real-time feedback\, setting goals\, and automating HR services based on specific individual need assessments. This research is aimed at understanding the influence of the AI coaching and chatbots on motivation\, active engagement\, and general employee satisfaction within technological organizations. This study employed primary data collected from questionnaires\, finding that AI coaching promotes motivation\, satisfaction\, and expansion of remote working opportunities. However\, lack of trust in AI and perceived ethical absence of transparency on the part of recommendations made by the AI can be seen as significant drawbacks. To overcome these challenges\, a hybrid form of human-AI coaching is suggested where AI maximum benefits are retained without missing out on the human touch and consideration that defines coaching. This may facilitate the understanding of the substantial influence that AI has on employee engagement and ultimately on the future of human resource management. The study also suggests how organizations can optimally leverage AI coaching and engagement chatbots while minimizing associated risks.
CATEGORIES:VIRTUAL ROOM 4C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:866b14fd056efe582aa1c4b3c28bcdca
URL:http://11tict4sd.sched.com/event/866b14fd056efe582aa1c4b3c28bcdca
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:Adaptive Key Authentication for Secure IoT: Addressing Security Threats and Optimizing
DESCRIPTION:Authors - Palak Patel\, Chintan Shah\, Premal Patel Abstract - The use of Internet of Things (IoT) in healthcare systems the world has also measured substantial improvement in patient care by offering real time monitoring and diagnosis. But growing connectivity means that sensitive medical data is also vulnerable to numerous security threats like man-in-the-middle (MITM) attacks\, key theft\, and device cloning\, among many others. These traditional methods for authentication\, based on either passwords or static keys\, are especially susceptible to such cyber-attacks\; thus\, they are ineffective in protecting healthcare IoT environments characterized by dynamic threat landscapes. This research article presents a new dynamic key-based authentication scheme for healthcare Internet of Things (IoT) networks. In the suggested framework\, mutual authentication between each of the healthcare server and the physical IoT client device is used to protect the sensitive data of the patients in transit\, along with providing real-time secure transmission. By focusing on how cryptographic keys develop\, the framework addresses current security threats\, including key theft and MITM attacks. Not only does this method keep computational and communication costs low\; it is also lightweight enough for resource-constrained medical IoT devices using a lightweight authentication protocol. This system is efficient with respect to data size between registration and authentication phases while performing better than all other systems. It also minimizes latency and bandwidth usage by improving the caching layer. The scheme is also computationally cheap\, making it feasible in resource-poor healthcare settings. The security analysis tools confirm that the suggested architecture is resistant to ordinary attacks such as MITM and replay attacks while incurring low overhead.
CATEGORIES:VIRTUAL ROOM 4D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:2adc84bc0acbeb55a8ac2c29f3cc7658
URL:http://11tict4sd.sched.com/event/2adc84bc0acbeb55a8ac2c29f3cc7658
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:American Sign Language Recognition Using Hybrid Deep Learning Architecture
DESCRIPTION:Authors - Abhijeet Kumar\, Yash Shekhawat\, Rahul Kumar\, Naresh K Abstract - Real-time sign language recognition depends on an enhanced CNN-LSTM architecture which uses ASL training data. A new preprocessing approach boosts image resolution to 46×46 pixels thus enhancing the recognition precision. Numerous frames enter the system which enables a deep learning model to analyze spatial and temporal features to identify different hand signals in real time. The system integrates with the Flask-React-based frontend which allows real-time predictions through webcam interfaces in order to support practical field use. The system translates acknowledged signs into speech audio through Text-to-Speech APIs which drives inclusion between hearing-impaired users and people who do not use sign language. The system’s effectiveness was validated through experimental testing which reached 95% accuracy. The system's development will advance by implementing support for local sign languages with mobile platform integration.
CATEGORIES:VIRTUAL ROOM 4D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:6d0371c106abbaa28b85b9c4927f714f
URL:http://11tict4sd.sched.com/event/6d0371c106abbaa28b85b9c4927f714f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:From Scroll to Screen: Emotional and Physiological Engagement in Text\, Comics\, and Virtual Reality
DESCRIPTION:Authors - Vitika Soni\, Sakshi Chauhan\, Harsho Mohan Chattoraj\, Varun Dutt Abstract - There have been few direct comparisons in the literature between the emotional and autonomic impact of virtual reality (VR) and traditional and visual-narrative media\, although VR has the potential to create vivid engagement. This study bridges this gap by using the culturally relevant narrative of King Harishchandra from the Indian Knowledge Systems (IKS) corpus to examine the impact of various storytelling media—text reading\, comics\, and virtual reality (VR)—on emotional and physiological engagement. We measured physiological arousal through Heart Rate Variability (HRV) indices\, including RMSSD (parasympathetic modulation) and LF/HF ratio (sympathovagal balance)\, and emotional reactions through the Positive and Negative Affect Schedule (PANAS) in 30 participants (N = 10 per group). Comics performed better than text and virtual reality in terms of ratings of positive affect. While the VR and text groups reported significantly higher RMSSD values\, reflecting more parasympathetic activity\, comics registered the lowest LF/HF ratios\, reflecting smoother cognitive-emotional processing and greater autonomic balance. The findings indicate that comics\, despite being frequently neglected\, can stimulate more stable emotional engagement than virtual reality immersion\, which has implications for future application in therapeutic design\, education\, and culturally-based digital media. By emphasizing the importance of narrative coherence and cognitive manageability in shaping user experience\, this research contributes to digital storytelling\, affective computing\, and media psychology.
CATEGORIES:VIRTUAL ROOM 4D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:72bd7b6818f50b4270b94e7e65b79f12
URL:http://11tict4sd.sched.com/event/72bd7b6818f50b4270b94e7e65b79f12
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:Generating realistic synthetic data using “CoreGAN” and balanced by “SMOTHE” for better distribution
DESCRIPTION:Authors - Zaibunnisa L.H. Malik\, Amena Niyaz Ahmed Malik\, Pooja Raundale Abstract - Autistic Spectrum Disorder (ASD) refers to a group of developmental disorders that affect the nervous system\, leading to challenges in social interaction\, communication\, and behavior. The severity of ASD symptoms can vary widely\, ranging from mild to severe. Diagnosing and predicting ASD with high accuracy requires the use of advanced machine learning models. However\, one of the major challenges in building such models is the availability of sufficient data. Open-source datasets often have a limited number of instances\, which may not be enough to train robust models that can generalize well to new\, unseen data. To overcome this limitation\, it is essential to augment the dataset with additional\, synthetically generated instances. In this context\, techniques like corGAN (Conditional Generative Adversarial Networks) are employed. This comprehensive dataset is then used to train a machine learning model\, which can more effectively predict ASD. The synthetic data ensures that the model has access to a richer\, more varied set of information\, ultimately leading to better performance and more accurate predictions for diagnosing and understanding ASD. We will also apply SMOTHE and Adaptive_Synthetic on GAN data\, and prove that SMOTHE on GAN data gave a better distribution than Adaptive_Synthetic on GAN data.
CATEGORIES:VIRTUAL ROOM 4D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:30b90b275e90bdd27ab8d543f86de39a
URL:http://11tict4sd.sched.com/event/30b90b275e90bdd27ab8d543f86de39a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:Graph Database: Comparative study of RDBMS vs NoSQL
DESCRIPTION:Authors - Amitabha Bhattacharyya\, Ayush Misra\, Sourkarjya Kundu Abstract - The Relational Database was widely used in Industry and academics for software development in the early 90's and it is continuing now even starting from Oracle 7.1.3 version. But with increasing complexity of data\, unstructured data or semi structured data capturing\, research has shifted the paradigm from SQL to NoSql. Variety of NoSql databases has emerged and could not sustain in the market because of their own limitations. The Relational Database System has complex join operation and is costly compared to NoSql. 80% market share is grabbed by Oracle for any new software development in 2005 and later on researcher started inventing NoSql with low cost\, open source and it’s quite challenging. In our IEEE 2020 we did survey different types of Graph databases and also showd the path on how to reengineer SQL to NoSql in the Springer publication. This paper aims to provide insights about Nosql MongoDB\, relational database Oracle or MySQL\, re-engineering methodologies study from sql to NoSql and also how much faster is SQL vs NoSql.
CATEGORIES:VIRTUAL ROOM 4D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:8d45c662d07a7f4d51a5ad188cc6e97b
URL:http://11tict4sd.sched.com/event/8d45c662d07a7f4d51a5ad188cc6e97b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:Parameter-Efficient Folk Art Generation: Fine-Tuning SDXL with LoRA for Madhubani Art Generation
DESCRIPTION:Authors - Devashree Kute\, Anjali Naik Abstract - This study introduces a resource-efficient method for generating artwork in the traditional Madhubani style using Stable Diffusion XL (SDXL). Using the low rank adaptation (LoRA) technique\, the model is fine-tuned with culturally relevant prompts and stylistic guidance to emulate the distinct characteristics of this Indian folk art form. To improve inclusivity and accessibility\, prompt multilingual support is incorporated\, covering Hindi\, Bengali\, Telugu\, and English\, through an automated translation mechanism\, which retains the keyword to ensure consistency between languages. The training process is optimized for standard consumer-grade GPUs utilizing FP16 precision\, CPU memory offloading\, and a fixed Variational Autoencoder (VAE)\, enabling stable 1024×1024 image generation. For evaluation\, a Contrastive Language Image Pretraining (CLIP) based scoring method is employed to assess the semantic alignment between prompts and generated images. The findings indicate that the style remains remarkably consistent across languages\, demonstrating that even minimal training can enable AI to effectively capture and preserve traditional art forms within digital media.
CATEGORIES:VIRTUAL ROOM 4D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:568acc96697406efb4c5c08fbc5d3516
URL:http://11tict4sd.sched.com/event/568acc96697406efb4c5c08fbc5d3516
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:Retrieval-Augmented Generation for Grape Leaf Disease Diagnosis and Treatment: A Deep Learning Approach
DESCRIPTION:Authors - Sonali Patil\, Adwait Jadhav\, Sahil Bhavsar\, Pradnya Kamble\, Arya Tandale Abstract - This research introduces the innovative AI-driven dashboard\, leveraging Transfer Learning on Efficient Net and Retrieval Augmented Generation (RAG) for recommendation generation. The research is focused on minimizing the losses incurred due to diseases thus increasing the crop yield to meet the increasing demand. Our system incorporates the pre-trained Efficient Net model to classify the leaf images into various disease categories while LLAMA 3.1 8B LLM model is used to generate the remedy insights. Our methodology not only tackles the issue of hallucinations as well as correctness of the information which are very common in LLM response generation\, but provides a personalized remedy plan for the farmer incorporating climatic conditions such as Temperature and Humidity as well. The suggested methodology not only aims at detecting diseases at early stages\, but also at securing the necessary food supply\, reducing the amount of pesticides used and promoting eco-friendly way of cultivation. Looking at the future\, the project visions of increasing the use of LLMs in the agriculture industry by continuously upgrading RAG as well as the supplied documents to maintain up to date responses.
CATEGORIES:VIRTUAL ROOM 4D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:e1878bc9ab2183588a02e1460c434b81
URL:http://11tict4sd.sched.com/event/e1878bc9ab2183588a02e1460c434b81
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:Seeing Through the Green Veil: How Greenwashing Perceptions Shape Sustainable Consumer Choices in India
DESCRIPTION:Authors - Athira T\, Abhishek H\, Deepak Gupta\, Shobhana Palat Madhavan Abstract - With growing environmental awareness\, consumers are drawn to sustainable offerings. Greenwashing — false environmental claims—has made consumers skeptical and impacted purchasing behaviour. Understanding greenwashing perception is vital to studying sustainable consumer behaviour\, especially in India where research is limited. This paper explores greenwashing and sustainable consumer choices through a quantitative research design\, using data from 223 Indian consumers analyzed via SEM and Mediated SEM in Stata. Employing the Value-Belief-Norm (VBN) Theory and the Theory of Planned Behaviour (TPB)\, the study examines the role played by environmental knowledge\, green skepticism\, social influence\, and perceived moral obligation in sustainable consumption and green purchase intention\, with greenwashing perception acting as a mediator. The findings suggest that perceived moral obligations are the strongest driver for greenwashing perceptions\, as well as for green purchase intentions and sustainable consumption behaviour. Environmental knowledge enhances green-washing perception and sustainable behaviour. Green skepticism affects sustainable choices indirectly via greenwashing perception. Social influence affects greenwashing perception in a negative manner but sustainable consumption and purchase intention in a positive manner. These insights can help shape strategies that build consumer trust and promote genuine sustainable practices.
CATEGORIES:VIRTUAL ROOM 4D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:75ca5088ee98b6514687411146e0b86a
URL:http://11tict4sd.sched.com/event/75ca5088ee98b6514687411146e0b86a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:THE DUAL EDGE OF AI IN CYBERSECURITY: MITIGATING RANSOMWARE THREATS THROUGH SELF-LEARNING HONEYPOTS AND BEHAVIORAL ANALYTICS
DESCRIPTION:Authors - Keyur Kurani\, Vaibhavi Machchhar Abstract - Ransomware is a rapidly evolving cyber threat with the potential to cause significant financial and operational disruption across industries. This paper analyzes the 2017 WannaCry attack\, which exploited a Microsoft Windows vulnerability to infect over 200\,000 systems globally\, severely affecting sectors such as healthcare\, including the UK’s NHS.The study explores ransomware’s progression from simple attacks to advanced\, AI-powered variants. These modern forms use real-time analysis\, intelligent target selection\, and adaptive evasion techniques to bypass traditional defenses and enhance social engineering tactics.To counter these threats\, organizations must adopt proactive strategies\, including AI-driven cybersecurity solutions\, behavior-based anomaly detection\, and targeted employee training. Understanding the AI–ransomware link is essential for building resilient defenses against future cyberattacks.
CATEGORIES:VIRTUAL ROOM 4D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:e29dd23479f016c4bbb3d54cfa88687d
URL:http://11tict4sd.sched.com/event/e29dd23479f016c4bbb3d54cfa88687d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:Web Application Firewall Using Machine Learning and Features Engineering
DESCRIPTION:Authors - Purvee Agrawal\, Sanika Bhosale\, Viraj Kakade\, Vishal Jaiswal\, Sarthak Baraliya Abstract - This paper presents the design and implementation of a Web Application Firewall (WAF) using machine learning models to effectively detect and mitigate three prominent web security threats: Distributed Denial-of-Service (DDoS)\, SQL injection\, and Cross-Site Scripting (XSS). The proposed system leverages separate machine learning models for each attack type\, optimizing detection by focusing on specific features unique to each threat. By analyzing traffic behavior\, request payloads\, and input structures\, the WAF ensures high accuracy in identifying and blocking malicious activities. This multi-model approach significantly reduces false positives and enhances real-time protection. The solution is scalable and can adapt to evolving attack patterns\, providing robust security for modern web applications and critical infrastructure.
CATEGORIES:VIRTUAL ROOM 4D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:24d3cee2a552ec6d4c9b936edf685af4
URL:http://11tict4sd.sched.com/event/24d3cee2a552ec6d4c9b936edf685af4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:Assessing Readiness for the Adoption of Industry 4.0 Technologies in Manufacturing MSMEs: A Case of Plastic Manufacturers
DESCRIPTION:Authors - Sivakami B U\, M. Suresh Abstract - As the conversation shifts toward Industry 5.0 (I5.0)\, how prepared are manufacturing Micro Small Medium Enterprises (MSMEs) to adopt the foundational technologies of Industry 4.0 (I4.0)? Despite MSMEs' economic importance\, they persistently face challenges in adopting essential technologies and undergoing digital transformation due to financial\, infrastructural\, and work-force-related barriers. The extant literature provides limited understanding of their readiness to implement individual I4.0 technologies. To address this gap\, this study aims to develop a Fuzzy Logic-based readiness assessment framework to evaluate adoption potential and identify weaker attributes. Using an ontological approach\, the framework identifies key enablers\, criteria\, and attributes. Findings reveal an “average ready” status\, with critical gaps in IoT\, Robotics\, Automation\, and Digital Twin technologies. The framework provides MSMEs with a roadmap to assess their readiness and strategically plan their I4.0 transition.
CATEGORIES:VIRTUAL ROOM 4E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:631906b7ce594185b734c3d7c9d6a007
URL:http://11tict4sd.sched.com/event/631906b7ce594185b734c3d7c9d6a007
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:Assessment for Construction 4.0 practice level using Fuzzy Logic: A Case of Construction Organization
DESCRIPTION:Authors - Drisya Murali\, Suresh M Abstract - This study aims to develop a novel assessment framework that measures the practice of organizations in the construction industry for Construction 4.0. The research helps to measure and improve construction organizations’ practice level for Construction 4.0 by identifying key enablers and criteria and addressing weaker attributes. This research seeks to use a fuzzy logic approach within a conceptual framework to evaluate the current practice level of these organizations. By assessing organizational members' perspectives regarding strategic management\, resource allocation\, technological readiness\, and socio-economic considerations\, the study aims to identify critical practice criteria and categorize them into four enablers with detailed attributes. The assessment was done at one location\, suggesting further evaluation could help improve practice levels across the organization. The management of a case construction organization would use the study to enhance the practice level of their organization to improve the Construction 4.0 practice. To enhance the organization's practice for Construction 4.0\, focus on improving identified weaker attributes based on the proposed assessment framework model. The ultimate goal is to provide recommendations for improving practice level\, thereby maximizing the benefits of Construction 4.0 in the construction industry. This study is the first to propose such a framework\, contributing valuable insights to the research on Construction 4.0 in this sector.
CATEGORIES:VIRTUAL ROOM 4E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:9ee923dbe17576fbc468efd9a28a330e
URL:http://11tict4sd.sched.com/event/9ee923dbe17576fbc468efd9a28a330e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:AUTOMATIC FIRE-FIGHTING ROBOT FOR WAREHOUSES & STORAGES
DESCRIPTION:Authors - Suhas Bhise\, Devesh Kulkarni\, Mayur Gaikwad\, Parth Nevase Abstract - Warehouses and storage facilities are highly vulnerable to fires due to the presence of flammable materials and high-density storage layouts. Traditional fire detection and suppression systems frequently encounter limitations in such environments\, such as delayed response times and restricted access to fire sources. The study describes the design and development of an automatic fire-fighting robot specifically for warehouses and storage facilities. The proposed robotic system combines advanced sensors for smoke\, heat\, and flame detection with real-time navigation capabilities to autonomously locate fire sources. Some of them uses thermal imaging cameras\, infrared sensors\, and LiDAR technology to precisely map the environment\, detect obstacles\, and navigate narrow aisles. The robot has a multi-directional water or foam nozzle for efficient fire suppression\, as well as a decision-making algorithm that prioritizes critical fire zones\, allowing for a quick and effective response. Furthermore\, the system supports remote monitoring and control via a user-friendly interface\, allowing for human intervention as needed. Heat-resistant materials and a long-lasting power supply improve the robot's durability\, allowing it to operate continuously in extreme conditions. Experimental results show that the robot is effective at detecting fires early\, responding quickly\, and minimizing damage. The study seeks to provide a dependable\, cost-effective solution for improving fire safety in warehouses and storage facilities\, thereby reducing risk to both property and human life.
CATEGORIES:VIRTUAL ROOM 4E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:7f6ae578639e0c43d873078fddedd9d8
URL:http://11tict4sd.sched.com/event/7f6ae578639e0c43d873078fddedd9d8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:Data-driven Optimization of Hybrid Renewable Energy Systems: Managing Net Metering Costs Through Machine Learning
DESCRIPTION:Authors - V. K. Abhang\, Y. A. Shinde\, S. N. Shingote\, Adik Somal\, Aglawe Nikhil\, Akolkar Vivek\, Ghondage Pratik Abstract - This project explores how machine learning can help optimize hybrid renewable energy systems\, with a special focus on managing net metering costs. By analyzing real-time data from renewable sources and consumer energy usage\, the goal is to create a smart\, efficient framework that improves energy reliability while keeping costs low. The idea is to strike a balance ensuring that energy production and consumption align seamlessly with changing demand and environmental conditions. To make this happen\, we’re using the Open Energy Modelling Framework (OEMOF)\, which helps optimize how energy is distributed\, stored\, and interacted with the grid. With OEMOF\, we can simulate energy flows\, make better decisions about energy trading and self-consumption\, and develop cost-effective net metering strategies. On top of that\, advanced predictive models for weather and energy demand forecasting allow for proactive system adjustments\, making sure the setup remains efficient and reliable.Beyond the technical side\, this approach directly supports the global shift toward sustainable energy. By making hybrid renewable energy systems more cost-effective and scalable\, it not only helps individuals and businesses save money but also contributes to reducing carbon footprints moving us one step closer to a greener future.
CATEGORIES:VIRTUAL ROOM 4E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:07a5e9ee855435370ce21c147eb4b509
URL:http://11tict4sd.sched.com/event/07a5e9ee855435370ce21c147eb4b509
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:Enhanced Feature Extraction for Phishing URL Detection: A Comprehensive Analysis of Structural\, Host-Based\, Content and N-Gram Attributes
DESCRIPTION:Authors - Lokesh Khedekar\, Suvarna Pawar Abstract - In order to increase the accuracy of threat detection for malicious URLs\, this paper proposes an improved feature extraction methodology. High false positive rates are a common consequence of traditional detection systems' restricted feature sets. The suggested method extracts a wide variety of lexical\, host-based\, content-based\, and character-level n-gram features in order to solve this. While host-based qualities offer contextual information like domain age and DNS validity\, lexical features record structural irregularities. While n-gram features identify obfuscation through frequent character sequences\, content-based features—such as login indicators\, file extensions\, and JavaScript references—reflect behavioral patterns. According to correlation analysis\, the majority of traits are still weakly associated\, providing a variety of complementary signals\, even while some are strongly related. By properly recognizing benign URLs with 100% confidence\, the suggested model showed dependable real-time prediction and obtained a high accuracy of 98.50% in detecting dangerous URLs.
CATEGORIES:VIRTUAL ROOM 4E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:4f5a8f8a90ffacd729137ac9cc7c6796
URL:http://11tict4sd.sched.com/event/4f5a8f8a90ffacd729137ac9cc7c6796
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:Exploring biases and interpretability of Deep learning models
DESCRIPTION:Authors - Shreya Pulluri\, Megha Mohan\, Madduri Aditya Vardhan Reddy\, Chitresh Simhadri\, Geetha M Abstract - Bias in race and gender classification models creates strong ethical dilemmas\, where disparities typically exist among groups of different demographics. In this research\, a Bias-Aware Grad-CAM method is proposed that embeds interpretability-driven bias reduction within training. A ResNet model was trained for gender and race classification using the UTKFace dataset\, which shows preliminary errors in minority group predictions. The Bias-Aware GradCAM framework proposed produces heatmaps to detect model attention areas\, calculating Intersection over Union (IoU) between the heatmaps and pre-defined face bounding boxes. A reweighting process in the loss function\, based on IoU scores\, is introduced to retrain the model. Results show enhanced classification performance by aligning model attention with appropriate facial areas better. This strategy emphasizes the promise of explainability-guided methods to real-time bias reduction in face recognition models.
CATEGORIES:VIRTUAL ROOM 4E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:c137e46a6c6c028a362dc6ddaaacb32c
URL:http://11tict4sd.sched.com/event/c137e46a6c6c028a362dc6ddaaacb32c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:Homomorphic Encryption for Privacy Preservation
DESCRIPTION:Authors - Swatee Nikam\, Nilima Kulkarni\, Amrita Manjrekar Abstract - With increasing data and its computation over clouds through various mediums it has become difficult to preserve privacy and then manipulate data according to the requirements. The third-party service providers have increased to do this manipulation over the bigdata which is collected over period of time. But again\, the privacy is breached when the third party is involved with data of individuals and thus mechanism of homomorphic encryption (HE) is introduced which allows computation to be done on encrypted data. The initial work on homomorphic encryption were impractical but recent work is established with efficiency over practical application latest being embedded in Microsoft Edge browser. This paper gives a brief introduction to homomorphic encryption and its categorization over libraries created over recent years. Practical implementation HE is programmed over C++ language\, with recent development the trial implementation is in progress over python language also. The libraries include such as Helib\, SEAL\, Blyss\, concrete\, etc. HE is developed as future enterprise for security and privacy guard up with application slated as geofencing with keeping secrecy of location or latigo polls for scheduling the meetings. The paper will discuss some other application with reference to new developed libraries under HE.
CATEGORIES:VIRTUAL ROOM 4E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:e5b22108f045c1c911010143810fb8b5
URL:http://11tict4sd.sched.com/event/e5b22108f045c1c911010143810fb8b5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:Interpretable Fake News Detection Using Neural Networks and LIME
DESCRIPTION:Authors - Mrunal Vibhute\, Anjali Naik Abstract - In an era where misinformation spreads rapidly\, the need for reliable and interpretable fake news detection systems is critical. This study evaluates deep learning models—Fully Connected Neural Networks (FCNN)\, Convolutional Neural Networks (CNN)\, Graph Convolutional Networks (GCN)\, Long Short-Term Memory (LSTM) networks\, and FastText—for fake news classification. To ensure interpretability\, Local Interpretable Model-agnostic Explanations (LIME) are applied to FCNN\, CNN\, and GCN\, generating human-understandable explanations for predictions. While LSTM and FastText are included for performance comparison\, they are excluded from interpretability analysis due to technical constraints. The models are trained using different input representations: TF-IDF for FCNN and CNN\, and graph structures for GCN. This paper analyzes trade-offs between accuracy and explainability\, offering insights into the effectiveness of deep learning models for fake news detection and contributing to the development of more transparent AI solutions.
CATEGORIES:VIRTUAL ROOM 4E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:b15c5dbc08f0f2081657317d06fd7858
URL:http://11tict4sd.sched.com/event/b15c5dbc08f0f2081657317d06fd7858
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:OcuXPlain: An Explainable AI Approach for Multi-Class Ocular Disease Detection
DESCRIPTION:Authors - Manan Parekh\, Anmol Aafre\, Parth Patel\, Vaidehi Lad\, Nirali Nanavati Abstract - The human eye is highly sensitive and vulnerable to various diseases that need timely attention\; otherwise\, they may lead to vision impairment. According to WHO (World Health Organization)\, most of these cases could be avoided through regular examinations. Therefore\, early detection of eye disease has become a necessity. In this manuscript we propose a novel approach for prediction and classification of ocular diseases like age-related macular degeneration\, cataract\, diabetic retinopathy\, glaucoma\, hypertensive retinopathy\, myopia\, and normal using fundus images collected from multiple public sources. Our study comprises the study of multiple contemporary deep learning models\, including EfficientNet-B4\, ConvNeXt\, DenseNet-201\, and ViT-16B. Our proposed solution involves the implementation of the ConvNeXt model on our dataset\, which achieves an accuracy of 94.14%. Our purpose behind this study is not only to develop an effective classification model but also to ensure its visual - understanding how and why the model is making certain decisions. To achieve this\, we implemented the Gradient-Weighted Class Activation Mapping (GradCAM) to provide an explainable AI (XAI) based solution to help researchers and practitioners in the field.
CATEGORIES:VIRTUAL ROOM 4E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:dca5d9aa1585edde3cf9e8b477783c59
URL:http://11tict4sd.sched.com/event/dca5d9aa1585edde3cf9e8b477783c59
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T040000Z
DTEND:20260825T060000Z
SUMMARY:Relay-assisted framework for mmWave 5G NR BS sys-tem in V2X Communications
DESCRIPTION:Authors - Vinodh Kumar Minchula\, Supraja Reddy A\, Sharathchand Kodam Abstract - This paper investigates the characteristics of 5G NR-V2X channels and explores the benefits of relay-assisted communication for ensuring reliable and robust connectivity in dynamic vehicular environments. A detailed analysis of mmWave Base Station (BS) deployments utilizing MIMO antenna arrays of varying dimensions is conducted for the 5G NR n258 frequency band at 26 GHz. To evaluate system performance\, a simulation framework is developed based on a Hyderabad Open Road Roundabout (ORR) scenario\, aligned with 3GPP specifications\, capturing vehicle mobility patterns and beam steering mechanisms to identify the optimal beam. To address the limitations of the NLoS\, relay nodes are introduced to assist NLoS users\, leading to measurable improvements in link quality and throughput. Experimental findings show that relay-assisted links yield up to a 4.1% increase in bit rate over direct NLoS connections. These results confirm the efficacy of relay-based architectures in enhancing signal robustness and extending mmWave communication range in V2X environments. The proposed framework provides a scalable and resilient solution for enabling high-performance vehicular networks in urban deployments. These findings clearly demonstrate that relay-assisted communication significantly enhances signal robustness and extends the effective range of mmWave links\, thereby facilitating improved reliability and efficiency in next-generation vehicular communication networks.
CATEGORIES:VIRTUAL ROOM 4E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:d4d010aa6557bbfe1934433b7b86c4b5
URL:http://11tict4sd.sched.com/event/d4d010aa6557bbfe1934433b7b86c4b5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T044500Z
DTEND:20260825T045000Z
SUMMARY:Lighting of the Lamp to Mark the Auspicious Begining
DESCRIPTION:
CATEGORIES:INAUGURAL SESSION
LOCATION:Assembleia 1\, Goa\, India
SEQUENCE:0
UID:133fb76d8e3e99f9202092327bb126ab
URL:http://11tict4sd.sched.com/event/133fb76d8e3e99f9202092327bb126ab
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T045000Z
DTEND:20260825T045500Z
SUMMARY:Presentation of Bouquet/Momento to the Guests
DESCRIPTION:
CATEGORIES:INAUGURAL SESSION
LOCATION:Assembleia 1\, Goa\, India
SEQUENCE:0
UID:b584388cf6948d8fc2ea90ef11cd20cb
URL:http://11tict4sd.sched.com/event/b584388cf6948d8fc2ea90ef11cd20cb
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T045500Z
DTEND:20260825T050500Z
SUMMARY:Welcome Remarks By
DESCRIPTION:
CATEGORIES:INAUGURAL SESSION
LOCATION:Assembleia 1\, Goa\, India
SEQUENCE:0
UID:f4030c4ba2239b81bd0e59a674ef9209
URL:http://11tict4sd.sched.com/event/f4030c4ba2239b81bd0e59a674ef9209
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T050500Z
DTEND:20260825T051500Z
SUMMARY:Special Guest Address By
DESCRIPTION:
CATEGORIES:INAUGURAL SESSION
LOCATION:Assembleia 1\, Goa\, India
SEQUENCE:0
UID:1eccaa8fe30acd229258d651990d737c
URL:http://11tict4sd.sched.com/event/1eccaa8fe30acd229258d651990d737c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T051500Z
DTEND:20260825T053000Z
SUMMARY:Address By Keynote Speaker
DESCRIPTION:
CATEGORIES:INAUGURAL SESSION
LOCATION:Assembleia 1\, Goa\, India
SEQUENCE:0
UID:a1eb548aaa2e6921b3d9d02fd99651ac
URL:http://11tict4sd.sched.com/event/a1eb548aaa2e6921b3d9d02fd99651ac
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T053000Z
DTEND:20260825T054500Z
SUMMARY:Address By Keynote Speaker
DESCRIPTION:
CATEGORIES:INAUGURAL SESSION
LOCATION:Assembleia 1\, Goa\, India
SEQUENCE:0
UID:2230b22e07e6daa5c8e1d0ccfcef2231
URL:http://11tict4sd.sched.com/event/2230b22e07e6daa5c8e1d0ccfcef2231
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T054500Z
DTEND:20260825T060000Z
SUMMARY:Special Guest Address By
DESCRIPTION:
CATEGORIES:INAUGURAL SESSION
LOCATION:Assembleia 1\, Goa\, India
SEQUENCE:0
UID:80907e7b8c9d31a5133660dc5ebb4435
URL:http://11tict4sd.sched.com/event/80907e7b8c9d31a5133660dc5ebb4435
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T060000Z
DTEND:20260825T061500Z
SUMMARY:Special Guest Address By
DESCRIPTION:
CATEGORIES:INAUGURAL SESSION
LOCATION:Assembleia 1\, Goa\, India
SEQUENCE:0
UID:a5fbee7c6c3a5a2a5572da135d98ea3e
URL:http://11tict4sd.sched.com/event/a5fbee7c6c3a5a2a5572da135d98ea3e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T060000Z
DTEND:20260825T060200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 4A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:292277391e7f8754283ef1df97f3ebad
URL:http://11tict4sd.sched.com/event/292277391e7f8754283ef1df97f3ebad
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T060000Z
DTEND:20260825T060200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 4B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:94017787308e43c193f8e0ac7cdb2982
URL:http://11tict4sd.sched.com/event/94017787308e43c193f8e0ac7cdb2982
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T060000Z
DTEND:20260825T060200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 4C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:327d35cef0a7b55d8a7cb08373277b4e
URL:http://11tict4sd.sched.com/event/327d35cef0a7b55d8a7cb08373277b4e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T060000Z
DTEND:20260825T060200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 4D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:6b8691d4389ec5e18ce46a58cf2dadeb
URL:http://11tict4sd.sched.com/event/6b8691d4389ec5e18ce46a58cf2dadeb
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T060000Z
DTEND:20260825T060200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 4E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:70b1c94e6cad7c90e247a1c076d928ae
URL:http://11tict4sd.sched.com/event/70b1c94e6cad7c90e247a1c076d928ae
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T060200Z
DTEND:20260825T060500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 4A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:5dc545360a760f1b386c7bfaf9dd3c6b
URL:http://11tict4sd.sched.com/event/5dc545360a760f1b386c7bfaf9dd3c6b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T060200Z
DTEND:20260825T060500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 4B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:ea958fe743272e5594fd03d57fbcd354
URL:http://11tict4sd.sched.com/event/ea958fe743272e5594fd03d57fbcd354
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T060200Z
DTEND:20260825T060500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 4C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:dfd1464682badaa5a9422076b8242013
URL:http://11tict4sd.sched.com/event/dfd1464682badaa5a9422076b8242013
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T060200Z
DTEND:20260825T060500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 4D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:f5566ed2f4d38d1910fd59126aa720fd
URL:http://11tict4sd.sched.com/event/f5566ed2f4d38d1910fd59126aa720fd
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T060200Z
DTEND:20260825T060500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 4E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:47289b2790bc3a95db74ed32f5aa4371
URL:http://11tict4sd.sched.com/event/47289b2790bc3a95db74ed32f5aa4371
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T061500Z
DTEND:20260825T062000Z
SUMMARY:Vote of Appreciation
DESCRIPTION:
CATEGORIES:INAUGURAL SESSION
LOCATION:Assembleia 1\, Goa\, India
SEQUENCE:0
UID:3a68daef6f959011f1880c816f29219c
URL:http://11tict4sd.sched.com/event/3a68daef6f959011f1880c816f29219c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T062000Z
DTEND:20260825T063000Z
SUMMARY:Group Photograph
DESCRIPTION:
CATEGORIES:INAUGURAL SESSION
LOCATION:Assembleia 1\, Goa\, India
SEQUENCE:0
UID:cf469dbee5ee3cadca54b3592a745829
URL:http://11tict4sd.sched.com/event/cf469dbee5ee3cadca54b3592a745829
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T063000Z
DTEND:20260825T070000Z
SUMMARY:Networking Tea & Coffee
DESCRIPTION:
CATEGORIES:INAUGURAL SESSION
LOCATION:Assembleia 1\, Goa\, India
SEQUENCE:0
UID:26d8da4baa5918e786598e8410d4f424
URL:http://11tict4sd.sched.com/event/26d8da4baa5918e786598e8410d4f424
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T065800Z
DTEND:20260825T070000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 5A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:61bcfcda1b6cfbf0a7ba2342d141a907
URL:http://11tict4sd.sched.com/event/61bcfcda1b6cfbf0a7ba2342d141a907
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T065800Z
DTEND:20260825T070000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 5B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:cce5a08c793a354df05e52334296a700
URL:http://11tict4sd.sched.com/event/cce5a08c793a354df05e52334296a700
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T065800Z
DTEND:20260825T070000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 5C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:d3fa89b804e38fba4ade5a815660caa6
URL:http://11tict4sd.sched.com/event/d3fa89b804e38fba4ade5a815660caa6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T065800Z
DTEND:20260825T070000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 5D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:4f9ef6a58bc24bc82fa059574f387d59
URL:http://11tict4sd.sched.com/event/4f9ef6a58bc24bc82fa059574f387d59
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T065800Z
DTEND:20260825T070000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 5E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:f3a0abef204adeff43aa1de0839ace47
URL:http://11tict4sd.sched.com/event/f3a0abef204adeff43aa1de0839ace47
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:AI-Driven Data Leakage Prevention: A Deep Learning-Based Framework for Securing Sensitive Information
DESCRIPTION:Authors - Yuvraj Nikam\, Viddulata Patil\, Pratik Kamble\, Bhagyashree Shendkar\, Viresh Vanarote\, Pankaj Chandre Abstract - The increasing risk of data breaches necessitates advanced security solutions\, with AI-driven Data Leakage Prevention (DLP) systems emerging as a key defense mechanism. This framework integrates deep learning techniques\, including CNNs\, LSTMs\, autoencoders\, and GANs\, for anomaly detection and simulated attacks. Threat detection is enhanced through anomaly scoring\, incident response\, and policy enforcement. Additionally\, security is fortified with federated learning\, blockchain for audit and integrity\, and homomorphic encryption for privacy preservation. This hybrid approach ensures scalable\, interpretable\, and resilient data protection against evolving cyber threats. Future research should focus on optimizing computational efficiency while maintaining high detection accuracy and privacy assurance
CATEGORIES:PHYSICAL TECHNICAL SESSION 1A
LOCATION:West 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:25dab4766ac3bdcbaed776a0e3e6f4fd
URL:http://11tict4sd.sched.com/event/25dab4766ac3bdcbaed776a0e3e6f4fd
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:An IoT based Real-Time Garbage Detection System using YOLOv8 and Raspberry Pi
DESCRIPTION:Authors - Samarth Yogesh Jadhav\, Rutuja Rajaram More\, Rohit Dnyaneshwar Kokate\, Aditi Dinkar Kadam\, Nikhil Subhash Patankar Abstract - In urban areas\, where waste production often surpasses outdated human sorting methods\, efficient trash management is becoming more and more challenging. Using a Raspberry Pi and a refined YOLOv8 object detection model\, we provide a real-time garbage identification solution. This hybrid technique enhances detection performance for localized waste categories\, including metal\, batteries\, and plastic containers\, by combining pre-trained YOLOv8 weights with unique fine-tuning on a domain-specific dataset. A Flask-based web application provides users with a straightforward monitoring interface. Additionally\, when waste is identified\, the technology automatically notifies collection staff via WhatsApp\, guaranteeing prompt notifications.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1A
LOCATION:West 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:14a52110f8967f8a80429df62c0ca533
URL:http://11tict4sd.sched.com/event/14a52110f8967f8a80429df62c0ca533
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:COLOUR BASED SORTING SYSTEM USING CONVEYER BELT
DESCRIPTION:Authors - Harish Harsurkar\, Shridhar Deshpande\, Rohit Kushwaha\, Shailesh Naik\, Suraj Chavan Abstract - Sorting is the act of carefully organizing things. In the wholesale market and many businesses\, object sorting by size\, form\, quality\, etc. is favored by hand. Unfortunately\, this approach is laborious\, inefficient\, and not always reliable. Current market solutions may sort a single object using either one or more parameters. An automated object sorting mechanism using an IR sensor is offered as a replacement for this conventional method of sorting. It detects and categorizes two distinct objects using two distinct methods for analyzing heights. The significance of process automation has grown in recent years due to the fact that it is directly related to the development of any industry. Industrial process robots equipped with advanced sensing technology are used for exact and accurate output. Image processing has established its supremacy and pervasiveness in many industrial processes in the contemporary era. A color-based item sorting system that makes use of machine vision and image processing procedures is presented in this research. Utilizing a real-time color image processing technique to continually assess and examine the color deformities utilizing camera-based machine vision\, the proposed work aims to produce a small\, user-friendly\, and accurate items sorting machine. The item is then classified into predetermined quality categories using the pick and place roboarm after the assessment of quality. The system discards the examined item if it does not conform to quality standards. Many different domains will find use for the suggested approach in their ongoing quality review processes.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1A
LOCATION:West 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:587f1abfdbc1623f7e201e063e09d064
URL:http://11tict4sd.sched.com/event/587f1abfdbc1623f7e201e063e09d064
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:Computational analysis of GWO and PSO optimization used for controlling water level in pharmaceutical bulk drug Industries
DESCRIPTION:Authors - Hafiz Shaikh\, Ajinkya Golande\, Sandeep Shelar\, Shivaji Bhosale Abstract - Water level management at a specific moment is a major topic of contention in bulk drug production companies. Production initially declines until the water level reaches the target level. However\, pharmaceutical companies could make more money if they could precisely regulate the water level at the beginning of production. Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) are introduced in this work to perfectly synchronize the water level with optimal performance parameters. The method for controlling the water proportion (level) in the spanned tanks for the MIMO system can be achieved by identifying the mathematical model. Monitoring the system’s open-loop reaction is the first step in identifying the system. By examining the connected tank’s real parameters\, this may be processed. A detailed explanation of state-space analysis of connected tanks and how it is converted into a transfer function is provided.The intrinsic parameters needed for the computation are examined in this work. The platform for viewing the responses is MATLAB. The PID controller’s observations show that a better controller is required to improve performance. This document presents performance analysis and its debate.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1A
LOCATION:West 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:989535eb7e9248febd4c9a3c8105489d
URL:http://11tict4sd.sched.com/event/989535eb7e9248febd4c9a3c8105489d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:MOTORIZED SHAPING MACHINE
DESCRIPTION:Authors - Husain Shaikh\, Dnyaneshwar Kamblajkar\, Sayyad Ruhan Quadri\, Shashikant Shirke\, Ganesh Kadam Abstract - A wide variety of reciprocating machines are available to most businesses\, allowing them to complete machine operations on relatively minor jobs. For smaller jobs with less material\, shapers\, broaches\, and planners are the tools of choice for machining. These machines are perfect for milling intricate patterns into tiny areas\, whether they're flat\, angled\, grooving\, or any combination of these. Only the forward stroke removes the materials from the work. Making the product takes longer due to the increased machining time. A compact twin shaper machine is created to address this issue by simultaneously shaping two work parts. This machine can remove material from two work pieces at once and has two directions of ram action. The result is a boost in output with a decrease in machining time
CATEGORIES:PHYSICAL TECHNICAL SESSION 1A
LOCATION:West 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:770063c01b6cb0e1aa2394fe64b24341
URL:http://11tict4sd.sched.com/event/770063c01b6cb0e1aa2394fe64b24341
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:Virtual Machine Migration Optimization in Cloud Data Centers: A Comprehensive Review
DESCRIPTION:Authors - Vaibhav Sawalkar\, Nitin More Abstract - Virtualization of the cloud data center (CDC) is achieved by operating multiple virtual machines (VM) in parallel processing on a single cloud server\, as cloud users increase rapidly. Using dynamic virtual machine migration can help meet the growing demand for resources like compute\, storage\, and connectivity. The majority of resource management strategies are employed for single or multiple virtual machine migrations in order to optimize the cloud data center's performance and efficiency. The workload that comes with a rapid rise in cloud users also causes a corresponding reduction in QoS (service quality). Only greater computing facilities\, managing huge numbers of cloud users\, and cutting down on time and energy spent in cloud data centers can be achieved with the help of the potent live VM migration technique. A modern cloud computing system's main component is live virtual machine migration. This document examines all the most recent live virtual machine migration approaches\, including their benefits and drawbacks. There has been a brief mention of their future range study and deployment. Through simulation\, the pre-copy and post-copy VM migration approaches have been compared with respect to CPU usage\, memory usage\, and network characteristics. This study examines the important components of virtual machine migration strategies and evaluates a variety of them while conducting a thorough examination of the current schemes. Key objectives guide the exploration of current systems. Some open research issues are also discussed in the domain of virtual machine migration.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1A
LOCATION:West 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:ecfcb7b9c93169ac77e41d0c935420c5
URL:http://11tict4sd.sched.com/event/ecfcb7b9c93169ac77e41d0c935420c5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:Wavelet-Integrated Framework for Large-Scale Underwater Image Enhancement
DESCRIPTION:Authors -&nbsp\;Yashas Vishwanathan\, Viraat Sai Palamanda\, Shreekara R Dandina\, Rashmi Ugarakhod\nAbstract -&nbsp\;Seafloor photographs generally experience severe degradation due to optical properties of light\, like absorption and scattering\, leading to color anomalies\, reduction of contrast\, and loss of details. Such vision loss conditions create severe issues for marine science and computer vision-based ocean exploration missions. Thus\, an underwater image restoration method is proposed using a wavelet-based U-Net architecture. By incorporating wavelet decomposition into a deep learning framework\, the proposed model can extract multiscale spatial information efficiently\, preserving the image clarity with structural and textural details. Wavelet-based model is trained on the Large-Scale Underwater Image (LSUI) and Enhancing Underwater Visual Perception (EUVP) datasets\, both of which offer diverse underwater scenes captured under various lighting conditions and water types. The model’s performance is evaluated quantitatively in terms of qualitative analysis metrics. For the LSUI dataset\, the proposed method achieved a peak signal-to-noise ratio (PSNR) of 23.47 dB and a structural similarity index (SSIM) of 0.9127. Evaluation on the EUVP dataset yielded a PSNR of 23.59 dB and an SSIM of 0.8572.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1A
LOCATION:West 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:9b2cf823b9825d12a94fc101c5f4dc87
URL:http://11tict4sd.sched.com/event/9b2cf823b9825d12a94fc101c5f4dc87
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:Anomaly Detection in Industrial Machines Using Echo State Networks
DESCRIPTION:Authors - Mann Patel\, Mithil Mistry\, Hasti Vakani\, Sachin Patel\, Tandel vaibhav\, Rishi Patel\, Amit Nayak\, Rajesh Patel Abstract - It’s paramount for the productivity of any company that industrial machinery is operating in an efficient manner and that the idle time is minimal. Anomaly detection plays a pivotal role in predictive maintenance – it focuses on abnormal behaviour patterns. The major challenge of RNNs is the computational complexity and the training of the models. On the other hand\, Echo State Networks (ESNs)\, which belong to the class of Reservoir Computing\, are generally more effective and efficient in modelling temporal relations. This paper investigates ESNs for the detection of industrial machinery anomalies using time-series sensor data. We describe the application of an ESN model on a standard industrial data set. Our results further indicate that ESN models are able to provide good results with less computation time and hence they can be used in real world industrial scenarios.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1B
LOCATION:West 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:61fb1b76eb7d6807ea92e1b9fa2abf17
URL:http://11tict4sd.sched.com/event/61fb1b76eb7d6807ea92e1b9fa2abf17
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:Enhanced Black Winged Kite Algorithm: A Hybrid Approach Using Latin Hypercube Sampling and Levy Flights
DESCRIPTION:Authors - Megha Gaur\, Purvi Rathore\, Jigyaasa Meena\, Surendra Nagar\, Vijay Kumar Bohat Abstract - Metaheuristic algorithms are widely used for solving complex optimization problems due to their flexibility and efficiency. Among them\, the Black Winged Kite Algorithm (BKA) has shown promising results but suffers from instability\, premature convergence\, and uneven distribution of the initial population. To address these limitations\, this paper proposes an Enhanced Black Winged Kite Algorithm (EBKA)\, which incorporates Latin Hypercube Sampling (LHS) for better initial diversity and L´evy flights to achieve a balanced exploration–exploitation trade-off. Experimental evaluations on CEC2022 benchmark functions demonstrate that EBKA consistently achieves superior stability\, faster convergence\, and improved solution quality compared to the original BKA and other recent metaheuristics.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1B
LOCATION:West 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:d34e5a6bc0d73c3e4eecc6511ea525a1
URL:http://11tict4sd.sched.com/event/d34e5a6bc0d73c3e4eecc6511ea525a1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:EXPLORING THE EFFICACY OF WAV2VEC AND LSTM MODELS IN SPEAKER DIARIZATION
DESCRIPTION:Authors - Devika Vijapur\, Nidhi Desai\, C M Tulasi\, Satish Chikkamath\, Suneeta V Budihal Abstract - Speech Emotion Recognition (SER) is identification and classification of emotions from various speech signals with high precision and reliability and interpreting emotions more meaningfully in real-world scenarios. Traditionally\,SER has focused on utterance level approaches\,treating emotions as attributes of an entire spoken segment. Emotions in speech can also be dynamic and regarded as distinct scheduled events with clear time demarcations rather than generalized attributes. To deal with this Speech Emotion Diarization (SED) is introduced. Much like Speaker Diarization which pursues answer for the question\,”Who speaks when?”\, SED tackles ”Which emotion appears when?”. SED focuses mainly on diarizing the input speech utterance into different emotions based on the occurrence of emotion. It also specially focuses on the individual frame analysis. It tells which emotion has occurred and when it has occurred. Emotion recognition and precise time segmentation is the key highlight of speech emotion diarization. SED can revolutionize and improve various aspects of our daily lives and also offers immense potential in fields such as enhanced mental health and well-being\, call centers and customer support\, education and e-learning\, entertainment and media\, safety and security\, social robots\, cross-cultural emotional understanding\, and many more.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1B
LOCATION:West 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:aec06c3566e56c60000058babfeba37a
URL:http://11tict4sd.sched.com/event/aec06c3566e56c60000058babfeba37a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:Improvised Mobile Sink based Data Survivability in Unattended Wireless Sensor Networks
DESCRIPTION:Authors - Nischaykumar Hegde\, Praahas Amin Abstract - In unattended sensor networks\, a major challenge in data survivability. The data collected by sensors can be lost due to various reasons like sensor node failures\, attacks etc. In our earlier work\, a data survivability solution based on mobile sink with increased focus on security and data compression was proposed. But the network structure lacked efficiency to enhance data survivability in terms of data backup at optimal locations and collection of them before any risks. This work addresses this problem and proposes a improvised mobile sink-based data survivability scheme. The data collection points are optimized with aim of enhancing data survivability using graph theory concepts. Through simulation analysis the proposed solution is found to increase data survival probability by atleast 3% compared to existing works. The proposed solution was also found to be energy efficient and increased the life time of sensor network by atleast 11% compared to existing works.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1B
LOCATION:West 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:f9f23d035e9cae7ea2b93dd287100534
URL:http://11tict4sd.sched.com/event/f9f23d035e9cae7ea2b93dd287100534
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:Performance Evaluation of QoS Parameters of UAV for Lunar Surface Exploration
DESCRIPTION:Authors - Sachin Kumar Gupta\, Vijay Kumar Sharma\, Kajal\, Ashish Suri Abstract - Unmanned aerial vehicles (UAVs) have significant potential for exploration of space\, particularly the Moon\, where rovers are often unable to mobilize adequately. This paper explores the operational viability of UAVs in the lunar environment by evaluating several flight parameters\, such as thrust performance\, stability factors\, and flight trajectories. Unlike Earth\, the Moon lacks an atmosphere\, meaning UAVs cannot rely on aerodynamic lift for flight and must be propelled instead. In this paper\, we model a UAV's desired trajectory and demonstrate these operational characteristics using MATLAB simulation for a UAV. The goal is for UAVs to exert thrust control\, navigate a UAV to the desired trajectory\, and deploy energy-efficient systems to stabilize flight. Understanding the UAVs for lunar exploration in upcoming space explorations will be beneficial. It expands the way to exploration in an autonomous environment and scientific contextual data collection on the lunar surface in the future.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1B
LOCATION:West 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:3ce0881252d879b969f712db09763a66
URL:http://11tict4sd.sched.com/event/3ce0881252d879b969f712db09763a66
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:Smart Traffic Sign Recognition: Enhancing Road Safety
DESCRIPTION:Authors - Puneet Mugabasav\, Shreyas M. Salotagi\, Gireesha H M\, P.C Nissimagoudar\, Nalini C. Iyer Abstract - This paper introduces a smart and reliable Convolutional Neural Network (CNN) designed to recognize traffic signs in real-time. Trained on the well-known GTSRB dataset\, the model works with high-resolution images from cameras mounted on vehicles. It helps cars ”see” signs like “Stop\,” “Yield\,” and speed limits—even when lighting is poor or signs are partially blocked. By using deep learning and advanced image analysis\, the system alerts drivers to signs they might overlook\, helping prevent accidents caused by missed warnings or speeding. It’s fast\, accurate\, and fits perfectly into modern driver-assistance systems and self-driving technology. The model shines in tricky spots like school zones\, busy intersections\, and construction areas where quick decisions matter most. Plus\, it’s designed to be flexible and easy to integrate into different vehicle systems. As it learns from more region-specific data\, it has great potential to make driving not just smarter\, but much safer—wherever the road takes you.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1B
LOCATION:West 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:bc966d9c0fdbd45a6c639d207f311654
URL:http://11tict4sd.sched.com/event/bc966d9c0fdbd45a6c639d207f311654
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:SmrutiPankh: An Automated Real-Time Assistance Device for Alzheimer’s Patients
DESCRIPTION:Authors -&nbsp\;Janvi S. Bhoyar\, Shantanu A. Lohi\, Umaima Syeda Fatima\, Sharvari R. Sonukale\, Devesh M. Patil\, Krishna Borakhade\, Archana W. Bhade\, Dilip R. Uike\, Rajesh M. Metkar\nAbstract -&nbsp\;Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that impairs cognitive functions\, affecting millions of individuals worldwide. Patients require continuous assistance to perform daily activities\, placing a heavy burden on caregivers. This paper presents Smrutipankh\, an AI-powered automated assistance device that utilizes real-time camera tracking and activity recognition to monitor Alzheimer’s patients. The system uses deep learning models to detect routine behaviors and abnormal events such as falls or wandering. By integrating cloud- based data storage and mobile notifications\, Smrutipankh enhances patient safety and reduces caregiver workload. The experimental results demonstrate high accuracy in activity recognition and anomaly detection\, making Smrutipankh a promising solution for Alzheimer’s care.c
CATEGORIES:PHYSICAL TECHNICAL SESSION 1B
LOCATION:West 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:895cc6c0b82f541597a48abf09524134
URL:http://11tict4sd.sched.com/event/895cc6c0b82f541597a48abf09524134
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:A Data Science Framework for Enhanced Diabetes Prediction: Integrating Mathematical Modeling\, Statistical Feature Engineering\, and Machine Learning
DESCRIPTION:Authors - Ansh Soni\, Aneri Shah\, Krish Modi\, Nishant Doshi Abstract - This paper expresses an innovative method of data science architecture for enhanced diabetes prediction\, embedding comprehensive mathematical framework\, statistical feature engineering\, and machine learning[I]. Utilizing the Pima Indian Diabetes dataset\, we design and extract key features that drive prediction - Insulin-to-Glucose Ratio (IGR)\, Diabetes Risk Index (DRI)\, Metabolic Syndrome Score (MSS) and more—to analyze complex clinical dynamics. A detailed attempt of comparison study across logistic regression\, decision trees\, random forests\, and deep neural networks enhanced by tuning model parameters [II] - indicates accuracy\, precision.. By integrating mathematical and statistical methodologies\, this methodology advances early diabetes detection\, supporting the revolutionary impact of AI-driven analytics in custom healthcare.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1C
LOCATION:South 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:3e1928c87e38d1945da20110d5cbe529
URL:http://11tict4sd.sched.com/event/3e1928c87e38d1945da20110d5cbe529
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:Comprehensive Analysis of ICT-Based Learning Management Systems: Best Practices for Enhancing Digital Learning Experiences
DESCRIPTION:Authors -&nbsp\;Arjun Singh Vijoriya\, Yogesh Parmar\, Bheem Singh Jatav\nAbstract -&nbsp\;Information and Communication Technology (ICT) has come up as impactful force in the drastic change in Higher Education. Among various ICT tools\, Learning Management Systems They have shown to have a great effect on the redefinition and improvement of the teaching process and the teaching and learning process itself. This paper presents an implementation proposal based on April of 2025 to identify and promote this paper provides best practices in utilizing ICT based LMS to enhance eLearning experiences. A comprehensive mixed methods approach for the research is adopted. First\, 300 participants were involved in a structured survey\, the participants included university students and faculty members\, to seek out insights regarding LMS usability\, satisfaction as well as expectation. Second\, the study incorporates The case studies are also done on globally recognized institutions such as Massachusetts. Focusing on their major bacteria from Institute of Technology (MIT) and Harvard University. LMS practices\, technology integration strategies\, and student engagement techniques. LMS usage data and analytics were also analyzed to determine performance patterns and patterns of user behavior. The main discoveries show that the enhancement of Artificial Intelligence (AI) features such as\; personal learning recommendations\, automates quizzes\, tests and quizzes\, other forms of tests\, and intelligent tutoring systems— yields a massive improvement user engagement and satisfaction. In detail\, 85 % said that they have experienced better satisfaction to the use of AI tune up of LMS platforms. while the remaining respondents expressed their satisfaction in the range of 60% toward the traditional non AI systems. Besides\, the study of the involvement of people focuses on the user-centered wellbeing or the lack of it in the practices of different organizations. Protection of design\, effective cybersecurity measures and the usage of data-based decisions. in LMS deployment. This research offers major benefits for schools that want to upgrade their computer-based educational platforms. It offers systematic guidance to educational stakeholders who want to successfully implement an LMS system and keep it functioning properly. The paper suggests ways to develop future digital education using blockchain to confirm credentials and creating Metaverse-based learning environments.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1C
LOCATION:South 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:f05fbda4c43b5d433856c40a503081f8
URL:http://11tict4sd.sched.com/event/f05fbda4c43b5d433856c40a503081f8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:DDOS Attack Detection in Cloud Computing Using Machine Learning
DESCRIPTION:Authors - Gaurav Kumar\, Lalit Kumar\, Deependra Rastogi\, Mahesh Swami\, Atiku Hasan Abstract - Cloud computing is the need of the day for computing but vulnerable to cyber-attacks\, particularly those presented by Distributed Denial of Service or DDoS attacks\, which essentially crash services through heavy-handed levels of attack traffic. Traditional methods of detection are not dynamic enough for cloud environments\, necessitating the evolution towards complex solutions such as machine learning (ML) for attack identification. This paper elaborates on three algorithms: Decision Tree\, Naïve Bayes\, and Support Vector Machine SVM in identifying DDoS attacks using the CICDDoS2019 dataset. The results of the study were based on accuracy\, precision\, recall\, and the computational cost for each model considered. It seems that the Decision Tree model is accurate up to 100% while accuracy in Naïve Bayes and SVM stands at 99.93%. However\, every algorithm has an accompanying limitation such as the overfitting of Decision Trees and high computational requirements of SVM. In this paper\, the adoption of hybrid machine learning models and feature engineering can potentially improve DDoS detection for reliability and robustness of cloud system.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1C
LOCATION:South 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:8e58cd50b15cffd51302234ab32fab5f
URL:http://11tict4sd.sched.com/event/8e58cd50b15cffd51302234ab32fab5f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:Experimental Evaluation of Information Leakage via Electromagnetic Emanation Using Channel Capacity
DESCRIPTION:Authors - Yusuke Murayama\, Hiromi Shima\, Hidema Tanaka Abstract - Against information leakage via electromagnetic emanation\, electromagnetic absorption tape and electromagnetic shielding tape are expected as countermeasures. In this paper\, we adopt these methods for HUBs and LAN connectors to conduct experiments and evaluate their effectiveness in reducing the amount of information leakage. We estimate the risk using channel capacity from the perspective of electromagnetic security. As a result\, we can observe decrease in power and frequency shift. However\, we conclude that although the frequency shift is effective as an EMC countermeasure\, it does not effectively reduce the risk of information leakage. We show that evaluation using channel capacity is suitable for information security\, such as estimating the effectiveness of a combination of multiple countermeasures and specifying the numerical goals to be achieved.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1C
LOCATION:South 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:cc9b0be2e9f6c516e055e0ca74502613
URL:http://11tict4sd.sched.com/event/cc9b0be2e9f6c516e055e0ca74502613
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:Hybrid AI-Driven Optimization for Transformer Power Systems: Multi-Agent Reinforcement Learning and Anomaly Detection
DESCRIPTION:Authors - Aneri Shah\, Krish Modi\, Ansh Soni\, Nishant Doshi Abstract - There is a need for efficient and optimized power distribution for the transformer system. This paper presents a hybrid AI-driven approach integrating Multi-Agent Deep Reinforcement Learning (MARL) and Anomaly Detection for transformer power system optimization. Three key components have been focused: (1) a Predictive Maintenance Model leveraging deep learning to estimate the Remaining Useful Life (RUL) of transformers\, (2) an Autoencoder-based Fault Detection Model for anomaly identification\, and (3) a Multi-Agent Deep Q-Network (DQN) system optimizing power distribution and balancing loads dynamically. The findings contribute to intelligent power system management\, reduced downtime\, increased transformer downtime and adaptive optimization framework for future smart grids. Such contributions are aligned with higher sustainability goals by minimizing wastage of resources and minimizing carbon footprints due to inefficient grid operations.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1C
LOCATION:South 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:f60c7de110c485f3808be9d5be7f75e0
URL:http://11tict4sd.sched.com/event/f60c7de110c485f3808be9d5be7f75e0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:Hyper-Localized Tax Optimization Using AI/ML: A Novel Approach
DESCRIPTION:Authors - Krish Modi\, Ansh Soni\, Aneri Shah\, Nishant Doshi Abstract - Tax optimization continues to be a complicated issue as financial habits are individual responses to regional tax policy changes. This research presented an innovative tax optimization framework powered by AI\, including multiple-output neural networks\, clustering algorithms and NLP\, to provide personalized\, hyper-localized tax savings strategies. The proposed framework accomplishes this by predicting salient financial metrics\, such as tax non-liability\, adjusted gross income\, and even refunds. The clustering algorithms predict user personas and group taxpayers in the same ZIP code in order to develop clustering profiles within a region\, discovering local patterns in different tax deductible expenses. The NLP (Natural language processing) engine self-identifies unapplied tax credits\, thus possibly increasing almost 35% of potential refunds. Because of real-time adaptivity\, the framework can respond as financial inputs change\, particularly with last-minute or end-of-year contributions and deductions\, which maximizes most deduction valuations and enhancements to accuracy will naturally reduce audit risk. The experimental results suggest prediction accuracy of over 93%\, in addition to greater refund optimizations\, greater audit reductions\, and more strategic financial layouts. The methodology proposed in the patent provides patent level innovations in clustering based tax strategy\, dynamic optimization\, and autonomous discovery of credits. As such\, the foundation for next-generation AI tax planning systems has been established for individual and small business use.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1C
LOCATION:South 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:c837fa8e9ad1964b41053290c2ea97f5
URL:http://11tict4sd.sched.com/event/c837fa8e9ad1964b41053290c2ea97f5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:Oblivious Transfer and Anonymous Password-Based Authenticated Key Exchange using PUF
DESCRIPTION:Authors - Ikuro Ego\, Hidema Tanaka Abstract - Physically Unclonable Functions (PUF) can generate random numbers based on semiconductor differences (silicon fingerprints) in integrated circuits (IC). In this paper\, we propose 1-out-of-n Oblivious Transfer and Anonymous Password-Based Authenticated Key Exchange (APAKE) using PUF. By using our protocols\, it is possible to get secret information without using direct input of secret key. At the same time\, mutual authentication can also be achieved in our proposed method. Our proposed protocols also have the advantage of enhancing security by physically exchange of PUF such as IC card and its reader.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1C
LOCATION:South 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:ea0856e9454a99411b4d42a1f7b81aef
URL:http://11tict4sd.sched.com/event/ea0856e9454a99411b4d42a1f7b81aef
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:Advancing Dermatological Diagnostics: Benchmarking YOLO Variants for Edge-Driven Skin Cancer Detection
DESCRIPTION:Authors - Niveditha N Reddy\, Pooja Agarwal Abstract - In critical field medical diagnostics\, early and accurate detection of skin cancer remains paramount for effective treatment\, yet significant challenges persist to the subtle visual differences between benign and malignant lesions and limited access to specialist care. Addressing these challenges\, skin cancer prediction study presents an innovative deep learning framework leveraging optimized YOLO architectures to revolutionize skin cancer classification. Utilizing a robust dataset of 33\,126 dermoscopic images - with all malignant cases confirmed by histopathology and benign cases validated through expert consensus or longitudinal follow-up and developed and compared lightweight versions of YOLOv5 YOLOv8\, Hybrid YOLO specifically engineered for clinical deployment. The methodology incorporated advanced optimizations including model pruning and TensorRT quantization to ensure efficient performance on edge devices like the NVIDIA Jetson Nano. This triad of models creates a comprehensive diagnostic ecosystem: YOLOv5’s precision reduces unnecessary biopsies\, YOLOv8’s speed enables accessible population screening\, and Hybrid YOLO’s enhanced sensitivity provides crucial safety-net detection for high-risk patients. Implemented together\, they address the full spectrum of clinical needs - from rural health posts lacking specialists to advanced dermatology centers. The Hybrid YOLO’s particular strength in detecting subtle malignancies (validated on histopathology-confirmed cases) could transform monitoring of high-risk patients and lesions with ambiguous visual features. By combining these architectures with rigorous clinical 2 validation\, bridge a critical gap between AI innovation and real-world patient care. The Hybrid YOLO’s 98.57% accuracy and superior recall demonstrate that thoughtfully combined approaches can outperform standalone models where it matters most - catching more cancers earlier while maintaining diagnostic reliability.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1D
LOCATION:South 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:0815fd8b11757c4dbd06e988c72dc0b4
URL:http://11tict4sd.sched.com/event/0815fd8b11757c4dbd06e988c72dc0b4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:AI Hallucination Prediction: A Novel Approach for Preventing False AI Outputs
DESCRIPTION:Authors - Arpita Kundu\, Aishwarya Malhotra\, Vimmi Malhotra Abstract - Generative AIs still keep picking up speed in nearly every industry\, but the trustworthiness of what they spit out is starting to worry people. The biggest headache by far is hallucination - when the model strings together something that sounds good but isn’t really true. In fields like health care\, teaching\, or money management\, that slip-up can cause real harm\, so no one wants to brush it off. Plenty of fixes have been tried already\, yet most are just clean-up crews that look for false claims after the damage is done. This paper instead rolls out a forward-looking shield that tries to spot trouble before it even leaves the keyboard. Our approach mixes guessing-game math\, relevance scoring\, and feedback loops so the model itself learns what to avoid. Because of that\, flaky sentences get tossed while still letting fluent\, on-topic prose pass through. Tests show hallucination drops sharply without losing the smooth feel readers expect. Taken together\, the work moves the field closer to generative models that people can safely trust.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1D
LOCATION:South 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:805e6df1f678dc8c08e59e17d2fe8e12
URL:http://11tict4sd.sched.com/event/805e6df1f678dc8c08e59e17d2fe8e12
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:Big Data Analytics in the AI Era: A Systematic Review of Frameworks\, Challenges\, and Future Directions
DESCRIPTION:Authors - Barkha Yadav\, Ritish Bansal\, Ashima Mehta Abstract - A paradigm shifts in the process of gaining insight from intricate\, massive datasets across industries is represented by big data analytics\, or BDA. Core methods (Hadoop\, Spark\, and Python libraries)\, applications (healthcare diagnostics that achieve 40 percent efficiency gains\, smart cities\, and financial modeling)\, and persistent problems (data privacy\, energy efficiency\, and talent gaps) are all deliberately examined in this review. As game-changing solutions\, emerging trends like edge AI\, quantum analytics\, and federated learning are highlighted. This paper gives researchers and practitioners a thorough road map for navigating an evolving BDA landscape while addressing ethical and scalability concerns by combining recent advancements (2020–2024) with practical limitations.. . .
CATEGORIES:PHYSICAL TECHNICAL SESSION 1D
LOCATION:South 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:7ecb24d6e3bc9934e92072b6648466de
URL:http://11tict4sd.sched.com/event/7ecb24d6e3bc9934e92072b6648466de
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:EVE-SIM: Evaluation of Vision-Based Event Simulators for Autonomous Driving Applications
DESCRIPTION:Authors - Mani.C\, Vinayak Nayak\, Ujwala Patil Abstract - In this paper\, we present a comparative study of Event processing that has attained much attention in recent years in the field of autonomous driving in adverse conditions. Frame-based camera simulators for different events capture images at prescribed times\, and it is often plagued by problems during high-speed events\, as motion blur can occur and fail to perform in dynamic environments. Event cameras\, on the other hand\, are an alternative that can capture asynchronous\, sparse\, and high-frequency pixel-level events at very low latency\, particularly well suited for applications like autonomous driving. However\, high-resolution event capture requires high-speed circuitry and accurate timing mechanisms\, and makes it technically challenging and expensive to manufacture the event cameras. To overcome these challenges\, several open-source simulators have been proposed to test and validate event generations. In this work\, we discuss the comparison of different open-source event simulators such as ESIM\, V2E\, V2CE\, and Recurrent Vision Transformer (RVT). We consider diverse driving conditions\, a broad spectrum of sensor noise\, and realistic environments for this analysis. Relevant quantitative metrics such as accuracy\, temporal resolution\, latency\, and noise handling are also included in the justification of the selection of event simulation in real-time applications. We observe that the RVT-based event simulator is promising for autonomous driving applications.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1D
LOCATION:South 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:2cc4d6c653313f856288d6cd99fc01c8
URL:http://11tict4sd.sched.com/event/2cc4d6c653313f856288d6cd99fc01c8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:Predictive Modeling of Geriatric Outcomes Using Factor Scores: Bayesian and Maximum Likelihood Estimation Approaches
DESCRIPTION:Authors - S. Amirtha Rani Jagulin\, A. Venmani Abstract - This paper explores the development of predictive models for geriatric health outcomes using factor scores derived from Bayesian and Maximum Likelihood Estimation (MLE) approaches. Factor scores\, representing latent dimensions such as mobility and social participation\, are used as predictors to model outcomes like walking difficulty and health impairments. The study compares the performance of various models using AIC\, BIC\, and R-squared metrics\, highlighting the strengths of Bayesian methods\, particularly those employing non-conjugate priors like Cauchy-Log Normal. Clustering analysis further validates the differentiation between models\, emphasizing the superior predictive accuracy and robustness of Bayesian approaches. These results underscore the utility of advanced statistical techniques in geriatric health studies and provide insights into their practical applications for early risk assessment.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1D
LOCATION:South 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:a04a0fe48ba444dbca95510ad11aa86c
URL:http://11tict4sd.sched.com/event/a04a0fe48ba444dbca95510ad11aa86c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:Smart Manufacturing in the Industry 4.0 Era: Technologies\, Trends\, and Future Prospects
DESCRIPTION:Authors - Aradhya\, Ashima Mehta\, Naman Yadav Abstract - Smart manufacturing is an entire paradigm change in production systems\; the traditional manufacturing mechanism has been easily supplemented with the most advanced digital technologies\, which include AI (artificial intelligence)\, deep learning\, IoT (the Internet of Things)\, and cyber-physical systems. This study reviews Email Correspondence and consolidates a very recent contribution and comparative analysis result from the most diverse sets of studies to provide the reader with a holistic approach to constituting the evolution and implementation of smart manufacturing paradigms in the much larger picture of Industry 4.0. The review is organized around topical themes such as the transition from traditional to intelligent manufacturing\, differences and similarities of Smart manufacturing with intelligent manufacturing\, and changes from digital technologies to improved process efficiency\, quality control\, and decision-making. It highlights the fact that AI and data-based methods are adaptive in solving challenging problems related to. However\, manufacturing also provides important aspects of physical cyber adoption\, interoperability\, cybersecurity\, and standardization. It also studies keywords in publications to perform bibliometric analyses\, which recognize and critically assess the main emerging issues of modern technology\, such as the integration of the human-cyber physical system\, digital twins\, predictive maintenance\, and sustainable production strategies.This statement captures both the latest advances and the challenges of high relevance in the field\, with a vision for The future of research and practice. Furthermore\, this would also serve as a handy document for researchers and industry practitioners by bringing together the latest state-of-the-art modern developments\, pointing out the gaps in the literature and even laying down a road map for future innovations in smart manufacturing systems.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1D
LOCATION:South 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:a5d0e4217f6bcae8dfa0540c8b929407
URL:http://11tict4sd.sched.com/event/a5d0e4217f6bcae8dfa0540c8b929407
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:Streamlining Navigation for Self-Driving Systems: A Practical Approach
DESCRIPTION:Authors - Harsh Vaddatti\, K.Sahana\, Raghavendra M. Shet\, Goutami Mangalgi\, Neela Patil\, Ujwala Patil\, Nalini Iyer Abstract - This paper presents a comparative study of A* and Rapidly-exploring Random Tree (RRT) algorithms for autonomous path planning. A*\, a heuristic-based method\, ensures optimal paths but is computationally intensive\, whereas RRT offers faster\, scalable solutions in dynamic environments at the cost of path smoothness. Both algorithms are evaluated under identical simulated conditions using metrics like trajectory quality\, computation time\, and search space coverage. The study also introduces enhancements—ripple reduction for A* and a refined RRT variant—to improve trajectory quality. Results and visualizations highlight trade-offs\, aiding in the informed selection of path planning strategies.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1D
LOCATION:South 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:3ec6a705533b3e0a11d927b7d5dbecc6
URL:http://11tict4sd.sched.com/event/3ec6a705533b3e0a11d927b7d5dbecc6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:Car Speed Estimation in ROI using OpenCV and YOLOv8
DESCRIPTION:Authors - K. Bhaskar Naik\, Nulakachandanam Praneeth Babu\, M. Vyshnavi Abstract - Vehicle speed estimation is vital for intelligent transportation systems\, and the use of digital image processing techniques like YOLOv8 and OpenCV has garnered significant attention. This paper introduces a novel approach that combines OpenCV and YOLOv8 to estimate car speeds within a designated Region of Interest (ROI). A dataset comprising a video from a traffic surveillance camera is collected and utilized to train and evaluate the car speed estimation system. OpenCV is employed to process the video data\, while the YOLOv8 model is employed in the detection of objects to identify cars within the ROI. The 'cv2.EVENT_MOUSEMOVE' constant from the OpenCV library is employed to obtain the ROI. By analyzing the car's motion within the ROI\, the vehicle's speed is accurately estimated. The proposed approach offers advantages over traditional methods. It provides a cost-effective solution with improved coverage compared to radar and laser-based systems. Accurate car speed estimation within the specified ROI is crucial for effective traffic management and the development of intelligent transportation systems. Evaluation results show that the suggested strategy is effective. The system successfully estimates car speeds within the defined ROI\, contributing to accurate speed limit detection and enhancing traffic management efficiency.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1E
LOCATION:Board Room 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:e011929eefde63766a97ac26e9af8645
URL:http://11tict4sd.sched.com/event/e011929eefde63766a97ac26e9af8645
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:Detection of News Media Bias Using Machine Learning: An Unsupervised Approach
DESCRIPTION:Authors - Nisha Shah\, Anjali Jivani Abstract - News media bias significantly influences public perception and trust in journalism. As news media outlets play a critical role in shaping public opinion\, detecting bias in reporting is essential to ensure balanced and fair communication. This study presents an unsupervised machine learning framework for identifying media bias\, leveraging a combination of natural language processing (NLP) techniques with statistical analysis. Large-scale news corpora are evaluated across multiple parameters\, including content\, tone\, emotion\, readability\, and balance to uncover patterns of bias. The choice of an unsupervised machine learning approach serves the objective to address the challenge of the unavailability of a gold standard labelled dataset. The proposed system demonstrated its effectiveness by analyzing the four most prominent Indian media outlets: The Times of India\, Hindustan Times\, Deccan Herald\, and India Today. Experimental results from these sources showcase the system’s effectiveness in detecting and differentiating bias levels across the selected parameters\, offering valuable insights into the landscape of media reporting in India.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1E
LOCATION:Board Room 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:1a8c6c4ad37e0da49f335efdfe1e658f
URL:http://11tict4sd.sched.com/event/1a8c6c4ad37e0da49f335efdfe1e658f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:Employee Motivation for IS: A Study of a Microfinance Organization in Nicaragua
DESCRIPTION:Authors - Vishnu Vinekar Abstract - This study focuses on employee motivation to use information systems to improve impacts of nonprofits in developing countries. The Nobel Prize winning Grameen Bank in Bangladesh pioneered microfinance to reduce poverty in developing countries. However\, microfinance organizations in developing countries suffer from poor business processes6 resulting in very high interest rates on loans charged to the poor. In this study\, we analyze the motivation of employees of a nonprofit microfinance organization toward information system to ameliorate these problems. To do this we build on Vroom’s (1964) theory of work motivation\, which includes three constructs: valence\, expectancy\, and instrumentality. We hypothesize that employees of NGOs in developing countries will not only have high valence for individual-level outcomes\; they will also have strong valence for both their organization and their community. However\, we also hypothesize that in developing countries\, employees have adequate instrumentality or expectancy regarding the use of information systems in achieving these outcomes. We test these hypotheses empirically and find support that these employees do have a strong valence for both organizational as well as community outcomes. However\, despite the high valence\, instrumentality and expectancy remain low. We use these to make suggestions to improve motivation for implementing information systems to improve microfinance objectives.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1E
LOCATION:Board Room 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:715a74db81a82dbcf97b038186fc1b61
URL:http://11tict4sd.sched.com/event/715a74db81a82dbcf97b038186fc1b61
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:Enhancing Cloud Security with AI and Attribute Based Encryption
DESCRIPTION:Authors - Gunik Chhabra\, Aashi Kataria\, Ashima Mehta Abstract - With the growing use of cloud computing\, protecting data from cyber threats has become a top priority. This study explores how AI-driven security\, Generative AI\, and Key-Policy Attribute-Based Encryption (KP-ABE) can strengthen cloud security. AI helps detect threats through pattern recognition and predictive analysis\, while Generative AI enhances threat intelligence. KP-ABE ensures that only authorized users can access sensitive data. Through a survey-based analysis\, this research highlights how combining these technologies can create a more secure and adaptive cloud security system.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1E
LOCATION:Board Room 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:a23ffba667859affab7d0e372f950bf3
URL:http://11tict4sd.sched.com/event/a23ffba667859affab7d0e372f950bf3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:MALWARE DETECTION FOR CYBER SECURITY
DESCRIPTION:Authors - Nisar S. Shaikh\, Dattatraya S. Bormane Abstract - Detecting malware is an essential component of cybersecurity to safeguard systems from malicious threats. An innovative hybrid model termed Long Short Feed Forward Base Convolution Neural Network (LSFFbCNN) is proposed for accurate and efficient malware detection. It combines Long Short-Term Memory (LSTM) networks and Feedforward Neural Networks (FFNN) to boost malware detection capabilities. The proposed method seeks to enhance the accuracy and efficiency of malware detection by exploiting the advantage of both sequential and non-sequential feature extraction techniques. The distinct feature vectors extracted from these networks are integrated through a concatenation layer\, creating a unified data representation. This combined feature vector is further refined by a dense layer that increases the model's ability to absorb information from sequential and non-sequential features. It employs CNNs as classifiers\, utilizing Reinforcement Learning (RL) and SoftMax functions to identify malware patterns. The proposed LSFFbCNN is evaluated on a ‘Malware detection’ dataset that includes malware and benign samples\, demonstrating superior accuracy and robustness in distinguishing malicious software. The proposed LSFFbCNN achieved 99.98% accuracy and 99.97% precision\, including F1-score and recall. The results highlight the efficacy of this hybrid approach\, presenting a promising solution for strengthening malware detection in cybersecurity applications.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1E
LOCATION:Board Room 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:a067dcdaf04ff357f28af33d1b5d32dc
URL:http://11tict4sd.sched.com/event/a067dcdaf04ff357f28af33d1b5d32dc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:The Impact of AI Tools on Academic Performance and Learning Among Young Aspirants
DESCRIPTION:Authors - Hirakjyoti Hazarika\, Pranjal Hazarika\, Kumar Konch\, Basera Sangma Abstract - The rapid emergence of Generative Artificial Intelligence (GenAI) tools—such as ChatGPT\, Google Gemini\, and Microsoft Copilot—has significantly influenced academic practices among young learners. This study investigates the impact of these tools on the academic performance and learning experiences of students aged 16 to 34. Through a structured survey of 350 participants across various institutions\, the research examines usage patterns\, perceived benefits\, and concerns related to AI-driven educational support. Findings reveal that AI tools notably enhance students' efficiency\, comprehension\, writing skills\, and academic output. Key advantages include time-saving\, personalized learning\, and improved understanding of complex topics. However\, the study also identifies critical concerns\, including potential over-reliance on AI\, ethical issues\, and the accuracy of AI-generated content. Data analysis indicates a predominantly positive perception of AI’s role in academics\, with over 60% of respondents reporting enhanced academic performance. Nonetheless\, a portion of users expressed neutral or negative experiences\, pointing to the need for balanced and mindful integration. The study underscores the dual nature of GenAI in education: offering transformative support while presenting challenges that warrant careful oversight. It calls for educational institutions to establish robust guidelines and promote responsible use\, ensuring that AI tools serve as supplements—not substitutes—for genuine learning. This research contributes to a deeper understanding of the evolving educational landscape and provides insights for educators\, policymakers\, and developers seeking to leverage AI ethically and effectively in academic contexts.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1E
LOCATION:Board Room 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:ae5473fa13ff754bed879c681a4d2faf
URL:http://11tict4sd.sched.com/event/ae5473fa13ff754bed879c681a4d2faf
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:Towards Secure Federated Learning: Understanding Vulnerabilities and Defense Mechanisms
DESCRIPTION:Authors - Rahul H. Bhole\, Sachin R. Sakhare\, Parikshit N. Mahalle\, Priyanka More Abstract - Federated Learning (FL) is a decentralized machine learning approach that enables collaborative model training without sharing raw data\, ensuring privacy preservation. Despite its benefits\, FL is vulnerable to adversarial threats such as Byzantine attacks\, where malicious clients send corrupted updates to disrupt the global model’s performance. This study introduces a simulation framework designed to evaluate the impact of such adversarial behaviors and assess the resilience of various aggregation techniques. The proposed methodology incorporates a mix of honest and malicious clients\, simulating their interactions across multiple training rounds. The results highlight significant fluctuations in the global model's performance caused by malicious updates\, exposing the limitations of standard aggregation methods like the simple mean. The visualized outputs emphasize the destabilizing effects of Byzantine attacks and underscore the necessity of implementing robust aggregation and anomaly detection strategies. This work provides a systematic foundation for understanding vulnerabilities in FL and guides the development of more secure\, reliable aggregation mechanisms.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1E
LOCATION:Board Room 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:04e8041ad5bf0166aaf53549d90de385
URL:http://11tict4sd.sched.com/event/04e8041ad5bf0166aaf53549d90de385
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:AI-Powered Mobile Applications for Early Detection of Chronic Diseases: A Federated Learning Approach
DESCRIPTION:Authors - Yogita Yadav\, Shruti Tajne\, Vimmi Malhotra Abstract - AI-powered early detection systems are revolutionizing healthcare by facilitating timely diagnoses\, customized treatments\, and improved patient outcomes. Utilizing machine learning algorithms\, these systems analyze vast medical datasets to identify risk factors for chronic illnesses such as diabetes\, cardiovascular diseases\, and neurodegenerative conditions—often before symptoms emerge. AI continuously evolves by incorporating new data\, enhancing both accuracy and efficiency. While AI offers significant benefits\, including greater diagnostic precision\, realtime monitoring\, and cost efficiency\, its adoption comes with challenges. Key concerns include data privacy risks\, integration complexities\, algorithmic bias\, and the need for clear regulatory guidelines. Additionally\, AI-driven chatbots are becoming increasingly popular for managing chronic diseases\, enhancing patient engagement and self-care. However\, further research is required to assess their safety\, technical functionality\, and long-term effectiveness. AI has vast potential in preventive healthcare\, drug discovery\, and personalized medicine. However\, addressing existing limitations is essential to developing ethical\, effective\, and accessible AI-driven healthcare solutions.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1F
LOCATION:Board Room 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:6fceec64797eea0c9b6d70525bc9a233
URL:http://11tict4sd.sched.com/event/6fceec64797eea0c9b6d70525bc9a233
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:Big Data Analytics: Trends\, Challenges\, and Applications
DESCRIPTION:Authors - Anuj Singh\, Ashima Mehta\, Suhani Ekbote Abstract - Big Data is revolutionizing data management\, analysis\, and decision-making across industries. Defined by volume\, velocity\, variety\, and veracity\, it requires advanced tools for efficient processing. Traditional systems struggle with exponential data growth\, necessitating parallel architectures. Digital advancements like social media\, IoT\, and Web 3.0 have fueled data expansion\, offering enterprises valuable insights. Companies like Google and Amazon leverage Big Data for optimization and customer satisfaction. Businesses increasingly adopt data-driven strategies to stay competitive\, but challenges like storage\, security\, and system coordination persist. Traditional data warehouses often fail to meet modern scalability demands. Big Data analytics enhances decisionmaking\, efficiency\, and innovation\, driving the fourth industrial revolution. AI-driven analytics\, cloud computing\, and computational intelligence help manage vast datasets. As industries evolve\, Big Data shapes policies\, transforms enterprises\, and fuels economic growth\, ensuring organizations maximize value while adapting to the ever-expanding data landscape.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1F
LOCATION:Board Room 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:2e3f340817795008cef32855f304c07f
URL:http://11tict4sd.sched.com/event/2e3f340817795008cef32855f304c07f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:Deep Learning Empowered Multi-Class Classification of Brain Tumors: Enhancing Diagnostic Accuracy
DESCRIPTION:Authors - Neha Verma\, Vijay Kumar Bohat Abstract - Classifying brain tumors is a critical aspect of medical image analysis\, significantly enhancing the precision of diagnosis and therapy planning. In recent years\, deep learning methodologies have exhibited considerable success in several medical image analysis applications\, including brain tumor classification. This research presents a deep learningbased methodology for the categorization of multi-class brain tumors using MRI data. To standardize the classification of different tumor types\, this method use convolutional neural networks (CNNs) to autonomously extract discriminative characteristics from MRI data. The methodology employs MRI scans of patients with pituitary tumors\, meningiomas\, and gliomas sourced from the FigShare dataset. The thorough testing and assessment confirm the efficacy of our approach to accurate brain tumor classification across multiple categories. The accuracy\, sensitivity\, and specificity of the results produced by the proposed model are remarkable for different tumor types\, demonstrating the potential of the CNN (ResNet50) architecture to enhance diagnostic processes in neuroimaging. Furthermore\, it provides critical insights into the characteristics of the acquired model and the therapeutic relevance of the results. This research enhances the knowledge concerning the utilization of advanced neural network methodologies to improve brain tumor diagnosis and emphasizes the significance of automated classification systems in clinical practice.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1F
LOCATION:Board Room 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:a1f9491befc967bf656e3ee19825c96d
URL:http://11tict4sd.sched.com/event/a1f9491befc967bf656e3ee19825c96d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:Generative AI for Metadata Creation: Enhancing Resource Discovery
DESCRIPTION:Authors - Hirakjyoti Hazarika\, Prasanna Kumar Konch\, Niharika Saikia\, Pranjal Hazarika\, Ajit Knowar\, Basera Sangma Abstract - This research examines the role of generative AI in the creation of metadata within digital libraries\, with an emphasis on improving user experience and efficacy. The results suggest that AI considerably enhances resource discoverability and expedites the cataloguing process. Nevertheless\, user satisfaction levels are inconsistent\, as they are influenced by personalization and accuracy\, and there are concerns regarding the contextual relevance and quality of AI-generated metadata in comparison to human-generated entries. The necessity of frameworks that encourage responsible AI usage is underscored by ethical implications\, such as accountability\, privacy\, and bias. Strategies for retraining staff and creating new roles that compliment AI technologies are necessary due to the potential impact on library employment. In summary\, generative AI presents significant potential for metadata improvement\; however\, it is essential to prioritise user satisfaction\, ethical considerations\, and quality. Libraries can guarantee comprehensive access to information for all users by establishing a digital environment that is more equitable and effective by combining AI with human expertise.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1F
LOCATION:Board Room 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:8109908a52e3a409fcb7468bb546d8c7
URL:http://11tict4sd.sched.com/event/8109908a52e3a409fcb7468bb546d8c7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:Orchestrating Adaptive AI in Video Games using Dynamic Sentiment Modulation and Dual-Memory Architectures
DESCRIPTION:Authors - Amaan Shaikh\, Harsh Chinchakar\, Ayush Kulshreshtha\, Priyanshi Jain\, Vandana Jagtap Abstract - This paper presents a novel conversational engine for non-player characters (NPCs) in role-playing games\, designed to deliver adaptive\, personality-driven dialogue without the need for training and maintaining custom models. The system integrates dynamic sentiment-based tone modulation with a dual-memory architecture that combines short-term conversational context and long-term narrative history. Advanced decay and cooldown mechanisms are implemented to gradually diminish the influence of older interactions and sentiments\, thereby enabling smooth transitions in NPC behavior. Our approach dynamically recalibrates NPC responses based on real-time player inputs\, world state\, and historical interactions\, resulting in immersive and context-aware dialogue that is highly humanlike. Coupled with the Generative AI models\, our framework generates NPC responses that convincingly emulate actual personalities and emotions\, while maintaining minimal reliance on the underlying large language model for fine-tuning. Experimental evaluations demonstrate that the engine produces coherent\, emotionally consistent interactions\, significantly enhancing the realism of NPC communication in gaming environments. This work contributes a scalable and technically robust framework that bridges the gap between traditional scripted dialogues and modern adaptive storytelling.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1F
LOCATION:Board Room 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:d8aee6c6c56437251b2706c08749b1e1
URL:http://11tict4sd.sched.com/event/d8aee6c6c56437251b2706c08749b1e1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:Precision Crop Recommendation Systems: Leveraging Environmental Computing and ICT for Sustainable Agriculture
DESCRIPTION:Authors - Vivek Chamoli\, Himani Binjola\, Kaushal Pandey\, Kamlesh Kukreti Abstract - With environmental computing and agricultural engineering fast emerging as important factors determining agricultural productivity\, Information and Communication Technology (ICT) is playing an extra role in im-proving advanced agricultural applications. This paper proposes a machine learning-based crop recommendation system based on environmental and soil parameters to enhance the decision-making process in crop selection. The study made use of ICT tools for collection and processing of data on nitrogen (N)\, phosphorus (P)\, potassium (K)\, temperature\, humidity\, pH\, and rainfall as a way of encouraging data-based decisions for farmers. Four machine learning algorithms were put to the test\, Logistic Regression\, Support Vector Machine (SVM)\, Random Forest\, and Gradient Boosting\, where the Random Forest algorithm scored the highest accuracy (99.32%). The main predictors of crop recommendations were soil nutrients\, temperature\, humidity\, rainfall\, and pH levels. The study demonstrates how environmental computing and engineering can enhance agricultural productivity and ensure sustainability through ICT-based solutions.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1F
LOCATION:Board Room 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:bfbb3ec3859253ffbee5729d36cf4978
URL:http://11tict4sd.sched.com/event/bfbb3ec3859253ffbee5729d36cf4978
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T083000Z
SUMMARY:Real Time Object Detection for Visually Impaired People
DESCRIPTION:Authors - Kanishka Sharma\, Vaishali Langote\, Kunwar Rambhia\, Anisha Gupta\, Rehan Shaikh Abstract - This paper explores an innovative real-time object detection system designed to empower visually impaired individuals with enhanced spatial awareness. Due to their inability to see their surroundings\, people with visual impairments constantly struggle to navigate daily life. The real-time object detection system presented in this research provides an easy-to-use method of identifying items in the surroundings by converting visual information into meaningful audio cues. Using AI-powered image processing for precise recognition\, the system's straightforward activation mechanism\, camera\, speaker\, and Raspberry Pi provide user-friendliness. The goal of this innovation is to improve the independence\, safety\, and confidence of visually impaired people by fusing assistive technology with practical applications. Our method combines little hardware with a sophisticated processing unit to deliver real-time audio input on things in the environment\, utilising the potential of embedded systems and artificial intelligence.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1F
LOCATION:Board Room 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:2622a579c3df79295c9451083bf63206
URL:http://11tict4sd.sched.com/event/2622a579c3df79295c9451083bf63206
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:A Novel Multi-Domain ECG Feature Analysis Approach for Precise Arrhythmia Diagnosis
DESCRIPTION:Authors - Gauri S. Bhagat\, Nitin S. More Abstract - Cardiovascular disorders continue to be a major global health issue\, with arrhythmias presenting significant hurdles in both diagnosis and treatment. This article presents a groundbreaking and thorough framework for ECG feature assessment that incorporates morphological\, temporal\, and frequency-domain elements\, all improved by advanced processing techniques and smart classification methods. By utilizing diverse features and tailoring approaches to individual patients\, the proposed system enhances diagnostic accuracy and dependability. Evaluations on standard datasets indicate improved classification efficacy\, marking a substantial advancement in automated systems for arrhythmia diagnosis. The framework’s adaptability further positions it as a strong prospect for integration into mobile and telemedicine platforms\,thus facilitating diagnostic processes to near real-time clinical application.
CATEGORIES:VIRTUAL ROOM 5A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:bc08a286564aadba20a5df879f689604
URL:http://11tict4sd.sched.com/event/bc08a286564aadba20a5df879f689604
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:Assessing the factors influencing customer comfort and identifying the areas for improvement in private logistics courier service
DESCRIPTION:Authors - Ann Mary Francis\, C. Rojalin Patri\, A. Varun Raj\, B. Harijith M Abstract - This study evaluates the extent of customer satisfaction with private logistics service providers\, where courier services are widely used. The study aims to comprehend customer perception of service quality by implementing a dual survey method: a customer service call survey and a walk-through audit. The key is to determine the areas of improvement and increase overall customer satisfaction. The findings indicate that more than 40% of the customers are dissatisfied with the service delivered. Findings indicate major problem areas are delivery experience\, communication channels\, and customer service interactions. The findings indicate that Order and tracking (improving order accuracy\, transparency\, and real-time tracking ability)\, Delivery (enhancing delivery timeliness\, reliability\, and communication during the delivery process)\, and Facility Factors (streamlining facility layout\, staffing\, and processes to facilitate smooth operations and effective service delivery) must be enhanced. Through the enhancement of these three aspects\, private logistics service providers can enhance overall customer satisfaction.
CATEGORIES:VIRTUAL ROOM 5A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:67b2d67681f6ff4ce6d7e27f6279878f
URL:http://11tict4sd.sched.com/event/67b2d67681f6ff4ce6d7e27f6279878f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:Enhancing Web-Page Prediction Accuracy Through an Ensemble of Logistic Regression\, Naive Bayes\, and Markov Models
DESCRIPTION:Authors - Sanjeev Kumar Punia\, Karanjeet Singh\, Fahar Imran Abstract - The exponential growth of the World Wide Web has increased the need for efficient Web-page prediction models to reduce access latency and enhance user experience. Traditional methods\, such as k-order Markov models\, struggle with balancing prediction accuracy and complexity. In this work\, we propose an ensemble model that combines Logistic Regression\, Naive Bayes\, and a First-Order Markov model to improve Web-page prediction accuracy. Logistic Regression identifies relationships in Web-log datasets\, Naive Bayes applies probabilistic reasoning\, and the Markov model captures transition probabilities between pages. By combining these models using a stacking classifier\, we aim to leverage their strengths for more robust predictions. Our results demonstrate that the ensemble model outperforms individual models\, achieving higher accuracy\, precision\, and recall. This hybrid approach can significantly improve Web navigation efficiency\, making it a promising solution for real-time Web-page prediction.
CATEGORIES:VIRTUAL ROOM 5A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:65e748710c2845daa4dc9f02c0ecd473
URL:http://11tict4sd.sched.com/event/65e748710c2845daa4dc9f02c0ecd473
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:File Storage and Sharing using Hybrid Cryptography
DESCRIPTION:Authors - Rupali Vairagade\, Shailesh Hiralal Yadav\, Harshal Raju Ismulwar\, Manoj Ramashish Gupta\, Nilakshi Jain\, Shwetambari Borade Abstract - Digital statistics develop at an extraordinary pace\, making the demand for cozy\, scalable\, green records more than ever before. Traditional encryption methods\, including symmetric and asymmetric encryption\, have long been extremely important for protected tag marks. However\, these traditional methods face major challenges consisting of complex mathematical calculations\, difficult intrinsic management\, and obstacles to scalability. These issues can be enjoyed by improving aid\, poor overall performance\, and bad users. Hybrid encryption structures provide an effective solution with the help of a combination of stable security of heterogeneous encryption for critical changes and administration and the efficiency of symmetric encryption. This twin technology allows for faster processing of records\, but the encryption key is protected and protected from unauthorized entries. By using the power of both encryption strategies\, hybrid encryption deals with scalability and overall performance issues. This is often seen in traditional systems and is ideal for large packages in a wide range of different fields. In the long run\, hybrid encryption is actually the main break in protection technology\, providing a balanced solution that improves security\, improves machine efficiency and ensures scalability. This approach is suitable for current desired developments for virtual environments\, allowing businesses to protect sensitive statistics without compromising performance or enjoying users.
CATEGORIES:VIRTUAL ROOM 5A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:ba932fc90f8d0fc42c80072f9570b946
URL:http://11tict4sd.sched.com/event/ba932fc90f8d0fc42c80072f9570b946
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:Flood Rescue: An Integrated GIS and Remote Sensing-Based Decision Support System for Flood Inundation Warning and Relief
DESCRIPTION:Authors - Dhruva R. Rinku\, Parimi Hema Sree\, D. Nagajyothi\, Anita Kulkarni Abstract - In recent years\, the frequency of floods has surged due to climate change and unchecked urbanization. In developing nations such as India\, floods wreak havoc that can take decades to recover from. To effectively mitigate the impact of flood disasters\, precise flood inundation warnings are essential. This system employs a Geographic Information System (GIS) model\, constructed using a Digital Elevation Model (DEM) and building shape-files\, to analyze land behavior during floods and identify inundation risks in various locations. This information is crucial for taking proactive measures to reduce flood-related destruction promptly. The Global Precipitation Measurement’s (GPM) half-hourly rain data is utilized to assess the current influence of rainfall on flood conditions across different regions\, thereby enabling timely warnings to residents in nearby areas. Furthermore\, this system incorporates a flood relief system\, which is web-based and developed using PHP as the web application and a MySQL database. This platform prepares donors to assist flood victims with various types of donations. Immediately following the release of a flood warning through a GSM module that sends SMS alerts\, donors are informed to be ready with their contributions. This timely communication equips authorities to provide refuge to flood victims. This system is specifically designed for Mumbai\, India’s largest city\, which is frequently affected by floods\, resulting in significant property and life damage annually.
CATEGORIES:VIRTUAL ROOM 5A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:430b98c444ff19c45bb095d5f56bb2ea
URL:http://11tict4sd.sched.com/event/430b98c444ff19c45bb095d5f56bb2ea
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:Lane Departure Warning and Correction System with Control logic on FPGA
DESCRIPTION:Authors - Nikita Patil\, Basawaraj Patil Abstract - The goal of this paper is to leverage Field Programmable Gate Array (FPGA) technology to create a Lane Departure Warning and Correction System. Its Advanced Driver Assistance System (ADAS) design attempts to improve vehicle safety by avoiding in advertent lane changes. Under a variety of driving circumstances\, such as changes in lighting\, weather and road types\, the system reliably detects lane boundaries by utilizing image processing techniques like Hough Transform and Canny Edge Detection. The Xilinx Zynq Ultra scale FPGA\, which combines high-performance processors and peripherals for real- time processing and control\, is used in the implementation. In order to guarantee that the car stays in its lane\, the system is made to deal with issues like faded markers\, shadows\, and construction zones. It does this by promptly sending out alerts or taking remedial action. By addressing real-world issues in autonomous and semi-autonomous vehicles\, this breakthrough highlights the emerging of strong hardware and cutting-edge algorithms\, greatly lowering the risks associated with lane departure incidents.
CATEGORIES:VIRTUAL ROOM 5A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:ba785f44b73dea46ea21a0679141755a
URL:http://11tict4sd.sched.com/event/ba785f44b73dea46ea21a0679141755a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:Leveraging Point Cloud Data for Autonomous Vehicle Systems: A Comprehensive Dataset Pipeline and ANN Model
DESCRIPTION:Authors - Gauri Gautam\, Vijay Kumar Sharma Abstract - This paper outlines an organized approach to creating a dataset and training an Artificial Neural Network (ANN) for autonomous vehicle driving systems. It utilizes the KITTI dataset and includes key preprocessing steps like converting binary files to CSV. Other steps comprise calibrating and plotting 3D point cloud data and creating 2D front-view projections with consistent sizes. A novel data preparation pipeline is presented\, ensuring homogeneity while addressing challenges like variable point distributions. It also confronts cropping consistency to improve data quality. The results demonstrate a robust methodology for generating training-ready datasets. A three-layer ANN is trained effectively for autonomous driving tasks. This work contributes significantly to autonomous systems. It provides a scalable approach that can adapt to various datasets.
CATEGORIES:VIRTUAL ROOM 5A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:e77309afc6b64ee214d53cf770c2549a
URL:http://11tict4sd.sched.com/event/e77309afc6b64ee214d53cf770c2549a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:MAXIMIZING DETECTION COVERAGE IN IDS THROUGH HYBRID DEEP LEARNING ARCHITECTURES
DESCRIPTION:Authors - Jaimin Dave\, Chintan Shah\, Premal Patel Abstract - Implementing effective control over harmful actions in a network through an Intelligent Detection System (IDS) is necessary for modern digital security\, but building robust techniques for coverage with high accuracy remains a challenge. To help overcome this challenge\, the study's contribution proposes a hybrid deep learning approach combining convolution neural networks (CNN) and Long Short Term Memory (LSTM) networks for maximum coverage of detections in IDS. Tests have been conducted on various datasets and the model achieved best results of 75% detection accuracy for different attack scenarios. This was better than what's achieved using traditional methods as the legacy Intelligent Detection Systems (IDS) techniques\, although increasing detection coverage\, reduced the level of falsely identified cases and improved adaptability towards new patterns of attacks. The results open new frontiers for the development of hybrid machine learning architectures capable of addressing the shortcomings of traditional Intelligent Detection Systems (IDS) models and improving network security.
CATEGORIES:VIRTUAL ROOM 5A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:4db164d2b3cb0dac4fa0676fc13c382b
URL:http://11tict4sd.sched.com/event/4db164d2b3cb0dac4fa0676fc13c382b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:ODDNet- Object Detection in Dark with Attention-driven RGB-Event Fusion
DESCRIPTION:Authors - Safa Imtihaz Sayyad\, Jyoti M Satihal\, Ujwala Patil Abstract - Object detection in dark conditions at night is challenging due to poor visibility\, leading to reduced accuracy and performance. We propose ODDNet\, a framework for object detection in the dark that combines enhanced RGB images with event-based data\, leveraging their complementary strengths. Using Zero-DCE\, RGB images are enhanced for low-light\, while event data is processed through a Temporal Multi-scale Aggregation to extract its temporal features. Attention mechanisms and specialized loss functions improve low-light imaging\, preserve spatial details\, and enhance detection accuracy. Ablation studies highlight the contributions of Zero-DCE and attention-based fusion. The proposed ODDNet model achieves a mean average precision (mAP) of 45.8% during the day and 28.3% at night at an intersection over Union (IoU) threshold of 0.5. These results demonstrate ODDNet’s capability to effectively address low-light challenges\, indicating its potential for autonomous systems and night-time surveillance.
CATEGORIES:VIRTUAL ROOM 5A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:e66ec0b0972f8be5f4e1819e9dbbf335
URL:http://11tict4sd.sched.com/event/e66ec0b0972f8be5f4e1819e9dbbf335
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:Personalized AI Doctor
DESCRIPTION:Authors - Aarti Amod Agarkar\, Harshal Vijay Chaudhari\, Sanskar Mukta Chaudhari\, Astha Sachin Chaudhari\, Om Yogesh Borse Abstract - The Personalized AI Doctor is a smart and user-friendly platform designed to make healthcare more accessible and efficient. It simplifies the process of booking medical appointments by using advanced technologies like artificial intelligence (AI)\, natural language processing (NLP)\, and secure cloud-based databases. Patients can easily register on the platform\, share their symptoms through a chatbot or voice commands\, and receive accurate disease predictions powered by AI. The system intelligently matches patients with the right doctors based on their specialization and availability\, ensuring timely care. It also allows users to book appointments and attend virtual consultations through automatically generated Google Meet links\, offering convenience and flexibility. Additionally\, the platform provides detailed insights and reports for administrators\, helping optimize doctor schedules and improve resource allocation. By combining AI\, modern database systems\, and cloud services\, this system transforms the healthcare experience\, making it simpler\, faster\, and more patient-centric.
CATEGORIES:VIRTUAL ROOM 5A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:87164baee8f317154a7ebdda8ece7be0
URL:http://11tict4sd.sched.com/event/87164baee8f317154a7ebdda8ece7be0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:A Self-Monitoring System for Internal Intrusion Detection and Protection using Data Mining and Forensic Techniques
DESCRIPTION:Authors - V. V. mandhare\, Poonam Bhokare\, P.S. Vikhe\, Chandrakant Kadu Abstract - These days\, billions of people use the internet worldwide. Technology for intrusion detection may be novel. a security technology generation that keeps an eye on the system to stave against malicious activity. The IDPS tracks malicious user behavior over time using a neighborhood procedure grid. Because of the rhetorical alternatives\, the system suggests a security system during this project called the Intrusion Detection and Protection System at call level\, which builds user pro- files to monitor usage activities. The proposed work is evaluated using intrusion detection systems and forensic techniques. The bottom paper includes a review of the literature on the Intrusion Detection System (IDS) and Internal Intrusion Detection System (IIDS). Internal Intrusion Detection System (IIDS)\, which employs predetermined algorithms or approaches to distinguish unauthorized user activity or attacks over a network\, was designed during this research.
CATEGORIES:VIRTUAL ROOM 5B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:0a46556aba6414ea8d2479d1271111d3
URL:http://11tict4sd.sched.com/event/0a46556aba6414ea8d2479d1271111d3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:A Systematic Performance Comparison of YOLO Models for Human Identification in Visual Scenes
DESCRIPTION:Authors - Dev Gandhi\, Adarsh Srivastava\, Rachit Soni\, Daksh Parekh\, Lokesh Heda Abstract - Recognizing people in images and videos is the main objective of computer vision-based person identification. The past ten years have seen a great deal of research in human detection. As single-stage algorithms\, YOLO is a desirable choice for object detection because it offers faster results than two-stage algorithms. The advantage of this approach is that it provides both a manual for choosing the most effective human detection methods for real-world applications and a thorough analysis of current methods. This research paper's objective is specifically to evaluate and compare the performance of YOLOv3\, YOLOv4\, and YOLOv5 models on various images to detect human in visual scenes. Additionally\, this paper discusses various parameters according to which the model’s efficiency is determined. Our experimental results demonstrate that YOLOv5 achieves higher accuracy\, precision\, and recall as compared to other YOLO models\; the highest accuracy attained by YOLOv5 is 0.94 with F1 score of 0.96.
CATEGORIES:VIRTUAL ROOM 5B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:d6562ba33d3606719e527552e2f7a157
URL:http://11tict4sd.sched.com/event/d6562ba33d3606719e527552e2f7a157
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:AGENTIC AI WITH MODEL CONTEXT PROTOCOL
DESCRIPTION:Authors - Shubhamm Kumaar\, Akshat Sharma\, Sukrati Chaturvedi Abstract - By connecting Agentic AI with Model Context Protocol Servers (MCPS) it is made possible to work towards autonomy of decision making. work-flow automation. Agent AI which is powered by large language models can provide a live context of information using MCPS. This work mostly focuses on the making of smarter workflows which are regular and pleasing. traditional automation. This integration improves using a decentralized multi agent framework. Makes decisions better and works smoother. Healthcare\, manufacturing\, smart\, and other fields. cities. Tesla's efficient production and Singapore's Smart city are real-life examples. Nonetheless\, various obstacles regarding scalability\, ethics\, computation\, and more can hinder its application. However\, challenges like scalability\, ethics\, and computational demands remain. This review synthesizes current research\, applications\, and future directions\, underscoring the promise of this technology for innovative automation solutions.
CATEGORIES:VIRTUAL ROOM 5B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:e7ff2f1e180fdae47f4f594310cc7b61
URL:http://11tict4sd.sched.com/event/e7ff2f1e180fdae47f4f594310cc7b61
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:Ensuring secure online Examination through QR Code Authentication and Automatic Question Paper Generation
DESCRIPTION:Authors - V. V. mandhare\, Hemlata Mali\, P.S. Vikhe\, M. R. Bendre\, M. R. Parkhe Abstract - This paper introduces a smart and efficient Hall Ticket Generation System that integrates QR code technology to improve the process of generating and managing hall tickets for academic examinations and events. Built using Java and MySQL\, the system is designed to simplify administrative work\, enhance security\, and promote eco-friendly practices by eliminating the need for paper-based hall tickets. The core functionality of the system is its ability to generate dynamic QR codes for each hall ticket\, which can be quickly scanned using an Android- based scanner application. This allows invigilators to instantly verify the identity and credentials of candidates\, reducing the chances of fraudulent entries or unauthorized access to the examination hall. By transitioning tothisdigitalmodel\,institutionsbenefitfromreducedpaperusage\,lower administrative costs\, and improved operational efficiency. The system is not only secure but also user-friendly\, making it easier for both staff and students to manage examination logistics. In Automatic Question Paper Generator Module\, which addresses the common challenges associated with manually preparing exam questions. To overcome these limitations\, the system uses a key word-based randomization algorithm to create question papers swiftly and securely. This method ensures that each set of questions is unique\, well-distributed across topics\, and free from duplication. The system is capable of storing and managing multiple question paper sets\, making it easier to conduct exams across different classes while ensuring comprehensive curriculum coverage. Overall\, this project offers a complete solution that not only simplifies exam management but also enhances the quality and integrity of academic assessments through automation and intelligent design.
CATEGORIES:VIRTUAL ROOM 5B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:f41ae5a236f708f563d8332f26661215
URL:http://11tict4sd.sched.com/event/f41ae5a236f708f563d8332f26661215
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:Heart Disease Prediction Using Demographic and Clinical Parameters: A Comprehensive Analysis
DESCRIPTION:Authors - Madhu Shukla\, Vipul Ladva\, Simrin Fathima Syed\, Neel H. Dholakia Abstract - Cardiovascular disease continues to be one of the leading causes of death globally\, demonstrating the critical role of efficient and reliable prediction models. Here in this study a dataset that is integrated from five heart disease datasets originated from publicly available sources such as UCI for Heart Attack risk prediction and analysis with 1\,888 instances are used. Fourteen primary factors that include age\,abnormality of cholesterol level\, type of chest pain\, as well as exercise related parameters from demographic and clinical dimensions were investigated in order to find the association with heart disease. Significant trends were found using data visualization to show high heart risks with certain chest pain types and high maximum heart rate. A correlation matrix illustrates important inter-feature relationships and sheds new light on the predictabilitheckability of the features. This study highlights the possibility to exploit demographic and clinical information for early detection of high risk individuals and in the future\, to allow medical interventions and improve health state.
CATEGORIES:VIRTUAL ROOM 5B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:5a6c9ea922362a6c902a15b1d61a0e8f
URL:http://11tict4sd.sched.com/event/5a6c9ea922362a6c902a15b1d61a0e8f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:Machine Learning based Smart Recommendation system for Selection of Routing Protocols in MANETs
DESCRIPTION:Authors - Bhukya Rakesh\, Dasari Yaswanth\, Kumari Nidhi Lal Abstract - Mobile Ad Hoc Networks (MANETs) are dynamic\, decentralized networks that require efficient routing mechanisms to ensure reliable communication. Traditional routing protocols struggle with issues such as high node mobility\, energy constraints\, and unpredictable topology changes. This research explores the integration of artificial intelligence\, specifically neural networks\, to enhance routing efficiency in MANETs. Our approach leverages deep learning models to predict optimal routes by analyzing network parameters such as node density\, mobility patterns\, and link stability. The proposed AI-driven routing mechanism dynamically adapts to network variations\, improving packet delivery ratio\, reducing latency\, and optimizing energy consumption. Comparative evaluations against conventional routing protocols\, such as AODV and DSR\, demonstrate significant improvements in network performance. The results highlight the potential of neural networks in revolutionizing adaptive routing for MANETs\, paving the way for more intelligent and resilient communication systems.
CATEGORIES:VIRTUAL ROOM 5B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:7144c340e9138d8613c5f42b8ac2c5a2
URL:http://11tict4sd.sched.com/event/7144c340e9138d8613c5f42b8ac2c5a2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:Online Payment Fraud Detection Using Machine Learning
DESCRIPTION:Authors - Ayush Tiwari\, Ayushi Tomar\, Prabhjot Kaur Abstract - Online banking fraud constitutes illegal access to accounts for the payment transfer purposes. There are several reasons for difficulty in the detection of such crimes-from the imbalance of data to the fraudsters' techniques that are ever-changing. The set of tools used for this purpose are machine learning\, economic optimization\, and risk assessment. By combining these techniques\, a maximum reduction in the losses due to fraud and false positives will be achieved. The machine learning models\, when validated against real datasets\, were able to reduce losses by 52%\, allowing for just 0.4% false positives. The improvement in behavior analysis for fraud detection is conducted through transaction clustering\, sliding window aggregation\, and adaptive classifiers. The algorithms such as KNN\, SVM\, Logistic Regression\, Local Outlier Factor\, and Isolation Forest are used to predict fraud in credit card transactions so that it is accurately detected\, false alarms being at a minimum\, and customers are availed against unauthorized charging.
CATEGORIES:VIRTUAL ROOM 5B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:17010b8b0c627bdb142af939357596eb
URL:http://11tict4sd.sched.com/event/17010b8b0c627bdb142af939357596eb
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:Optimized Machine Learning Models for Fertilizer Recommendation Using Feature Engineering and Neural Networks
DESCRIPTION:Authors - Nisarg Chaudhari\, Urva Dave\, Zeel Patel\, Manasvi Vachhani\, Dweepna Garg\, Bhavika Patel\, Kashyap Patel\, Parth Goel Abstract - Fertilizer recommendation plays a crucial role in optimizing crop yield while minimizing resource wastage. The integration of machine learning techniques enables precise fertilizer prediction based on soil and environmental conditions\, leading to improved agricultural productivity. However\, traditional methods often result in overuse or underuse of fertilizers\, negatively impacting soil health and crop growth.This study employs various machine learning algorithms\, including RandomForest\, XGBoost\, LightGBM\, HistGradientBoosting\, CatBoost\, and Neural Networks\, to classify and recommend fertilizers based on soil parameters. The models were trained on a synthetic fertilizer dataset containing diverse soil compositions and fertilizer requirements.Experimental results indicate that Neural Networks outperform tree-based models\, achieving the highest testing accuracy of 84.10%\, demonstrating strong generalization capabilities. Accuracy and loss trends over epochs confirm stable learning\, while a confusion matrix reveals minimal misclassifications. This study highlights the effectiveness of deep learning in optimizing fertilizer recommendations\, contributing to more sustainable and efficient agricultural practices.
CATEGORIES:VIRTUAL ROOM 5B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:99da2437ca65fc82cfa312f662f8abcc
URL:http://11tict4sd.sched.com/event/99da2437ca65fc82cfa312f662f8abcc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:Secure and Cost-Effective IoT-Based Water Quality Monitoring Framework
DESCRIPTION:Authors - Nirav Narayan\, Martin Parmar\, Parth Shah\, Mrugendra Rahevar Abstract - Water quality is a critical concern for human health\, ecosystems\, and sustainable resource management. Traditional water quality monitoring methods are expensive\, time-consuming\, and often lack real-time data availability. This research proposes a Secure Water Quality Monitoring framework integrating IoT\, LoRa WAN\, and secure data transmission to enable real-time\, cost-effective monitoring in remote and urban areas. The system employs low-power LoRa technology for long-range communication\, ensuring reliable data transmission even in connectivity-challenged regions. ESP32 microcontrollers process sensor data\, measuring key parameters such as pH\, turbidity\, TDS\, EC and DO. Security is ensured using AES-128-bit encryption and SHA-256 hashing\, safeguarding environmental data against tampering. The proposed framework addresses challenges in conventional monitoring\, such as high costs and limited scalability\, by offering a low-cost\, energy-efficient\, and scalable approach. This study demonstrates the potential of IoT-driven smart monitoring framework to enhance water resource management and environmental sustainability\, paving the way for future advancements in sensor technology\, energy efficiency\, and data security.
CATEGORIES:VIRTUAL ROOM 5B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:36802229c0c593726d0e7c52761f4224
URL:http://11tict4sd.sched.com/event/36802229c0c593726d0e7c52761f4224
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:THE ROLE OF INTERNAL FAMILY NETWORKS IN SUCCESSION CHOICE
DESCRIPTION:Authors - Amrithesh TV\, Sajin John Shaji\, Sangeeth Gopinath Abstract - Family businesses play significantly in the world economy\; yet\, the majority can somehow manage to survive through leadership change from impacts of family relationships. Unlike some business corporations with formalized transitions\, family companies dominantly depend on family relationships and informal decision-making\, hence bringing about business instability as well as conflicts. This research examines just how internal family systems indeed influence successor choice\, and shape leadership transition on business resilience. Through in-depth qualitative research with strict thematic analysis of family firm owner interviews\, the research discovers a number of important succession determinants\, such as traditional heirarchy\, mentering\, and resistance to modernization of the modern kind. The research does uncover that in fact successor selection is usually highly influenced by family influence\, in contrast to planned planning\, and therefore jeopardizing some degree of business stability. This paper suggests the adoption of comprehensive succession planning and participative decision-making. Systematic leadership development programs\, coupled with open communication\, will ensure successful leadership succession and long-term business success.
CATEGORIES:VIRTUAL ROOM 5B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:dfc03cb603d5c53e85fd22b9af209bef
URL:http://11tict4sd.sched.com/event/dfc03cb603d5c53e85fd22b9af209bef
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:Analyzing Airline Sentiment in a Multilingual Twitter Landscape via Vectorization and ML Models
DESCRIPTION:Authors - Vanishree Pabalkar\, Anuja Bokhare Abstract - Sentiment analysis in multilingual social media data is a challenging and critical task due to the diversity of languages and sentiments expressed by users worldwide. In this study\, we focus on conducting sentiment analysis on the Twitter US Airline Sentiment dataset\, which includes tweets in English from users expressing their opinions about various US airlines. We address the research gap of multilingual sentiment analysis by leveraging advanced NLP techniques and machine learning algorithms. Count Vectorization and TF-IDF Vectorization is used during the study to extract features after cleaning up the data and processing the text. To categorize tweets as having positive or negative sentiment\, we assess the effectiveness of three classifiers: Multinomial Naive Bayes\, Bernoulli Naive Bayes\, and Logistic Regression. We examine these classifiers' accuracy on a different collection of unlabeled tweets without ratings in more detail. The work provides valuable insights into the opinions posted by individuals on Twitter about US airlines and intends to develop multilingual sentiment analysis\, especially for social media data. The findings serve as a basis for creating sentiment analysis algorithms that are more precise and reliable and that can be used to various language groups on social media platforms.
CATEGORIES:VIRTUAL ROOM 5C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:616ecd87f288967dd034a5fb1ce82f1b
URL:http://11tict4sd.sched.com/event/616ecd87f288967dd034a5fb1ce82f1b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:Classification of Brain Images using Bit Plane Approach
DESCRIPTION:Authors - Tanuja R. Patil\, Samiksha Dandgall\, Vishwanath P. Baligar Abstract - Early diagnosis of brain diseases is very important and challenging nowadays. Detecting neurological disorders such as brain tumors using magnetic resonance imaging (MRI) has become an important research topic. Recently many machine learning and deep learning models have been proposed to detect and classify the brain abnormalities. Many of these models have high time complexity and still efficient models are required. The proposed model makes use of a Novel and Low Complexity Approach to solve the problem of classification of brain images. This approach is a less complexity deep learning model which uses novel methods for Denoising\, Segmentation\, Feature extraction and Classification of brain tumors. Here\, it makes use of the advantages of bit plane approach and a unique feature extraction method. The proposed model makes use of the data set from Kaggle in which\, size of the training data set is 2870 with four classes namely No Tumor\, Glioma Tumor\, Meningioma Tumor and Pituitary Tumor. The size of the testing data set is 394. A feature vector which matches most with the feature vector of the input image is considered as the class of the input image. The proposed method makes use of advantages of time domain and able to give good results. The overall performance of the proposed algorithm considering both training and testing data set is 97.34%. The proposed idea is comparable with the many existing models and the results are compared with three models CNN\, VGG19 and Inception-V3 models and found to be promising.
CATEGORIES:VIRTUAL ROOM 5C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:92d210bab6dc09358959764f53686444
URL:http://11tict4sd.sched.com/event/92d210bab6dc09358959764f53686444
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:CLOUD BASED PLANT HEALTH MONITORING SYSTEM
DESCRIPTION:Authors - Arnav Rahul Jade\, Jatin Santosh Jaiswal\, Nishad Sachin Kamat\, Vedant Mahesh Kandarkar\, Amruta Pabarekar Abstract - The Cloud-Based Plant Health Monitoring System is designed to help farmers and agricultural experts precisely identify plant diseases using artificial intelligence and cloud technology. Traditional plant health assessments rely on manual inspection\, which can be time-consuming and prone to errors. This project automates the process by allowing users to upload images of plant leaves\, analyzed by a machine learning model hosted on a cloud platform. The system identifies whether the plant is healthy or has a disease\, providing instant results through a simple mobile or web application. To achieve this\, the system uses a Convolutional Neural Network (CNN) trained on a dataset of plant leaf images\, covering both healthy and diseased conditions. The application is designed to be user-friendly\, allowing even non experts to access plant health information easily. This approach reduces the need for excessive pesticide use\, saves time\, and supports sustainable farming practices by helping users respond to plant health issues promptly. Keywords-plant health monitoring\, artificial intelligence\, convolutional neural network (CNN)\, plant disease detection\, cloud computing\, mobile application and machine learning.
CATEGORIES:VIRTUAL ROOM 5C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:ecef653c9b3ca0a6901645b1c9d5d961
URL:http://11tict4sd.sched.com/event/ecef653c9b3ca0a6901645b1c9d5d961
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:Deep Tune Network: An Approach Towards Music Classification and Recommendations
DESCRIPTION:Authors - Arunabh Barooah\, S. Saranya Rubini Abstract - In the context of the digital music industry\, accurate classification of music into genres and the ability to recommend appropriate genres for a user greatly improves usability of various streaming platforms. Several traditional machine learning approaches that rely on metadata face significant limitations\, such as inconsistencies in the data\, the growing diversity of musical styles\, and a lack of focus on the actual musical content of songs. These shortcomings often result in suboptimal performance\, particularly in recommendation systems. In response to these issues\, this work proposes Deep Tune Network (DTN)\, a deep learning system for automated genre analysis and discovery of music based on similar acoustic patterns. This system uses Convolutional Neural Networks (CNNs) and Mel-frequency cepstral coefficients (MFCCs) to identify the repetitive patterns inside audio signal needed to classify music into different genre. The model achieves a maximum test accuracy of 93.01%\, demonstrating its reliability in real-world applications. Additionally\, a cosine similarity-based recommendation system is implemented to suggest acoustically similar songs\, bridging accurate genre classification with personalized playlist curation.
CATEGORIES:VIRTUAL ROOM 5C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:30be8e813e6ef4ef46986d370247134b
URL:http://11tict4sd.sched.com/event/30be8e813e6ef4ef46986d370247134b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:FarmTech: Enhancing Agricultural Equipment Utilization with Machine Learning-Based Price Prediction
DESCRIPTION:Authors - Om Gadhvi\, Srushti Pawar\, Shravan Gadhvi\, Mansi Jadhav\, Manya Gidwani Abstract - Farmers in Maharashtra\, India face substantial costs and low utilization rates on equipment\, leading to significant financial strain. We suggest implementing an AI-powered Dynamic Machine Price Prediction Model that we can use for a digital platform that enables renting out equipment. This model uses Linear Regression\, Random Forest\, and Gradient Boosting to output what prices equipment should sell for\, given the age\, how often farmers use it\, when they use it\, and market demand. Gradient Boosting turned out to be the most accurate model in our exams\, giving us a 94% R² score so that our model predictions are trustworthy. The website is built using the MERN stack\, and it employs secure transactions through PayPal and a feature that enables farmers to search for equipment based on their location. By adjusting equipment prices on the go\, we provide insights to farmers into how much they should charge for renting out their equipment in all conditions The proposed system enhances resource utilization\, sustainability\, and economic resilience in the agricultural sector.
CATEGORIES:VIRTUAL ROOM 5C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:9d8d317db469d93e422d1157c85750ec
URL:http://11tict4sd.sched.com/event/9d8d317db469d93e422d1157c85750ec
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:IoT-Enabled Real-Time Monitoring for Predictive Maintenance in DC Motors
DESCRIPTION:Authors - Elakkiya R\, Sagunthala G\, Tanisha Sinha\, Gugapriya G Abstract - This project presents an IoT-based predictive maintenance system based on machine learning algorithms—Random Forest\, Logistic Regression\, SVM\, and LSTM—to identify motor faults precisely. Realtime data such as sound\, vibration\, and RPM are recorded through hardware prototyping\, while Simulink simulates speed and torque. Data is transmitted to Firebase for real-time monitoring\, prompting automated fault notifications. This system improves industrial efficiency by minimizing sudden failures\, reducing maintenance expenses\, and increasing machinery lifespan through prompt\, data-driven interventions.
CATEGORIES:VIRTUAL ROOM 5C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:d2c5a0631431f5ef5ed6ef04b3427584
URL:http://11tict4sd.sched.com/event/d2c5a0631431f5ef5ed6ef04b3427584
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:MediLink: Blockchain Based Comprehensive Web framework for Maintaining Health Records
DESCRIPTION:Authors - Atharva. Makode\, Yash. Kasar\, Yatish. Gharat\, Yuvraj. Gage\, Mandar Ganjapurkar\, Kiran Deshpande Abstract - This paper proposes MediLink\, a blockchain-based platform to store\, share\, and manage secure medical records. Patients and care providers in healthcare systems today typically face problems of data privacy compromises\, system-to-system noninteroperability\, and bureaucratic administration. These present risks to compromised patient care as well as expose data security gaps. MediLink addresses these challenges head-on by developing a decentralized\, transparent system that places patients in control\, with full control over their medical information while ensuring the integrity and confidentiality of the same. The platform employs smart contracts to carry out important functions such as processing insurance claims\, handling patient consent\, and keeping track of audit trails. Not only does this reduce human errors between humans but also saves administrative burdens and costs as well. Utilizing the strength of blockchain technology\, MediLink not only secures data—yet also streamlines the easy exchange of patient records between healthcare providers. The end result? Seamless care coordination\, enhanced patient outcomes\, and a smoother experience for all
CATEGORIES:VIRTUAL ROOM 5C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:7a8107fab2877865778987d2b797cb01
URL:http://11tict4sd.sched.com/event/7a8107fab2877865778987d2b797cb01
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:Optimized Wallace Multipliers Using Approximate Adders with ALU Error Correction
DESCRIPTION:Authors - D.V.N. Bharathi\, K.P.K. Lalitha Vitala\, K. Bala Sindhuri\, Sai Nikilesh Kudapa\, M.L. Hiranya\, Lingam Swamy Surendra\, Manoj Kumar Juttuka\, Kishore Varma Manthena Abstract - This paper explores the design and efficiency of optimized Wallace multipliers integrated with approximate adders to enhance energy efficiency\, reduce delay\, and minimize hardware complexity in the first address. Five distinct departments are introduced: architectures (AA1–AA5)\, each offering unique trade-offs regarding power consumption\, processing speed\, and circuit area. These adders are incorporated into Wallace multipliers\, which improve computational speed while lowering energy requirements and design complexity. The proposed designs are evaluated using 90 nm technology to determine their applicability in resource-constrained and error-tolerant domains\, such as image processing\, machine learning\, and IoT applications. The findings demonstrate a versatile balance between accuracy and resource efficiency\, making these designs well-suited for real-time systems with different performance demands.
CATEGORIES:VIRTUAL ROOM 5C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:3170e77a3ac15dba9e948f63f3d3c0dc
URL:http://11tict4sd.sched.com/event/3170e77a3ac15dba9e948f63f3d3c0dc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:Sustaining Community Health Workforce Through E-Governance: Addressing Motivation\, Retention\, and Public Health Resilience
DESCRIPTION:Authors - Deepa Unni\, Murale Venugopalan\, Sanju Kaladharan Abstract - Community Health Workers (CHWs) does an important role in delivering health to the public. CHW programmes often fail due to the impracticable expectations\, lack of proper planning and also the efforts required to implement these activities are often underestimated. Prior to the COVID-19 pandemic\, many of the developing countries used digital health technologies to address a range of health issues. Limited research has been held so far to discover the role of e-governance in addressing sustainable community health workforce. This paper bridges this gap by proposing an E-Governance Enabled Sustainability Model for CHWs.
CATEGORIES:VIRTUAL ROOM 5C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:bb2577d609c7bb781eb65bed10a98754
URL:http://11tict4sd.sched.com/event/bb2577d609c7bb781eb65bed10a98754
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:Tomato Leaf Disease Detection Using Fusion of Thepade’s SBTC and Haralick Moments (GLCM) Features with Machine Learning Algorithms
DESCRIPTION:Authors - Aparna Joshi\, Moreshwar A. Mahale Abstract - Crop disease diagnosis forms one of the most significant facets of precision farming\, which owes a great boost to the power of machine learning. This work presents a new method through which the use of the Tomato Leaf Disease Dataset to classify the tomato leaf's disease is achieved. The database contains 1609 images for ten disease classes: Bacterial Spot\, Early Blight\, Late Blight\, Leaf Mold\, Septoria Leaf Spot\, Spider Mites\, Target Spot\, Tomato Yellow Leaf Curl Virus\, Tomato Mosaic Virus\, and Healthy Leaves. The features are extracted through the combination of Thepade's Sorted Block Truncation Coding (TSBTC) and Haralick Moments (GLCM) to improve texture and intensity description. Extracted features are categorized into multi-level classification (2-ary\, 3-ary\, 4-ary\, 5-ary). The results achieved are stored in an Excel file and re-run using the support of Weka tool where classifiers like Naïve Bayes\, Logistic Regression\, Sequential Minimal Optimization (SMO)\, J48\, Random Forest\, and Random Tree. are employed. The study identifies the optimal model so that accurate and automated diagnosis of crop disease can be performed.
CATEGORIES:VIRTUAL ROOM 5C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:00331ae1d6d1b2a1807f54416be8c60e
URL:http://11tict4sd.sched.com/event/00331ae1d6d1b2a1807f54416be8c60e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:A Sequence-to-Sequence Approach for Text Summarization Using Bi-LSTM Networks
DESCRIPTION:Authors - Amulya Naik\, Pallavi Dhaded\, Shireesh Hakki\, Satish Chikkamath\, Suneeta V Budihal\, Sujata Kotabagi Abstract - A text processing framework that applies Encoder-Decoder architecture with attention mechanism functions as the main focus of this research for resolving Natural Language Processing predictive problems. The research first outlines base technologies along with methodologies and frameworks required to build the system design. Detailed analysis of the dataset happens at this phase through observing dataset structure and calculating statistical summaries to detect missing or duplicate values. Better understanding of the dataset by using descriptive analytics to identify potential problems which leads them to improve the dataset. Data cleaning serves multiple functions during the process by eliminating unneeded columns together with missing value management and text normalization methods. The normalization procedure entails converting text into either upper or lowercase format and executes tag stripping alongside URL replacement and shorthand elimination and emoji and contraction removal. The processing begins after tokenization divides the text into segments. After cleaning the data the input and output components get separated while padding is used to maintain consistent dimensional structure.The model base incorporates an Encoder-Decoder framework combined with attention functionality while implementing a BiLSTM network. Through this specific model configuration both past and future inputs can be read contextually which boosts the prediction accuracy. The designed model produces 90.23 percent achievement in accuracy which highlights its strong capability in processing intricate NLP operations.
CATEGORIES:VIRTUAL ROOM 5D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:5891b31cd7c39ad6514c12b11e919814
URL:http://11tict4sd.sched.com/event/5891b31cd7c39ad6514c12b11e919814
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:AI ate my Job: Impact of AI Anxiety on Career Anxiety and Career Uncertainty
DESCRIPTION:Authors - Abirami K\, Megha Nayanar\, Shobhana Palat Madhavan\, Deepak Gupta Abstract - This study explores the impact of AI anxiety on career anxiety and career uncertainty\, using Self-Determination Theory as its theoretical framework. As Artificial Intelligence (AI) continues to reshape industries\, it presents both opportunities and challenges in career related factors. AI anxiety\, driven by concerns over job security and skill obsolescence\, affects individuals' confidence in their career choices and decision-making processes. As a result\, many individuals may experience uncertainty about their professional future. By analyzing data from 237 respondents across India\, this study identifies AI anxiety as a significant factor influencing career anxiety and career uncertainty. The findings reveal that individuals with higher AI anxiety are more likely to experience higher anxiety and indecisiveness in their career. However\, the overall life satisfaction helps in lowering career anxiety by providing individuals with a higher sense of well-being that counteracts the anxiety. In contrast\, factors such as relatedness\, competence\, and AI resilience do not show significant influence on either career anxiety or career uncertainty. This study enhances the understanding of how AI anxiety shapes career-related concerns\, offering insights into how individuals navigate career decisions in an AI-driven world.
CATEGORIES:VIRTUAL ROOM 5D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:f59d92e98c5cadf0c7159284882043ab
URL:http://11tict4sd.sched.com/event/f59d92e98c5cadf0c7159284882043ab
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:AI-Generated Realms: Crafting Images from Text with Stable Diffusion Model
DESCRIPTION:Authors - Shreya Ratagal\, Parvati Navalur\, Amruni Joshi\, Satish Chikkamath\, Sujata Kotabagi Abstract - With the instant advancement of generative artificial intelligence\, this study investigates the domain of text-to-image generation\, concentrating on the utilization of the Stable Diffusion model. The research examines the creation of visual content from written descriptions by leveraging sophisticated neural network architectures and underscores the importance of Natural Language Processing (NLP) in producing high-quality results. An extensive analysis was performed\, integrating both qualitative and quantitative assessments to evaluate the model’s performance\, scalability\, and ability to adapt to various inputs. The study emphasizes potential uses in creative content production\, virtual environments\, and educational resources while tackling ethical issues to promote responsible AI practices. The results highlight the revolutionary effects of text-to-image generation in transforming the process of visual content creation.
CATEGORIES:VIRTUAL ROOM 5D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:7bed17188b4fd66e3ac8fba4771e7bd9
URL:http://11tict4sd.sched.com/event/7bed17188b4fd66e3ac8fba4771e7bd9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:IOT FOR EARLY WARNING FLOOD SYSTEM
DESCRIPTION:Authors - Nishant Sharma\, Mohit Mahlawat\, Mohit Sharma\, Gagandeep Singh\, Ayush Kumar Singh\, Kamlesh Sharma Abstract - An Early Warning System (EWS) utilizing Internet of Things (IoT) technology represents a transformative approach to disaster prevention and management. By leveraging interconnected devices\, sensors\, and real-time data transmission\, IoT-based EWS enhances the ability to detect potential hazards—such as natural disasters\, industrial failures\, or environmental threats—at their earliest stages. These systems enable timely alerts and response strategies\, minimizing risks to human life\, infrastructure\, and ecosystems. IoT technology plays a crucial role in gathering precise\, real-time data from various sources\, including seismic sensors\, weather stations\, water levels\, and air quality monitors. This data is then transmitted to centralized platforms for analysis\, allowing authorities and stakeholders to predict\, assess\, and act swiftly before a disaster strikes. With cloud computing and AI integration\, IoT-enabled EWS can also deliver highly accurate forecasts and automated decision-making\, further enhancing disaster resilience. As the world faces increasing threats from climate change\, environmental degradation\, and urbanization\, IoT-based Early Warning Systems are becoming essential tools for safeguarding communities\, enhancing preparedness\, and ensuring a more resilient future
CATEGORIES:VIRTUAL ROOM 5D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:3033779ec59826b7a92eeb6fc203ba26
URL:http://11tict4sd.sched.com/event/3033779ec59826b7a92eeb6fc203ba26
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:IoT-Based Health Monitoring System Using Four in One Electrogram Sensor
DESCRIPTION:Authors - Jigme Nidup\, Adithya Gattadi\, Naresh K Abstract - Many serious health conditions\, such as atrial fibrillation (AF)\, neuropathy\, muscle disorders\, and sleep-related neurological diseases\, often go undiagnosed until complications arise. To address these challenges\, this paper presents an advanced health monitoring system that integrates a multifunctional 4-in-1 electrogram sensor capable of measuring muscle activity using Electromyography (EMG)\, eye movement using Electrooculography (EOG)\, brain activity using Electroencephalography (EEG)\, and heart rhythm using Electrocardiography (ECG)\, along with a body temperature sensor\, into a compact and wearable device at low cost. The device leverages the ESP32 Wi-Fi module to process and enable seamless data transmission to a Message Queuing Telemetry Transport (MQTT) cloud platform\, ensuring secure\, efficient\, and scalable storage and analysis of collected health data. The system uses multiple pre-trained CNN models\, each specialized in detecting specific diseases. Tests show an average accuracy of 90.3% making it a cost effective and efficient solution.
CATEGORIES:VIRTUAL ROOM 5D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:5dbc9d22ce43aebfcd39fadefb19007f
URL:http://11tict4sd.sched.com/event/5dbc9d22ce43aebfcd39fadefb19007f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:Kicking Goals with AI:Football Analysis Using YOLO and OpenCV
DESCRIPTION:Authors - Harsha M R\, Jyotiradhitya Kallimani\, Nitishgouda Patil\, Tohid Bijalikhan\, Satish Chikkamath\, Suneeta V. Budihal\, Sujata S. Kotabagi Abstract - In India what does it take to go PRO in football? Players from their childhood shred sweat\,blood and their precious time. And in sport we know that the next-gen superstar is guaranteed to start off his career from local and youth leagues. And honestly speaking these leagues do not offer the resources to invest. Resources in the sense that include technical skills\, opponent player data\, and event data(player stats). Currently\, event data is mostly collected manually by human individuals\, who gather data in several steps and through numerous persons involved. And this manually collecting data requires a lot of human resources and requires multiple checks and for that reason collection data is not practical in local or youth leagues. And this process takes a lot of time. So Automatic event detection could provide event data faster. which players can take use to analyse players and their own performance. And through which scouts would be able to ensure no player is missed or overlooked.
CATEGORIES:VIRTUAL ROOM 5D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:a3e5dfb21d6a1142ba3c9b9958f17665
URL:http://11tict4sd.sched.com/event/a3e5dfb21d6a1142ba3c9b9958f17665
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:PCOS Detection in Ultrasound Images Using Transfer Learning with InceptionV3 and ResNet50
DESCRIPTION:Authors - Arushi Madaan\, Sunita Garhwal\, Anu Bajaj Abstract - Women today are most likely to be experiencing Polycystic Ovary Syndrome (PCOS)\, a hormonal imbalance disorder. This disorder mostly affects women’s ovaries\, where a large number of tiny fluid-filled sacs called cysts—also referred to as follicles—form around the ovary’s periphery. The exact root cause of PCOS is still unknown despite advances in science. Using ultrasound (US) scans to identify numerous follicles is an efficient way to diagnose PCOS early and schedule treatment. The primary purpose of this article is to determine whether or not a woman has PCOS or not without supervision from a physician. In this work\, we provide a deep learning (DL) method based on transfer learning for PCOS classification using US ovarian images\, with the goal of improving diagnostic efficiency and precision. InceptionV3 and ResNet50 models\, which had accuracy rates of 99.68% and 97.5%\, respectively\, were used for this research. The study’s findings show that\, as compared to conventional machine learning (ML) techniques\, transfer learning-based classification performs better in PCOS variant classification. This study aims to accurately diagnose PCOS in patients and use our proposed model to treat PCOS. Gynaecologists and other medical professionals can benefit from our model’s ability to provide a prompt\, reliable\, and correct response.
CATEGORIES:VIRTUAL ROOM 5D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:ba12194dabef9fbeb8b341ac3dabd411
URL:http://11tict4sd.sched.com/event/ba12194dabef9fbeb8b341ac3dabd411
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:Predictive Analysis and Clustering of Autism Spectrum Disorder in Children Using AQ10 Data
DESCRIPTION:Authors - Sushma Vispute Priya Surana\, Shubhangi Vairagar\, Sujit Shaha\, Omkar Shinde\, Sameer Sambhare\, Krushna Salbande Abstract - Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition affecting social interaction\, communication\, and behavior. Autism is the third most common developmental disorder in the world. In India\, the prevalence of autism is increasing and is estimated to be around 1 in 68 children. With its rising prevalence\, early detection is crucial for timely intervention. This paper serves as both a review and a research study. The review explores existing ASD detection methodologies\, highlighting machine learning approaches such as multinomial logistic regression (MLR)\, support vector machines (SVM)\, and convolutional neural networks (CNN)\, along with tools like eye-tracking and EEG analysis. The research component applies machine learning models—including Logistic Regression\, Decision Tree\, Random Forest\, SVM\, KNeighbors\, Naive Bayes\, and Neural Networks—on AQ10 survey data (1054 samples\, 19 features) to evaluate their effectiveness. SVM achieved the highest accuracy. Further analysis examined the necessity of all 10 AQ10 questions\, revealing that AQ4 and AQ10 contribute the least to predictive accuracy. Heatmap analysis confirmed weak correlations with the total ASD score. These findings suggest that refining ASD screening tools by removing less informative questions can improve efficiency while maintaining diagnostic reliability.
CATEGORIES:VIRTUAL ROOM 5D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:9754d6673353b352025fc9f83009e9de
URL:http://11tict4sd.sched.com/event/9754d6673353b352025fc9f83009e9de
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:Sensing the Future: Revolutionizing Pest Detection in Agriculture through Sensor-Driven Deep Learning Techniques
DESCRIPTION:Authors - Vijayalakshmi S Katti\, Usha J Abstract - Pests\, pose a pervasive threat to agriculture on a national scale. Their voracious feeding on plant roots results in diminished crop yields\, leading to economic losses for farmers and potential food security challenges. The integration of advanced sensor technologies and deep learning offers a promising avenue to address the impact of Pests\, enabling timely and accurate detection. This\, in turn\, allows for targeted and efficient pest management strategies\, mitigating the widespread repercussions of infestations and fostering sustainable agricultural practices on a national level. This paper explores the transformative synergy between sensor technologies and deep learning techniques for the identification and density detection of pests in agriculture. Traditional methods face limitations\, prompting a shift towards advanced technologies. We survey the landscape of sensor technologies\, including image sensors\, acoustic sensors\, and soil sensors\, highlighting their real-time\, high-dimensional data contribution. Integration with deep learning models\, such as Convolutional Neural Networks and Recurrent Neural Networks\, offers a precise and adaptive approach to pest management. The potential impact of this integration is substantial\, promising increased crop yields\, reduced reliance on broad-spectrum pesticides\, and improved environmental sustainability. The review underscores the importance of continuous adaptation and scalability\, setting the stage for a future where technology plays a pivotal role in ensuring the health and productivity of agricultural landscapes.
CATEGORIES:VIRTUAL ROOM 5D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:ecef74e7dde03b1d75a1c5c2a3da40c8
URL:http://11tict4sd.sched.com/event/ecef74e7dde03b1d75a1c5c2a3da40c8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:UrbanFix AI: Smart Reporting System for Road Safety and Management
DESCRIPTION:Authors - Ashwini Jarali\, Sanskruti Lad\, Snehal Kavathekar\, Prajwal Lalpotu\, Shreya Jadhav Abstract - Potholes on roads significantly impact safety and road infrastructure\, leading to accidents and increased vehicle damage. Timely detection and repair are crucial to address these issues effectively. This paper presents an AI-based system for automated pothole detection and reporting\, aimed at improving pothole management for road maintenance authorities. The system uses the YOLO (You Only Look Once) object detection model to accurately identify potholes in real-time road imagery\, combined with GPS for precise localization. Detected potholes are automatically reported to the relevant authorities via email\, ensuring swift corrective action. The YOLO model is trained on a diverse dataset of pothole images\, achieving high detection accuracy across various pothole sizes and shapes. Additionally\, the system tracks the status of reported potholes to ensure repairs are completed. This solution enhances road safety and reduces manual effort\, providing a comprehensive approach to modern road maintenance.
CATEGORIES:VIRTUAL ROOM 5D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:42bb5c45d760c6b0eb869c5a94ab9f73
URL:http://11tict4sd.sched.com/event/42bb5c45d760c6b0eb869c5a94ab9f73
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:Brain Tumor Segmentation in MRI Images using U-Net
DESCRIPTION:Authors - Nirav Bhatt\, Purvi Prajapati\, Nikita Bhatt\, Jiten Bhalavat Abstract - A crucial stage in medical image analysis for brain tumor diagnosis\, treatment planning\, and patient monitoring is brain tumor segmentation. It entails locating the tumor and any of its subregions\, including the necrotic core\, peritumoral edema and an enlarging tumor. Manual segmentation takes a lot of time and is prone to mistakes\, which makes it unsuitable for regular clinical use. Recently\, deep learning-based techniques have shown promise as a method for automatically segmenting brain tumors. In this work\, we suggest a deep learning method for automatically segmenting brain tumors from magnetic resonance imaging (MRI) scans using a UNet architecture. A deep learning architecture created especially for image segmentation is the U-Net model. It is composed of an encoder-decoder structure\, where the decoder reconstructs the input image and the encoder extracts features from it. On a range of medical image segmentation tasks\, including brain tumor segmentation\, the U-Net model has demonstrated state-of-the-art performance.
CATEGORIES:VIRTUAL ROOM 5E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:0f80e0aa0e1ddfc4d522533eb4751c08
URL:http://11tict4sd.sched.com/event/0f80e0aa0e1ddfc4d522533eb4751c08
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:Breaking the influence: The Role of De-influencers in shaping Anti- Consumption and Conscious Consumption
DESCRIPTION:Authors - Divya Lakshmi R\, Jayasri R\, Deepak Gupta\, Shobhana Palat Madhavan Abstract - The rise of social media has significantly influenced consumption behaviour\, with influencers shaping purchasing decisions across industries. However\, a countermovement— de-influencing—has emerged\, urging consumers to rethink their buying habits and embrace conscious consumption. This study explores how de-influencers impact consumer decision-making by discouraging excessive and unsustainable purchases. This study employs the theoretical framework of Consumer Resistance Theory. Through an online survey of 192 respondents\, the research examines factors such as trust in de-influencers\, follower congruence\, ethical concerns\, and message content in shaping anti-consumption tendencies. The findings show that de-influencers are linked to attitudes that support sustainable consumption. There is a positive relationship between follower alignment and brand avoidance\, while ethical concerns are strongly tied to anti-consumption and conscious consumption. A minimalist mindset also aligns with these patterns\, indicating that de-influencers influence attitudes that lead to more sustainable choices. The study offers valuable insights for marketers\, policymakers\, and sustainability advocates\, shedding light on the growing digital influence landscape and its implications for sustainable conscious consumption practices.
CATEGORIES:VIRTUAL ROOM 5E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:f83a1047bee0f364c5ebee153551ef5f
URL:http://11tict4sd.sched.com/event/f83a1047bee0f364c5ebee153551ef5f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:Context-aware Proactive Algorithm for Recommendation based on Internet of Behavior (IoB)
DESCRIPTION:Authors - Pranali G.Chavhan\, Ritesh V. Patil Abstract - The race between the rapid spread of ubiquitous computing and the Internet of Behavior has opened up a whole new avenue for the provision of personalized and context-aware services. To this end\, the work presents a proactive recommendation algorithm that is meant to capitalize on IoB data to predict user behavior and deliver tailor-made content in an unobtrusive manner. With real-time behavior information added to the mix\, it intends to go a step further from traditional recommendation systems. What differentiates this approach is its use and manipulation of a multitude of contextual factors: geographical context\, temporal context\, context of what device is being used - perhaps even emotional context as well. This live blending affords the system the ability to respond to what is happening in the real world\, thus making it more reactive and relevant. The simulation results show that this awareness of context is going to generate a significantly better user engagement and accuracy of recommendation rather than traditional systems.
CATEGORIES:VIRTUAL ROOM 5E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:9b78443ed6fee36140a8c030b6b33653
URL:http://11tict4sd.sched.com/event/9b78443ed6fee36140a8c030b6b33653
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:Detection of Parkinson Disease in the Early Stage
DESCRIPTION:Authors - D Kishore Babu\, K Subba Rao\, Nagesh Babu Dasari\, Kumara Raja\, Golla Mary Prakash Kumari\, Vijayakumar Chilamkurthi Abstract - A neurological disorder identified as Parkinson's disease (PD) is described through a continuing loss of dopamine producing brain cells\, which results in bradykinesia\, rigidity\, and tremors. Symptoms typically appear after 60 - 80% of these cells have disappeared. 7 - 10 thousand people suffer with Parkinson's disease throughout the world\, primarily affecting those over fifty\, while 4% of cases also affect younger age groups. 90% of Parkinson's disease patients experience speech issues early in the disease. Machine learning algorithms offer a practical means of diagnosing Parkinson's disease early on by analyzing speech features. By using voice datasets from the UCI Machine Learning Repository\, these methods may effectively and with low error rates classify Parkinson's disease (PD). This raises the likelihood of an early diagnosis and course of action.
CATEGORIES:VIRTUAL ROOM 5E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:88be67adda0531209346da11d92cb443
URL:http://11tict4sd.sched.com/event/88be67adda0531209346da11d92cb443
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:HARDWARE ACCELERATION OF K-MEANS CLUSTERING ALGORITHM
DESCRIPTION:Authors - Manasi Sangamnerkar\, Prachi Mukherji\, Seema Rajput\, Nandini Kendre\, Vaishnavi Mudaliar Abstract - This paper gives a comparative study of the K-means clustering algorithm run on three platforms: a CPU\, an FPGA\, and a hybrid CPU-FPGA setup\, focusing on execution efficiency and scalability. The CPU version is suitable for small datasets due to its simple serial processing ability\, while the FPGA shows superior performance for larger datasets with hardware acceleration and parallel processing. The hybrid setup employs the ARM Cortex-A9 processor in addition to the programmable logic of the Xilinx ZedBoard (ZYNQ-7000 SoC). The algorithm is run through Vitis on the CPU\, while AXI-interfaced IP cores\, developed using Vivado\, provide signal monitoring and real-time debugging through the Integrated Logic Analyzer (ILA). This setup provides dynamic software control and high-speed processing. The FPGA showed an execution time of 38.077 nanoseconds\, compared to the 0.015519 seconds on the CPU\, providing a speedup of about 106 times. Implementation issues\, such as the lack of native floating-point support and reliance on fixed-point approximations\, have been noted for future improvement. Additionally\, 8-bit binary representations of centroids are visualized using LEDs on the FPGA\, providing a physical and intuitive visualization of the clustering process. This paper illustrates the effectiveness of FPGAs and hybrid CPU-FPGA setups in accelerating compute-intensive machine learning algorithms and the benefits of hardware-based optimization in real-time and embedded systems.
CATEGORIES:VIRTUAL ROOM 5E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:4426d4ba260ffbf501b35a5b34f6e3f0
URL:http://11tict4sd.sched.com/event/4426d4ba260ffbf501b35a5b34f6e3f0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:Integrating Machine Learning with Geo-Spatial Temporal Satellite data for Improved Flood Susceptibility Assessment
DESCRIPTION:Authors - Roshni De\, Debatosh Chakraborty\, Dwijen Rudrapal\, Baby Bhattacharya Abstract - Floods are one of the most dangerous natural disasters\, both frequent and dynamic due to continuous land use changes and climate change. This causes difficulty in predicting the areas most vulnerable due to their complex nature\, causing heavy loss and damage. The study presents a data-driven framework for flood susceptibility mapping\, examining the influence of multiple satellite-derived geo-spatial and temporal features in the Cachar district of Assam\, India—a region frequently impacted by monsoonal flooding. By integrating Machine Learning with features derived from NDVI (Landsat 8)\, LULC (Sentinel-2)\, topographic variables (SRTM DEM)\, soil texture (OpenLandMap)\, and monsoon precipitation (CHIRPS)\, alongside flood extent information obtained from NDWI and Sentinel-1 SAR data\, the model aims to enhance predictive accuracy in flood-prone\, data-constrained environments. A rigorous feature selection process using IGR and VIF score and comparative evaluation across various classifiers was used to optimize the model. The study highlights the importance of integrating machine learning with remote sensing data to construct a precise flood risk model to aid the disaster management team in identifying vulnerable regions.
CATEGORIES:VIRTUAL ROOM 5E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:bcbba28fccae3a92a324e757a95d5932
URL:http://11tict4sd.sched.com/event/bcbba28fccae3a92a324e757a95d5932
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:MLP Powered IoT-Enabled Smart Cane for the Visually Impaired: Mobility Enhancement and Fall Detection Through Sensor Based Behavior Analysis
DESCRIPTION:Authors - Shri Harini A\, Niranjana Shaji\, Karthick rajaa A S\, Gugapriya G Abstract - Visual impairment increases fall risk\, particularly among older adults with low vision facing a 16% higher likelihood of falls and those with blindness experiencing a 40% increased risk. To address this\, the research presents an ML-driven\, IoT-enabled smart cane equipped with sensor-based behavior analysis for real-time fall detection and mobility assistance. The system analyzes motion patterns and sudden orientation changes that helps in detecting falls while integrating obstacle detection with multi-modal feedback. Designed with low-power and cost-effective embedded components\, the system ensures efficiency on resource-constrained devices\, while IoT connectivity enables remote monitoring and caregiver communication. This smart cane offers a costeffective\, scalable solution to improve independence and quality of life for visually impaired individuals.
CATEGORIES:VIRTUAL ROOM 5E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:8a8501b10a52bdeceea76b15d5aef108
URL:http://11tict4sd.sched.com/event/8a8501b10a52bdeceea76b15d5aef108
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:Smart Health Monitoring Empowered With IoT
DESCRIPTION:Authors - Subhashree Banerjee\, Ranit Roy\, Anirban Chattopadhyay Abstract - Having utilized the latest scientific and knowledge developments\, Wireless-Sensing node Technology has made many strides in healthcare today. Many people\, however\, are made to suffer from different health-related issues and even death due to several illnesses\, often from an absence of proper medical attention. There is an urgent need for an efficient\, modern real-time patient monitoring system realized through the power of IoT. Continuous real-time tracking of vital health parameters\, like temperature\, blood pressure\, oxygen saturation measurement\, and electrocardiogram (ECG)\, and displaying their values other than ECG on LCD\, along with alerts issued in case of deviations or abnormality for the reported health factor to the concerned healthcare professional\, and storing the real-time stats of the patient in graphical form\, makes up the proposed innovative system. Incorporated with different specialized sensors\, including the temperature sensor\, blood pressure sensor\, SPO2 sensor\, and ECG sensor\, and also providing two microcontrollers accompanied by software\, the system is intended to meet the primary purpose of establishing a robust patient management infrastructure rooted in IoT. By adopting this high-tech system\, the health workers will be better placed to monitor their patients anywhere they might be\, either in a hospital setup or even at the convenience of their homes\, through an integrated IoT-enabled healthcare platform. The overarching goal of this initiative is to guarantee the delivery of top-tier patient care services while promoting enhanced health outcomes and overall well-being for individuals under medical supervision.
CATEGORIES:VIRTUAL ROOM 5E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:cbbd3ee2763a978252521b3810bd38bf
URL:http://11tict4sd.sched.com/event/cbbd3ee2763a978252521b3810bd38bf
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:SmartVote: Biometric-Backed Voting on the Blockchain
DESCRIPTION:Authors - Jyotsna More\, Suvarna Aranjo\, Martina D’souza\, Siddhi Awlegaonkar\, Saahil Chaurasia\, Aditya Ghadge\, Shreya Jadhav Abstract - Traditional voting systems suffer from several challenges\, including voter impersonation\, ballot tampering\, multiple voting\, and lack of transparency\, which compromise electoral integrity. To address these issues\, this paper proposes a Blockchain-Based Biometric Voting System that integrates fingerprint authentication with blockchain technology to enhance the security\, transparency\, and reliability of elections. Biometric authentication ensures that only registered voters and administrators can access the system\, eliminating impersonation and fraudulent voting. The R307 fingerprint module is used for real-time voter authentication\, preventing unauthorized access. Once verified\, votes are recorded on a blockchain ledger\, ensuring data immutability and decentralization. Unlike conventional databases\, blockchain technology eliminates single points of failure\, preventing vote manipulation and unauthorized modifications. The system also incorporates a web-based interface that allows voters to register\, authenticate\, and securely cast their votes\, while administrators can manage the election process with full transparency. Blockchain’s decentralized nature ensures that all transactions\, including candidate registration\, vote counting\, and election results\, are securely stored and verifiable\, preventing tampering and external interference. Initial testing demonstrates high accuracy in biometric verification and efficient blockchain-based vote storage\, making the system scalable for local\, state\, and national elections. By integrating biometric security with blockchain's trustless nature\, this system provides a fraud-resistant\, transparent\, and tamper-proof voting solution\, fostering greater public confidence in electoral processes.
CATEGORIES:VIRTUAL ROOM 5E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:b6f8cce633a1ea7e00fafdc2614bfb8d
URL:http://11tict4sd.sched.com/event/b6f8cce633a1ea7e00fafdc2614bfb8d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T070000Z
DTEND:20260825T090000Z
SUMMARY:Waste Management System
DESCRIPTION:Authors - Mathesh H P\, Shanthini E\, Praveen S\, Praneshwaran M S Abstract - Environment with green practices depends heavily on efficient waste management\, especially in parts of the economy where garbage is produced and processed in huge quantity Plastic bottles are one of the largest impediments to waste material due to their bulk use and destructive effect on the environment Inability of conventional methods of garbage segregation to offer the required precision and speed to function in most situations renders garbage segregation ineffective. To counter the issue\, in this project\, an improved waste sorting mechanism using the YOLOv8 object detection algorithm is utilized. Image processing and real-time machine learning are utilized by the system to separate and filter plastic bottles from the rest of the waste materials on a conveyor belt accurately. On grounds of efficiency and effectiveness\, the YOLOv8 algorithm is superior to traditional methods and previous models. It is also highly renowned for detecting objects with a very high degree of accuracy. Other methods did not extend beyond detection\, but auto-sorting ensures that plastic trash is sorted according to what needs to be recycled. This raises the overall processes of waste management and lowers contamination. Because it can provide businesses with a more and more scalable option\, this invention is a giant leap for automated waste management.
CATEGORIES:VIRTUAL ROOM 5E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:f195bc9735a56b07a24c762beb08f404
URL:http://11tict4sd.sched.com/event/f195bc9735a56b07a24c762beb08f404
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:AI and Technology Adoption as Drivers of Firm Performance: BERTopic Modeling and Text Mining Approach
DESCRIPTION:Authors -&nbsp\;Diya Rajesh\, Priya Dharshini R\, Uma Shankar VM\, Sangeetha Gunasekar\nAbstract -&nbsp\;Technology and innovation play a critical role in promoting sustainable development. However\, these technological adoptions at firm level can have varying impact on firm performance. Research in recent years has focused on disruptive technology and its impact on how businesses are run today. Firms to enhance their profitability and productivity find it necessary to adopt these new technologies including AI technology in their businesses. The present study analyses the impact of technology adoption on firms’ profitability for NSE 500 non-financial firms. The study covers the post pandemic years of 2022 to 2024. Identification of technology related theme was done using BERTopic modeling. Further technology adoption index was created based on text mining approach. Results from heteroscedasticity corrected model estimations indicate that technological adoption has a significant positive impact on firm performance. The results have managerial implications.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2A
LOCATION:West 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:f5295fe9d9edad851da989bdd3205f2b
URL:http://11tict4sd.sched.com/event/f5295fe9d9edad851da989bdd3205f2b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:AI Revolution in CAD: Pioneering the Future of Design
DESCRIPTION:Authors - Amit Uttarkar\, Shubham G Pathak\, Aman Kamble\, Harshad Lokhande\, Abhishek Dhore\, Rajkumar Patil Abstract - The integration of Artificial Intelligence (AI) into Computer-Aided Design (CAD) has opened up unprecedented opportunities for the future of design. This paper explores the revolutionary impact of AI in CAD\, highlighting its potential to transform traditional design processes and pave the way for innovative\, efficient\, and intelligent design solutions. By automating repetitive tasks\, enhancing creativity\, and enabling rapid prototyping\, AI-driven CAD empowers designers and engineers to push the boundaries of what is achievable. This abstract delves into the key advancements\, challenges\, and prospects presented by AI in CAD\, emphasizing its role in shaping the future of design across various industries. As we embrace this technology\, we stand at the cusp of a new era\, where AI collaborates with human ingenuity to create groundbreaking designs and redefine the possibilities of innovation.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2A
LOCATION:West 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:d1bcf2a3a15d9b467ec781986bb86181
URL:http://11tict4sd.sched.com/event/d1bcf2a3a15d9b467ec781986bb86181
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:Cartoon Analysis - Chhota Bheem Character detection and Screen time analysis using YOLO (You Only Look Once)
DESCRIPTION:Authors - Sakshi Korde\, Rasika Kanitkar\, Vaishnavi Gaikwad\, Hitali Khachane\, Rashmi Apte\, Mangesh Bedekar\, Rakhi Dongaonkar Abstract - The influence of children’s media on young audiences is both profound and far-reaching\, playing a pivotal role in shaping their values\, behaviours\, attitudes\, and overall cognitive development. Television programs and cartoons\, in particular\, have a unique ability to capture the attention of young viewers and leave a lasting impact on their understanding of the world around them. Recognizing this\, the current work focuses on an in-depth analysis of the popular Indian cartoon series Chhota Bheem. The main objective of this work is to assess and analyse the screen time allocated to key characters within the series\, paying special attention to how positive and negative roles are represented. By applying advanced AI-based techniques for character recognition and screen time measurement\, the study seeks to uncover critical insights about the narrative structure and character emphasis in the series. These insights can then be used to evaluate the extent to which such portrayals contribute to shaping children’s perceptions of morality\, relationships\, and problem-solving abilities. In addition\, this research delves into the balance between the portrayal of positive and negative influences within the show\, offering a nuanced perspective on how these dynamics can affect children’s psychological development. Ultimately\, the work aims to provide meaningful and actionable insights that are relevant to various stakeholders\, including parents\, educators\, and creators of children’s media. These findings serve as a valuable resource for fostering responsible media consumption and production\, ensuring that children are exposed to programming that nurtures their development in positive and meaningful ways. The influence of children’s media on young audiences is both profound and far-reaching\, playing a pivotal role in shaping their values\, behaviours\, attitudes\, and overall cognitive development. Television programs and cartoons have a unique ability to capture the attention of young viewers and leave a lasting impact on their understanding of the world around them. Recognizing this\, the current work focuses on an in-depth analysis of the popular Indian cartoon series Chhota Bheem.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2A
LOCATION:West 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:7edbb2006d290c5e4501b725f44275fd
URL:http://11tict4sd.sched.com/event/7edbb2006d290c5e4501b725f44275fd
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:Modelling and Classifying Sleep Disorders with Machine Learning Algorithms
DESCRIPTION:Authors - Moushmee Kuri\, Pratibha Jadhav\, Swapnil Patil\, Priya Goure\, Pankaj Chandre\, Pratik Kamble Abstract - The sleep disorders significantly affect quality of life and overall health. Accurately diagnosing and classifying these disorders is crucial for effective treatment. This study explores the use of machine learning techniques\, specifically Random Forest\, Logistic Regression\, and Support Vector Machine (SVM) algorithms\, to classify sleep disorders using the Sleep Health and Lifestyle dataset from Kaggle. The dataset includes information on sleep duration\, quality\, physical activity\, stress\, and other lifestyle factors. The models are evaluated for accuracy\, precision\, recall\, and F1-score. The Random Forest model performed best with an accuracy of 91.67%. However\, further refinement is needed for classes such as insomnia and sleep apnea\, which show lower recall scores.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2A
LOCATION:West 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:a1a2a51a9da86a24733d0d18076e9940
URL:http://11tict4sd.sched.com/event/a1a2a51a9da86a24733d0d18076e9940
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:Performance Enhancement of Deep Learning Techniques for Predicting Drug Reactions with Multi-Omics Data
DESCRIPTION:Authors - Vikram Kishor Abhang\, Baisa L. Gunjal Abstract - The field of deep learning-based drug-response prediction is examined in this overview of the literature\, with an emphasis on more recent developments including multi-omics data. Accurate and customized medication response pre-diction techniques are becoming more and more important as precision medicine gains traction. Conventional methods frequently fail to capture the intricate relationships between biological variables affecting the effectiveness of drugs. The article looks at the difficulties that current approaches are facing and emphasizes how deep learning techniques can help to overcome these constraints. In particular\, it addresses how to predict medication responses by integrating transcriptomics\, proteomics\, metabolomics\, and genomes as a multi-omics data. Through the combined investigation of many chemical characteristics with deep neural networks\, these techniques provide a thorough grasp of the biological principles that underpin therapeutic efficacy. The review summarizes new research findings and offers insights regarding multi-omics data.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2A
LOCATION:West 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:6737abf48d92f847bfbbd8e4857866a4
URL:http://11tict4sd.sched.com/event/6737abf48d92f847bfbbd8e4857866a4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:Query Optimization: Techniques and Strategies for MySQL Performance Improvement
DESCRIPTION:Authors - Pranali Kosamkar\, Gautam Sharma\, Lakshya Upadhyaya\, Bhavya Shah\, Samarth Patel Abstract - In this paper\, we will focus on optimization of MYSQL queries. Different factors are taken into account when calculating the cost of a query\, such as the number of disk accesses\, CPU processing time\, and the number of rows scanned. Considering all this\, we can create a price estimate\, price analysis of all the different ways to write questions\, metrics\, constraint integrity\, JOINS\, nested queries\, and other queries that come up when writing a lot of code from questions. Interfacing with the data logic and cost calculations is done in Python and is used with libraries like Matplotlib to find a more efficient comparison query writing strategy. MySQL's EXPLAIN keyword extracts execution details like the number of rows scanned and usage metrics and combines them with Python's timing module for execution time. These factors lead to the final price estimate and are plotted using graphs to help to draw conclusions. We also developed a MySQL user-defined function called calc_query_cost\, which includes a formula that estimates query cost based on the number of queries\, the number of rows scanned\, the processing time\, and usage metrics. Experimental results demonstrate that indexed queries were found to reduce the average query execution time by 70%-90%\, with non-indexed queries taking up to 5x longer in certain scenarios also JOIN operations proved to be 2-3x more efficient than nested queries in terms of execution cost\, especially for larger datasets. Optimizing query-writing strategies resulted in an execution time improvement of up to 60% for complex queries.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2A
LOCATION:West 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:36f81a5b0df3e4acff2578acb8514404
URL:http://11tict4sd.sched.com/event/36f81a5b0df3e4acff2578acb8514404
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:Retinal Optical Coherence Tomography Image Analysis for text report generation using Deep Learning
DESCRIPTION:Authors - Uday Mande\, Pathan Mohd Shafi\, Pankaj Chandre Abstract - Prolonged diabetic Macular Edema (DME) is a disease especially seen in diabetic patients. It could result in blindness. Therefore\, in order to avoid irreparable vision loss\, early detection and treatment are essential. Images from optical coherence tomography (OCT) have been utilized extensively to aid in the detection of various illnesses. Manual screening is costly\, time-consuming\, and prone to human error. In order to get around these limitations\, artificial intelligence (AI) approaches have been used extensively. This research contributes towards development of textual reports\, helpful for ophthalmologists to increase overall performance. Here\, a variety of machine learning (ML) and deep learning (DL) approaches are used to develop retinal health detection models. It is found that Convolutional Neural Networks (CNN). Models were extensively employed in DL and ML methods for the analysis of OCT images. Model is developed for detecting and analysing Diabetic macular edema using methods of artificial intelligence throughout the past ten years. We have talked about the shortcomings of the current techniques and offered ideas for new approaches to precisely identify and confirm eye conditions.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2A
LOCATION:West 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:742d7dabcb3546dde78ff52f44ce4f87
URL:http://11tict4sd.sched.com/event/742d7dabcb3546dde78ff52f44ce4f87
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:A Predictive Analytics Approach to College Recommendation Using XGBoost
DESCRIPTION:Authors - Harshvardhan Tibile\, Tanvi Handigol\, Suraj Kajagar\, Satish Chikkamath\, Kaushik Mallibhat\, Suneeta V Budihal Abstract - In the proposed work\, we develop a predictive model using the XGBoost algorithm to recommend suitable colleges for students based on the Karnataka Common Entrance Test (KCET) rank\, category\, preferred branch\, and location. The model is developed using a dataset with 1\,731 entries and 28 columns\, detailing rank thresholds for various admission categories in colleges across Karnataka. Pre-processing activities include encoding categorical features\, handling missing values\, and normalizing numerical features. The XGBoost classifier is optimized with hyperparameters to achieve a balance between precision and generalization\, yielding a test accuracy of 93.5%. The results indicate that the model successfully provides accurate and personalized college recommendations\, significantly enhancing the decision-making process for students. Future work will focus on adding additional student attributes\, continuously refreshing data\, and developing an interactive chatbot to improve the accessibility and adaptability of the system. The proposed approach aims to reduce administrative workloads\, improve clarity\, and allow students to make informed academic choices.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2B
LOCATION:West 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:e36124f0fcf55a560f8555777fb6a061
URL:http://11tict4sd.sched.com/event/e36124f0fcf55a560f8555777fb6a061
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:Attire Classification in Indian Cinema
DESCRIPTION:Authors -&nbsp\;Niharika Patil\, Pradnya Patil\, Maitreyee Patil\, Khushi Jha\, Rashmi Apte\, Mangesh Bedekar\, Neeta Maitre\nAbstract -&nbsp\;The increasing influence of cinema on fashion trends in India has prompted a need for data-driven analysis to quantify its impact. Prior research has explored general fashion classification or cultural impacts through surveys\, but none have automated the study of cultural attire in movies. This work addresses that gap by developing a machine learning-based system to analyze movies and classify the attire of actresses as Indian or Western. Using tools such as OpenCV\, TensorFlow\, and face recognition libraries\, the system extracts movie frames\, detects actress presence\, and categorizes attire. Results from around 100+ movies highlight a significant shift in representation\, with Indian attire percentages varying from 1.44% to 99.72% across films. For example\, Sholay (1975) demonstrated 99.72% Indian attire\, while Genius (2018) depicted only 1.44%. These findings reveal trends correlating with cultural changes over decades. The work concludes that cinema plays a pivotal role in shaping fashion preferences\, providing a foundation for further studies to include comprehensive cultural analyses. Future enhancements aim to broaden attire classification and explore thematic cultural representations.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2B
LOCATION:West 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:6afa35903c3e8c4e355c0f74835c6c50
URL:http://11tict4sd.sched.com/event/6afa35903c3e8c4e355c0f74835c6c50
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:Breast Cancer Prediction Project using Machine Learning
DESCRIPTION:Authors - Manav A. Thakur\, Priya Gawhane\, Kalyani Ghogale\, Neha Ghule\, Dev Jadhav Abstract - Breast cancer\, a significant health concern among women\, requires prompt and accurate diagnosis to improve treatment outcomes and survival rates. Traditionally\, specialized doctors perform the diagnosis\; however\, advancements in machine learning algorithms are enabling supportive diagnostic tools. In this study\, we employ a hybrid approach combining Convolutional Neural Networks (CNNs)\, OpenCV\, and Random Forest algorithms to classify breast cancer cases as malignant or benign. The dataset\, sourced from the University of Wisconsin\, includes 357 malignant and 212 benign tumors\, with clinically relevant features extracted using feature engineering techniques. OpenCV is utilized for image preprocessing\, ensuring standardized input quality for model analysis\, particularly in image-based features. Following data normalization and preprocessing\, the dataset is divided into training and testing sets. CNNs are applied to image data for in-depth feature extraction\, identifying patterns indicative of malignancy.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2B
LOCATION:West 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:a5583a2d286c200b8b33d6f40baf9402
URL:http://11tict4sd.sched.com/event/a5583a2d286c200b8b33d6f40baf9402
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:Interference Management in NB-IOT: A Hybrid Beamforming Approach
DESCRIPTION:Authors - Kalmesh Narendra\, Prasanna Patil\, Ankit Kulkarni\, Amogh Kuber\, Kiran M R\, Suneeta V Budihal Abstract - Beamforming sends the beam through the desired directions and suppress signals from other ways. Multiple-Input Multiple-output systems combined with beamforming would be of next generation wireless systems. Techniques applied in cellular telecommunications\, such as beamforming in 5G\, are largely being used at present. In this paper\, hybrid beamforming is adopted to enhance interference management in NB-IoT devices by optimising parameters such as Signal-to Noise Ratio and interference power. The focus lies on comparing different forms of beamforming algorithms (analog\, digital\, hybrid). We find that interference power of hybrid beamforming is 10.48 and signal to noise ratio is 20.45db which is better than other two beamforming techniques. These innovations highlight the possibilities of hybrid beamforming for reliable and efficient networks in dense NB-IoT deployment.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2B
LOCATION:West 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:d15feb9ae582d82085193ac49c477957
URL:http://11tict4sd.sched.com/event/d15feb9ae582d82085193ac49c477957
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:Llama 3.2 Vision Instruct for Indian Traffic Scene Understanding and Object Detection in Dashcam Footage
DESCRIPTION:Authors -&nbsp\;Mohan S.G\, Venkat Narayanan B\, Kedar Rajesh Bhagat\, Aarnav N R Kiran\nAbstract -&nbsp\;This study evaluates the capabilities of the Llama 3.2 Vision Instruct model on vision tasks related to Indian traffic data. A custom dataset of 200 images extracted from dashcam footage was created\, capturing diverse Indian traffic scenarios. The model was deployed locally and tested using 20 prompts across four categories: vehicle information\, human presence\, object detection\, and road information. Results indicate Llama 3.2 performs consistently in detecting vehicles and humans\, with processing times correlating to prompt complexity. The model showed strengths in basic object recognition but faced challenges with more complex scene understanding tasks. Fluctuations in performance highlight areas for potential improvement\, particularly in handling the unique complexities of Indian traffic environments. This evaluation provides insights into the model's applicability for autonomous driving and traffic analysis in the Indian context\, while also identifying directions for future enhancements in multimodal vision-language models for specialized domains.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2B
LOCATION:West 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:90f74201349d569d202d61e9259e6f0c
URL:http://11tict4sd.sched.com/event/90f74201349d569d202d61e9259e6f0c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:Pre-trained CNN Models Based Dog Video Summarization: A Comparative Analysis
DESCRIPTION:Authors - Tejas Chauhan\, Bhagyesha Pandhi\, Krishna Jariwala\, Mitesh Patel\, Divya Kavathiya Abstract - The nature of data has changed significantly as a result of the quick development of technology. Text-based datasets have given way to visual data\, such as photos and videos. This shift necessitates the development of cutting-edge technologies that can effectively process and analyze visual input\, allowing the creation of intelligent systems that can precisely extract insightful information. Convolutional neural network (CNN) models that have already been trained are now essential resources for this project. In this paper\, the effectiveness of three well-known CNN models—AlexNet\, GoogleNet\, and SqueezeNet—in picture classification tasks is thoroughly compared. Our assessment concentrates on object detection performance on Dog’s dataset obtained from the Stanford Dogs Breed dataset\, offering important information about the advantages and disadvantages of each model.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2B
LOCATION:West 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:4ded9de5dc6637c4387748e73b796d86
URL:http://11tict4sd.sched.com/event/4ded9de5dc6637c4387748e73b796d86
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:Structured Relevance Assessment for Robust Retrieval-Augmented Language Models
DESCRIPTION:Authors - Astitva Veer Garg\, Aryan Raj\, D. Anitha Abstract - This paper addresses the challenges faced by Retrieval-Augmented Language Models (RALMs) in reducing factual errors by introducing a framework for structured relevance assessment. The aim is to enhance the robustness of RALMs by improving document relevance evaluation\, balancing intrinsic and external knowledge\, and managing unanswerable queries effectively. We propose a multi-dimensional scoring system for document relevance\, considering semantic matching and source reliability. Our approach includes embedding-based relevance scoring\, the use of synthetic training data with mixed-quality documents\, and specialized benchmarking on niche topics. Additionally\, we implement a knowledge integration mechanism and an ”unknown” response protocol to handle queries where knowledge is insufficient. Preliminary evaluations show significant reductions in hallucination rates and improved transparency in reasoning processes. This work advances the development of more reliable question-answering systems capable of functioning effectively in dynamic environments with variable data quality. While challenges remain in accurately distinguishing credible information and balancing system latency with thoroughness\, the proposed framework marks a step forward in enhancing the reliability of RALMs.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2B
LOCATION:West 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:95594c6d562d2b9daaf5cda8341f4e31
URL:http://11tict4sd.sched.com/event/95594c6d562d2b9daaf5cda8341f4e31
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:A Review on Mobile Forensic Needs\, Challenges\, Approaches\, Process Model\, Tools and Standard
DESCRIPTION:Authors - Chitrashri K\, Karuna C Gull\, SushilaDevi V.\, Sansktruti V.B.\, Pavan K. Korlahalli Abstract - Cybersecurity is a critical field focused on safeguarding digital systems from cyber threats. Within this domain\, digital forensics plays a key role in investigating cyber offenses by analyzing evidence from various digital platforms. With the rapid growth of mobile technology and the widespread use of smartphones for communication and transactions\, mobile forensics has become an essential subfield. It involves the acquisition\, analysis\, and preservation of mobile device data to support legal investigations. This paper presents a comprehensive review of mobile forensics\, examining its evolution\, methodologies\, forensic process models\, tools\, challenges\, and the application of machine learning and deep learning techniques. It also references established standards from NIST and SWGDE to recommend best practices\, address research gaps\, and suggest future directions in mobile forensic investigations.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2C
LOCATION:South 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:adfe58ac98a5b366132c7ad7753b17cd
URL:http://11tict4sd.sched.com/event/adfe58ac98a5b366132c7ad7753b17cd
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:Automated OT Sterilization using UV Radiation and Hydrogen Peroxide Vapor
DESCRIPTION:Authors -&nbsp\;Trissa Rose\, Rithukesh Pillai\, Manya Murali\, K.Neethu Sathyan\, Vidya G S\nAbstract -&nbsp\;Ensuring a sterile environment in operation theatres is paramount for infection prevention and patient safety. This paper presents an automated sterilization system combining UV-C radiation and hydrogen peroxide vapor to achieve efficient and thorough disinfection. The proposed system integrates germicidal UV-C lamps to neutralize airborne microorganisms and an ultrasonic mist generator to disperse hydrogen peroxide vapor\, reaching inaccessible surfaces and enhancing sterilization coverage. Safety is prioritized through real-time motion detection sensors that halt the process upon human presence\, preventing accidental exposure. A programmable microcontroller supervises the entire cycle\, offering reliability and ease of use. Future advancements include IoT-enabled features for remote monitoring\, data logging\, and predictive maintenance. This solution addresses limitations of traditional methods\, streamlining hospital sterilization protocols while ensuring safety\, efficiency\, and adaptability to modern healthcare demands.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2C
LOCATION:South 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:92450b8025d218cafc9ab373918c9458
URL:http://11tict4sd.sched.com/event/92450b8025d218cafc9ab373918c9458
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:CFD-Based Blood Flow Simulation in Microfluidic Device
DESCRIPTION:Authors - Veerraj Satish Chitragar\, Nihal Ravindra Jain\, Nidhi S Chickerur\, Samudyata Minasandra\, Guruprasad Konnurmath Abstract - Grasping the dynamics of blood flow is essential for exploring cardiovascular health and identifying irregularities. The research presents an innovative method for simulating blood circulation in microfluidic devices by combining sophisticated modeling techniques with an emphasis on mimicking arterial conditions. The research begins by creating a two-dimensional (2D) serpentine microfluidic model to analyze flow patterns and pressure distributions. The insights gained from the initial model inform the development of a three-dimensional (3D) microfluidic model\, enhanced with slice simulations to accurately represent velocity fields and pressure gradients. A crucial element of the research involves developing a microfluidic device that mimics the physical and material properties of real arterial systems. The model features layers that resemble the characteristics of media and adventitia\, essential for mimicking the mechanical traits of arterial walls. The final simulation includes a lifelike arterial wall constructed from myocardium material\, enabling thorough analysis of velocity profiles and pressure distributions. The research effectively demonstrates the ability to replicate authentic arterial blood flow dynamics in microfluidic systems. This is validated by visual representations of velocity and pressure profiles\, highlighting the intricate interactions between fluid dynamics and the mechanics of arterial walls. By connecting biological systems to computational modeling\, the research establishes a robust foundation for exploring cardiovascular hemodynamics. The findings emphasize the potential of microfluidic systems for diagnostic and therapeutic applications\, significantly contributing to the advancement of both research and treatment.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2C
LOCATION:South 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:68b9329c7e3ce5650a5baf8895f60f99
URL:http://11tict4sd.sched.com/event/68b9329c7e3ce5650a5baf8895f60f99
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:Flight Price Forecasting Employing Machine Learning Methodologies
DESCRIPTION:Authors - Pinky Yadav\, Tanvi Rustagi\, Garima\, Mahesh Swami\, Prakhar Consul\, Gaurav Kumar Abstract - The distance travelled\, the time of purchase\, the cost of fuel\, the airports of exodus and influx\, dates of commute\, aviation company and the class of travel are just a few of the many factors that affect flight prices. Prediction is a well-known field of study that uses predictive modelling methods and historical flight data to make precise predictions about airline ticket pricing. Every carrier uses a unique set of rules and algorithms to calculate the right price. This paper's goal is to analyse several hypothesis tests to increase the authenticity of the flight booking record set that was acquired from "Ease My Trip."The record set would be skilled and a sustained target parameter would be forecasted utilizing various machine learning techniques. By choosing a particular set of factors that affect airline ticket prices\, this study investigates the problem of flight pricing. The schedule\, destination\, length of the journey\, and other events like holidays or vacations all affect how much a ticket costs. Before planning a trip\, people may preserve time and money by being aware of fundamentals of airline fares. Six distinct ML models are utilized to forecast travel costs after Ease My Trip dataset is examined to provide insights into airline fares. To find the primary determinants of flight costs\, the performance of different models is compared\, XGBoost classifier outperform with test score of 0.95927654 and MAE of 2616.627154.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2C
LOCATION:South 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:7ae1c7ae705afda1784e19211aed82fc
URL:http://11tict4sd.sched.com/event/7ae1c7ae705afda1784e19211aed82fc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:GenAI-Driven Portfolio Review System: Leveraging AI for Smarter Investment Decision
DESCRIPTION:Authors - Sumanth S\, Rencita Maria Colaco Abstract - Making smart financial decisions can be overwhelming due to complex terminology\, diverse investment options\, and varying risk levels. This paper introduces a solution that simplifies financial decision-making through the integration of Large Language Models (LLMs). The proposed system acts as a virtual financial assistant\, allowing users to ask questions in natural language—like “Things to be aware of before investing in XYZ stock”—and receive clear\, personalized answers based on expert-reviewed financial insights\, explained in simple\, easy-to-understand terms. The system retrieves live financial data\, evaluates risks\, and uses conversational AI to guide users toward informed decisions. By reducing reliance on technical jargon and costly advisory services\, this AI-driven approach promotes financial literacy and confidence. It empowers users of all backgrounds to make smarter\, faster\, and more personalized financial choices.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2C
LOCATION:South 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:191e0f387c2e9d46bedc11c404899814
URL:http://11tict4sd.sched.com/event/191e0f387c2e9d46bedc11c404899814
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:Knee Osteoarthritis Stage Analysis using Convolutional Neural Networks
DESCRIPTION:Authors - Jyoti Patil Devaji\, Sushma Garawad\, Nirmala S. R.\, Suneeta V. B. Abstract - This study proposes the use of deep neural networks (DNNs) for the classification and diagnosis of knee osteoarthritis (KOA)\, a common chronic joint ailment characterized by a wide range of symptoms. A number of health-related criteria\, including age\, gender\, body mass\, hormone profile\, and genetic predisposition\, must be evaluated in order to make an accurate diagnosis of KOA. Using deep learning algorithms to accurately classify the severity of KOA is the main goal of this research. The suggested method classifies separate subgroups according to factors such as age\, sex\, and degree of obesity by using self-reported clinical characteristics. The superiority of deep learning for KOA diagnosis has also been demonstrated by a comparison analysis that was done to assess the performance of the suggested DNN versus conventional machine learning models.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2C
LOCATION:South 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:9aad72a600da0f700797d96d87b3fc62
URL:http://11tict4sd.sched.com/event/9aad72a600da0f700797d96d87b3fc62
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:Mindful or Distracted? Understanding Student Mobile Behavior through Digital Phenotyping
DESCRIPTION:Authors -&nbsp\;Maitri Vaghela\, Ansh Patel\, Megh Shah\nAbstract -&nbsp\;Smartphones have become an integral part of students’ daily lives\; however\, excessive and unconscious usage can impact their productivity and mental well-being. Traditional survey-based studies are often limited by self-reporting inaccuracies\, leading to the need for more objective assessment methods. This study adopts a smartphone sensing approach rooted in digital phenotyping to collect real-time behavioral data from 30 university students. Using an in-house developed app\, data on app usage\, screen unlock patterns\, sleep indicators\, and stress levels were collected. Behavioral features like mobile usage\, sleep quality\, and stress levels were used to predict distracted and mindful usage windows. Findings indicate that high non-academic app usage during study hours\, frequent nighttime phone activity\, and elevated stress levels correlate with distracted behavior. Conversely\, healthier sleep patterns and the use of academic-related apps are associated with mindful engagement. These insights aim to promote greater self-awareness and healthier smartphone habits among students.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2C
LOCATION:South 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:5c0c5be5df1119246dbc255dec801599
URL:http://11tict4sd.sched.com/event/5c0c5be5df1119246dbc255dec801599
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:A Deep Q-Network-Based Recommendation model for Dynamic Traffic Signal Control in Multi-Intersection Networks
DESCRIPTION:Authors - Ataus Samad\, Vandana Bhatia\, Atal Bhatia Abstract - Urban traffic congestion continues to pose serious challenges to mobility\, energy efficiency\, and environmental sustainability. To address this problem\, this paper proposes a deep Q-Network (DQN)-based real-time recommendation model to manage dynamic traffic signals across multiple intersections. The proposed model integrates reinforcement learning with a data-driven recommendation engine within the Simulation of Urban Mobility (SUMO) environment\, utilizing traffic attributes such as queue length\, vehicle speed\, waiting time\, and throughput. A novel reward function\, which combines queue minimization and wait time reduction\, significantly enhances traffic flow efficiency. The experimental results reveal substantial improvements in average speed\, queue length\, and aggregate waiting time relative to baseline models. This paper demonstrates the feasibility and effectiveness of DQN for real-time\, multi-intersection traffic signal management and its use in recommendation system.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2D
LOCATION:South 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:ebe21c189bb4d561f5f0fb7f61ba6ab5
URL:http://11tict4sd.sched.com/event/ebe21c189bb4d561f5f0fb7f61ba6ab5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:A Survey Paper on Techniques and Trends of AI Driven Skincare
DESCRIPTION:Authors -&nbsp\;Kalyanshetti Praneet Vijay\, Aarushi\, Kartik Sadanand Naik\, Krishan V Naikmasur\, Saritha\nAbstract -&nbsp\;The global skin care industry has been evolving at an exponential rate. Established or even the broad skincare industry has been shifting with high acceleration due to the incorporation of artificial intelligence and deep learning technologies. All these innovations are geared towards filling essential voids in skin condition assessment\, prescription of products\, and the representation of the skin of color. As this survey paper aims\, the relevant studies on various algorithms to analyze facial images\, skin condition identification\, as well as ingredient recommendations are explored. Further\, it lists the limitations such as dataset biasness\, real time performance re-optimization\, data privacy issues along with the prospectus of AI based skincare solutions. This survey paper also focuses on things like why skincare is necessary and how are different chemicals or their compositions useful in fighting multiple facial skin defects. It elaborates on the existing solution shortcomings and suggests possible categories for further research\; the development of comprehensive skincare management that incorporates life changes\, the extension of the datasets with regards to the minor skin diseases for improved precision of the model. Alleviating these challenges will enable AI-driven skincare systems to transform the beauty market and make customized skincare accessible\, understandable\, and available to a worldwide population.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2D
LOCATION:South 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:13f19cb1fb249cb198e7fd2360f46a58
URL:http://11tict4sd.sched.com/event/13f19cb1fb249cb198e7fd2360f46a58
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:Accident Detection and Emergency Support System for Scooters Based on Edge AI Technology
DESCRIPTION:Authors -&nbsp\;Tanmay M S\, Neeraj Rajiv Shivam\, Veena Shivanna\, Sathya D\, Chandramouleeswaran Sankaran\nAbstract -&nbsp\;This paper presents the development and deployment of an AI-powered accident detection system for scooters\, leveraging edge computing and machine learning for real time crash and fall detection with an alert system. The system architecture is divided into three layers. Layer 1 focuses on sensor data acquisition in real-time\, integrating an MPU6050 sensor (accelerometer and gyroscope) with an ESP32 microcontroller for localized data processing\; it’s also equipped with a GPS module to provide location data to improve emergency response. Layer 2\, AI-based accident detection layer\, uses a sequential neural network that is trained on diverse datasets that comprises of crash scenarios\, fall scenarios and internally collected normal riding. Layer 3\, the decision and notification layer\, deploys real-time alerts communicating critical information such as name\,blood group and GPS coordinates to emergency services and personal contacts.The proposed system combines machine learning and edge computing to provide a scalable real-time solution for improving the safety of scooter riders.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2D
LOCATION:South 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:492408d7dc5b3669e3a61902b9e2081a
URL:http://11tict4sd.sched.com/event/492408d7dc5b3669e3a61902b9e2081a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:Disease Prediction & Diagnosis by Various Pattern Matching Techniques using Biological DNA Sequences
DESCRIPTION:Authors - Banothu Ramji\, Veerender Aerranagula\, Raju Bhukya Abstract - Pattern matching allows users to search the database for specific DNA sequences. As biological data increases exponentially\, researchers are striving to improve solutions in various areas of bio-informatics. Real applications require faster algorithms with lower error rates. Therefore\, in this work\, we provide parallel implementation of four pattern-matching algorithms developed to speed up the search for DNA sequence patterns. We implement these algorithms both serially and parallelly. Parallel implementation of these algorithms provides a more optimized\, time-efficient way of performing pattern-matching in DNA Sequences. Experimental results show that the parallel version of these proposed algorithms is faster than their serial versions. The paper finally runs all four algorithms’ serial and parallel versions for NCBI ‘Homo Sapiens’ Dataset. Output and time-efficiencies for all algorithms are recorded and compared with respect to sequential and parallel.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2D
LOCATION:South 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:22a0f6918c9b0620746dcecf20fa7b16
URL:http://11tict4sd.sched.com/event/22a0f6918c9b0620746dcecf20fa7b16
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:Intelligent Weather Prediction for Accurate Forecasting using Machine Learning
DESCRIPTION:Authors - Rajeshree Khande\, Navnath Shete\, Sheetal Rajapurkar\, Abhishek Supe Abstract - Weather forecasting is essential to many businesses\, such as agriculture\, transportation\, and disaster assistance. Traditional forecasting methods rely on numerical models\, which are helpful but may not accurately predict very complex and dynamic weather patterns using machine learning (ML) algorithms to improve conventional forecasting techniques. models a way to increase the accuracy of weather forecasting. Models seek to improve conventional numerical weather prediction techniques by utilizing their capacity to identify intricate patterns in large datasets. These machine learning algorithms can identify complex interdependencies and nonlinear interactions that may be missed by traditional forecasting methods by integrating historical meteorological data that includes a variety of characteristics such as temperature\, humidity\, wind speed\, precipitation\, and air pressure. By combining the results of several models\, ensemble approaches like Random Forests enable the creation of forecasts that are more reliable and accurate. Neural networks\, especially deep learning architectures like recurrent or convolutional neural networks\, are skilled at identifying spatial and temporal correlations in meteorological data sequences\, offering important new information about changing weather patterns. Understanding the complex links between meteorological variables is greatly aided by decision trees\, which are renowned for their interpretability and capacity to capture nonlinear correlations. This research attempts to develop hybrid models that combine the advantages of both conventional numerical approaches and cutting-edge machine learning methodologies by utilizing these various ML techniques and training them on sizable datasets. The ultimate objective is to provide weather forecasts that are more accurate\, dependable\, and timely—essential for sectors that depend on exact weather predictions. This combination of approaches aims to lessen the drawbacks of traditional approaches and greatly improve forecasting abilities in several industries\, including disaster assistance\, transportation\, and agriculture.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2D
LOCATION:South 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:84542ed651893d1eb79d72df57eefcfb
URL:http://11tict4sd.sched.com/event/84542ed651893d1eb79d72df57eefcfb
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:IOT in Underwater Exploration: Enabling Smart Oceanographic Devices
DESCRIPTION:Authors - Ananya Agrawal\, Vedant Agrawalla\, Sunil Kumar\, Surendra Solanki Abstract - Investigating ocean depths beyond 5\,000 meters presents significant difficulties: pressures surpassing 80 MPa\, complete darkness\, and intricate landscapes. Conventional ROVs are limited in their ability to adapt in real time and lack high-resolution environmental awareness. This study presents JalX\, a next-generation Unmanned Underwater Vehicle (UUV) designed for autonomous missions in deep-sea environments (5–8 km)\, including search efforts for shipwrecks and lost cities. JalX utilizes ACO-based strategies for adaptive pathfinding\, real-time SLAM through the integration of sonar and LiDAR\, and AI-driven vision enhanced by GANs for detailed 3D reconstruction. Its hull is made of titanium alloy that withstands extreme underwater pressures\, while a hybrid communication system - UWoC\, acoustic modems\, and satellite links - facilitates uninterrupted data transmission to surface stations. Sophisticated AI manages propulsion and navigation with energy efficiency in mind\, allowing for more than 48 hours of autonomous operation in deep-sea conditions. Through system-level design\, algorithmic testing\, and preliminary field experiments\, JalX showcases impressive mapping precision\, strong object identification capabilities\, and durable communication systems. By merging marine engineering with artificial intelligence and robotics\, JalX establishes a new standard for adaptable and intelligent underwater exploration platforms that have the potential to transform oceanographic and archaeological studies.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2D
LOCATION:South 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:c4f16120f59f3ef1008aae73b0b50f5a
URL:http://11tict4sd.sched.com/event/c4f16120f59f3ef1008aae73b0b50f5a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:Machine Learning-Driven E-Governance Framework for Mitigating Challenges of International Students in Indian Institutions: A Policy-Centric Approach
DESCRIPTION:Authors - Himani Binjola\, Kaipee Luther Newray\, Vivek\, Vidushi Negi\, Shweta Bajaj\, Smriti Tandon Gupta\, Rekha Verma\, Shikha Tyagi Abstract - This paper uses machine learning to investigate the problems faced by foreign students in Indian universities from 2021 to 2023\, therefore identifying systematic flaws in ICT-driven governance systems. Using mixed-method analysis of 1\,680 respondents\, the study found notable policy flaws in digital integration including disconnected visa systems\, poor language assistance tools\, and ineffective grievance redressal procedures. By means of predictive analytics\, blockchain-secured platforms\, and AI-enabled cultural adaption tools\, the proposed machine learning model defines a four-tier ICT governance framework in line with India's e-government operations and addresses UN SDG 4 (Quality Education).
CATEGORIES:PHYSICAL TECHNICAL SESSION 2D
LOCATION:South 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:a664742ae2675307fd58fb416e6b13e2
URL:http://11tict4sd.sched.com/event/a664742ae2675307fd58fb416e6b13e2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:AI/ML for Toor Dal Classification
DESCRIPTION:Authors - Khushi Khanchandani\, Nilkamal More\, Nishtha Kumar\, Lipy Kothari\, Krish Shah\, Kaustubh Nair Abstract - The classification of Toor Dal (pigeon pea) based on quality is essential for fair pricing\, consumer trust\, and efficient industrial processing. Traditional methods rely heavily on manual inspection\, which is often subjective\, labor-intensive\, and inconsistent. This paper presents a comprehensive study of AI-driven approaches for automated Toor Dal classification using both classical machine learning and modern deep learning techniques. Various image preprocessing and feature extraction methods such as Gray Level Co-occurrence Matrix (GLCM)\, Histogram of Oriented Gradients (HOG)\, and histogram analysis are used to enhance visual inputs. Classical models including Support Vector Machines (SVM)\, K-Nearest Neighbors (KNN)\, and Decision Trees are evaluated alongside deep learning models like Convolutional Neural Networks (CNN)\, ResNet\, and MobileNet. The You Only Look Once (YOLO) object detection framework is also implemented using annotated images via Roboflow\, achieving an accuracy of 91%. Comparative analysis highlights performance based on accuracy\, processing time\, and F1-score\, with deep learning models generally outperforming traditional approaches. Challenges such as dataset limitations\, real-time deployment\, and mobile optimization are addressed\, and possible future directions such as hybrid AI models\, edge deployment\, and AI-IoT integration are discussed. The findings demonstrate the potential of AI in enhancing food quality assurance and establishing scalable grading systems in agriculture.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2E
LOCATION:Board Room 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:353d66b668e0c21b5b97b2f301751f38
URL:http://11tict4sd.sched.com/event/353d66b668e0c21b5b97b2f301751f38
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:An Unsupervised Learning Framework for Solar Flare Forecasting Using Clustering and Anomaly Detection on SDO Magnetogram Data
DESCRIPTION:Authors - Gokapay Dilip Kumar\, Ravikumar Munaganuri\, Dhruv Diguvapalli\, Smayan Paul\, A S Akhil\, Mallu Shiva Rama Krishna\, Prem Swarup Mallipudi\, Kanchapogu Naga Raju\, Sivanagaraju Vallabhuni Abstract - Solar flares are bursts that can disrupt terrestrial and space-based operations. This study aims to develop an unsupervised learning framework to anticipate flares and mitigate their impact. We applied clustering to unlabeled Solar Dynamics Observatory magnetic field time-series data to group similar activities and detect anomalies indicative of impending flares. Features were preprocessed and analyzed with K-Means and DBSCAN. Models differentiated regular activity from anomalous patterns preceding flares\, revealing clusters with higher flare propensity. Unsupervised learning effectively forecasts solar flares by autonomously identifying critical patterns and yielded an MSE of 0.06. Integration of wind data and historical records with real-time prediction can be done in the future.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2E
LOCATION:Board Room 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:fe329f0722c35aa01da24bd85a432f76
URL:http://11tict4sd.sched.com/event/fe329f0722c35aa01da24bd85a432f76
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:Healthcare Fraud Detection Utilizing Machine Learning Techniques
DESCRIPTION:Authors - Harjas Bajaj\, Diksha Joshi Abstract - Fraud in the healthcare industry remains one of the most profound issues globally\, with billions lost each year. This research focuses on one of the most recent methods\, applying Machine Learning (ML) algorithms\, to automate the detection of fraudulent claims. Multiple models including Logistic Regression\, Random Forest\, and XGBoost and Neural Networks as well as Isolation Forest were trained and evaluated on an accuracy dataset that contained 1 million claims insurance. Several challenges such as redundant features and noisy data were successfully overcome using data preprocessing and visualization methods. These models were compared on the basis of accuracy\, precision\, recall\, and F1-score. XGBoost achieved the greatest accuracy of 92.1%\, and Neural Networks and Random Forests followed close behind. Although unsupervised\, Isolation Forest proved useful in identifying anomalous patterns in situations with little labeled data. ML techniques require model interpretability and computational efficiency to detect fraud in real-world healthcare systems.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2E
LOCATION:Board Room 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:82798d50f686ded5b40af4d532033a9e
URL:http://11tict4sd.sched.com/event/82798d50f686ded5b40af4d532033a9e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:Logistic-Map Based Image Steganography Technique
DESCRIPTION:Authors - Pratham Vasa\, Vansh Dhoka\, Vivean Arya\, Aneri Patel\, Avani Bhuva Abstract - This paper presents a logistic map-based image steganography technique to enhance security and resistance against steganalysis. Unlike traditional methods like Least Significant Bit (LSB) substitution\, our approach leverages chaos theory to determine dynamic embedding locations\, improving imperceptibility and robustness. Experimental results show high PSNR and SSIM values\, ensuring minimal distortion and secure data concealment. The proposed method effectively balances security\, capacity\, and adaptability\, with future improvements aimed at optimizing computational efficiency and resistance to detection.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2E
LOCATION:Board Room 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:3a03f53bcf67e2f6e50fac2a0619f545
URL:http://11tict4sd.sched.com/event/3a03f53bcf67e2f6e50fac2a0619f545
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:Optimal Post-High School Course Selection System Leveraging Machine Learning
DESCRIPTION:Authors -&nbsp\;Varsha Lokare\, Iram Jhetam\, Prakash Jadhav\, A. W. Kiwelekar\nAbstract -&nbsp\;The transition from high school to higher education marks a serious stage in an individual's academic journey\, where selecting the most suitable career path assumes thoughtful implication. The plenty of choices following high school completion necessitates a robust framework for guiding students toward optimal course selections. This paper introduces an innovative approach\, the Best Course Selection System (BCSS)\, designed to facilitate informed decision-making leveraging machine learning methodologies. It incorporate essential criteria such as personal interests\, employment prospects\, eligibility criteria\, affordability\, duration of study\, and course suitability. The BCSS integrates classifiers like Decision Tree(DT)\, AdaBoost\, Support Vector Machine (SVM)\, Artificial Neural Network (ANN)\, etc. Three major streams—Arts\, Commerce\, and distinct Science streams—are considered for comprehensive analysis. Comparative evaluations based on Accuracy\, Confusion Matrix\, Precision\, Recall\, and F1-Score metrics highlight the superior performance of Support Vector Machine\, Artificial Neural Network\, and Decision Tree classifiers over AdaBoost. The proposed BCSS\, trained and tested on a specific database\, demonstrates an impressive accuracy rate of approximately 98% in predicting the most favorable course options. This system promises to serve as a tool for students navigating the complex landscape of post-secondary education choices\, aiding in informed decision-making for a successful academic path.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2E
LOCATION:Board Room 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:3bafae3c2d72fb5bd320643e866b2de8
URL:http://11tict4sd.sched.com/event/3bafae3c2d72fb5bd320643e866b2de8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:Revolutionizing Financial AI with Federated Learning: A Secure and Scalable Approach
DESCRIPTION:Authors - Saptarshi Kundu\, Ruchir Chhabra\, Abhivardhan Yadav\, Surendra Solanki\, Gaurav Kumawat\, Deepjyoti Choudhury Abstract - Financial institutions face major challenges with regards to data privacy\, fraud detection\, and credit risk assessment as a result of the weaknesses in centralized AI systems that deal with sensitive financial data. These are susceptible to cyberattacks\, breaches of privacy\, and inability to comply with regulation. In an effort to solve these challenges\, we propose FinSec-FL\, a robust Federated Learning (FL) framework to enhance secure\, scalable\, and privacy-oriented AI in financial services. It leverages homomorphic encryption\, secure multiparty computation\, and differential privacy methods that allow cooperative AI model training while preserving raw data in confidence. Moreover\, it includes blockchain-smart contracts for maintaining some model verification and auditability\, which is also transparent and tamper-resistant for confidential information. This method adds higher resilience of the system against malicious activity and trust for the decentralized financial networks. The framework contains a method known as Adaptive Federated Learning\, which facilitates rapid fraud detection\, and also federated reinforcement learning to handle credit risk in dynamic environments. This research proves that FinSec-Fl is more accurate than all other centralized models for fraud detection\, which protects privacy and computational efficiency\, which is crucial. Future work would involve quantum-resistant cryptography and federated graph neural networks to further detect neural networks to detect fraud patterns\, which is the future objective of the project.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2E
LOCATION:Board Room 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:1658760831c44f34bf45e7de3156eda8
URL:http://11tict4sd.sched.com/event/1658760831c44f34bf45e7de3156eda8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:SkillSage: AI-Powered Placement Preparation Platform for Engineering Students
DESCRIPTION:Authors - Manikrao Dhore\, Ayush Sasane\, Amankumar Kumare\, Aditya Kadlag\, Gaurang Khanderay Abstract - Over the years\, placement preparation has posed serious daunting problems to most of the engineering students\, such as lack of efficiency with Applicant Tracking System (ATS)\; limited\, unrealistic practice of interviews\; scattered and often obsolete resources. Such a situation results in low selfesteem\, misaligned applications for jobs\, and ineffective preparation. Such problems are addressed by this paper\, wherein SkillSage\, an AI-based platform\, is developed for closing these gaps with integrated modules such as resume optimization\, mock interviews\, job matching\, and skills assessment. SkillSage applies advanced NLP models like GPT-4\, Whisper API to provide personalized feedback\, adaptive learning pathways\, and real-time speech-to-text simulation for interviews. ATS compliant resume scoring\, skill-based job suggestions\, and AI quizzes are additional features. Early evaluation results have shown considerable improvement in interview performance\, acceptance of resumes\, and job-skill matching accuracy. In this way\, SkillSage provides the entire intelligent solution to address the gap between what academia prepares students for and what the industry expects.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2E
LOCATION:Board Room 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:d552ec721d15393077a4d760ca8c98e1
URL:http://11tict4sd.sched.com/event/d552ec721d15393077a4d760ca8c98e1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:A Novel Deep Learning-Based Technique for Automatic Source Code Summarization
DESCRIPTION:Authors - Shruthi D\, Chethan H. K\, Agughasi Victor Ikechukwu Abstract - A set of encoder-decoder large language models (LLMs) called CodeT5+ was created for a range of code-related applications. It presents adaptable architecture that may be tailored to many downstream uses\, including text-to-code retrieval\, code creation\, completion\, and math programming. Text-code matching\, span denoising\, contrastive learning\, and causal language modeling spanning unimodal and bimodal multilingual code corpora are among the varied collection of pretrained objectives used to train the model. Using frozen off-the-shelf LLMs for initialization is a significant breakthrough that enables the models to scale effectively without requiring training from scratch. The instruction-tuned CodeT5+ 200m model outperforms current benchmarks and records better state-of-the-art results in code summarization among the variants. The performance metrics were impressive: ROUGE-1 at 78.5%\, ROUGE-2 at 66.2%\, ROUGE-L at 77.4%\, BLEU-4 at 56.3%\, METEOR at 64.7% and CodeBLEU at 71.5%. These outcomes demonstrate how well Co-deT5+ comprehends and produces code for a variety of programming tasks.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2F
LOCATION:Board Room 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:69b858b0f588bcafc2a02a3af3521d0c
URL:http://11tict4sd.sched.com/event/69b858b0f588bcafc2a02a3af3521d0c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:Energy Consumption Optimization of 5g uplink in NB-IoT
DESCRIPTION:Authors - Shreyas Sutar\, Sanjeev Gani\, Prajwal R A\, Vaibhav. P. Garag\, Samrudh Katti\, Kiran M R\, Suneeta V Budihal Abstract - This study offers optimization techniques for energy usage in NB-IoT devices\, with the main emphasis on integrating the Extended Discontinuous Reception (eDRX) algorithm. eDRX allows devices to stay in a low-power sleep state for a longer period before waking up for network connectivity\, only as and when needed. This is a technique for overcoming significant obstacles to IoT deployments in remote or hostile geographies by allowing devices to last much longer on battery power. The research also provides a comparative analysis of eDRX against other power management mechanisms and shows its energy efficiency and operational stability superiority. The research validates the development of sustainable and scalable IoT solutions.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2F
LOCATION:Board Room 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:94dd20af55c0192c7b51eb0d9c4b7750
URL:http://11tict4sd.sched.com/event/94dd20af55c0192c7b51eb0d9c4b7750
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:Expirio– Simplifying Subscription and Deadline Management System
DESCRIPTION:Authors - Shreyas Kshirsagar\, Masira Kulkarni\, Deepika Ajalkar\, Pranav Dighade\, Prathamesh Karajkar Abstract - Expirio is a web-based platform designed to simplify subscription and deadline management for individuals and businesses. It offers a centralized system to track services\, monitor payments\, categorize expenses and income\, and receive reminders for renewals. A key feature is the service analysis score\, which evaluates the cost-effectiveness of subscriptions\, supporting informed financial decisions. Built using the MERN stack with Next.js for front-end performance\, MongoDB for flexible storage\, and JWT-based authentication for security\, Expirio ensures responsive and secure access. Its interface streamlines financial tracking while providing detailed reports and analysis for both personal and business users. Automated reminders and financial tools reduce manual effort and minimize missed payments. Designed to scale\, Expirio supports users ranging from individuals to SMEs and subscription service providers\, promoting efficient and effective subscription and financial management.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2F
LOCATION:Board Room 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:f10b46192a7dc3b636d86939db8f9699
URL:http://11tict4sd.sched.com/event/f10b46192a7dc3b636d86939db8f9699
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:Integrating Social Media’s Role in Elections with the Theory of Planned Behavior: A Comprehensive Exami-nation of Voter Intention
DESCRIPTION:Authors -&nbsp\;Anita Shalehah\, Massoud Moslehpour\, Khoirul Amin\, Hanif Rizaldy\, Ankita Manohar Walawalkar\nAbstract -&nbsp\;This paper integrates a systematic review of social media’s role in elections with an extended Theory of Planned Behavior (TPB) framework to examine how online political marketing influences voter intention. The first part provides a review of 17 studies on the impact of social media\, detailing how platforms—particularly Twitter and Facebook—affect political campaigns. The second part extends the TPB by incorporating Candidate Image (CI) as a critical factor mediating the relationship between Political Marketing on Social Media (PMSM) and voter intention. By extending the Theory of Planned Behavior to include Candidate Image as a mediating factor\, this paper introduces a novel framework that bridges rational predictors of intention with the symbolic and emotional impact of online candidate branding—filling a critical gap in political marketing theory. Empirical findings from multiple contexts suggest that while social media can substantially influence voter choice—especially when campaigns deploy two-way communication and salient image-building—some campaigns still rely on one-way “broadcast” approaches that limit true voter engagement. This synthesis reveals how strategic social media engagement can improve attitudes (AT)\, shape subjective norms (SN)\, and heighten perceived behavioral control (PBC). Recommendations emphasize the need for interactive content\, credible messaging\, and authenticity in building candidates’ online presence. The paper concludes by discussing the practical implications for campaign teams and the theoretical refinements required to fully account for digital-era political behavior.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2F
LOCATION:Board Room 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:dbce44145ea0b17de3f0e68dd5c254e8
URL:http://11tict4sd.sched.com/event/dbce44145ea0b17de3f0e68dd5c254e8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:Intelligent System for Accessible Content Creation by Design
DESCRIPTION:Authors - Charudatta Jadhav\, Sai Chandana Sirisha Gorasa\, Manjiri Sathe\, Kunal Shrivastava Abstract - Enterprises produce a broad range of digital documents\, such as bank statements\, pay slips\, e-books/bills & others which transformed the way consumers engage with brands but a section of the world population with print disabilities is still deprived of such digital facilities because they are mostly inaccessible. There are diverse ways in which documents are created by enterprises like - via text editor\, raw content coming from Database or CMS. In all scenarios\, enterprises take a reactive approach to implement accessibility postproduction\, which is not right and practical because it’s a laborious and manual task\, which requires specialized training\, high cost & effort. In this paper\, we are presenting our solution approach of ‘Inclusive Content by DESIGN’\, where we are building a core AI technology capable of processing raw and unstructured content derived from traditional document formats like PDF\, Text\, HTML\, or from enterprise databases or Content Management Systems. Our technology identifies accessibility gaps at element level e.g.\, headings\, images etc. and at page and document level\, introduce accessibility fixes based on noncompliance and later publish diverse accessible formats like accessible PDF\, EPUB3\, DAISY text/audio and E braille. It will envisage the enterprise system to take care of end-to-end process from content generation to content production with ‘Machine First’ approach and to disseminate in inclusive formats. Our vision is to bring a holistic approach of solving all types of document accessibility in one platform by disseminating the right content at right time at enterprise level by integrating accessibility at root.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2F
LOCATION:Board Room 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:a1f953c0dfa5c6afbb05333b966bb391
URL:http://11tict4sd.sched.com/event/a1f953c0dfa5c6afbb05333b966bb391
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:Position Estimation of a vehicle using Particle Filters
DESCRIPTION:Authors - Saiprasad Teli\, Krishna Kulkarni\, Vivek Maragal\, Uday Hiremath\, Supriya Katwe Abstract - Localization is vital in autonomous driving\, which an accurate vehicle position enables. There are many studies on different localization algorithms\, like Montecarlo and Kalman filters\, for sensor fusion and localization of the vehicle. Particle filters are one of the techniques that help accurately estimate the vehicle’s position\, even in non-linearities. Such an algorithm is presented in this study for vehicle position estimation integrating data from GPS and IMU sensors\, which is generated from a sensor\, and simulation is carried out for demonstration. The algorithm differs from the available study in that it can create particles differently. The results demonstrate that increasing the number of particles in the filter significantly enhances accuracy and reduces error. This approach is particularly effective for autonomous navigation in complex and dynamic environments\, making it an ideal candidate for future autonomous driving and Advanced Driver Assistance Systems (ADAS).
CATEGORIES:PHYSICAL TECHNICAL SESSION 2F
LOCATION:Board Room 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:051425b1d20dddb473b8b6f7d698cc6d
URL:http://11tict4sd.sched.com/event/051425b1d20dddb473b8b6f7d698cc6d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T083000Z
DTEND:20260825T100000Z
SUMMARY:Zero-Shot Classification with NLI DeBERTa V3: Evaluating MultiNLI for NLP Tasks
DESCRIPTION:Authors - Samriddhi Gupta\, Sankrishna Goyal\, Khushi Goyal\, Ram Kishan Dewangan Abstract - Zero-Shot Classification (ZSC) has indeed become one of the main topics of research in Natural Language Processing (NLP)\, as it allows the model to predict the test data belonging to classes that the model has never seen before with no access to any samples from such classes. In this paper\, the capability of DeBERTa V3\, one of the advanced pre-trained language models\, is explored when it comes to zero-shot classification by means of Natural Language Inference (NLI). To this end\, we concentrate on the application of MultiNLI dataset for demonstrating the ability of DeBERTa V3 in classifying the text into new classes. Semantic relations and contextualized embeddings allow DeBERTa V3 to perform well in tasks requiring generalization to unseen situations\; It can classify cases based on premise-hypothesis pairs without requiring tuning to the specific task. To address these issues\, we present a suggested procedure for preprocessing\, feature engineering\, andemos prompt tuning. The experimental setting measures the model’s usability via accuracy and top-k accuracy parameters. The evaluation also confirms that DeBERTa V3\, with the suitable hypothesis template and prompt-tuning\, surpasses typical zero-shot strategies\, which significantly improve the reliability of practical applications in various NL abilities. This work demonstrates the potential of the zero-shot learning paradigm for practical scenarios where access to labeled data is limited\, especially in the settings with multiple languages and domains.
CATEGORIES:PHYSICAL TECHNICAL SESSION 2F
LOCATION:Board Room 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:322693dab3d03d1c6a3d1fa67a92ac32
URL:http://11tict4sd.sched.com/event/322693dab3d03d1c6a3d1fa67a92ac32
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T090000Z
DTEND:20260825T090200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 5A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:cd33bf89b8831f16c83eaf26a9a3cf50
URL:http://11tict4sd.sched.com/event/cd33bf89b8831f16c83eaf26a9a3cf50
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T090000Z
DTEND:20260825T090200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 5B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:462f36079d95d35385fe791aec9f7d04
URL:http://11tict4sd.sched.com/event/462f36079d95d35385fe791aec9f7d04
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T090000Z
DTEND:20260825T090200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 5C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:f6bd45da9bb27aba5a48b2d5c680253e
URL:http://11tict4sd.sched.com/event/f6bd45da9bb27aba5a48b2d5c680253e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T090000Z
DTEND:20260825T090200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 5D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:24e096b17ea6205cc540926ffbd97e3f
URL:http://11tict4sd.sched.com/event/24e096b17ea6205cc540926ffbd97e3f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T090000Z
DTEND:20260825T090200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 5E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:5ecf5280707b27932bf16ff9dd376618
URL:http://11tict4sd.sched.com/event/5ecf5280707b27932bf16ff9dd376618
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T090200Z
DTEND:20260825T090500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 5A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:fa10e7624f93f143b011829b3a76e233
URL:http://11tict4sd.sched.com/event/fa10e7624f93f143b011829b3a76e233
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T090200Z
DTEND:20260825T090500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 5B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:51fca805ba0e52069f1e8a49dfe4936c
URL:http://11tict4sd.sched.com/event/51fca805ba0e52069f1e8a49dfe4936c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T090200Z
DTEND:20260825T090500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 5C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:6c50c175ba46c6a43d5cbc28730d688c
URL:http://11tict4sd.sched.com/event/6c50c175ba46c6a43d5cbc28730d688c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T090200Z
DTEND:20260825T090500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 5D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:2853e382ebd5474799b0c324110266c7
URL:http://11tict4sd.sched.com/event/2853e382ebd5474799b0c324110266c7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T090200Z
DTEND:20260825T090500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 5E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:13626f4ef1ba453864e4574bb3ed67fd
URL:http://11tict4sd.sched.com/event/13626f4ef1ba453864e4574bb3ed67fd
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T095800Z
DTEND:20260825T100000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 6A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:e3e057e40a8ebcfcfada0d147f53b960
URL:http://11tict4sd.sched.com/event/e3e057e40a8ebcfcfada0d147f53b960
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T095800Z
DTEND:20260825T100000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 6B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:308dcd130c631c31eaee419183d5b72e
URL:http://11tict4sd.sched.com/event/308dcd130c631c31eaee419183d5b72e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T095800Z
DTEND:20260825T100000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 6C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:89673f2e19f6aa21ba00b7a72ccb3f3e
URL:http://11tict4sd.sched.com/event/89673f2e19f6aa21ba00b7a72ccb3f3e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T095800Z
DTEND:20260825T100000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 6D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:f9089e15c804347bb917d0ec12e21a93
URL:http://11tict4sd.sched.com/event/f9089e15c804347bb917d0ec12e21a93
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T095800Z
DTEND:20260825T100000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 6E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:fd5945bfb4702bea9c4a851b2cff8189
URL:http://11tict4sd.sched.com/event/fd5945bfb4702bea9c4a851b2cff8189
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T113000Z
SUMMARY:A Hypothetical Case study of SDN parameters for network-based container setup on raspberry Pi
DESCRIPTION:Authors - Bimlendu Shahi\, V. Pushparajesh Abstract - The depth of behavioral analysis using a hypothetical case study on Software-Defined Networking (SDN) Parameters provides solution for better functioning and performance improvements [1]. In the present time in world of Information Technology Industry\, the leading container technology is Docker [2]. Further\, this case study helps to characterize their alterations or variations for container based SDN setup and non-container based SDN setup. In this paper\, we have performed hypothetical case study focusing on Software-Defined Networking (SDN) parameters implemented container setups on Raspberry Pi devices. A hypothetical case study was carried out typically in two network states: one at healthy operating condition without any impacts in the network and another at Congested Network condition. In a Software-Defined Networking (SDN) environment\, understanding the variations in performance metrics under different conditions is crucial. In this paper\, a hypothetical study was performed on the network-based container setup on raspberry Pi using the statistical methods by understanding the p-value and confidence interval. The hypothesis testing results shows that SDN performance metrics over time or different level of interactions (i.e.\, throughput\, jitter and latency) vary from Normal Operating condition to congested condition.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3A
LOCATION:West 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:c95fede6de674b5f214cd5ce18810147
URL:http://11tict4sd.sched.com/event/c95fede6de674b5f214cd5ce18810147
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T113000Z
SUMMARY:Automated Height-Based Object Separator Using a Double-Acting Cylinder and PLC
DESCRIPTION:Authors - Hafiz Shaikh\, Harshad Mane\, Aditya Tapkire\, Khomane Nishant\, Samarth Pawar Abstract - In this research\, a double-acting cylinder and a Programmable Logic Controller (PLC) are used to build and implement an automatic height-based object separator. By combining cutting-edge sensor technology for height detection with the actuation capabilities of a double-acting cylinder\, this system automates the separation process\, minimising errors and reducing reliance on manual labour. Its goal is to improve efficiency and accuracy in sorting objects of different heights in industrial environments. The process entails setting up the PLC to process data from height sensors\, accept input\, and adjust the actuation mechanism as necessary. According to experimental data\, the system reliably sorts items with a high degree of dependability\, demonstrating a significant improvement in separation efficiency. Potential solutions are addressed along with implementation challenges like response times and calibration. The results open the door for further advancements in industrial automation technology and highlight the value of automated systems for height-based sorting applications. By emphasizing the value of innovation in production process optimisation\, this study adds to the expanding corpus of knowledge in automation and robotics. Using a non-contact sensor\, a conveyor belt moves items to a detecting zone where their heights are determined. The PLC processes the sensor data and uses the object’s height to decide the proper action. The item is then physically separated onto the appropriate bin or conveyor belt by activating a double-acting cylinder.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3A
LOCATION:West 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:2e0fca3e4f1b777b002978077e1130a3
URL:http://11tict4sd.sched.com/event/2e0fca3e4f1b777b002978077e1130a3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T113000Z
SUMMARY:Evaluating Convolutional Neural Network Models: Performance Perspective in Video Summarization
DESCRIPTION:Authors - Rachit Adhvaryu\, Dipesh Kamdar\, Krishna Raulji\, Shreya Dholariya\, Krunal Vaghela\, Munindra Lunagaria Abstract - The nature of data has changed significantly as a result of the quick development of technology. Text-based datasets have given way to visual data\, such as photos and videos. This shift necessitates the development of cutting-edge technologies that can effectively process and analyze visual input\, allowing the creation of intelligent systems that can precisely extract insightful information. Convolutional neural network (CNN) models that have already been trained are now essential resources for this project. In this paper\, the effectiveness of three well-known CNN models—ResNet\, DenseNet\, and VGG—in picture classification tasks is thoroughly compared. Our assessment concentrates on object detection performance on three different datasets—animals\, birds\, and flowers—obtained from the online repository of Kaggle\, offering important information about the advantages and disadvantages of each model.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3A
LOCATION:West 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:cf4bdb2e5a5c7152014b5ae6785c8aa5
URL:http://11tict4sd.sched.com/event/cf4bdb2e5a5c7152014b5ae6785c8aa5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T113000Z
SUMMARY:Lightweight Cryptography for IoT Security
DESCRIPTION:Authors - V Tanisha\, Varun Kashyap\, Vytla Sri Hanvith\, V Yamini\, Santhameena S Abstract - The increasing adoption of Internet of Things (IoT) technology has raised security concerns due to the computational and power constraints of embedded devices. Traditional cryptographic algorithms often impose high resource demands\, making them impractical for IoT environments. This paper investigates the use of lightweight cryptographic algorithms\, specifically PRESENT and SIMON\, for securing data transmission in IoT applications. The implementation involves an ESP32 microcontroller interfaced with a DHT11 sensor\, where the collected environmental data is encrypted before being transmitted to the cloud platform\, ThingSpeak. The performance of the encryption algorithms is analyzed based on computational efficiency\, memory consumption\, and security strength. Experimental results demonstrate that lightweight cryptographic techniques provide an effective trade-off between security and performance\, ensuring data integrity and confidentiality without significantly impacting device resources. The study highlights the importance of integrating lightweight cryptography into IoT networks to mitigate security vulnerabilities while maintaining operational efficiency. These findings contribute to the development of secure and scalable IoT systems that address the growing demand for resource-efficient security solutions.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3A
LOCATION:West 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:452f7251ab4c6b5d5d8bf4109eb07003
URL:http://11tict4sd.sched.com/event/452f7251ab4c6b5d5d8bf4109eb07003
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T113000Z
SUMMARY:Sentiment-Enhanced Natural Language Processing for Fictional Character Analysis: Classifying Moral Alignments
DESCRIPTION:Authors -&nbsp\;K Yashita Varsha\, K Chinmay Naag\, Divyansh Maurya\, Rashmi Ugarakhod\nAbstract -&nbsp\;A deep learning approach to classify fictional and comic characters into four alignment categories (Good\, Bad\, Neutral and Antihero) based on their back-stories using natural language processing (NLP) has been presented. Our methodology incorporates comprehensive text preprocessing\, including data cleaning\, WordNet-based augmentation and dataset balancing techniques. The provided classification model employs a neural network architecture with vectorizers\, embedding layers\, global max pooling\, and regularized fully connected layers\, optimized using Adam with early stopping along with it to enhance the user experience\, a cosine similarity retrieval system is integrated which identifies the most similar character in our database based on input descriptions and provides that along with the alignment. The model achieves approximately 91% validation accuracy which is better compared to all previous state of art models\, demonstrating its effectiveness for text-based classification tasks. Beyond entertainment applications\, this approach shows potential for adaptation to criminal profiling and behavioral analysis which can be helpful for analyzing class of a criminal profile using a similar NLP model\, where textual descriptions of backgrounds and psychological factors could predict behavioral patterns
CATEGORIES:PHYSICAL TECHNICAL SESSION 3A
LOCATION:West 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:7416d676651bb6889ff50a31efa35a82
URL:http://11tict4sd.sched.com/event/7416d676651bb6889ff50a31efa35a82
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T113000Z
SUMMARY:The Role of Digital Forensics in Cybercrime Investigations: Methods\, Tools\, and Legal Considerations
DESCRIPTION:Authors - Rahul Bhole\, Rahul P. More\, Yuvraj Nikam\, Vikas More\, Rupali Bhatkhande\, Ishwari Raskar Abstract - Digital forensics is a key area of cybercrime and crime investigation with digital evidence. Due to the rapid expansion of digital devices and use of the internet\, forensic processes have evolved to address data collection\, analysis\, and admissibility in the court of law. This essay outlines some of the significant digital forensics categories like hard disk forensics\, memory forensics\, and network forensics\, and how they are vital in cybercrime investigations. The research also analyzes the formal forensic investigation process\, technical concerns\, legal limits\, and the necessity of standardized procedures. The use of computers in forensic investigations and the influence of forensic tools on evidence gathering are also discussed. As cyber threats continue to change\, extending forensic processes\, integrating artificial intelligence\, and international legal collaboration are required to improve digital investigations. This research emphasizes the necessity of digital forensics in the provision of cybersecurity\, supporting law enforcement\, and maintaining justice in the digital world.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3A
LOCATION:West 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:088e20cb1f89eed51159e5a8a8d2d244
URL:http://11tict4sd.sched.com/event/088e20cb1f89eed51159e5a8a8d2d244
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T113000Z
SUMMARY:Early-Stage Alzheimer’s Detection: Comparing CNN and Advanced Neural Architectures
DESCRIPTION:Authors - Krish Mithaiwala\, Vidhiben Ka Patel\, Sachin Patel\, Dhwanil Chauhan\, Margi Shah\, Ankur Patel Abstract - Alzheimer's disease (AD) is a neurological illness that affects memory and cognitive function over time. There is currently no cure or treatment to stop the disease's progression. Early diagnosis is essential but challenging because symptoms typically don't show up until serious brain damage has occurred. This study investigates how convolutional neural networks (CNNs)\, a type of deep learning\, can be used to increase the precision of brain imaging diagnosis. Conv2D\, MaxPooling2D\, and dense layers were used to train the proposed CNN model on a Kaggle MRI dataset that was divided into three dementia levels: low\, mild\, and severe. SMOTE was used to resolve class imbalance\, improving the model's capacity to categorise under-represented groups. The CNN had a relatively low F1 score\, a robust recall\, and a high classification accuracy of 96.35%. On the same MRI dataset\, comparative comparison with other neural architectures such as VGG16 and Inception V3 also showed dependable performance\, underscoring CNNs' potential to support early detection. This study highlights the potential of deep learning to transform Alzheimer's diagnosis and improve clinical decision-making for improved disease treatment\, despite difficulties in identifying small brain alterations.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3B
LOCATION:West 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:9114397363908c4c0759ba7c94aa683d
URL:http://11tict4sd.sched.com/event/9114397363908c4c0759ba7c94aa683d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T113000Z
SUMMARY:Leveraging AI for Green Hydrogen Production: A Sustainable Pathway for India’s Energy Future
DESCRIPTION:Authors - Kanubhai K. Patel\, Hasnain Inayatali Narsandawala\, Sandeep Gaikwad\, Nilay Vaidya\, Krishna Kant\, Dharmendra Patel Abstract - This paper explores the integration of machine learning with green hydrogen production to address India’s growing energy demands\, climate goals\, and vision for Viksit Bharat 2047. By combining renewable energy sources with advanced machine learning models\, the study aims to optimize hydrogen production processes\, enhance efficiency\, and reduce costs. The research leverages published frameworks to forecast renewable energy availability and applies Artificial Neural Networks (ANNs) and other predictive algorithms to green hydrogen production. Key findings highlight green hydrogen’s potential to decarbonize critical sectors. While contributing to Sustainable Development Goals. The paper concludes that green hydrogen\, empowered by machine learning\, offers a transformative solution for India’s energy transition\, fostering economic growth\, sustainability\, and global leadership in clean energy innovation by 2047.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3B
LOCATION:West 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:dd3aa80b7a9cda84e355d93a2c0a5f18
URL:http://11tict4sd.sched.com/event/dd3aa80b7a9cda84e355d93a2c0a5f18
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T113000Z
SUMMARY:Mamdani Fuzzy Inference System Based on Multi-Textural Biomarkers for Alzheimer 's Stage Detection
DESCRIPTION:Authors -&nbsp\;Kavitha A. R\, Ramya M\, Charanya T. N\, Lita Pansy. P\, E. BHUVANESWARI\nAbstract -&nbsp\;The chronic brain disease known as Alzheimer’s disease (AD) primarily affects short-term memory while advancing through its neurodegenerative stages. The initial symptoms of the disease develop gradually before the condition deteriorates thus early detection becomes vital. A Machine learning approach powers the Disease Diagnostic System (DDS) that analyzes T2 weighted Magnetic Resonance Imaging (MRI) scans from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The paper examines the Hippocampus and amygdala located in the left hemisphere of the human brain as Region of Interest (ROI) which is extracted from small cohort MRI scans. The μ±3σ normalization method applies to segmented ROI while first-order histogram features extract Skewness and Kurtosis values. The Region of Interest receives two-dimensional wavelet features which include the Max norm of the original image and the Diagonal Detail Coefficient. The extracted textural markers serve as inputs to build a Mamdani FIS which defines minimum rules for AD stage diagnosis. The proposed classification method delivers accuracy levels of 96.13 percent for AD vs NC diagnosis and 94.73 percent for MCI vs NC diagnosis and 93.11 percent for AD vs MCI diagnosis. Multiple feature extraction through biomarker texture analysis enables better decision generation within the expert system framework.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3B
LOCATION:West 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:8891a8e7be201ffcbd79f822b431719d
URL:http://11tict4sd.sched.com/event/8891a8e7be201ffcbd79f822b431719d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T113000Z
SUMMARY:NeuroInsight: Automated EEG Pattern Analysis for Critical Care
DESCRIPTION:Authors -&nbsp\;Roopa Ravish\, Priyadarshi Sivakumaran\, Darshana Vedavalli\, Sai Sooraj Ramagiri\, Nitish R\nAbstract -&nbsp\;The complexity of the analysis of electroencephalography (EEG) is a difficulty for application in clinical practice\, it has historically relied on expert interpretation in an effort to identify discrete patterns of signals\, and that makes scalability and efficiency difficult. In response to this challenge\, a method of EEG signal automation classification using deep learning has been developed to help in the diagnosis of neurological disease through the EEG signal classification of six types: seizure\, lateralized periodic discharges (LPD)\, generalized periodic discharges (GPD)\, lateralized rhythmic delta activity (LRDA)\, generalized rhythmic delta activity (GRDA)\, and other. This approach uses a multi-class EEG classifier based on ResNet blocks\, 1D convolution\, and GRU layers to learn EEG signal multi-scale and temporal features.The data used are EEG samples divided into training\, validation\, and test sets\, and the architecture is optimized for processing sequences efficiently and hence ideal for pattern detection specific to every neurological disorder. Performance measures reflect the model's strength\, with an accuracy of 83.28%\, a macro F1 measure of approximately 79.85%\, and a Kullback-Leibler (KL) divergence loss of 0.26 during training. The high F1 score also reflects the model's strength for clinical application\, with the potential to simplify diagnostic procedures. The results reveal the strength of convolutional networks to classify EEG and enable large-scale automated neurological evaluation solutions of the future.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3B
LOCATION:West 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:93f52722beab1ba4b2fa726a5792c889
URL:http://11tict4sd.sched.com/event/93f52722beab1ba4b2fa726a5792c889
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T113000Z
SUMMARY:Real-Time Human Sitting Posture Detection Using YOLOv5
DESCRIPTION:Authors -&nbsp\;Mahalakshmi Bodireddy\, Aditi Aher\, Aditi Dabhade\, Pranjali Deshpande\, Shriniwas Dhage\nAbstract -&nbsp\;Prolonged sitting in workplaces contributes to health issues like musculoskeletal disorders\, making proper posture maintenance essential yet challenging. This study presents a real-time sitting posture detection system using You Only Look Once version 5 (YOLOv5) to classify postures as "Good" or "Bad" and provide instant feedback for healthier habits. A key contribution is a dataset of 1\,342 images collected to capture diverse user profiles and comprehensive views of sitting postures\, reflecting real-world conditions\, including background noise\, ensuring reliable performance even in challenging environments. The model demonstrates substantial accuracy in detecting and classifying postures by achieving a mAP@0.5 of 78.9%\, a precision of 74.4%\, and a recall of 79.7%. Integrating webcams and leveraging cloud platforms offers efficient\, accurate\, and non-invasive feedback\, promoting workplace ergonomics and healthier sitting practices.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3B
LOCATION:West 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:47b2cbfb69aef3358a1b10a5eb9b03cc
URL:http://11tict4sd.sched.com/event/47b2cbfb69aef3358a1b10a5eb9b03cc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T113000Z
SUMMARY:Sentiment Analysis Driven by AI for Employee Retention: Prompt Identification of Burnout and Disengagement at Work
DESCRIPTION:Authors - Nilay Vaidya\, Kamini Solanki\, Krishna Kant\, Jay Panchal\, Kanubhai K. Patel\, Tejas Thakkar Abstract - Organizations face persistent challenges in employee retention\, with burnout and disengagement being primary drivers of attrition. Traditional retention strategies often rely on reactive approaches\, failing to identify early warning signs. This study proposes an AI-driven sentiment analysis framework that integrates Natural Language Processing (NLP) and Multiple Criteria Decision Making (MCDM) to detect early indicators of disengagement and burnout in workplace communication. By analyzing emails\, chat logs\, HR feedback\, and survey responses\, the model identifies sentiment trends\, emotional tone\, and linguistic patterns linked to declining employee engagement. The extracted sentiment scores are incorporated into an MCDM-based decision model\, ranking key risk factors such as leadership influence\, job satisfaction\, and workload. This enables HR teams to implement personalized interventions for at-risk employees. The proposed methodology is validated using real-world organizational datasets\, focusing on the accuracy of early detection and intervention effectiveness. Findings indicate that AI-powered sentiment insights\, when combined with structured decision models\, significantly enhance retention strategies by proactively addressing workplace stressors. This research contributes to AI-driven HR analytics\, offering a scalable solution for improving employee engagement and reducing turnover.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3B
LOCATION:West 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:b317b36ea5083ab601b31eb6a9211a7b
URL:http://11tict4sd.sched.com/event/b317b36ea5083ab601b31eb6a9211a7b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T113000Z
SUMMARY:A Comprehensive Speaker Diarization System Utilizing Pyannote audio for Segmentation\, ECAPA-TDNN for Embedding\, and SA-EEND for Speaker Assignment
DESCRIPTION:Authors - Jatin Kumar Singh\, Satwik Bhat\, Varunkumar Salimath\, Satish Chikkamath\, Suneeta V. Budihal\, Sujata S. Kotabagi Abstract - Speaker diarization—the process of partitioning audio streams into segments associated with individual speakers—is a critical task in speech analytics\, transcription systems\, and human-computer interaction. This paper presents a comprehensive and modular speaker diarization pipeline that integrates state-of-the-art components to ensure high accuracy and scalability across diverse acoustic environments. The proposed system leverages Pyannote.audio for precise speech activity detection and segmentation\, ECAPA-TDNN for robust and discriminative speaker embedding extraction\, and SA-EEND (Speaker-Attributed End-to-End Neural Diarization) for intelligent speaker assignment\, even in scenarios with an unknown number of speakers. Our pipeline addresses limitations in conventional systems by employing a hybrid framework that combines modular interpretability with the flexibility of end-to-end learning. Each component is independently fine-tuned and integrated to form a cohesive diarization system. Pyannote.audio ensures temporal segmentation with high recall\, ECAPA-TDNN embeddings provide speaker-specific features with robustness against overlapping speech\, and SA-EEND enhances attribution accuracy through attractor-based modeling. The system is validated on publicly available datasets with diverse speaker distributions and overlapping conditions. Evaluation across standard diarization metrics such as DER\, JER\, and SCER demonstrates notable improvements over baseline methods. The modularity of the system also enables future expansion toward multi-modal diarization and domain-specific applications.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3C
LOCATION:South 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:2fa14c79ef77d54cbb3822e892b39355
URL:http://11tict4sd.sched.com/event/2fa14c79ef77d54cbb3822e892b39355
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T113000Z
SUMMARY:Automated Laryngoscope using AI Image Detection
DESCRIPTION:Authors -&nbsp\;Rachel Reju John\, Eisha M\, Safla P\, Sreekutty K S\, Vidya G S\nAbstract -&nbsp\;Automated laryngoscopes enhance airway management by enhancing intubation accuracy and safety\, particularly in difficult cases. The technology aims to reduce reliance on operator skill\, and intubation is made safer and more reliable in emergency situations. Automated laryngoscopes distinguish from conventional devices in employing AI and machine learning for real-time feedback and enhanced airway visualization. Image detection and recognition from the mouth to the epiglottis is the first step using python. Motion integration to automate tube insertion along the lungs is a possible future research direction.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3C
LOCATION:South 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:020e7f9585978772783bcdee92f60067
URL:http://11tict4sd.sched.com/event/020e7f9585978772783bcdee92f60067
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T113000Z
SUMMARY:Availability Evaluation and Performance Analysis of Steam Generating System in Thermal power plant Through RAMD Approach
DESCRIPTION:Authors - Jagriti Singh Chundawat\, Ashish Kumar\, Monika Saini Abstract - Thermal power plants are the backbone of the electricity generation industries. The steam generating unit is the core element of the thermal power plants. The performance and maintenance of this system faces a lot of challenges over time. RAMD analysis plays a crucial role in evaluating the performance of any system. The main purpose of this study is to evaluate the RAMD measures such as reliability\, availability\, maintainability\, dependability\, mean time between failure (MTBF) and mean time to repair (MTTR). For this purpose\, for each subsystem the diagram of transition between state to state are drawn and using Markov chain process their corresponding Chapman Kolmogorov differential equations derived. An Exponential distribution follows by Both the failure and repair rates for all subsystems. This RAMD analysis shows that the boiler subsystems perform best with highest availability i.e. 0.99912 among all the subsystems. The overall availability of a steam generating system is 0.98008. The main findings of these studies are helpful for the thermal power plant designers\, containing comprehensive knowledge that will promote productivity and efficiency.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3C
LOCATION:South 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:925eea4d0c15352f15c583d9d91f478f
URL:http://11tict4sd.sched.com/event/925eea4d0c15352f15c583d9d91f478f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T113000Z
SUMMARY:Bridging Linguistic Scripts: A Comprehensive Survey of Transliteration Techniques Across Languages
DESCRIPTION:Authors -&nbsp\;Devika Deshpande\, Pranjali Deshpande\nAbstract -&nbsp\;Transliteration is the process of converting text from one script to another while keeping phonetic and orthographic accuracy. It’s a key technique for enhancing interlingual communication and fostering accessibility in multilingual settings. The review encompasses a thorough examination of transliteration systems considering several languages and scripts. Examining their benefits\, drawbacks and applications\, the review classifies transliteration methods into grapheme-based\, phoneme-based and hybrid systems. While phoneme-based models ensure better phonetic alignment but require significant processing power\, grapheme-based approaches provide simplicity but lack in phonetic correctness. Hybrid models use statistical and deep learning techniques to integrate contextual and linguistic data\, resulting in enhanced adaptability and accuracy as compared to the traditional approaches. The survey focuses on the importance of transliteration systems for Natural Language Processing (NLP)\, Cross-Lingual Information Retrieval (CLIR)\, and multilingual communication. It also addresses the challenges like inconsistency\, limited data\, and phonetic ambiguity. It also explores the possibilities in enhancement of current transliteration systems that will help create strong\, flexible\, and context-sensitive solutions to linguistic ambiguities and improve access to digital resources in multilingual setting.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3C
LOCATION:South 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:5e090f9f481524ebe768823d6d487391
URL:http://11tict4sd.sched.com/event/5e090f9f481524ebe768823d6d487391
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T113000Z
SUMMARY:COMPARATIVE ANALYSIS OF RAINFALL USING MACHINE LEARNING AND DEEP LEARNING
DESCRIPTION:Authors - Apoorva Appanna Bhoi\, Swathi Raghavendra Kulkarni\, Hitashi Dinesh Haldonkar\, Satish Chikkamath\, Suneeta V. Budihal\, Sujatha S. Kotabagi Abstract - Rainfall forecasting is one of the most critical management methods associated with water levels\, agriculture\, and disaster prevention. But it is not so simple to precisely forecast rainfall because the climatic condition is fluctuating along with other environmental conditions. There can be destruction and floods with the death of many people if the heavy rain is unforeseen. Thus\, in this research study\, the emphasis will be placed on determining the efficiency of ML and DL in accurately forecasting rainfall. Machine learning and deep learning models made the rainfalls’ predictions easier\, yet the development stage. The techniques employed for rainfall prediction are Logistic regression\, Decision tree\, Random forest\, SVM(Support vector machine)\, and CNN(Convolustion neural network). These models are validated using a test database and accuracy and ROC curve measures to find out the best model for rainfall prediction. This work attempts to compare some machine learning algorithms with deep learning algorithms and informs us which of the approach is optimal. As this study’s result indicates\,the proposed CNN model in this paper is the most precise model compared to the others.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3C
LOCATION:South 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:d3cfa491ec2777dc440538dd82ae2ea8
URL:http://11tict4sd.sched.com/event/d3cfa491ec2777dc440538dd82ae2ea8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T113000Z
SUMMARY:Performance Analysis of Different Dimensionality Reduction Techniques in Classification of Cancer
DESCRIPTION:Authors - Jayshree Thota\, Surajgouda M Patil\, Satish Chikkamath\, Suneeta V Budihal Abstract - Breast cancer is a serious disease that affects both men and women\, although women are more susceptible. It is one of the leading causes of cancer-related deaths\, particularly among women. In its early stages\, breast cancer often presents few observable signs\, making diagnosis challenging and delaying effective treatment. However\, it is important to note that with early diagnostics and appropriate intervention\, the mortality rate from this disease can be significantly reduced. In addition to standard diagnostic methods\, artificial intelligence has shown great potential in assessing the early risk of breast cancer by analyzing enriched health data. In this study we attempted to see accuracy with and without PCA\, apply Logistic Regression to classify the cancer. The results shows an in-sight that with PCA the best results are obtained as the most useful information is restrained in the data. Subsequent research will aim to combine fuzzy logic controllers with rule-based controllers to improve the detection rate of breast cancers. Additionally\, expert rules will be utilized to evaluate the validity of the proposed model.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3C
LOCATION:South 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:58318a82a530920f845667e2e14740db
URL:http://11tict4sd.sched.com/event/58318a82a530920f845667e2e14740db
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T113000Z
SUMMARY:Title: Advances\, Challenges\, and Future Directions of Deep Convolutional Neural Networks in Medical Imaging: A Systematic Review
DESCRIPTION:Authors - Hetal M. Bhatt\, Sunil Bajeja Abstract - Background: DCNNs have led to revolution of medical imaging for disease diagnosis\, segmentation and prognosis. Such application has brought undeniable gains in accuracy on diverse clinical domains. Objective: This study offers a systematic review of the applicability\, challenges\, and proposed work directions in medical imaging in DCNNs. Methods: PubMed\, IEEE Xplore\, Scopus and Google Scholar were searched in a comprehensive manner\, and the studies were deemed relevant\, methodologically rigorous and performance metrics based. Results: Overall\, DCNNs have resulted in much higher diagnostic accuracy when used for tumour classification\, lung disease detection\, brain lesion segmentation and cardiac abnormality identification. However\, there are still a few challenges\, including the issue of scarcity of data\, the lack of the model interpretability and the clinical adoption.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3C
LOCATION:South 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:b96e3bc9cc248eb6ba8ba7eccb73ec84
URL:http://11tict4sd.sched.com/event/b96e3bc9cc248eb6ba8ba7eccb73ec84
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T113000Z
SUMMARY:Automated Workflow for Manufacturing and Assembly Using Factory I/O
DESCRIPTION:Authors -&nbsp\;Prathamesh S Anvekar\, Prateek P Prabhakar\, Rohangouda Patil\, Shrihari katti\, Anand Lakundi\, Satish G J\, Madhusudhana H K\nAbstract -&nbsp\;Industrial automation has transformed manufacturing and assembly due to its enhanced efficiency\, accuracy\, and scalability. This research will therefore explore the potential of Factory I/O as a strong simulation tool in the creation and testing of automated workflows within manufacturing and assembly. The main aim is to show how Factory I/O can be used efficiently in the creation\, evaluation\, and optimization of automation systems before they are applied in real situations. The work addresses challenges such as high initial investment costs\, potential risks during deployment\, and the need for flexible testing environments. Factory I/O enables the creation of virtual models of industrial systems\, offering a safe and cost-effective platform for experimentation. By integrating this tool\, the study aims to bridge the gap between theoretical designs and practical implementations. It describes a methodology—simulation of the complete manufacturing workflow\, material handling\, processing\, and assembly tasks followed by analysis. Some key findings emphasize Factory I/O’s ability to discover design flaws\, minimize downtime\, and raise system reliability. This paper establishes the importance of simulation tools to support innovation and increase adaptability in dynamic manufacturing environments. This piece of work contributes to the literature in providing practical insights into Factory I/O in automation and underlines the potential for contributing to increasing efficiency in manufacturing. The results bear significance for the industries with respect to process optimization at minimized cost and risk . . .
CATEGORIES:PHYSICAL TECHNICAL SESSION 3D
LOCATION:South 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:9e0dbe2cc475c037b41074a2f8f65e2b
URL:http://11tict4sd.sched.com/event/9e0dbe2cc475c037b41074a2f8f65e2b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T113000Z
SUMMARY:Automatic Road Maintenance Robot
DESCRIPTION:Authors - Kalyani Kulkarni\, Dipti Varpe\, Gargi Kathale\, Shreya Patil\, Drishi Dhamale Abstract - This paper aims to presents the design and development of a robotic system able to autonomously detect and fill potholes in roads\, a significant challenge in civic structure conservation. The proposed system utilizes a combination of detectors and selectors to achieve this task effectively. An ultrasonic detector is employed to detect the presence of potholes by measuring the distance to the road face. When a pothole is detected\, a servo motor activates a filling medium to introduce a suitable material to repair the disfigurement. To ensure safe navigation\, an infrared (IR) detector is used to descry obstacles in the robot's path\, allowing it to avoid collisions and implicit damage. Global Positioning System (GPS) technology is integrated into the system to track the robot's position in real time\, enabling remote monitoring and control. This data is transmitted to a pall-grounded platform\, similar to Blynk\, where it can be penetrated and imaged through a mobile operation. This allows for effective operation of the robot's operations and ensures that potholes are repaired instantly\, perfecting road safety and reducing conservation costs. A motordriver and DC motors are responsible for the robot's movement\, allowing it to navigate to pothole locales and carry out the form process. The overall system armature is designed to be effective\, dependable\, and able to operate autonomously without mortal intervention. By automating the process of pothole discovery and form\, the proposed system can significantly ameliorate the condition of roads and enhance the overall quality of life for citizens.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3D
LOCATION:South 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:3018f64977999cc503c8b8e7c5f03edb
URL:http://11tict4sd.sched.com/event/3018f64977999cc503c8b8e7c5f03edb
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T113000Z
SUMMARY:Decoding Emotions: Using LSTM Neural Networks for EEG-Based Emotion Recognition
DESCRIPTION:Authors - Ramesh M Tirakanagoudar\, Lavanya Joshi\, Sujay Badiger\, Satish Chikkamath\, Suneeta V Budihal\, Sujata Kotabagi Abstract - This study looks into how brain signals\, recorded using EEG (electroencephalogram) technology\, can help identify human emotions through deep learning. It focuses on three emotional states—negative\, neutral\, and positive—and examines how certain patterns in brain activity can reflect how we feel. To analyze this\, the research uses Long Short-Term Memory (LSTM) networks\, which are well-suited for understanding time-based data like EEG signals. The model is carefully designed to pick up on meaningful patterns\, reduce the risk of overfitting\, and improve how accurately it can classify emotional states. Before training the model\, the EEG data goes through detailed preparation\, including cleaning\, labeling\, and splitting into training and testing sets. Training is carried out using efficient methods that help save on resources and avoid unnecessary computations. The study points to real-world use cases—such as tools for monitoring mental well-being or creating technology that responds to users’ emotions. Overall\, it shows how deep learning and EEG data together can deepen our understanding of emotional states.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3D
LOCATION:South 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:4c7fdc5d43191ced514cb160c4240810
URL:http://11tict4sd.sched.com/event/4c7fdc5d43191ced514cb160c4240810
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T113000Z
SUMMARY:Deep Learning For IoT Data Analytics
DESCRIPTION:Authors - Abhinav Thakur\, Bhushan\, Ashima Mehta Abstract - The fast growth of the Internet of Things (IoT) has produced an immense pool of smart\, networked devices generating enormous amounts of data every day. From transport and health to agriculture and intelligent cities\, these devices are becoming must-haves for service improvement\, operation optimization\, and innovation stimulation. However\, the sheer number and diversity of data coming from IoT sources overwhelm traditional data analysis methods. This is where deep learning steps in—offering great tools that can learn patterns\, detect unusual pattern\, and process information in real time. Of them\, Long Short- Term Memory (LSTM) networks have shown highly promising results\, particularly for sequential data analysis such as time series\, and integration with probabilistic models increased accuracy and re-liability even more. This paper discuss significant techniques and uses of deep learning to revolutionize IoT data analytics\, investigating important techniques and uses\, mentioning present opportunities\, challenges\, and new promising research opportunities before us in this rapidly changing field
CATEGORIES:PHYSICAL TECHNICAL SESSION 3D
LOCATION:South 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:668f7e40a439853dcc319ff235d69cdb
URL:http://11tict4sd.sched.com/event/668f7e40a439853dcc319ff235d69cdb
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T113000Z
SUMMARY:Hybrid Machine Learning System for Recognizing Vehicle Number Plates in Hazy Environments is utilized for Safety and Security at Tourist Destinations
DESCRIPTION:Authors - Pharindra Kumar Sharma\, Neha Jain\, Rahul Kumar Singh\, Mayank Sharma\, Manoj Kumar Sharma\, Manish Jain\, Shyam Akashe\, Ranjeet Singh Toma Abstract - In general\, license plates are recognized under normal circumstances but it becomes very critical to track in hazy and foggy environment. Such conditions are often faced at hilly or terrain area where foggy weather and hazy environment creates challenges for safety and security. Mainly tourists are the soft target at such types of tourist destinations looted and theft through vehicles. Safety and security is a prime concern for an international tourist while explore any other country. Despite this\, recognizing vehicle license plates is very difficult\, especially when there is fog present in a particular global environment. Most of the time\, fog or haze blurs the boundaries and characters of license plates\, making them difficult to detect or identify. The purpose of this paper is to propose a hybrid machine learning algorithm to remove haze\, improve image quality and identify a vehicle. To make license plate recognition more accurate in foggy weather\, this algorithm uses two hybrid machine learning approach\, which will be conducive to ensuring the safe operation of vehicles in foggy weather.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3D
LOCATION:South 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:ca56673869bab8050ce6b5f92489f308
URL:http://11tict4sd.sched.com/event/ca56673869bab8050ce6b5f92489f308
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T113000Z
SUMMARY:Markov Based Availability Assessment of PV Solar Power Plant
DESCRIPTION:Authors - Kanak Saini\, Monika Saini\, Ashish Kumar\, Dinesh Kumar Saini Abstract - The objective of this research is to optimize the availability of a photovoltaic (PV) solar power plant. A PV solar plant is a complex system in which multiple subsystems are connected to each other. It consists of three subsystems PV modules\, inverter\, and transformer. All three subsystems are connected to each other in series configuration. The steady state availability is formed by using normalizing equations and Chapman Kolmogorov equations to improve the availability of PV solar power plants. All the failure and repair rates of the system are statistically independent and exponentially distributed. Through numerical results the availability of PV solar plant was found to increase from 0.983005 to 0.984553 as the repair rate increases.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3D
LOCATION:South 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:f7cff405394b81f1d159744c7d0d1058
URL:http://11tict4sd.sched.com/event/f7cff405394b81f1d159744c7d0d1058
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T113000Z
SUMMARY:Real-Time Resume Screening Using Kafka Based Applicant Tracking System
DESCRIPTION:Authors - Animesh Giri\, Arigela Likhith\, Atul Kumar Patra\, Balabhadra Sai Siri Abstract - Recruitment is a fundamental yet resource intensive process for organizations\, especially when handling large volumes of applications. Manually screening resumes to identify qualified candidates is time consuming\, error prone\, and difficult to scale. Human judgment may overlook suitable candidates or inconsistently apply criteria\, resulting in delayed hiring and reduced efficiency. Traditional systems also struggle to keep up with the dynamic demands of large scale recruitment workflows. This paper suggests a distributed\, real time ATS for automating resume ingestion\, parsing\, and ranking. The system\, built with Apache Kafka and Spark Streaming\, eliminates rule based static filtering by taking advantage of RAKE\, regular expressions\, and TF-IDF scoring to extract and score structured resume data. The pipeline is automated from file ingestion to candidate ranking while offloading workloads onto multiple FastAPI based microservices. A load balancer based on hashing distributes resumes equally among processing servers. A resume is parsed to candidate details\, and these are published to Kafka topics. Candidate resumes are scored against job descriptions using TF-IDF scoring. This allows unbiased\, real-time candidate evaluation for multiple positions. Kafka achieves high availability with topic replication and offset management\, and Spark Streaming achieves continuous processing and recovery through checkpointing. Structured outputs in JSON format enable downstream analytics and integration with external systems. Performance measurements show the system processes growing volumes of resumes with invariant latency and high performance. Fault tolerance controls also guarantee seamless operation in the event of server or network failure. Scalable performance is verified by complexity analysis for different job types and resume contents. With automated resume screening through distributed stream processing\, this system eliminates most human effort\, reduces hiring time\, and offers a fault resilient\, data driven alternative to conventional ATS implementations.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3D
LOCATION:South 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:0713ddc89b5a75c5b8c4ed4180d68db3
URL:http://11tict4sd.sched.com/event/0713ddc89b5a75c5b8c4ed4180d68db3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T113000Z
SUMMARY:EchoSight: An Assistive Device for the Visually Impaired
DESCRIPTION:Authors - Komal Papanwar\, Selina Shrivastava\, Anjali Askhedkar Abstract - This paper presents an intelligent assistive navigation system designed to enhance the mobility and spatial awareness of visually impaired individuals. The system is built on a compact and low-power Raspberry Pi platform\, enabling portability and affordability. It integrates real-time object detection using the YOLOv8 deep learning model with AprilTag-based localization to estimate the user’s position within a mapped environment. A calibrated monocular camera captures visual input to detect both objects and tags\, while geometrical calculations estimate distances and directional angles to guide navigation. Users can input their desired destination via speech\, and the system provides auditory instructions using a text-to-speech engine. By combining computer vision\, lightweight edge computation\, spatial localization\, and audio feedback\, the system transforms visual data into actionable navigation commands. Experimental evaluations in controlled indoor environments demonstrate the system’s ability to deliver accurate\, real-time guidance. This work advances the development of portable\, intelligent\, and cost-effective assistive technologies for independent mobility among the visually impaired.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3E
LOCATION:Board Room 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:9681fbc9794b0324c2d651547158308f
URL:http://11tict4sd.sched.com/event/9681fbc9794b0324c2d651547158308f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T113000Z
SUMMARY:Empowering Healthcare Decisions: The Impact of Big Data and Predictive Modeling
DESCRIPTION:Authors - Priyanka Pawar\, Anagha Kulkarni\, Prajakta Pawar\, Bhavana Pansare\, Manisha Bhende\, Harshal Raje Abstract - The healthcare industry is undergoing a change thanks to the quick development of Big Data Analytics (BDA)\, which makes data-driven decisions that improve patient outcomes possible. Predictive modeling offers previously unheard-of possibilities for predicting health outcomes and identifying at-risk groups due to the massive volumes of data generated by the healthcare industry\, including genomic information\, wearable technology\, and electronic health rec- ords (EHRs). This study examines how predictive analytics is being used in the healthcare industry\, outlining strategies that use statistical and machine learning methods to better allocate resources\, lower readmission rates\, and foresee disease outbreaks. Even with its revolutionary potential\, integrating BDA is fraught with difficulties\, such as the requirement for standardized data formats\, interoperabil- ity problems\, and data protection issues. For predictive models to be successfully adopted\, these obstacles must be removed. The paper explores the potential of predictive analytics in transforming healthcare from reactive to proactive\, enhancing patient care and operational efficiency. It proposes the AAI-CDS frame- work\, a structured approach to enhance clinical decision-making through AI\, aiming for adaptability and integration into the healthcare landscape.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3E
LOCATION:Board Room 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:1c47727f1799ba6d2191a9ecbfa792ab
URL:http://11tict4sd.sched.com/event/1c47727f1799ba6d2191a9ecbfa792ab
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T113000Z
SUMMARY:Experimental Study of Statis Analysis Frameworks for Incremental Data Flow Analysis
DESCRIPTION:Authors -&nbsp\;Smakshi Alhat\, Aayushee Gujarathi\, Bhakti Chougule\, Shreya Mokalikar\, Chhaya Gosavi\nAbstract -&nbsp\;Incremental data flow analysis is a crucial technique aimed at efficiently updating analysis results when modifications are made to a program\, reducing the need for a complete reanalysis. In this study\, we conducted an in-depth evaluation of various static analysis tools to assess their capabilities for incremental data flow analysis. The tools explored include Soot and SootUp\, known for their flexibility in static analysis\; Heros\, a framework for interprocedural data flow analysis based on the IFDS/IDE framework\; and Boomerang\, SparseBoomerang\, and SPDS\, which focus on demand-driven approaches. Additionally\, we examined Vasco and Reviser\, tools specifically tailored for incremental analysis. While Reviser appeared promising for its dedicated focus on incremental analysis\, its non-functional state\, as confirmed by its creators\, rendered it impractical for use. This paper offers a comparative assessment of these tools\, highlighting their theoretical support\, practical limitations\, and potential for enabling incremental data flow analysis in future tool development. Furthermore\, we implemented a program using SootUp that\, while not achieving true incremental analysis\, efficiently analyzes only the modified portions of code across different program versions\, significantly reducing analysis time. Our findings offer a critical perspective on the current capabilities of analysis tools and suggest directions for future research and tool development.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3E
LOCATION:Board Room 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:373f8bc6b125cd42f07b4b1ce30dfb16
URL:http://11tict4sd.sched.com/event/373f8bc6b125cd42f07b4b1ce30dfb16
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T113000Z
SUMMARY:Leveraging AI and Blockchain Technology for Enhancing Healthcare Data Management and Patient Care
DESCRIPTION:Authors - Anagha Kulkarni\, Priyanka Pawar\, Harshal Raje\, Bhavana Pansare\, Manisha Bhende Abstract - Although there have been notable technology developments in the healthcare industry\, issues with handling data\, security\, and interoperability still exist. Large volumes of complicated data\, such as patient records\, diagnostic pictures\, treatment histories\, and genetic profiles\, are produced by the healthcare ecosystem. Although AI has the potential to improve predictive analytics\, individualized treatment planning\, and enhanced diagnostics\, mainstream use is hampered by worries about biases in algorithms\, privacy of information\, and legacy system integration. Blockchain technology solves problems like security of data\, traceability\, which is and patient consent by providing a decentralized\, impenetrable framework for safe healthcare data transmission. Using multidisciplinary techniques from healthcare informatics\, ML\, cryptography\, user experience\, and ethical compliance\, this project attempts to create an integrated framework that combines blockchain for safe data governance with AI for intelligent data analysis.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3E
LOCATION:Board Room 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:8e912afdca371776a1e4bbf66ce2e16a
URL:http://11tict4sd.sched.com/event/8e912afdca371776a1e4bbf66ce2e16a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T113000Z
SUMMARY:NEST FINDER
DESCRIPTION:Authors - Mohit Deore\, Nishk Dongre\, Lokesh Doshi\, Mahek Dudhale\, Geeta. S. Navale Abstract - Nest Finder is a modern House Rental Service website developed using the MERN stack (MongoDB\, Express.js\, React.js\, Node.js)\, designed to offer a broker-free platform that enables direct communication between property owners and tenants. It simplifies property rentals and sales by eliminating the traditional brokerage system\, making transactions more transparent\, secure\, and cost-effective. The platform features a clean\, user-friendly interface with advanced search and filter options to help users quickly find suitable properties. An integrated AI chatbot provides real-time assistance\, enhancing the overall user experience by answering queries and guiding users through the process. Nest Finder focuses on secure property management and streamlined communication\, delivering an efficient\, scalable\, and trustworthy solution for the real estate market.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3E
LOCATION:Board Room 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:6861e836a3f5874acff1acdac98e37c9
URL:http://11tict4sd.sched.com/event/6861e836a3f5874acff1acdac98e37c9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T113000Z
SUMMARY:The Role of Indo-Israeli Cyber Cooperation in Countering Cyber Threats
DESCRIPTION:Authors - Niraj Kumar Singh\, Salmiati\, Sunil Bajeja Abstract - In the modern era\, where technology is connected to all aspects of life\, new challenges arise in cyberspace\, making cybersecurity a key element in protecting data and systems from cyber threats. The complexity of cybersecurity challenges requires countries to collaborate in combating cyberattacks that transcend national borders. India and Israel possess highly developed cyber capabilities\, with Israel becoming one of the world leaders in cybersecurity\, particularly in defense and highly effective responses\, while India excels in software development and advanced cybersecurity. Despite both countries having adequate cyber capabilities\, they still face cybersecurity challenges. India and Israel\, as countries that frequently face cyberattacks from both state and non-state actors\, have raised awareness about the importance of cyber cooperation to protect national security and critical infrastructure. The theory used in this research is Network Security Theory. The research examines How do India and Israel collabo-rate in addressing cyber attack and what are the challenges of Indo-Israeli cooperation in implementing cybersecurity? As a result of this research\, Israel and India are conducting their cyber defense\, which includes the signing of a Memorandum of Understanding (MoU)\, CERT partnerships\, joint cyber training\, and the protection of critical infrastructure. However\, the cooperation still faces challenges such as differences in policy approaches\, lack of public awareness regarding cybersecurity\, and geopolitical threats targeting the country from both state and non-state actors.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3E
LOCATION:Board Room 1\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:949f307ae1322869a1dff22af0e42fac
URL:http://11tict4sd.sched.com/event/949f307ae1322869a1dff22af0e42fac
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T113000Z
SUMMARY:An Improved Content-Based Recommendation System Integrating Ontology-Based Inferences with Hybrid Causal Representation Learning and Reinforcement Learning
DESCRIPTION:Authors - Ekta Dalal\, Parvinder Singh Abstract - Recommendation system is very important in e-commerce\, entertainment\, education\, and healthcare. It is to help users to find about relevant content. The recommendation systems analyse its attributes and user preferences to create opinions. Although\, current methods face lots of problems like cold-start problem\, filter bubbles\, lack of adaptability\, data sparsity\, and high dependency. It is often relied on correlation-based learning\, it can reinforce biases and repetitive recommendations\, and it leads to underprivileged personalization. To address these issues this research proposed a state-of-the-art content-based recommendation system that engages Ontology-Based Inferences with Hybrid Casual Representation Learning (CRL) and Reinforcement Learning (RL). The goal is to enhance recommendation accuracy\, fairness\, and responsiveness by applying ontology for knowledge representation in a structured manner\, CRL for identifying true cause-effect relationships in user preferences\, and RL for real-time adaptive learning. The novelty of the method lies in its ability to reduce bias\, boost diversity in recommendations\, and dynamically respond to shifts in user interests. In order to check the efficiency of the proposed approach\, conducted experiments with benchmark datasets and compared it to traditional content-based approaches. The results indicate that method considerably enhances recommendation accuracy\, personalization\, and user satisfaction and surpasses the drawbacks of current methods efficiently. Experiments were conducted using the Amazon Reviews 2023 dataset\, and the model was implemented using Python\, TensorFlow\, and Scikit-Learn. The findings indicate that the proposed method outperforms traditional content-based approaches\, showing a 12% improvement in accuracy\, higher personalization\, and increased user satisfaction. These results confirm that integrating ontology\, CRL\, and RL creates a more intelligent\, unbiased\, and adaptive recommendation system\, effectively overcoming the limitations of existing methods.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3F
LOCATION:Board Room 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:bf827a50c95e05db49210e95467f4221
URL:http://11tict4sd.sched.com/event/bf827a50c95e05db49210e95467f4221
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T113000Z
SUMMARY:Comparison of LLM Models of AI: A Comprehensive Analysis
DESCRIPTION:Authors - Dhruvin Kotak\, Yamini Barge\, Tanvi Patel\, Nitin Pandya\, Rachit Adhvarvyu Abstract - Large Language Models (LLMs) have significantly advanced the field of artificial intelligence by enabling state-of-the-art performance in numerous natural language processing tasks. This paper presents a comprehensive comparison of several LLM models\, analyzing their architectures\, training methodologies\, performance metrics\, scalability\, and practical applications. We present an in-depth review of established and emerging models\, detailing experimental evaluations across multiple benchmarks. Our findings contribute to a better understanding of the trade-offs between model complexity\, scalability\, and application-specific performance\, while offering recommendations for future research directions.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3F
LOCATION:Board Room 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:e5ef2ecec4d8a7d37a2637f41e09e7b6
URL:http://11tict4sd.sched.com/event/e5ef2ecec4d8a7d37a2637f41e09e7b6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T113000Z
SUMMARY:Digital Hospitals: How the Metaverse is Reshaping Healthcare Services
DESCRIPTION:Authors - Bhavana Pansare\, Anagha Kulkarni\, Priyanka Prabhakar Pawar\, Manisha Bhende Abstract - The rapid integration of metaverse technology into healthcare is trans-forming medical services by establishing digital hospitals\, enhancing telemedicine\, and introducing immersive virtual reality-driven treatments. By incorporating unconventional technologies like VR\, AR\, AI\, and Blockchain\, digital hospitals reshape patient care delivery\, medical training\, and healthcare accessibility. From remote consultations to AI-assisted surgeries and blockchain secured medical records\, the metaverse is enabling a paradigm shift in how healthcare professionals interact with patients\, manage data\, and deliver services in a virtual environment. This paper explores the vast opportunities digital hospitals present\, including improved healthcare accessibility\, cost-effective solutions\, enhanced medical training\, and AI-powered precision diagnostics. Furthermore\, it critically examines the security and ethical challenges related with metaverse driven healthcare\, such as patient data privacy\, cybersecurity risks\, digital inequality\, and the AI ethics implications in medical decision-making. As digital hospitals become an integral part of the healthcare landscape\, it has been crucial to evolve regulatory frameworks\, ethical guidelines & technological safeguards to ensure their responsible and inclusive deployment. By analyzing real-world case studies and emerging technologies\, this re-search provides an extensive summation about how the metaverse can be effectively harnessed to improve healthcare outcomes while addressing ethical dilemmas and security concerns.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3F
LOCATION:Board Room 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:1eb5a2feaa91355a7132e08e58e33787
URL:http://11tict4sd.sched.com/event/1eb5a2feaa91355a7132e08e58e33787
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T113000Z
SUMMARY:LiDAR-GPS Integrated System for Real-Time Pothole Detection and Visualization Using Google Earth
DESCRIPTION:Authors - Vishal B Pattanashetty\, Prakash Pawar Abstract - Potholes pose a significant threat to road safety\, leading to accidents\, infrastructure degradation\, and increased maintenance costs. This paper presents an efficient approach for detecting and visualizing potholes by integrating LiDAR point cloud data with Google Earth through GPS georeferencing. A YDLIDAR G2 sensor is employed to collect spatial data\, which is transformed from polar to Cartesian coordinates and mapped onto GPS locations using Python-based processing. The methodology follows a structured workflow: data acquisition\, coordinate transformation\, georeferencing\, and visualization in Google Earth\, facilitating precise pothole localization. The proposed technique is compared with Martin Isenburg’s streaming pipeline method for large-scale LiDAR visualization\, highlighting distinctions in computational efficiency and real-time application. The results demonstrate accurate mapping of road anomalies\, enabling municipalities to optimize maintenance strategies. This study underscores the potential of LiDAR-based Road surface analysis for infrastructure monitoring\, with future applications in real-time assessment and autonomous navigation.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3F
LOCATION:Board Room 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:00e56b88e991d62bec7cd960132cf09a
URL:http://11tict4sd.sched.com/event/00e56b88e991d62bec7cd960132cf09a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T113000Z
SUMMARY:SCALING A DISTRIBUTED SERVICE MESH SYSTEM
DESCRIPTION:Authors - Anupriya N\, Sathiyamooorthy E Abstract - In order to improve scalability and flexibility in the microservice application\, this paper proposes a novel way to integrate Istio service mesh continuous integration/continuous deployment pipelines and Puppets automated configuration management. The suggested architecture ensures better system reliability and the efficient resource usage\, while streamlining the microservice deployment management and scaling. By using continuous integration/continuous deployment for automated testing and deployment\, Istios for traffic management and observability and Puppets for infrastructure provisioning. This integrated solution optimizes the development and operations of the microservice applications. According to this work deployment the efficiency\, manual error rates and system scalability have been significantly increased. Its wise counsel can be very helpful for the developer architects and organizations trying to build scalable\, flexible and reliable of the microservice applications.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3F
LOCATION:Board Room 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:7c034700f638423e55c1b28bb45484c2
URL:http://11tict4sd.sched.com/event/7c034700f638423e55c1b28bb45484c2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T113000Z
SUMMARY:Smile Prediction for Mental Health Monitoring from Video Sequences Using Deep Learning
DESCRIPTION:Authors - Krishna Kant\, Dipti B. Shah\, Nilay Vaidya\, Kanu Patel\, Kamini Solanki Abstract - Smile prediction has enormous promising demand for tracking mental health providing information about a person’s emotional wellness.This study suggests a unique method for predicting smiles in mental health monitoring by tracking smile expression and correlating it with emotional states using video sequences. This work has created a more reliable and accurate model for evaluating mental health by combining facial recognition for smile prediction.The proposed research work uses deep CNN along with the ResNet-50 architecture for smile prediction using CK+ data set. The study explores the potential uses of smile prediction systems in clinical contexts such as the early identification of anxiety\, depression and emotional discomfort. The deep CNN and ResNet-50 architectural model solves the issues of overfitting and vanishing gradient. The proposed work is tested on CK+ datasets and contrasted with current techniques\, the suggested system demonstrates that this proposed approach performs better than other conventional models and attained the results of 95%.
CATEGORIES:PHYSICAL TECHNICAL SESSION 3F
LOCATION:Board Room 2\, Taj Cidade de Goa Horizon\, Goa\, India
SEQUENCE:0
UID:1191f5aa980bc0886b56644da2bb5947
URL:http://11tict4sd.sched.com/event/1191f5aa980bc0886b56644da2bb5947
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:Air Quality Monitoring System Implementation Using ARIMA And LSTM
DESCRIPTION:Authors - Ganesh Puri\, Pratik Jadhav\, Mayuri Gawande\, Gadekar Gayatri\, Tushar Badakh Abstract - Air quality is a key determinant of public health\, increasingly impacted by natural events such as wildfires and volcanic eruptions\, as well as human-induced sources like vehicular and industrial emissions. This research focuses on predicting air pollution levels using two advanced models: Long Short-Term Memory (LSTM) networks and Auto-Regressive Integrated Moving Average (ARIMA). A dataset is developed by combining precise ground station data from multiple ground stations. It also features real-time inputs from low-cost ESP32-powered IoT sensors. The sensor fusion approach enhances both spatial coverage and data granularity\, offering a more comprehensive representation of environmental conditions. The ThingSpeak IoT platform is employed for live data collection\, visualization\, and remote monitoring. LSTM networks are selected for their capability to model complex\, long-term dependencies in time-series data\, while ARIMA provides a robust statistical baseline for comparison. Experimental results demonstrate that models trained on the fused dataset significantly outperform those using individual data sources\, resulting in more accurate and reliable air quality forecasts. This study presents a scalable and cost-effective framework for air quality monitoring\, supporting timely\, data-driven interventions by environmental agencies and contributing to improved public health management in both urban and rural areas.
CATEGORIES:VIRTUAL ROOM 6A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:6ccdb46c8a58f445292abce945e436e7
URL:http://11tict4sd.sched.com/event/6ccdb46c8a58f445292abce945e436e7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:Cloud based Crop Health Monitoring System
DESCRIPTION:Authors - Dasari Keerthi Sai Naga Sudha\, Navaneeth Rajamohan\, Chakravaram Hari Priya\, Mandapati Bindu Sree\, Beena B.M. Abstract - The present project illustrates a comprehensive crop health monitoring and recommendation system using environmental condition data in the context of optimizing agricultural practices. Crop health monitoring utilizes AWS SageMaker Studio in training and deploying a machine learning model to classify crops as either healthy or unhealthy based on environmental inputs like temperature\, humidity\, rainfall\, N\, P\, K and Ph. A Flask application\, developed in SageMaker Studio\, is designed as the interface for a real-time crop health prediction tool\, giving farmers actionable inputs for timely intervention. Finally\, a crop recommendation system\, using AWS SageMaker\, analyzes the environmental dataset to suggest the most suitable crop for a given region. These systems combined create sustainable farming practices and contribute positively to the improvement of agriculture productivity.
CATEGORIES:VIRTUAL ROOM 6A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:4ae33b20355d3fb5e4b645e39c5a8c24
URL:http://11tict4sd.sched.com/event/4ae33b20355d3fb5e4b645e39c5a8c24
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:Evaluating 5G Network Performance: A Simulation Study of Beamforming\, Massive MIMO\, and Small Cells
DESCRIPTION:Authors - Narayan Irkal\, Sanjana Ambore\, Praveen Bachalapur\, Nihar Kulkarni\, Mohammed Azharud-din\, Suneeta V Budihal Abstract - This paper presents a simulation-based study of standalone 5G networks focusing on such key performance metrics as latency\, throughput\, and packet loss. Three network configurations-Small Cells\, Massive MIMO\, and Beamforming-are considered for evaluating their impact on network performance. Three types of traffic-Voice\, Video\, and IoT\, with different data requirements-are also considered in this study. The simulation models the real-world scenario using a 3.5 GHz carrier frequency and 100 MHz bandwidth with different modulation schemes (QPSK\, 16QAM\, 64QAM) and coding rates (0.5\, 0.7\, 0.9). The performance metrics are calculated through a combination of signal-tonoise ratio\, resource block allocation\, and transport block size. The results are visualized by line and bar plots\, emphasizing the efficiency and trade-offs of different configurations under diverse traffic scenarios. This work explains the main issues of making 5G networks more dependable and efficient based on the upsurge in demand of high-speed-low-latency in modern applications.
CATEGORIES:VIRTUAL ROOM 6A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:2c771586b340d48aa5d3300c2dc910a3
URL:http://11tict4sd.sched.com/event/2c771586b340d48aa5d3300c2dc910a3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:Optimization of Routes of SDN Using GNN and DRL
DESCRIPTION:Authors - Zeeshan Mirji\, Muzammil Kharadi\, Prajwal Shiggavi\, Abdul Razzak R Yergatti\, Mohammed Azharuddin Adhoni\, Suneeta V Budihal Abstract - Software-defined networks (SDNs) enable flexibility by decoupling the control and data planes\, but their complex\, dynamic structures challenge traditional optimization methods like rule-based algorithms and Queuing Theory (QT). To address this\, we propose a framework using graph neural networks (GNNs) and deep reinforcement learning (DRL). GNNs model network components as nodes and connections as edges\, learning efficient representations to improve metrics like delay\, jitter\, load balancing\, and scalability. Our approach delivers scalable\, real-time SDN optimization\, significantly outperforming QT in simulations\, paving the way for advanced data-driven network control.
CATEGORIES:VIRTUAL ROOM 6A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:4d8919d4b472132adbd707a5b216366f
URL:http://11tict4sd.sched.com/event/4d8919d4b472132adbd707a5b216366f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:Performance Analysis of Wireless Routing Protocols
DESCRIPTION:Authors - Iliyas Kinnal\, Yashwant Danaraddi\, Amogh Gujamagadi\, Aditya Deshpande\, Mohammed Azharuddin\, Sunita V Budihal Abstract - Analyzing the performance of the communication system in terms of real-time applications can be judged by network performance analysis. Wireless networks consist of transmission power\, which determines all the key performance metrics\, such as throughput\, delay\, packet loss\, and energy efficiency. With an increase in transmission power\, it can cover greater signal strength and reduce packet error but will lead to increased amount of interference\, energy consumption\, and potential network congestion. The NS3 network simulator provides a robust platform for modeling and analyzing the impact of transmission power on wireless networks. It will allow researches to simulate real-life scenarios that alter transmission power levels and hence the effects it will have on the performance of the network under greatly differing conditions. Critical parameters such as SNR\, RSSI\, and link reliability can effectively be measured through NS3\, thus providing the optimum network configuration insights.
CATEGORIES:VIRTUAL ROOM 6A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:8f37970ddcfa92d289717c17a1a5fdbd
URL:http://11tict4sd.sched.com/event/8f37970ddcfa92d289717c17a1a5fdbd
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:Plant Leaf Disease Detection System Using Deep Learning
DESCRIPTION:Authors - Soni R. Ragho\, Rohan R. Swami\, Tanuja S. Gaikwad\, Aaditi P. Narke\, Shubham M. Atak Abstract - India's agricultural sector is a vital pillar of its economy\, providing livelihoods to millions of people. However\, plant diseases position a major threat to crop productivity\, leading to significant financial losses. The unpredictable nature of climate change has further exacerbated the spread of these diseases\, highlighting the need for early and accurate detection to prevent large-scale crop damage. Traditional disease identification methods\, which rely on human observation\, are often ineffective\, subjective\, and prone to misdiagnosis. Incorrect assessments may result in the misuse of pesticides\, causing economic burdens and environmental harm. Consequently\, the development of an advanced and reliable plant disease detection system is essential for promoting sustainable farming practices. The rise of artificial intelligence and image processing has introduced innovative techniques for detecting plant diseases. Deep convolutional neural networks (CNNs) have demonstrated exceptional efficiency in identifying and classifying plant diseases with high accuracy. These models utilize sophisticated machine learning techniques to analyze high-resolution leaf images\, ensuring fast and precise disease detection. This study aims to design an advanced CNN- based model to improve the accuracy and effectiveness of plant disease identification\, providing farmers with actionable insights for better disease control. By harnessing AI-driven technologies\, this research seeks to reduce agricultural losses\, enhance crop yields\, and contribute to the long-term sustainability of India’s agricultural sector. Additionally\, integrating such intelligent systems can optimize resource management and reduce reliance on harmful chemical treatments.
CATEGORIES:VIRTUAL ROOM 6A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:a8a24af29a2ba3ad06c8f1c4ff1e3179
URL:http://11tict4sd.sched.com/event/a8a24af29a2ba3ad06c8f1c4ff1e3179
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:PneumoSense: Smart Pneumonia Detection using Deep Learning
DESCRIPTION:Authors - Prerna Agrawal\, Savita Gandhi Abstract - Pneumonia is a predominant cause of illness and death globally\, particularly affecting vulnerable groups such as children\, the elderly\, and immunocompromised individuals. Timely and precise diagnosis is essential for effective therapy\; however\, conventional chest X-ray (CXR) evaluation by radiologists is prone to human error and constrained availability\, especially in resource-limited environments. Recent breakthroughs in deep learning have facilitated automated and highly precise medical picture analysis\, presenting a possible alternative for pneumonia identification. About 473\,780 cases of pneumonia were reported in India in 2022–2023 and throughout this time\, pneumonia was caused by 11\,497 baby fatalities that aged between 1 to 12 months and 4\,571 pediatric pneumonia-related deaths have been reported that aged between 1 to 5 years. In 2024 the annual incidence rate of community acquired Pneumonia in India is estimated to be between 5 and 11 per 1\,000 people. This research proposes a system named PneumoSense\, an intelligent automatic pneumonia diagnosis system employing DenseNet121\, a deep learning model recognized for its efficacy in medical imaging applications. PneumoSense is an automated\, intuitive interface that allows users to upload an x-ray image for analysis to ascertain the presence of pneumonia. Upon detection\, the algorithm generates a prediction score and advises medical consultation. The model underwent comprehensive testing against various lung illnesses\, such as COVID-19 pneumonia\, fibrosis\, and effusion\, confirming its reliability in practical applications. Experimental findings indicate that DenseNet121 surpasses other deep learning algorithms\, including CNN\, ResNet50\, and NASNet\, attaining superior recall of 0.9795 and AUC score of 0.9808. The workflow of PneumoSense is also discussed in the paper. PneumoSense diminishes diagnostic inaccuracies\, improves accessibility\, and provides a feasible AI-driven substitute for manual diagnosis. This study underscores the revolutionary capacity of deep learning in medical diagnostics\, facilitating early pneumonia detection\, enhancing patient outcomes\, and alleviating the workload on radiologists.
CATEGORIES:VIRTUAL ROOM 6A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:fc694625a4b9d5ddc2d4cd5ffa70349d
URL:http://11tict4sd.sched.com/event/fc694625a4b9d5ddc2d4cd5ffa70349d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:Real-Time Sentiment Analysis of Helpdesk Calls Using LSTM and NLP for Emotion-Aware Customer Support
DESCRIPTION:Authors - Vani K S\, Aditya Andotra\, Tanmay Sinha\, Kaatyaini Jaiswal\, Roshan Kumar Sahu Abstract - It’s very important for understanding and responding to customer emotions in real time to have an effective and satisfiable customer service. This study introduces an automated Sentiment Analysis System for Helpdesk Calls\, leveraging Long Short-Term Memory (LSTM) networks and advanced Natural Language Processing (NLP) techniques to enhance service efficiency. Traditional sentiment analysis methods mostly fail to capture the nuances of spoken language\, which reminds the need for a more robust approach. The proposed system processes helpdesk calls recordings\, applying speech normalization\, noise reduction\, and feature extraction using Mel-Frequency Cepstral Coefficients (MFCCs) before classification. By integrating machine learning and deep learning models\, the system provides real-time sentiment insights\, allowing operators to prioritize and address calls based on emotional tone. Performance evaluation using accuracy\, precision\, recall\, and F1-score ensures continuous model refinement. This scalable solution and methodology reduce manual effort\, enhances customer interactions\, and fosters improved satisfaction and loyalty\, representing a significant advancement in automated customer service technology.
CATEGORIES:VIRTUAL ROOM 6A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:673fbee5dd0db7bb7b57036eb7493651
URL:http://11tict4sd.sched.com/event/673fbee5dd0db7bb7b57036eb7493651
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:V2X Communication for Enhanced Vehicular Safety
DESCRIPTION:Authors - Pavan.B.Shivalli\, Mahamadshiraj.B\, Nitin.P.Savvase\, Samit.Patil\, Sarvesh.R.Karkannavar\, Mohammed Azharuddin\, Suneeta.V.Budhihal Abstract - A simulation model for inter-vehicle communication is presented in this research with the goal of improving traffic control and road safety during collisions. The model mimics data transfers between vehicles using OMNET++ and SUMO\, allowing for the real-time identification and notification of traffic accidents. The framework makes use of "VEINS" to seamlessly integrate traffic and network modeling\, enabling efficient data packet transfers for both routine and urgent messages. Dedicated Short-Range Communication (DSRC) with a 300-meter range and optimized power transfer at 9mW\, which increases energy efficiency\, are two important aspects. The framework achieves high-speed data transfer by using the UDP protocol\, which is necessary for prompt response in accident situations. The findings show that vehicles can communicate reliably with one another\, which could speed up emergency responses and lessen traffic interruptions caused by accidents.The model can be modified to accommodate different traffic situations and allows for the addition of more safety beacons. This model may be expanded in the future to support practical Intelligent Transportation Systems (ITS) applications.
CATEGORIES:VIRTUAL ROOM 6A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:01f7d063e9cf81344949abb1ab71d6e5
URL:http://11tict4sd.sched.com/event/01f7d063e9cf81344949abb1ab71d6e5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:Water Quality Prediction Using AWS and Machine Learning
DESCRIPTION:Authors - BG.Shresta\, P.Hari Sankar\, Pinninti Anju Chowdary\, Beena B.M Abstract - This project is to utilize cloud computing in deploying an ML-enabled Flask application for the prediction of water quality. The model\, trained on historical datasets of water quality\, is integrated into a user-friendly web interface constructed using Flask. Specifically\, the main objective is to explore the deployment and hosting of the applications across the AWS cloud platform to ensure scalability\, reliability\, and efficiency. Other deployment methods scrutinized include AWS Elastic Beanstalk\, AWS Lambda\, and EC2. For deploying\, Elastic Beanstalk was adopted as the principal deployment because it can natively host the application and handle scalability while providing end-to-end management of infrastructure. However\, issues arose with regards to Lambda’s resource limitation in handling gigantic ML models\, while configuration problems of EC2 were inevitable in scaling. The project itself reflects strengths and limitations of these services and points towards very robust infrastructure in resource-intensive ML applications. It’s applicable not only in the scalable solution of real-time water quality prediction but also comparative with studies on deploying Flask applications within AWS\, with insights to optimize performance and future applications based on cost-effectiveness.
CATEGORIES:VIRTUAL ROOM 6A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:c1a34b9933d2e2cc47d7d63eba97a181
URL:http://11tict4sd.sched.com/event/c1a34b9933d2e2cc47d7d63eba97a181
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:AI Powered Smart Glasses for Visually Impaired Individuals
DESCRIPTION:Authors - Ketki Kshirsagar\, Samarth Chikane\, Dev Desai\, Avdhut Hande\, Aniruddha Deobhankar\, Arun Govind\, Shubham Derkar\, Arjun Gupta Abstract - The one of the most important sensory organ of our body is an eye\, it is the reason why people can enjoy its beautiful surroundings. What if we would not have this important organ?\, the answer is quite obvious\, it would be very challenging\, he would be isolated. There are millions of people across the globe living such miserable lives. So to overcome this challenge we come up with an idea to build an assistive aid for blind needy. The project is AI-Powered Smart glasses for visually impaired. This basically notifies the blind person about the obstacle in front of him/her. This tool has the ability to tell the user the distance of the obstacle and what particularly the obstacle is like the tree is 60 cm away. This paper includes the brief information about how this glasses work. The glasses are full advanced technical tools like ultrasonic sensors\, IR sensors\, ESP32 microcontroller\, ESP32 cam module\, earpiece for user’s enhanced listening. The software we used is YOLOv5 for object recognition\, Tensor Flow Lite\, text-to-speech software\, MATLAB\, etc.
CATEGORIES:VIRTUAL ROOM 6B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:735cd828fa4441466207984ebdc100eb
URL:http://11tict4sd.sched.com/event/735cd828fa4441466207984ebdc100eb
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:AI-Driven Hemodynamic Profiling: Integrating Computational Fluid Dynamics and Machine Learning for Cardiovascular Health Monitoring
DESCRIPTION:Authors - Sparsh Kumar\, Satyadhyan Chickerur\, Prashanth Kumar Malkiwodeyar Abstract - Accurately capturing unsteady flow phenomena and complex fluid dynamics is essential for understanding and predicting arterial blood flow behavior. This study leverages computational fluid dynamics (CFD) and machine learning (ML) to detect regions of elevated shear stress in the aorta\, focusing on the dynamic responses of arteries under varying hemodynamic conditions. The geometric model of the human aorta was sourced from the Vascular Database\, which is supported by SimVascular and includes all necessary boundary conditions. Simulations were performed using the Navier-Stokes equations within SimVascular to generate vtk files containing velocity and pressure data. Since these files could not be directly used for ML training\, additional postprocessing was conducted in ParaView. By applying specific functions\, we extracted key metrics such as pressure\, velocity\, wall shear stress\, and other parameters at multiple spatial coordinates. This resulted in a CSV dataset comprising 23\,777 points with corresponding attributes for further analysis of high wall shear stress regions. A Random Forest classifier was trained on this dataset to predict regions of high wall shear stress of the aorta by analyzing attributes like pressure\, velocity\, and wall shear stress\, providing precise coordinate-based predictions of areas with abnormal hemodynamic stress. In our analysis\, regions of elevated shear stress were detected at 1\,288 points\, representing 5.42% of the total dataset. By integrating CFD with ML\, we successfully identified regions of high wall shear stress in the aorta\, enhancing clinical diagnostic accuracy and offering a data-driven alternative to traditional cardiovascular diagnostic techniques. This study underscores the value of combining CFD and ML to reduce reliance on traditional cardiovascular tests\, streamlining diagnosis and therapeutic planning while reducing costs and complexity.
CATEGORIES:VIRTUAL ROOM 6B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:379e22f602b13f1bf2f45efb68f5b7f5
URL:http://11tict4sd.sched.com/event/379e22f602b13f1bf2f45efb68f5b7f5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:Application of Sentiment Analysis in Marketing
DESCRIPTION:Authors - Vanishree Pabalkar\, Ruby Chanda\, Yash Yadav\, Megha Patil Abstract - Sentiment analysis\, is termed as opinion mining\, is a significant tool to assess customer’s opinions and expressions by analyzing textual data from various digital platforms. In marketing\, sentiment analysis provides invaluable insights into customer feedback\, helping companies to customize the products and services\, and marketing strategies to meet consumer needs. This paper explores the application of sentiment analysis specifically through a case study of the Samsung Galaxy S24 Ultra. The study involves collecting data from various sources\, like the news forums\, and news articles\, and employing natural language processing (NLP) techniques to classify and analyze sentiments into positive\, negative\, or neutral categories. The outcome conveys the essence of sentiment analysis in identifying consumer preferences and issues\, such as high prices or software problems\, which directly impact marketing strategies and product development. By using sentiment analysis\, companies like Samsung can make data-driven decisions to retain satisfied customers and ensure brand loyalty. This study also highlights the issues and constraints of current sentiment analysis methods\, that include the need for improved accuracy in sentiment classification and the handling of complex linguistic nuances. Future research directions include enhancing ML tools to classify the sentiment detection and exploring the use of sentiment analysis in real-time applications to provide instant feedback for marketers. The implications of sentiment analysis extend beyond marketing into areas like public relations\, customer service\, and product innovation\, making it an indispensable tool in today's digital age. As digital communication continues to grow\, the role of sentiment analysis is expected to expand\, offering inputs into consumer behavior and enabling more personalized\, effective strategies.
CATEGORIES:VIRTUAL ROOM 6B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:0036a64d9ccf18dcc54cbf35f8b2c436
URL:http://11tict4sd.sched.com/event/0036a64d9ccf18dcc54cbf35f8b2c436
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:ARTIFICIAL NEURAL NETWORKS APPLICATIONS IN SOCIAL MEDIA
DESCRIPTION:Authors - Kumar Rahul\, Ramjee Prasad Gupta\, Neeraj Arora\, Surender Kumar Kulshrestha Abstract - Artificial Neural Network (ANN) plays an important role in shaping modern social media platforms. The networks assist in presenting content recommendations\, analyzing user engagement\, detecting sentiment\, and automating moderation processes. Through the processing of user data\, ANNs enhance personalization\, improve advertising strategies\, and detect harmful content\, ensuring a seamless and engaging user experience. This paper explores the diverse applications of ANNs in social media\, highlighting their impact on user interaction\, content curation\, and platform security. This review highlights key advancements\, challenges\, and open issues related to data variations\, and evaluation criteria in social media analysis. Additionally\, a structured framework is proposed for future studies focused on leveraging ANNs to gain social media insights.
CATEGORIES:VIRTUAL ROOM 6B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:c21a381b1c2694ed0cef6195679fb037
URL:http://11tict4sd.sched.com/event/c21a381b1c2694ed0cef6195679fb037
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:Classification of Brain and Lungs Images using Deep Learning Models and Low Complexity Algorithm
DESCRIPTION:Authors - Tejas Nadagadalli\, Vishwanath Baligar Abstract - The assistance of deep learning for medical image diagnosis is often crucial in the timely treatment of patients suffering from diseases like brain tumors and lung cancer. This paper evaluates the performance of VGG 16 and Efficient-Net deep learning models for the classification of MRI and CT scans of the brain and lungs. A new low complex algorithm referred to as PDBS was promoted to increase the efficiency of model optimizations. The lesion detection models were assessed for accuracy\, training time\, and level of generalization attained. Experimental results highlight that the PDBS model consistently outperformed traditional CNN architectures\, achieving higher classification accuracy with 97% testing accuracy for brain MRI scans and 96.5% for lung CT scans while maintaining efficiency. These results above illustrate the depth of the contribution offered by deep learning methods to the enhancement of medical image analysis to support clinical workflow.
CATEGORIES:VIRTUAL ROOM 6B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:ef5d14d50f350580ddc16c562a26c6d2
URL:http://11tict4sd.sched.com/event/ef5d14d50f350580ddc16c562a26c6d2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:Enhancing Healthcare Data Security: Integrating NLP\, Deep Learning\, and Blockchain for Privacy and Compliance
DESCRIPTION:Authors - Sweety Singhal\, Uma Sharma Abstract - Healthcare domains deal with massive amounts of sensitive data\, such as patient health records\, diagnostic results\, and clinical notes\, which must be secured under privacy regulations (like HIPAA). Traditional security technologies cover many problems but struggle against more advanced iterative threats. Implementing deep learning algorithms has resulted in the creation of texts of advanced encryption techniques\, which can discover obvious patterns and improve the security of health systems. When combined with Natural Language Processing (NLP)\, these algorithms can also anonymize and de-identify patient information\, allowing healthcare providers to share and collaborate on data without violating patient confidentiality. This will prove valuable for further medical research and improving the quality of telemedicine\, where better information transfer is key for better treatment results. Key Approaches: Data encryption\, differential privacy\, tokenization\, and access control are all essential methods of protecting healthcare data\, and NLP plays a vital role in ensuring that sensitive health information is handled securely.
CATEGORIES:VIRTUAL ROOM 6B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:a6cbd4e17acb85c94bd311582df3483d
URL:http://11tict4sd.sched.com/event/a6cbd4e17acb85c94bd311582df3483d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:INTELLIGENT BRAIN TUMOR DETECTION USING MACHINE LEARNING MODELS
DESCRIPTION:Authors - Madhuri Badole\, Rohit Rathod\, Pavan Bachhav\, Devanshu Parulekar\, Harsh Kulkarni Abstract - Brain tumor diagnosis is a critical area of medical research as it directly impacts patient survival and treatment. Early diagnosis is essential for improving prognosis and facilitating therapy. Magnetic Resonance Imaging (MRI) is particularly reliable for detecting brain tumors due to its superior image quality and contrast. This study provides a comprehensive review of recent advancements in brain tumor diagnosis methods. Evolutionary algorithms based on natural selection principles offer optimal strategies for image processing\, segmentation\, feature extraction\, and classification in tumor diagnosis. Random Forest algorithms are used in this study to classify MRI images\, distinguishing between normal brain tissues and malignancies. Additionally\, the study explores hybrid models that integrate evolutionary algorithms with neural networks (CNN) to enhance accuracy. This research offers insights into the benefits and limitations of these approaches\, paving the way for further neuropsychology research.
CATEGORIES:VIRTUAL ROOM 6B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:2acbc222c217f428daa41b5a5f0c2dd9
URL:http://11tict4sd.sched.com/event/2acbc222c217f428daa41b5a5f0c2dd9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:Optimizing Hybrid Solar-Wind Systems with Differential Evolution for Energy and Stability
DESCRIPTION:Authors - Pritee Parwekar\, Chinmayee Ambarish Parwekar\, Kshitij Bhushan Abstract - As growing dependence on wind and solar energy brings about new challenges in both maximizing energy generation and ensuring the stability of grids due to the intermittent nature by virtue of dependence on variable atmospheric conditions\, optimization of hybrid solar-wind plant output is achieved through the present study by means of a DE algorithm for maximum energy yield enhancement and grid robustness. The approach includes a simulation of a small hybrid energy system\, which consists of a 10 m² solar panel and three wind turbines\, each with a capacity of 2 kW\, over a period of 24 hours. Using real meteorological data in the form of solar irradiance and wind speed profiles\, the differential evolution (DE) algorithm minimizes two most important parameters: tilt angle of solar panels from 0 to 90 degrees and the spacing of the wind turbines\, variable from 5 to 50 meters. The objective function is to maximize energy output in total and minimize hour-by-hour power oscillations\, a surrogate for grid stability. The results indicate that the optimized configuration with tilt angle 15.23° and turbine spacing of 35.67 m produces a total of 135.82 kWh\, up 12.7% from the baseline (120.45 kWh at tilt angle 30° and 10 m spacing). Moreover\, the stability penalty\, expressed as the sum of hourly output differences\, reduces from 48.73 to 42.19\, reflecting better grid compatibility. These results underscore the potential of DE as a successful method for the optimization of renewable energy\, with a real application to harmonize energy production and stability in hybrid systems. This research supports more efficient and trustworthy renewable grids and contributes to sustainable energy infrastructures transition
CATEGORIES:VIRTUAL ROOM 6B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:8c82c1e5e4e350a50111fecb005f4c31
URL:http://11tict4sd.sched.com/event/8c82c1e5e4e350a50111fecb005f4c31
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:Physics-Informed Neural Networks and Simulated Cardiac Data for Arrhythmia Classification
DESCRIPTION:Authors - Amogh M\, Satyadhyan Chickerur\, Prashanth Kumar Malkiwodeyar Abstract - Cardiac arrhythmias pose a significant challenge in clinical diagnostics\, necessitating accurate and efficient detection methods. This study explores the classification of arrhythmias using advanced machine learning models\, including Convolutional Neural Networks (CNNs)\, Long Short-Term Memory (LSTM) networks\, and Physics-Informed Neural Networks (PINNs). A dataset of 16\,000 simulated ECG signals\, generated using SimVascular\, provided the foundation for training and evaluation. CNNs achieved high accuracy in spatial feature extraction\, while LSTMs excelled in capturing temporal dependencies in sequential ECG data. PINNs emerged as the most robust model\, achieving a training accuracy of 97.8% and a testing accuracy of 97.2%\, leveraging domain-specific constraints from the FitzHugh-Nagumo equations. The results highlight the complementary strengths of these models\, with PINNs offering superior interpretability and physiological consistency. Future work will focus on integrating multi-modal data and developing real-time systems to advance arrhythmia diagnostics and improve cardiac care outcomes.
CATEGORIES:VIRTUAL ROOM 6B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:08abb1a1eeb7ce36ce4cc65ba6d74750
URL:http://11tict4sd.sched.com/event/08abb1a1eeb7ce36ce4cc65ba6d74750
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:Smart Financial Learning: An Intelligent Agent with Chatbot Assistance
DESCRIPTION:Authors - Siddhant Sawant\, Sajal Nampalliwar\, Ansh Masand\, Variza Negi Abstract - Financial literacy is a crucial yet often overlooked skill in formal education. Our research presents an AI-driven framework that democratises financial education through four interactive modules: budgeting tools\, a newsletter\, a virtual market simulator and a structured course. At the core lies an Intelligent Learning Agent. A chatbot first gauges prior knowledge\, then steers each learner to the appropriate level. Machine-learning\, NLP and reinforcement-learning techniques continually adapt the pathway in response to engagement signals. We detail the conceptual framework and system architecture\, illustrating how AI delivers scalable\, personalised financial learning.
CATEGORIES:VIRTUAL ROOM 6B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:4b4ca732dffdbdb75d74dbb591c2ebcb
URL:http://11tict4sd.sched.com/event/4b4ca732dffdbdb75d74dbb591c2ebcb
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:AI-Vision: Forecasting Diabetic Retinopathy for Preventive Care
DESCRIPTION:Authors - Prema Sahane\, Shreyas Borse\, Kartik Narkhede\, Manasi Choudhari\, Pradnya GaikWad\, Ashwini Bhosale\, Rutuja Khedkar Abstract - An innovative strategy to address one of the main causes of blindness in diabetic patients is presented in AI-Vision: Forecasting Diabetic Retinopathy for Preventive Care. Through sophisticated predictive modelling\, this study uses artificial intelligence (AI) to transform the diagnosis and treatment of diabetic retinopathy (DR). Our approach improves the accuracy of DR diagnosis and makes it easier to identify risk factors that contribute to the progression of the disease by combining cutting-edge Convolutional Neural Networks (CNNs) with extensive medical datasets. Healthcare practitioners may now use individualized preventative tactics based on patient profiles thanks to our cutting-edge model\, which uses real-time data analytics to deliver actionable insights. By empowering doctors to intervene promptly\, this proactive approach not only seeks to identify DR in its early stages but also lowers the likelihood of serious sequelae .Our research also shows how AI can be used to streamline automated screening processes. AI-Vision hopes to establish a new benchmark in preventative healthcare by bridging the gap between ophthalmology and AI\, with the ultimate goal of eradicating avoidable blindness in diabetic populations worldwide.
CATEGORIES:VIRTUAL ROOM 6C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:d1d53cac59616b5b52f2e62c4d0887cb
URL:http://11tict4sd.sched.com/event/d1d53cac59616b5b52f2e62c4d0887cb
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:An IoT-Powered Real-Time Cattle Health Monitoring System for Enhanced Agricultural Productivity
DESCRIPTION:Authors - Jalindar Gandal\, Nilesh Gorade\, Kunal Sonawane\, Khushal Patil Abstract - Cattle health and productivity are fundamental to the livelihoods of agriculturalists and the overall agricultural economy. Traditional cattle health monitoring methods are often time-consuming and lack the capacity for real-time assessment of cattle health\, resulting in reduced milk productivity and economic losses. To address these challenges\, we propose the implementation of an Internet of Things (IoT) technology-based\, low-cost\, real-time cattle health monitoring system. The system comprises wearable sensors for continuous monitoring of vital parameters such as body temperature\, heart rate\, and activity level. These sensor values are relayed wirelessly to a cloud server\, where data is processed and analyzed to identify anomalies indicative of potential health-related problems. This information is presented to the farmer through a user-friendly mobile application\, which displays real-time alerts and suggests preventive or remedial actions. The system facilitates early disease detection\, leading to improved cattle health\, enhanced milk production\, and enhanced farm profitability. The system emphasizes cost-effectiveness by utilizing readily available hardware\, thereby increasing accessibility for smallholder farmers. The system offers a long-term solution for cattle health management. This paper aims to demonstrate the transformative potential of integrating low-cost IoT technologies with livestock farming to establish precision agriculture and enhance the prosperity of rural farming communities.
CATEGORIES:VIRTUAL ROOM 6C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:c5ce900f7a6ed3ba5d61e0ecc6953442
URL:http://11tict4sd.sched.com/event/c5ce900f7a6ed3ba5d61e0ecc6953442
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:Child Mortality Prediction in India: A Time Series Approach Using ARIMA and SARIMA Models
DESCRIPTION:Authors - Samadhan Pujari\, Hetvi Saroliya\, Vedika Gawde\, Ekansh Manral\, Jalpa Mehta\, Deepika Patil\, Rashmi Malvankar Abstract - Mortality prediction is crucial for public health\, aiding resource allocation\, policy-making\, and preventive strategies. This study applies ARIMA and SARIMA models to analyze mortality trends in India (1990–2022) using data on infectious diseases such as Malaria\, HIV/AIDS\, Tuberculosis\, as well as non-communicable diseases like Nutritional deficiencies and neonatal disorders. ARIMA [1\, 3\, 4] captures non-seasonal trends\, while SARIMA\, incorporating seasonality\, proves more accurate. Implemented using Python libraries like pandas\, stats models\, and scikit-learn\, their accuracy is assessed using Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). Findings indicate that SARIMA outperforms ARIMA\, emphasizing the role of seasonality in mortality patterns.[6\,7\,16] A significant decline in deaths from infectious diseases like Malaria and Measles is observed\, attributed to public health initiatives\, immunization programs\, and improved healthcare facilities while neonatal and non-communicable diseases remain pressing concerns. Accurate data collection is essential for improving predictive modeling\, and ARIMA/SARIMA provide critical insights for public health planning. Future research could integrate factors such as climate change\, economic conditions\, and machine learning techniques to refine forecasting models further. This study reinforces the significance of time series forecasting in public health decision-making and strategic healthcare interventions.
CATEGORIES:VIRTUAL ROOM 6C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:91dfc01beddcc985bcd365f6cf208be7
URL:http://11tict4sd.sched.com/event/91dfc01beddcc985bcd365f6cf208be7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:Emerging Trends and Innovations in Sentiment Analysis: A Comprehensive Review
DESCRIPTION:Authors - Vaishali S. Katti\, Kailash J. Karande Abstract - This paper reviews significant advancements in sentiment analysis\, emphasizing various innovative applications in fields such as poetry analysis\, human resources\, customer feedback\, and mental health monitoring. The study systematically examines methodologies adopted in recent research\, elucidating their contributions to the field while presenting diagrams to enhance understanding. This overview not only highlights the evolution of sentiment analysis but also explores its implications across diverse sectors.
CATEGORIES:VIRTUAL ROOM 6C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:05391ab6638d1ef2372d0859f7d30c3a
URL:http://11tict4sd.sched.com/event/05391ab6638d1ef2372d0859f7d30c3a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:FPGA Implementation of Elliptic IIR Filter for Denoising ECG Signals
DESCRIPTION:Authors - Manjunath Inamati\, Goutami Naragund\, Chetan Paranatti\, Saroja Siddamal Abstract - Electrocardiogram (ECG) signals are vital for diagnosing cardiovascular conditions. However\, they are often contaminated by noise\, hindering accurate analysis. This paper presents the FPGA implementation of Infinite Impulse Response (IIR) elliptic filters for denoising ECG signals. Elliptic filters were chosen for their sharp roll-off and computational efficiency\, while an FPGA platform was utilized for real-time\, low-latency processing. The design leveraged MATLAB as the primary tool for filter parameterization and hardware-oriented signal processing due to its comprehensive functionality\, ease of use\, and seamless integration with HDL Coder for Verilog code generation. Synthesis and hardware deployment were performed using Xilinx Vivado. The system was validated using both synthetic and real ECG signals\, demonstrating effective noise suppression while preserving diagnostic features. Results indicate the potential of FPGA-based digital filters\, designed with MATLAB\, for portable and efficient biomedical applications.
CATEGORIES:VIRTUAL ROOM 6C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:40fce5d62857724347c1e38fe5298ccf
URL:http://11tict4sd.sched.com/event/40fce5d62857724347c1e38fe5298ccf
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:Multimodal Media Creation: Integrating LLMs for High-Quality Video Generation
DESCRIPTION:Authors - Aswin Sreerag\, Meenakshy P\, Akhil A\, Anargha Ranjit\, Anoop S. Babu Abstract - The field of text-to-video generation is going through a transformation\, by the advancements in Artificial Intelligence (AI) and deep learning. AI-powered video generation models helps us in the conversion of textual descriptions into visual content\, unlocking new possibilities in the fields of education\, entertainment\, and multimedia content creation. But the existing systems face challenges such as maintaining temporal coherence\, such as ensuring smooth transitions\, and correctly aligning video sequences with advanced textual inputs. This research combines Large Language Models (LLMs) and Generative Adversarial Networks (GANs) to develop a high-quality\, temporally consistent text-to-video generation framework. The system uses the Gemini model to change textual prompts into detailed and structured descriptions. These descriptions are initially given to the Stable Diffusion model to get the corresponding text-image before being input into a fine-tuned MoCoGAN model for video synthesis. The VATEX dataset\, which has extensive video and text pairs\, is the primary training resource\, this ensures meaningful alignment between textual descriptions and generated visuals. Apart from that\, the research also explores the DAMO ViLab which is a diffusion model\, that operates without additional training\, so that it provides a comparative analysis of different generative approaches. The results shows enhanced video smoothness\, improved scene consistency\, and stronger semantic alignment with the textual prompts. This work advances text-to-video generation by addressing key limitations\, using AI driven storytelling and visualization applications.
CATEGORIES:VIRTUAL ROOM 6C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:5313b8e3689e6cf84f411b375dcf1434
URL:http://11tict4sd.sched.com/event/5313b8e3689e6cf84f411b375dcf1434
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:Object detection using camera and LiDAR sensors in autonomous vehicles
DESCRIPTION:Authors - Jyoti Patil Devaji\, P. C. Nissimagoudar\, Prerana Savant\, V.S.Nandana\, Vaishnavi Harlapur\, Vidhi Agarwal Abstract - The project aims to focus on improving object detection and safety measures for autonomous vehicles by combining LiDAR and camera data. The main goal is to increase object detection accuracy and resilience by combining LiDAR data with the advanced object detection model YOLOv5. The system detects objects\, recognizes and tracks cars\, and uses a simple depth estimation technique based on bounding box width to determine how far away vehicles stand. To provide more accurate object localization in 3D space\, bounding boxes are constructed around identified objects\, and the related depth information is computed using the LiDAR data. A safety function that improves the situational awareness of the autonomous vehicle by generating an audio alert when a vehicle is spotted too close is also included in the system.
CATEGORIES:VIRTUAL ROOM 6C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:0dbd2470dfac5d372d897b965da16ab3
URL:http://11tict4sd.sched.com/event/0dbd2470dfac5d372d897b965da16ab3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:Privacy Preservation For Healthcare Data Using Partial Masking Technique
DESCRIPTION:Authors - Vismay Tank\, Khush Sanghavi\, Vyom Gandhi\, Pramila Shinde\, Jalpa Mehta\, Vaishali Korade Abstract - Everyone in the current world hopes that his/her personal information won’t be revealed in any way. Security insurance is essential for protecting personal information from prying eyes. The information may be extensive\, and it is important to minimize risk and ensure sensitive information is protected. This analysis addresses the drawbacks of previous customized security and other anonymization techniques by implementing a progressive modified protection saving technique. The core of the suggested technique is composed of two main components. Two additional states that are used in the report table but are hidden in the primary segment are sensitive data and fragile weight. The Fragile Data (DI) of the record holder determines whether the mystery should be retained or\, conversely\, whether it should be disclosed. Sensitive weight (DW) illustrates how brittle a characteristic's value is in comparison to the others. The following section discusses the Recurrence Circulation Block (FDB) and Semi Identifier Dispersion Block (QIDB)\, two other portrayals used for anonymization. Exploratory findings show that the suggested framework performs faster and loses less information than existing approaches.
CATEGORIES:VIRTUAL ROOM 6C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:023ad7f0f4c2a1d016705c019e12271b
URL:http://11tict4sd.sched.com/event/023ad7f0f4c2a1d016705c019e12271b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:Smart Ingredient Tracker: Product Safety and Allergy Detection Application
DESCRIPTION:Authors - Manas Tiwari\, Rohit Sharma\, Nyasa Singh\, Sandhya Avasthi Abstract - Although packaged foods are generally considered safe and hygienic\, many consumers are unaware of the additives they contain\, which can pose serious health risks. This lack of awareness has contributed to a rise in health issues such as allergies\, asthma\, diabetes\, and other chronic illnesses. To address this problem\, a mobile application is proposed that helps users make healthier food choices by analyzing packaged food ingredients. The application enables users to scan or upload images of ingredient labels\, utilizing image preprocessing techniques (grayscale conversion\, Gaussian blur) and Optical Character Recognition (OCR) to extract the ingredients. Users can also input personal health conditions like diabetes\, asthma\, or allergies\, allowing the system to tailor its analysis. By referencing a comprehensive database such as OpenFoodFacts\, the application provides immediate health-related insights on the detected ingredients. For ease of understanding\, ingredients are classified using a color-coded system: red for highly harmful substances\, orange for moderately harmful ones to be consumed in moderation\, and green for safe ingredients. This approach empowers consumers to make informed\, health-conscious decisions regarding packaged food consumption.
CATEGORIES:VIRTUAL ROOM 6C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:5a8db7af58f798604e27b52b1d0fb8ac
URL:http://11tict4sd.sched.com/event/5a8db7af58f798604e27b52b1d0fb8ac
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:Understanding the FIRE (Financial Independence and Early Retirement) Movement: Key Motivators and Factors Driving Its Adoption
DESCRIPTION:Authors - Nivedya Krishnan M T\, Mariya P Jose\, Amrita V S Abstract - The Financial Independence\, Retire Early (FIRE) movement is primarily focused on giving people financial security through saving and investing in such a way that they become less dependent on regular jobs. This empowers them to stop working earlier than regular retirement ages. The study explored major motivations for adopting FIRE through work-life attitudes\, desire for freedom\, financial well-being\, frugality and minimalism\, social influence\, and spousal/family support. A survey was administered to collect primary data from students and professionals from rural\, urban\, and semi-urban areas in India\, to reach a sample size of 400 respondents. The structured questionnaire includes a 5-point Likert scale\, binary\, and frequency-based questions. Data were coded and analyzed using SPSS\, with regression analysis employed to test the impact of independent variables on motivation to FIRE. Regression results showed that by far the best predictors of motivation for FIRE were frugality and minimalism\, desire for freedom\, and spousal support. The model was statistically very highly significant (p
CATEGORIES:VIRTUAL ROOM 6C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:21e5848088a3445ce84fe4f6bc60e5de
URL:http://11tict4sd.sched.com/event/21e5848088a3445ce84fe4f6bc60e5de
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:A Vision Based Blind Spot Warning System For Autonomous Driving
DESCRIPTION:Authors - Harshit Kadam\, V Harshavardhan\, Shrigouri S G\, Bhavana Gadagin\, Nalini Iyer\, Prabha Nissimagoudar Abstract - One of the key challenges for blind spot detection systems is the ability to detect and track objects in irregularly shaped regions. This problem becomes much more severe in addition to considering different vehicle velocities\, motions due to other objects\, and many different environmental conditions. Ordinary systems have fixed parameters for operating conditions\, which either are slow or may not adapt to changeable driving scenarios. The proposed solution is an adaptive continuous monitoring approach that provides the defined polygons with the ability to report on any encroachments while giving a proximity risk based on context data. This real-time adaptability allows the system to provide accurate and timely notifications to the driver\, thereby increasing the safety of critical events such as lane changes\, parking maneuvers\, or heavy traffic situations where blind spot threats are most prevalent. Initial results show that this adaptable system can outperform traditional precision and time response methods\, improving overall safety while driving.
CATEGORIES:VIRTUAL ROOM 6D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:5766c20ddeea924506a8bf525d67539b
URL:http://11tict4sd.sched.com/event/5766c20ddeea924506a8bf525d67539b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:Design and Implementation of a Secure QR Payment System Using Visual Cryptography
DESCRIPTION:Authors - Sharim Iqbal\, Purnima Ahirao\, Deepti Patole Abstract - QR code payment systems have become increasingly popular thanks to their speed\, simplicity\, and convenience. However\, as their usage grows\, so do concerns around security—issues like tampering\, spoofing\, and man-in-the-middle attacks are becoming more common. To address these vulnerabilities\, this paper introduces a novel approach to securing QR-based transactions using visual cryptography. Visual cryptography works by splitting an image into multiple shares\, which individually reveal nothing but can reconstruct the original image when overlaid—without the need for complex decryption algorithms. This research proposes a secure QR payment system that leverages visual cryptography to enhance data integrity\, prevent fraud\, and strengthen the overall security of mobile payments. The study covers current security challenges\, outlines the proposed system architecture\, implementation details\, and evaluates its performance.
CATEGORIES:VIRTUAL ROOM 6D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:63a56b991d4f4e4d1172703e2b412f1b
URL:http://11tict4sd.sched.com/event/63a56b991d4f4e4d1172703e2b412f1b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:Enhancing Sentiment Analysis of Movie Reviews
DESCRIPTION:Authors -&nbsp\;A.Akshaya\, G.Veera yasaswini\, P.Akshith\, K.Mahimanusha\, M.TanviSahasra\, Sushmarani\nAbstract -&nbsp\;Sentiment analysis is among the primary natural language processing (NLP) tasks and is widely utilized for extracting emotions and sentiments from text corpora. This paper proposes a comprehensive sentiment analysis approach for movie reviews based on Word2Vec\, TextBlob\, VADER\, and Gated Recurrent Units (GRU). Word2Vec is employed for word embeddings to extract semantic word relationships for improved feature representation. TextBlob and VADER are implemented as lexicon-based sentiment analysis tools\, for which TextBlob is interested in polarity and subjectivity and VADER is engineered for short texts with clear-cut sentiment indications. Besides\, deep learning architecture in the form of GRU is employed for extracting long dependencies and context associations between words of text corpora for enhanced sentiment classification. Methods are experimented and contrasted on the basis of a benchmark IMDB dataset with reference to accuracy\, precision\, recall\, and F1 score. Experimental findings substantiate that sentiment handling by deep learning-based approaches\, i.e.\, GRU via Word2Vec embeddings\, is better than traditional lexicon based approaches. The work provides insights to NLP-based opinion mining researchers and practitioners regarding the merit of utilizing hybrid approaches towards sentiment classification.
CATEGORIES:VIRTUAL ROOM 6D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:732ae762d6c04bc59c1460db9e7c462a
URL:http://11tict4sd.sched.com/event/732ae762d6c04bc59c1460db9e7c462a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:Face Recognition Using Support Vector Machines
DESCRIPTION:Authors - V M Aparanji\, Chaithanya C K\, Nanditha N\, Poornima H S\, Shreya A N Abstract - Face recognition is a vital biometric technology with growing applications in security\, access control\, and automation. Despite challenges such as lighting variation\, facial expressions\, pose angles\, and aging effects\, Support Vector Machine (SVM) has proven effective in modeling and classifying facial features due to its ability to construct optimal decision boundaries in high-dimensional spaces. In this study\, SVM was applied to a face recognition system for smart lock operations\, yielding strong performance metrics of 89.9% accuracy\, 89.7% precision\, 89.5% recall and an F1 score of 89.7%. These results demonstrate the capability of SVM to effectively manage facial variability while maintaining high accuracy in face recognition tasks.
CATEGORIES:VIRTUAL ROOM 6D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:36010e0078e24f879afcb30c3b5386e7
URL:http://11tict4sd.sched.com/event/36010e0078e24f879afcb30c3b5386e7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:Medicine Recommendation System Using NLP(Natural Language Processing)
DESCRIPTION:Authors - Ajay Talele\, Amruta Mankawade\, Aryan Sutar\, Nishit Shelar\, Urvesh Somwanshi\, Anushka sonde\, Shiv Sagar Singh\, Sharvari Savardekar\, Shivanand Satao\, Shridhar Sarda\, Raj Bapat Abstract - A Medicine Recommendation System intended to use user-provided symptoms to identify possible diseases and provide personalized suggestions for safety measures\, diets\, drugs\, and exercise regimens. The system predicts symptoms using Natural Language Processing (NLP) and Fuzzy Matching\, guaranteeing accurate identification even in the presence of noisy inputs. It makes predictions about likely diseases and obtains overarching information for each by comparing extracted symptoms with a disease- symptom dataset. The system\, which was developed with the Flask framework\, provides an intuitive online interface for smooth communication. This initiative aims to direct users toward informed medical treatment by showcasing potential in early disease identification. Future research will concentrate on improving accessibility more broadly and integrating healthcare data in real-time.
CATEGORIES:VIRTUAL ROOM 6D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:39a23b9e3988d00078a223268ac2058e
URL:http://11tict4sd.sched.com/event/39a23b9e3988d00078a223268ac2058e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:Modelling Comprehensive Web framework for Enhancing Administrative Efficiency in An Educational Institute
DESCRIPTION:Authors - Praniv Warungshe\, Siddhi Desale\, Sushant Mhatre\, Rujata Chaudhari\, Saylee Lapalikar Abstract - After graduation\, alumni often face difficulties in managing important academic documents such as Leaving Certificates (LCs)\, marksheets\, Letters of Recommendation (LORs)\, and convocation updates. Traditional processes are manual\, time-consuming\, and lack real-time tracking\, leading to delays and repeated campus visits. To address these challenges\, a cross-platform application was developed using React Native to simplify and digitize post-graduation work-flows. The application enables alumni to request\, verify\, and correct documents through a single digital platform with real-time status updates. LCs and marksheets are available on-screen\, making the verification process faster and more convenient. Convocation details can also be managed easily within the app. Faculty benefit from tools to handle LORs and other academic tasks efficiently\, while administrators use a centralized dashboard to track applications\, generate reports\, and respond to urgent requests quickly. The cross-platform nature ensures seamless access across various devices\, enhancing user convenience. By uniting all stakeholders on one platform\, the system boosts transparency\, reduces manual workload\, and modernizes the overall process\, offering an efficient and user-friendly solution to manage alumni post-graduation needs.
CATEGORIES:VIRTUAL ROOM 6D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:e9a19e1909281df1f8384192bd588d70
URL:http://11tict4sd.sched.com/event/e9a19e1909281df1f8384192bd588d70
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:Oral Disease Detection Using Multimodal Fusion
DESCRIPTION:Authors - Abhishek A Joshi\, Vasudhaika S\, Sinchana Chindi\, Kaushik Mallibhat\, Satish Chikkamath Abstract - The proposed work aims to present a novel framework to classify the oral diseases through multimodal data consisting of images and symptoms. The outcome of the work helps towards early diagnosis of oral diseases. Oral diseases are neglected by most people in the initial stages due to lack of awareness\, accessibility\, and availability of dental care. Early diagnosis of oral disease is necessary\, and early research focused on either the image of the affected area or just the textual description (symptom) to predict the illness\; both are essential. The study employs multimodal approach and utilizes an image dataset comprising seven categories of affected areas\, sourced from Kaggle. Additionally\, a textual symptoms dataset was developed\, consisting of 150 combinations for each illness. Confidence scores of both the model (image-classifier model and symptom-based illness prediction model) with the true label\, a new dataset is generated and it is trained with logistic regression to get the final predicted class. Image classifier model achieved 81% of accuracy whereas symptom-based model 97%\, resulting in final multimodal accuracy of surpassing both the model accuracies.
CATEGORIES:VIRTUAL ROOM 6D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:a35fe04829593b1adab3fb4da84a0c06
URL:http://11tict4sd.sched.com/event/a35fe04829593b1adab3fb4da84a0c06
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:Sentiment Analysis from Kannada text
DESCRIPTION:Authors - Laxmi Sanjay Badiger\, Keerti\, Sumangala Basavaraj Donkanavar\, Satish Chikkamath Abstract - Sentiment analysis is a vital task in NLP. That identifies the emotions in the text. Many studies concentrate only on the English language. There is an insufficient resource in Kannada language emotion analysis. This paper examines the sentiment in Kannada text using a manually prepared dataset. The datasets are divided into three classes positive\, negative\, and neutral. The processing techniques like context cleaning\, tokenization\, and sequence padding are used. The model uses RNN-LSTM which is efficient in handling the sequential data. The embedding layer is used to represent the words\, the LSTM layer is used to get the context of the sentence\, the Dropout layer is used to reduce the over fitting of the model and the dense layer is used to classify the sentiments into categories. The effectiveness of the model was measured using evaluation metrics like precision\, recall and f1 score. By predicting sentiments for Kannada text\, the paper also exhibits practical use of the model. This study demonstrates that LSTM-based models work well for sentiment analysis in Kannada. It also emphasizes the importance of creating and using manual datasets for low resource languages. The findings would be helpful to further research\, and the out-comes can be directly applied in many areas\, including social media monitoring\, customer feedback analysis\, and regional language processing.
CATEGORIES:VIRTUAL ROOM 6D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:1b7a42e2dcc245c111cd7871f45727ef
URL:http://11tict4sd.sched.com/event/1b7a42e2dcc245c111cd7871f45727ef
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:Sustainable Electronic Waste Management through Efficient Power Management for a Greener Future
DESCRIPTION:Authors - Bhadouriya Khushi Mukeshsingh\, Rajput Adityasingh Shashikantsingh\, Parmar Smit Dharmeshkumar\, Tiwari Prashant Dineshkumar\, Soumya Kiran Prajapai\, Nirav D. Mehta\, Anwarul M. Haque Abstract - The accelerating proliferation of electronic devices has led to a surge in electronic waste (e-waste)\, presenting a significant environmental and resource management challenge. Conventional e-waste disposal practices are inadequate\, often resulting in the release of hazardous substances and the loss of valuable materials. This paper explores a sustainable framework for e-waste reduction by leveraging advancements in power electronics and promoting standardization across electronic design and manufacturing. Key areas of focus include the adoption of fixed-type ports for power and data transfer\, implementation of mandatory certification and testing standards for electronic components\, and the design of modular and fixed PCBs to facilitate component reuse. Emphasis is placed on developing universal and multipoint-compatible components\, particularly in the context of electric vehicle (EV) charging infrastructure\, where interoperability can significantly reduce hardware redundancy. The integration of circuit protection mechanisms is proposed as a means to extend product lifespan and minimize failure-induced waste. Furthermore\, strategies for the reuse and remanufacturing of components are examined as critical elements of a circular economy. By combining technical\, regulatory\, and design-driven approaches\, this study outlines a comprehensive pathway toward reducing the environmental footprint of electronics and fostering sustainable innovation in power electronics.
CATEGORIES:VIRTUAL ROOM 6D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:ba1aca4e1eadcfb490afe1a543bfdbda
URL:http://11tict4sd.sched.com/event/ba1aca4e1eadcfb490afe1a543bfdbda
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:Textile loop: A Circular Economy Initiative in the Textile Sector
DESCRIPTION:Authors - Ayush Kayasth\, Vrund Raval\, Keren Khambhata\, Nirali Nanavati Abstract - India leads the world in textile production and exports\, but it also faces an increasing environmental problem: the country produces about 7\,800 kilotons of textile waste a year\, or 8.5% of the world's total. Even with dispersed efforts at recycling and reuse\, progress is still hampered by the lack of a centralized\, digitalized infrastructure for managing textile waste. In order to reduce waste in the Indian textile industry\, our study proposes TextileLoop\, a mobile application based on the ideas of the circular economy. The research employs a qualitative methodology\, with focus group discussions conducted among key stakeholders in Surat India’s leading textile hub. These conversations uncovered important issues\, such as small buyer networks\, reliance on offline trade\, and traditional players' reluctance to adopt new technologies. The results served as a guide for creating Textile Loop\, a B2B platform with an MVC architecture developed with Flutter and Appwrite. In addition to providing educational materials and a collaborative environment for industry stakeholders\, the application makes it easier to exchange excess textiles\, faulty goods\, and used machinery. Its integrated approach\, which combines digital infrastructure with circular economy strategies\, is what makes it innovative and the first circular economy based platform in India. Our platform aims to scale to a B2C and C2C model\, allowing for greater engagement. With implications for both industry and policy\, this work offers a workable plan for converting India's textile sector into a circular and sustainable ecosystem.
CATEGORIES:VIRTUAL ROOM 6D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:87cd376323a445cf120537192fe821c8
URL:http://11tict4sd.sched.com/event/87cd376323a445cf120537192fe821c8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:AI Driven CAPTCHA-based Security Alert for Identification and Preventing Malacious Bots
DESCRIPTION:Authors - Kaushal Kotkar\, Samiran Deore\, Riddhi Tak\, Tejal Deshmukh\, Rupali Vairagade\, Nilakshi Jain Abstract - Traditional CAPTCHAs often hinder users more than they stop bots. This project proposes a passive\, user-friendly alternative that monitors behavior—like mouse movement\, typing speed\, and clicks—to distinguish humans from bots. Built with Python\, FastAPI\, MongoDB\, and XGBoost\, the system defends against threats like DoS/DDoS attacks while remaining seamless. It adapts over time through model updates and achieved 95.3% accuracy with minimal false positives. With response times under a second\, it outperforms conventional CAPTCHAs in both speed and usability.
CATEGORIES:VIRTUAL ROOM 6E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:3141b573685beca74788ef072e3a278c
URL:http://11tict4sd.sched.com/event/3141b573685beca74788ef072e3a278c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:An Optimized Deep Event-Based Network Framework for Credit Card Fraud Detection
DESCRIPTION:Authors - Samrudhi Pustole\, Hemal Rajput\, Linisha Thakor\, Dhanashri Gawai\, R. B. Murumkar Abstract - Fraud detection is a critical challenge in financial transactions\, requiring advanced machine learning models to distinguish between genuine and fraudulent activities. This project focuses on LSTMbased fraud detection\, leveraging historical transaction data to identify suspicious patterns. The model processes multiple attributes\, including transaction amount\, category\, user job type\, geolocation\, and time-based parameters\, to assess fraud risk. In addition to the LSTM model\, we conducted single-attribute fraud analysis using various models\, evaluating their individual impact on fraud detection. This helped determine the most influential features in predicting fraudulent transactions. The system is integrated into a web-based application built with React and Flask\, allowing users to input transaction details and receive a fraud score in real-time. The backend ensures efficient data preprocessing using feature scaling and categorical encoding\, aligning new transactions with the trained model’s feature space. Through extensive testing with high-risk and low-risk transaction scenarios\, the system demonstrates its ability to detect fraudulent transactions with high accuracy\, making it a valuable tool for financial security. . . .
CATEGORIES:VIRTUAL ROOM 6E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:715a5819835560f1dd2727147662f19d
URL:http://11tict4sd.sched.com/event/715a5819835560f1dd2727147662f19d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:Autoencoder based Feature Engineering for Android Malware Detection using Ensemble Classifiers
DESCRIPTION:Authors - Shirina Samreen\, K. Shailaja Abstract - The goal of this research is to accurately classify Android applications as either malware or legitimate software using machine learning techniques. This accuracy is attained through a well-organized approach for dimensionality reduction utilizing an Autoencoder to transform a high-dimensional feature space to a compact representative feature space. This approach is crucial for reducing dimensionality\, especially since the novel NATICUSdroid dataset used for Android malware classification contains a large number of features\, including both native and custom permissions. The primary contribution of the research is the design of a Machine Learning Pipeline that prioritizes the most relevant features\, ensuring high accuracy with a minimal set of features. Classification is performed using various ensemble classifiers. Predictive ability of the proposed MLP is assessed through various evaluation metrics using a confusion matrix.
CATEGORIES:VIRTUAL ROOM 6E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:fe7af320aabb90522ee95d13aba4f642
URL:http://11tict4sd.sched.com/event/fe7af320aabb90522ee95d13aba4f642
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:Automated Incident Response System for Cybersecurity Threat Mitigation
DESCRIPTION:Authors - Akshat Sharma\, Alka Chaudhary Abstract - This study aims to develop an Automated Incident Response System (AIRS) for real-time detection and mitigation of cybersecurity threats. The system employs Random Forest for anomaly detection using live network traffic data. SMOTE is applied to address class imbalance\, and Optuna optimizes model parameters for enhanced accuracy. A web-based dashboard provides real-time visualization of security incidents. Performance evaluation on the UNSW-NB15 dataset demonstrates high accuracy (94.7%) and reduced false positives\, ensuring robust cyber defense.
CATEGORIES:VIRTUAL ROOM 6E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:0dda60d9eece89fd29e321f6527023ce
URL:http://11tict4sd.sched.com/event/0dda60d9eece89fd29e321f6527023ce
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:Digital Twins in Agriculture: Revolutionizing Climate Resilience with AI and IoT
DESCRIPTION:Authors - Swati Suman\, Sumit Ray\, Ajay Kumar Prusty\, Umesha C\, Girish Prasad Rath\, Sabyasachi Patnaik\, Ankita Priyadarshini\, Swagat Shubhadarshi\, Pavan Kumar Pandey\, Lalithamma M Abstract - Climate change has a significant influence on agriculture\, affecting developing nations' food security and financial condition. Thus\, the use of Digital Twins\, Internet of Things (IoT) devices\, and Artificial Intelligence (AI) may play an important role in transforming agriculture that is data-enabled in real time for crop development\, high productivity\, or climate mitigation. These technologies would aid in predicting drought start periods\, optimizing irrigation scheduling to react to any specific climatic shift\, and driving crop rotations in a given area. To power climate-resilient farming development\, AI and IoT must be combined\, resulting in DTs. This technology incorporates agricultural offices\, animal monitoring\, crop harvests\, crop protection\, and a DT for predictive maintenance purposes. AI is transforming agriculture by analyzing large volumes of data to forecast climate change consequences. Precision agriculture\, a key AI tool\, uses micro-localized applications based on syntactic sensory data\, drones\, and satellite data. Smart agriculture uses IoT\, AI\, Big Data analytics\, and DTs to gather\, integrate\, and analyze data from various sources. AI-powered models can forecast future weather patterns\, insect infestations\, and disease outbreaks\, enabling earlier intervention and higher output. These insights enable improved resource allocation\, agricultural practice optimization\, and enhanced farm output in the face of climate change and hence making the DT the possible game changer in the field of agriculture while keeping sustainability as one of its important cornerstones.
CATEGORIES:VIRTUAL ROOM 6E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:dd69f3a5a3c5ea75b9ed2102e8ea9450
URL:http://11tict4sd.sched.com/event/dd69f3a5a3c5ea75b9ed2102e8ea9450
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:Hierarchical Clustering of States with Crime against Children
DESCRIPTION:Authors - Sreelasya Changalasetty\, Lalitha Saroja Thota\, Seshagiri Rao Kandukuri\, Suresh Babu Changalasetty\, Ahmed Said Badawy\, Wade Ghribi\, Sajid Ali Khan\, Syed Asif Basha\, Firdouse Banu Abstract - Child-related criminal offenses are amongst the utmost heinous\, targeting the vulnerable youth of the society. These crimes include physical & sexual abuse of children\, child labor\, child trafficking\, cyberbullying etc. The World Health Organization (WHO) estimates that up to 1 billion crimes against children have occurred globally. In India alone\, more than 350 such crimes are reported each day. This study focuses on identifying crime hotspots related to children in India using hierarchical clustering\, a machine learning technique. The research utilized crime data from the National Crime Records Bureau (NCRB) India for 2016–2020\, alongside state-wise child population estimates\, to group states according to the gravity of offenses committed against children. The data was pre-processed\, normalized\, and analyzed using KNIME software\, which applied a bottom-up hierarchical clustering approach to create a dendrogram for visualizing crime clusters. The results revealed three category Indian states of crime zones in India: high\, medium\, and low. Delhi state was identified as the primary hotspot with a very high crime rate\, followed by 17 states in the medium-risk category\, and 12 states in the low-risk category. These findings underscore the need for targeted interventions and enhanced child protection policies\, especially in Delhi. The study illustrates how hierarchical clustering can be effectively applied to criminology for identifying high-risk areas and informing policy decisions. Future research may include the use of localized data and exploration of other clustering algorithms to refine and improve crime analysis and prevention strategies.
CATEGORIES:VIRTUAL ROOM 6E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:adfd1e67f2d726e9e2771dc45341252a
URL:http://11tict4sd.sched.com/event/adfd1e67f2d726e9e2771dc45341252a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:Phishing URL Detection: A Comprehensive Survey of Machine Learning Approaches
DESCRIPTION:Authors - G. B. Sambare\, Gauri Pawar\, Soham Vhanamane\, Tejas Sonar\, Omkar Gouroji Abstract - Phishing\, a deceptive practice aimed at acquiring sensitive information through fraudulent websites mimicking legitimate ones\, remains a significant cybersecurity threat. This paper presents a survey of machine learning (ML) algorithms applied for the detection of phishing URLs. We explore various feature categories derived from URL structure\, domain characteristics\, and HTML/JavaScript content. In particular\, the characteristics of interest involve address-bar-based features (i.e.\, URL length\, redirection patterns\, existence of IP addresses)\, domain-based features (e.g.\, DNS records\, website age\, web traffic)\, and HTML/JavaScript-based features (e.g.\, iframe redirects\, disabling the right click). The accuracy of a variety of classification methods\, namely Decision Tree\, Random Forest\, XGBoost\, and Support Vector Machines (SVM)\, is covered. By our results as well as available literature\, we point out the effectiveness of XGBoost\, which\, in our testing\, reached a comparatively high value of 86.8% accuracy and exhibited its prowess as a solid detector of phishing URLs. This paper offers some vision into the benefits and shortcomings different machine learning approaches are for phishing assaults.
CATEGORIES:VIRTUAL ROOM 6E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:fc1a6b98a8edcbfdd89af1d3e688e48f
URL:http://11tict4sd.sched.com/event/fc1a6b98a8edcbfdd89af1d3e688e48f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:SkillTrax: Personalized Skill Development Tracker
DESCRIPTION:Authors - Ajay K.Talele\, Gayatri Bhurguda\, Siddhant Yenpure\, Purva Ratnaparkhi\, Jidnya Santosh Jadhav\, Rugvedi Nimbhore\, Advait Mhalungekar\, Tanishka Kalokhe\, Riddhi Rathi\, Roshan Raut\, Pratha Sawant Abstract - In an era where skill development is essential for career growth\, learners often struggle to track progress\, find curated resources\, and stay motivated. SkillTrax is a personalized learning tracker built to address this gap. The platform helps users enter their skills\, select proficiency levels\, set learning goals\, and track their progress with recommended resources and quizzes. It is powered by object-oriented design principles\, ensuring modularity and scalability. This paper outlines the motivation\, design\, and implementation details of SkillTrax\, focusing on its core features\, backend architecture\, and educational impact.
CATEGORIES:VIRTUAL ROOM 6E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:65977825e5f1cd960f69d6334dbdab12
URL:http://11tict4sd.sched.com/event/65977825e5f1cd960f69d6334dbdab12
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:SyncVox: Synchronized AI Based Video Dubbing
DESCRIPTION:Authors - Owais Ansari\, Hemangini Patel\, Tejas Maroo\, Morvi Panchal\, Nikita Raichada Abstract - In recent years\, emotional voice conversion and expressive speech synthesis have gained attention due to their applications in areas such as automated dubbing\, human-computer interaction\, and assistive technologies. Our research proposes an AI-based dubbing system\, SyncVox\, which presents a seamless voice dubbing custom pipeline designed to provide seamless voice conversion across languages. It addresses low-resource video dubbing using various advanced technologies like speech recognition\, translation\, and style transfer. The pipeline combines speaker embeddings with advanced techniques to produce natural-sounding\, speaker-alike voice synthesis. By employing multitask learning with Text-To-Speech\, the pipeline is capable of capturing rich linguistic information while retaining languages\; this allows content creators to dub videos without compromising the original speaker’s intent and naturalness. Early results show that this system effectively synthesizes natural-sounding speech with high emotional fidelity.
CATEGORIES:VIRTUAL ROOM 6E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:2ff20458ac542cc1e4c8ed1014b6e311
URL:http://11tict4sd.sched.com/event/2ff20458ac542cc1e4c8ed1014b6e311
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T100000Z
DTEND:20260825T120000Z
SUMMARY:The Transformative Role of AI in the Programming of ICT in the Present Corporate World
DESCRIPTION:Authors - Sunitha Ratnakaram\, Venkamaraju Chakravaram\, Chakravaram Sri Surya Narayan Raj Abstract - Artificial Intelligence (AI) has significantly reshaped the landscape of Information and Communication Technology (ICT)\, particularly in the corporate sector. Integrating AI into programming and ICT development has led to automation\, enhanced efficiency\, and optimized business operations. AI-driven solutions are not only transforming software development but also impacting key business functions such as cybersecurity\, finance\, marketing\, and decision-making processes. AI-powered automation has enabled businesses to achieve unprecedented productivity levels\, making operations faster and more accurate while reducing human intervention. Companies now rely on AI for predictive analytics\, customer insights\, fraud detection\, and even real-time strategic decision-making. This research paper explores the transformative role of AI in ICT programming\, highlighting its vast impact on corporate efficiency\, software development methodologies\, cybersecurity frameworks\, and business functions such as marketing and finance. It also delves into the ethical considerations of AI implementation\, the challenges posed by AI-driven ICT automation\, and the potential of AI to reshape the global economy. This study presents various case studies and empirical findings that illustrate how organizations have successfully incorporated AI into their ICT programming strategies to gain competitive advantages. Furthermore\, the research investigates future trends\, exploring how AI is expected to evolve within the corporate sector and the potential risks it may pose. The researcher used descriptive exploratory research methodology. The findings of this paper contribute to the ongoing discourse on AI's role in shaping the digital landscape\, offering insights into both opportunities and concerns surrounding AI adoption in the modern corporate world.
CATEGORIES:VIRTUAL ROOM 6E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:db64bbed60d74b137349b72bd4016291
URL:http://11tict4sd.sched.com/event/db64bbed60d74b137349b72bd4016291
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T120000Z
DTEND:20260825T120200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 6A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:9b90c911c2b43c00d3ff9b9499bc4a62
URL:http://11tict4sd.sched.com/event/9b90c911c2b43c00d3ff9b9499bc4a62
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T120000Z
DTEND:20260825T120200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 6B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:b69edcbd03867700e8f242e61862eaf8
URL:http://11tict4sd.sched.com/event/b69edcbd03867700e8f242e61862eaf8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T120000Z
DTEND:20260825T120200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 6C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:0f4142e47b6596b265d7f44516b81c8a
URL:http://11tict4sd.sched.com/event/0f4142e47b6596b265d7f44516b81c8a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T120000Z
DTEND:20260825T120200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 6D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:3fa3cbdef7ea3a848e7f2bb0ec781fc4
URL:http://11tict4sd.sched.com/event/3fa3cbdef7ea3a848e7f2bb0ec781fc4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T120000Z
DTEND:20260825T120200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 6E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:2b88bb21b0dbeed26f139f39c42c70b1
URL:http://11tict4sd.sched.com/event/2b88bb21b0dbeed26f139f39c42c70b1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T120200Z
DTEND:20260825T120500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 6A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:51c076300e64c6680e1422c47c88b771
URL:http://11tict4sd.sched.com/event/51c076300e64c6680e1422c47c88b771
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T120200Z
DTEND:20260825T120500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 6B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:947d6af266579b032ec6da57998a8105
URL:http://11tict4sd.sched.com/event/947d6af266579b032ec6da57998a8105
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T120200Z
DTEND:20260825T120500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 6C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:edf4d33e080fbf8e9177284e7a18e6e3
URL:http://11tict4sd.sched.com/event/edf4d33e080fbf8e9177284e7a18e6e3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T120200Z
DTEND:20260825T120500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 6D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:6133cc10914f2fae3f065c865cc3e5eb
URL:http://11tict4sd.sched.com/event/6133cc10914f2fae3f065c865cc3e5eb
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260825T120200Z
DTEND:20260825T120500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 6E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:12d739bf6bfd54a5d1fcf67894deb022
URL:http://11tict4sd.sched.com/event/12d739bf6bfd54a5d1fcf67894deb022
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T035800Z
DTEND:20260826T040000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:303d0adccbd9013204cdc44dcba9421b
URL:http://11tict4sd.sched.com/event/303d0adccbd9013204cdc44dcba9421b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T035800Z
DTEND:20260826T040000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:401704397377d0af55ad2fd618fe5a95
URL:http://11tict4sd.sched.com/event/401704397377d0af55ad2fd618fe5a95
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T035800Z
DTEND:20260826T040000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:2e78a5de83cac22f3fe972da897c389c
URL:http://11tict4sd.sched.com/event/2e78a5de83cac22f3fe972da897c389c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T035800Z
DTEND:20260826T040000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:9728edffd4f955b6638d1d366933698b
URL:http://11tict4sd.sched.com/event/9728edffd4f955b6638d1d366933698b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T035800Z
DTEND:20260826T040000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 7E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:7c84b9ac4606b3dd4958d244b4e3cd61
URL:http://11tict4sd.sched.com/event/7c84b9ac4606b3dd4958d244b4e3cd61
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:A Study on Brand Preference of Health Drinks with Special Reference to Coimbatore City
DESCRIPTION:Authors - Dhavasironmani R.R\, Maria Joel. J\, Siddharth S.V\, Ajith Sundaram Abstract - Marketing research is essential to get the correct information about the consumers’ needs and their changing preferences. The evaluation of the Consumer Behaviour\, attitude\, perception and satisfaction level has been the subject of the market research very frequently. Health Drinks indeed are essential for every individual. The quantity of intake may vary according to the age\, occupation\, income level\, size of the family\, but everyone accepts that in order to cope up with the energy demands of the day-to-day life\, and to defend oneself from the polluted environment\, one should definitely consume any health drink supplementary to the food intake. Preferences get converted into a habit which is hard to change. It is evidenced from the study that certain health drinks are being consumed through generations that the customers develop a high degree of brand loyalty towards that brand.
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:a5614c69dcc36607d55d26a5cad15cac
URL:http://11tict4sd.sched.com/event/a5614c69dcc36607d55d26a5cad15cac
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:AI Based Predictive Framework for Maternal Health Timeline in Indian Women
DESCRIPTION:Authors - Bhuvaneswari Perumal\, Vaishnavi Moorthy\, Gladius Jennifer H Abstract - According to UNICEF-India\, 46% of maternal fatalities and 40% of neonatal fatalities transpire during labor or within the initial 24 hours post-delivery. Antepartum care includes routine surveillance\, assessment of risks\, and appropriate actions to enhance the health of the mother and fetus. Intrapartum care provides for safe labour and delivery with surveillance and appropriate management of complications by skilled personnel.This study aims at identifying the importance of holistic care in these stages and how it helps in preventing complications through the identification of high risk pregnancies which will be useful in avoiding the development of severe problems in future. The study uses analytical tools and Machine Learning models to analyze the health data and risk factors of pregnancy. In existing state of art they have inadequate early risk prediction with poor personalization. So the collected data includes several risk factors identified and classified based on their level of risk. The results of the attempts of applying various machine learning models and EDA methods to define the most important risk factors. This is a very large reduction and in line with the United Nations Sustainable Development Goals for the year 2030.The aim is to reduce maternal and neonatal morbidity and mortality. Lack of adequate management of intrapartum care can lead to postpartum problems to a large extent and thus affect the prenatal and fetal well-being.
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:1e8bd6cf7c86441a3c05fa23f9d78e63
URL:http://11tict4sd.sched.com/event/1e8bd6cf7c86441a3c05fa23f9d78e63
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:AURA: Adaptive User-guided Rendering Architecture for Robust Interior Design
DESCRIPTION:Authors - Yash Sharma\, Bramhansh Agarwal\, Sindhu Chandra Sekharan\, C. Kavitha\, S. Umamaheswari Abstract - In recent years\, interior design has played an increasingly important role in improving the look and usability of residential and commercial spaces. Although professional designers are often employed for this purpose\, the process can be time-consuming and costly\, with limited flexibility for personalized input. To address these limitations\, an AI-assisted solution has been developed. This system employs Conditional Generative Adversarial Networks to analyze photographs of indoor environments alongside text descriptions that reflect user preferences such as desired furniture style\, color schemes\, and spatial arrangements. After processing the information\, the tool provides a range of design suggestions tailored to the user’s specific needs. This method eliminates the need for repeated consultations and allows for rapid generation of unique\, realistic interior layouts. The approach supports a more inclusive and affordable design experience\, enabling individuals to explore personalized decor ideas efficiently. By merging visual data with linguistic inputs\, the system presents a novel pathway for intuitive and responsive interior design support.
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:7bab432c6b91c38b4b2f8601ccd32185
URL:http://11tict4sd.sched.com/event/7bab432c6b91c38b4b2f8601ccd32185
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:BiteSage: A Snake Bite Antidote Suggester
DESCRIPTION:Authors - Puja Cholke\, Om Yogesh Suhagir\, Maroof Mustaq Mohammed Gadiwale\, Srushti Pancham Mane\, Sanika Suresh Mohite\, Shreya Ramesh Phalke Abstract - Snake bites pose a severe public health risk\, especially in rural and tropical regions\, where delayed treatment often leads to fatalities. Existing systems struggle to classify snakes accurately based on symptoms\, causing delays in administering the correct antidote. To address this issue\, BiteSage (Snake Bite Antidote Suggester) utilizes data science and machine learning to classify snake bites as venomous or non-venomous based on user-reported symptoms and recommend the appropriate antidote. A chatbot interface assists users in symptom formulation and provides real-time counseling. Additionally\, the system offers visualization tools to analyze global trends in snake bites\, enhancing awareness and preparedness. The model ensures high precision in bite classification and antidote recommendations\, backed by comprehensive data analytics. This research benefits medical professionals in remote areas and educates the public\, helping to reduce fatalities and improve emergency response. By integrating AI-driven analysis\, real-time assistance\, and data visualization\, BiteSage enhances medical decision-making and public awareness\, ultimately saving lives.
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:c4f24581a213ddfbfcd83d94b7682590
URL:http://11tict4sd.sched.com/event/c4f24581a213ddfbfcd83d94b7682590
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:Decoding Disease Through Pixels: A Deep Learning Approach to Image-Based Diagnosis
DESCRIPTION:Authors - Kruthiga S\, Sindhu Chandra Sekharan\, H.Summia Parveen\, C. Kavitha\, S. Umamaheswari Abstract - The transformative potential of deep learning techniques to revolutionize the landscape of medical image analysis\, enabling accurate and efficient multi-disease prediction across a spectrum of critical health conditions. This work provides a solution to the early detection challenge of disease through prediction for Tuberculosis\, Pneumonia\, Glaucoma\, and Brain Tumors using deep learning methods. By leveraging the expressive power of convolutional neural networks and transfer learning strategies\, we have developed a robust framework capable of learning intricate patterns and subtle features indicative of diseases such as brain tumor\, glaucoma\, pneumonia\, and tuberculosis. Through meticulous data preprocessing\, model selection\, and rigorous training and validation procedures\, our approach ensures the reliability and generalizability of disease predictions\, offering clinicians a powerful tool for early diagnosis and personalized treatment planning. The integration of Python programming language facilitates seamless implementation and deployment of our framework\, making it accessible to healthcare practitioners and researchers alike. Overall\, our study represents a significant advancement in the field of medical image analysis\, with the potential to improve patient outcomes and revolutionize healthcare delivery on a global scale.
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:c4a655a415a83fb51f56d91beb6b1009
URL:http://11tict4sd.sched.com/event/c4a655a415a83fb51f56d91beb6b1009
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:DSEA: A Dynamic Selective Encryption Algorithm for Enhanced Security and Resource Efficiency in Wireless Communications
DESCRIPTION:Authors - Pranay Meshram\, Prakash Prasad Abstract - In the rapidly evolving digital landscape\, data security has become paramount\, necessitating innovative encryption techniques that balance computational efficiency with robust protection. This research introduces the New Efficient Selective Encryption Algorithm (DSEA)\, a novel approach to selective text encryption that addresses critical challenges in current cryptographic methods. By leveraging intelligent message analysis and strategic encryption\, by providing a robust approach to safeguard valuable information at the same time as minimizing resource usage\, DSEA addresses the need for privacy in a progressive manner. The approach utilizes proximity to structural properties of the message\, such as the ratio of alphabetic characters\, presence of vowels\, and semantic connections\, to inform the selection of encryption techniques. DSEA thus allows encryption to be applied at a more granular scale\, using its identification and prioritization of sensitive text segments\, which results in a significantly lower computation overhead compared to traditional techniques for full-document encryption. Experimental results show that DSEA has a better performance comparing with the existing selective encryption schemes\, especially in the encryption time percentage\, encryption processing time\, and encryption proportion.
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:2c145c06796beb1193606af6b4af9277
URL:http://11tict4sd.sched.com/event/2c145c06796beb1193606af6b4af9277
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:Employee Promotion Prediction Model Using Machine Learning
DESCRIPTION:Authors - Bendre M. R.\, Vikhe V.P.\, Vanve G.B. Abstract - Within the carrier and business industries\, there would be an ongoing demand for employees who are promoted to higher positions in the service and corporate sectors. The human resource team faces significant pressure to maintain employee commitment and motivation. Incentives such as promotions\, bonuses\, and wages are applied to motivate employees to feel closer to their work. The employee promotions are primarily deliberate\, expressing gratitude for the employee's dedication to enhancing business standards\, ensuring team competency\, preventing talent from seeking other opportunities\, and upholding the excessive degree of overall performance\, all through the assessment year\, human resources gather a significant quantity of facts on all elements of worker engagement events and activities. The data collected is continuously expanding in terms of employee service\, but it is of little value if it does not provide meaningful insights. As a result\, machine learning plays a crucial role in human resource analytics by extracting valuable information from collaborative employee data. The issue lies in the conventional approach to promotion\, which is both time- and resource-intensive due to the numerous steps required for segregating and promoting employees. This had a significant impact on the smooth transition of employees into their new positions. Because of this reason\, it's miles greater sensible if human assets can predict which workers are more legal and appropriate for advancement or upgrade\, earnings increase\, and so on. This research aims to propose or expect worker promotion. Utilizing machine learning techniques to forecast which employee might be eligible for a promotion\, contingent on the data gathered and their previous achievements. To determine the likelihood of advancement probabilities the classification algorithms together with decision trees (DT)\, logistic regression (LR)\, random forests (RF)\, and k-means clustering are considered broadly utilized within the field. The k-nearest neighbors (K- NN)\, random forest (RF)\, and decision tree (DT) classifiers are applied to make the expected forecast.
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:59bb70f72e0417537196c5e330b2f051
URL:http://11tict4sd.sched.com/event/59bb70f72e0417537196c5e330b2f051
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:Enhancing Navigation for Railway Station Facilities and Locations Using Augmented Reality
DESCRIPTION:Authors - Nishant Survase\, Chitti Saharsh\, Sachin Dhadwe\, Krishnadeep Thakare\, Yash Ishwarkar\, Nilesh Pinjarkar Abstract - Railway stations\, key transportation nodes\, frequently have complicated layouts that disorient travelers\, leading to delays. Conventional signage alone is not enough for effective navigation\, particularly with increasing urban populations. Augmented Reality (AR) becomes a solution\, superimposing virtual\, step-by-step directions onto actual views through smartphones or AR glasses. This paper explores AR's capability to improve navigation in stations by combining GPS\, GLONASS\, and adaptive machine-learning algorithms. Both marker-based and markerless AR approaches\, combined with realtime locationing\, also offer custom guidance. Analytics of learning further refine user engagement\, with increased feedback mechanisms as well as operational effectiveness. As such\, AR can efficiently handle congestion\, enhance accessibility for the disabled\, and optimize passenger flows. Keywords: Augmented Reality (AR)\, Railway
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:58042dedaa654241f6ae74f72fcd26ea
URL:http://11tict4sd.sched.com/event/58042dedaa654241f6ae74f72fcd26ea
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:Machine learning approach to predict type of mental disorder using mental status parameters
DESCRIPTION:Authors - Prafulla Bafna\, Punam Nikam Abstract - Mental illness can be the reasons of extreme behavioral\, emotional\, and physical health issues. Majorly there are 4 mental disorders which are based on disposition\, uneasiness\, identity and insanity. Most of the times symptoms pertaining to these mental diseases are common. But remedies on each mental disorder is different. Due to the commonly existing symptoms of each disease\, identifying the exact type of mental disorder is difficult. To smoothen the process of identifying exact mental disorder we use machine learning algorithms. The algorithms are executed on 1020 patient records containing nine parameters which show mental status such as l consciousness level\, general behavior\, and so on. To predict the exact type of mental clutter/disorder \, KNN and SVM are implemented using 80 :20 ratio of training-to-testing data. SVM proved to be more accurate that is low misclassification error and greater recall. The accuracy of prediction is steady for 300 to 1020 records.
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:23f0d62d37d1e142b41d4ae6e18f9d07
URL:http://11tict4sd.sched.com/event/23f0d62d37d1e142b41d4ae6e18f9d07
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:Real-Time Fraud Detection in Credit Card Transactions: Leveraging Face Detection and Machine Learning Techniques
DESCRIPTION:Authors - Supriya\, Ananya G Bhat\, Chandana B A\, Niharika P Abstract - This study seeks to enhance the accuracy of credit card fraud detection by utilizing advanced machine learning techniques\, with a specific focus on the XG Boost algorithm. Various ML approaches\, including Decision Trees\, Logistic Regression\, Naive Bayes\, Random Forest\, and XG Boost\, are evaluated for their efficiency in detecting fraudulent transactions using patterns derived from historical data. Recent advancements highlight the integration of diverse authentication methods and randomized training datasets to mitigate vulnerabilities in fraud detection systems.
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:542f8b38e2323b2d2629d81a015fc4c3
URL:http://11tict4sd.sched.com/event/542f8b38e2323b2d2629d81a015fc4c3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:AI-Driven Disaster Prediction: Integrating Earthquake and Flood Forecasting for Enhanced Resilience
DESCRIPTION:Authors - Saraswati Patil\, Mustafa Limdiyawala\, M.S. Dawngliana Fanai\, Meghaj Kharwadkar\, Shivshankar Mahajan Abstract - Natural disasters such as earthquakes\, floods\, and tsunamis pose severe threats to human lives\, infrastructure\, and economies. Effective prediction and response strategies are vital for minimizing their impact. This paper introduces an AI-driven Disaster Prediction and Relief Dashboard\, an integrated platform leveraging machine learning and geospatial mapping to forecast natural disasters and optimize relief operations. Using Random Forest and Gradient Boosting algorithms trained on historical data\, the system predicts the likelihood\, magnitude\, and severity of disasters. Geospatial visualization highlights high-risk zones and delivers real-time situational awareness for authorities. Additionally\, the platform streamlines relief management by dynamically allocating resources based on predicted disaster severity and location. By integrating predictive analytics with operational planning\, the system enhances preparedness and responsiveness\, contributing to more resilient disaster management.
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:3f75ad3e62dc7652dd8efff2690aae5d
URL:http://11tict4sd.sched.com/event/3f75ad3e62dc7652dd8efff2690aae5d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:Developing a CNN Model (Rebbica) for effective Lung Cancer Classification on Histopathology images
DESCRIPTION:Authors - N.N.S.S.S. Adithya\, P. Vanishree Sah\, B. Jyothirmai\, Nidhi Mishra\, D. Indira Abstract - Early prediction of lung cancer is crucial for reducing the death rate. Artificial intelligence\, particularly deep learning\, is employed to analyze CT scan images for more accurate automated prediction of types of lung cancer. This process of prediction is called classification. Lung cancer classification can be done with pre-networks such as VGG16 and ResNet50.But the main drawback of these techniques is that cancer cannot be detected on Histopathology images (i.e. image of tissues). As VGG16 and ResNet50 are designed for more general usage\, they are not suitable for analyzing Histopathology image. This study involves the development of a customized neural network model which can solve the problem of analyzing Histopathology images. This CNN model can help us detect lung cancer at a very early state in lung tissues. Detecting lung cancer at a very early stage can help doctors to cure the patient and save the life of a patient.
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:cc697a9a40b45c3641a7ef54a3076b46
URL:http://11tict4sd.sched.com/event/cc697a9a40b45c3641a7ef54a3076b46
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:Effect of Music Therapy on Children Suffering with Neurological Disorder
DESCRIPTION:Authors - Sneha S. Biradar\, Suvarna Kanakaraddi\, Neha Tarannum Pendari Abstract - This personalized music therapy framework for children with Autism Spectrum Disorder (ASD) involves data collection (AQ scores\, age\, gender\, and demographics)\, severity identification\, and customized music creation. Using a publicly available dataset\, children were classified by severity\, allowing the design of therapeutic music with varying duration\, tempo\, and complexity. Compositions were set to 15 minutes for low severity\, 30 minutes for moderate\, and 50-60 minutes for high severity. Preliminary results suggest this approach boosts engagement and may improve cognitive\, emotional\, and social outcomes\, demonstrating the potential of combining advanced analytics with personalized music therapy for ASD.
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:abc0a536d439ed344635d35eb07bc143
URL:http://11tict4sd.sched.com/event/abc0a536d439ed344635d35eb07bc143
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:Enhancing Customer Experience (CX) with Generative AI-Based Net Promoter Score (NPS) Prediction
DESCRIPTION:Authors - Ruby S Chanda\, Vanishree Pabalkar\, Priya Pradipkumar Tiwary Abstract - Companies are increasingly using data analytics and AI to personalize interactions and provide tailored recommendations. Additionally\, there is a growing focus on emotional intelligence and understanding customers' needs beyond their transactional behavior. Some real-world examples are – Netflix uses AI to analyze viewing history and preferences\, recommending personalized content\, Spotify leverages data on listening habits to create tailored playlists and discover new music. The need for AI models in NPS and CX enhancement arises from the increasing complexity of customer interactions and the vast amount of data generated. AI can help in predicting consumer’s future behavior by analysing their demographic and purchase data and identifying patterns. This empowers businesses to create more tailored and meaningful customer experiences\, resulting in greater satisfaction and loyalty. This project aims to develop a Generative AI-based Net Promoter Score (NPS) predictor to enhance Customer Experience (CX) in the retail industry. By leveraging advanced AI techniques like VAEs and GANs\, the model will be able to analyze vast datasets of consumer behavior and demographics\, providing more accurate and personalized NPS predictions.
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:846fe512a9673f6badd2fef3c8b5d0fc
URL:http://11tict4sd.sched.com/event/846fe512a9673f6badd2fef3c8b5d0fc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:Enhancing IOT-based fruit picking with Reinforcement Learning\, Transfer Learning and Neuroevolution
DESCRIPTION:Authors - Shriraj A. Patil\, Chudaman D. Sukte\, Jayesh R. Patil\, Chinmay R. Mhaske\, Mandar Dakhorkar\, Manohar K. Kodmelwar Abstract - This paper presents a novel IoT-based fruit-picking system that integrates Reinforcement Learning (RL)\, Transfer Learning (TL)\, and Neuroevolution to address the inefficiencies of current robotic harvesting methods. As demand for efficient agricultural practices rises\, traditional fruit-picking systems face significant challenges\, including operational inefficiencies\, fruit damage\, and limited adaptability to diverse environments. Our proposed solution leverages RL to optimize picking strategies through adaptive learning\, enhancing the robotic arm's efficiency over time. TL is employed to improve fruit recognition capabilities\, utilizing pre-trained models for accurate ripeness detection\, even with limited training data for specific fruit varieties. Additionally\, Neuroevolution evolves control strategies for the robotic arm\, enabling it to adapt to dynamic harvesting conditions. Comprehensive simulations demonstrate significant improvements in picking accuracy\, efficiency\, and adaptability compared to existing methods. The findings highlight the potential of integrating these AI models within IoT frameworks to revolutionize fruit harvesting\, ultimately contributing to smarter farming practices and enhanced agricultural productivity. This research underscores the interdisciplinary nature of modern agriculture\, combining advancements in AI\, robotics\, and IoT technologies to provide innovative solutions for the challenges facing the agricultural sector today.
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:f56fd97199f32788ae0e1d50aa98509d
URL:http://11tict4sd.sched.com/event/f56fd97199f32788ae0e1d50aa98509d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:Enhancing MRI Tumor Detection : A Survey On Image Upscaling\, GAN Based Data Augmentation and Federated Learning in Convolutional Neural Networks
DESCRIPTION:Authors - Gatla Vijayendher\, K Sai Karthikeya\, E Jayanth Madhav\, Tadepalli Satya Kiranmai Abstract - The increasing number of tumor cases has caused an alarming situation in the health care space. The tumor detection and diagnosis is a very computationally heavy and requires multiple medical imaging devices such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). It is very vital in order for early detection of tumor which can be done by precisely measuring their size which can improve a treatment by a huge factor in the patients. Existing diagnostic approaches often face challenges due to the diverse appearances of tumors and the constraints of current models. This study examines the different cutting-edge deep learning methods\, with a focus on utilizing Generative Adversarial Networks (GANs) to enhance tumor identification across various categories\, types\, and imaging techniques. We also investigate the role of data augmentation strategies in enhancing the model performance. Furthermore\, we examine the integration of Convolution Neural Networks (CNNs) to achieve accurate and robust results while preserving the data privacy. The goal of this study is to understand the detailed current scenario of the early detection and ways about the different techniques in various kinds of tumors which are present in the human body.
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:f33a034e3830c979639e3295a2db10dc
URL:http://11tict4sd.sched.com/event/f33a034e3830c979639e3295a2db10dc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:Evaluating Heat Transfer in Various Heat Sink Designs under Controlled Experimental Conditions
DESCRIPTION:Authors - Amol More\, Sanjeev Kumar\, Sandeep Kore Abstract - This study carefully checks how well three different types of heat sinks move heat under controlled laboratory conditions. The main focus is on Copper Pin Fin Heat Sinks\, Aluminum Phase Change Material (PCM) Pocketed Heat Sinks\, and Aluminum Plate Fin Heat Sinks (PFHS). These were put through a wind tunnel test that simulated forced convection. This gave a thorough comparison of how well they kept heat in. To make the experiments work\, heat was applied to the bottom of the heat sinks with 10W\, 20W\, and 30W of power\, to represent various thermal loads. The speed of the air was changed from 1 m/s to 5 m/s to see how speed affected how well heat was removed. It was also improved by making changes like adding a copper plate to the aluminum fins and making holes in both the shield connection and the pin fins which were used in the experiment. By changing the surface area and turbulence\, these changes are meant to see if they can improve the rate of heat transfer. The study's results should give us useful information about how to build heat sinks so they work best in a wide range of situations\, from home electronics to industrial systems. It is expected that the results will help make thermal management solutions that work better\, which will improve the performance and life of heat-sensitive parts.
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:6f94126db61004cdee63f619597364f3
URL:http://11tict4sd.sched.com/event/6f94126db61004cdee63f619597364f3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:Evaluating Lexicon-Based and Transformer-Based\, Approaches for Sentiment Analysis in Amazon Fine Food Reviews
DESCRIPTION:Authors - Ayinampudi Siva Rama Raju\, Suneetha Dwarapu\, Gaddiboyina Sai Jahnavi\, Tummalapalli Sai Sri Varshit\, Kalimahanthi Sai Nikhil Kartikeya Abstract - Sentiment analysis\, a key task in natural language processing (NLP)\, identifies emotional tone in text. This study compares two sentiment classification approaches: a lexicon-based method using VADER (Valence Aware Dictionary and sEntiment Reasoner) and a transformer-based deep learning method with RoBERTa (Robustly Optimized BERT Pretraining Approach). Using the Amazon Fine Food Reviews dataset of 568\,454 customer reviews\, the analysis categorizes sentiments as positive or negative. Preprocessing steps\, including text normalization and handling missing values\, ensure data reliability. VADER efficiently processes short\, informal texts using a predefined lexicon but struggles with complex linguistic structures and contextual subtleties. RoBERTa leverages transformer-based architectures to capture intricate word relationships\, enabling superior accuracy and nuanced sentiment detection in contextually rich texts. A comparative evaluation demonstrates that RoBERTa outperforms VADER by a significant margin\, underscoring the strengths of deep learning for detailed sentiment analysis. These findings emphasize the trade-offs between speed and contextual depth in sentiment analysis models and provide valuable insights for customer feedback interpretation\, opinion mining\, and broader NLP research.
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:a0e413528ea55e9b67e708f250f6b469
URL:http://11tict4sd.sched.com/event/a0e413528ea55e9b67e708f250f6b469
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:Sentiment Analysis of Financial Tweets & News Using Machine Learning to Identify Trading Opportunities
DESCRIPTION:Authors - Ruby S Chanda\, Rahul Dhaigude Abstract - In order to predict stock values\, this study investigates the combination of machine learning with sentiment analysis. The quick spread of news and the growth of social media sites like Twitter have made public opinion a bigger factor in financial markets. This study extracts market sentiment from tweets and news stories using Natural Language Processing (NLP) techniques\, namely the VADER sentiment analysis tool\, which has been tailored with financial lexicons. To forecast stock price fluctuations for firms like Amazon and Tesla\, sentiment data is included into a Generative Adversarial Network (GAN) model together with technical indicators like moving averages and Bollinger Bands. The model is evaluated using performance metrics like Root Mean Square Error (RMSE)\, demonstrating its ability to capture price trends and market sentiment dynamics. While results highlight the potential of GANs for real-world applications in financial trading\, the study also acknowledges limitations such as data quality and model uncertainty. Future directions include improving sentiment algorithms and incorporating additional market factors..
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:7ec063c1b7d87d4312e23f1f6f939502
URL:http://11tict4sd.sched.com/event/7ec063c1b7d87d4312e23f1f6f939502
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:The Role of Social Media Information Sharing on Generation Z's Green Purchase Intentions
DESCRIPTION:Authors - Aanjaneya K\, Anjana P\, S Sameera\, Ajith Sundaram Abstract - The environment-related worries of Generation Z together with sustainability-based activities established them as leaders who champion green consumerism. Digital natives of this generation opt to make buying choices on social media platforms according to their established reputation. The platforms of Instagram together with YouTube and LinkedIn function as essential spaces for spreading sustainability content which affects how people behave regarding their purchasing choices. Social media promotes consumer engagement through direct communication and enables fast information flow about green events so it stands as a key factor in developing positive green purchasing attitudes. Current research analyzes the impact of social media information sharing on Gen Z sustainable buying motivation through an investigation of green-value and subjective-norms as intervening variables. This research depends on the Stimulus-Organism-Response (SOR) model to see how social media leads consumers toward buying green products. This research study addresses the mental factors behind environmentally conscious buying to provide concrete recommendations for business organizations and government institutions. Companies can use the research results as a foundation to create better sustainability-oriented marketing plans that aim at Gen Z consumers.
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:562b0264cbfcc3af1f094c8d7a791cf9
URL:http://11tict4sd.sched.com/event/562b0264cbfcc3af1f094c8d7a791cf9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:Customized Convolutional Neural Network for Accurate Human Motion Forecasting
DESCRIPTION:Authors - Navneet S Patil\, Shashidhar Kumbar\, Sakshi Bhantanur\, Arjav Jain\, Satish Chikkamath\, Sujata Kotabagi Abstract - Human movement prediction is a key machine learning domain whose purpose is to predict future movement from previous motion patterns and context information\, with usage in autonomous vehicles\, virtual reality\, video games\, and health care. In this study\, the goal is to apply Convolutional Neural Networks (CNNs) for predicting human movement from the UCF50 dataset\, whose collection contains action videos with a wide variety of actions. CNNs excel at discovering spatial and temporal patterns from video data and\, thus\, can be used in understanding motion complexities. In this work\, a CNN-based approach is developed using a CNN architecture to assess motion dynamics and make accurate forecasts about future moves. By systematically preprocessing the dataset and optimizing the model’s architecture\, the study achieved an accuracy of 99.09demonstrating the reliability and efficiency of CNNs in motion prediction tasks. Furthermore\, the paper discusses existing methodologies in human motion prediction\, comparing their performance and highlighting the advantages of CNNbased models in processing visual data. The results here bring out the potential of CNNs for real-world applications and set the foundation for future advancements in human activity recognition. The current study adds insight into machine learning methods and how they can be used to enhance motion prediction\, with implications toward innovations in those fields that rely on precise modeling of human activities
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:472e25cba16c96d923cc97c24c9cd653
URL:http://11tict4sd.sched.com/event/472e25cba16c96d923cc97c24c9cd653
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:Docker Container Security: A Scanning-Centric Security Framework
DESCRIPTION:Authors - V.Sudeep\, V.Nishant\, MM.Mohamed jasir Faiez\, T.Monish\, Yuvaraj kumar.GP\, Akhil K J\, Praveen.K Abstract - Docker containers are central to modern software development and deployment due to their portability\, efficiency\, and scalability. By isolating applications and dependencies\, they provide a lightweight alternative to virtual machines\, enabling consistent environments across platforms. However\, Docker containers pose security challenges\, including shared kernel risks\, vulnerabilities in container images\, and misconfigurations\, which can lead to breaches.This paper examines security concerns in Docker containers and proposes a framework to identify and address vulnerabilities. The framework helps detect issues like outdated components and misconfigurations\, offering insights to enhance security. Through practical use cases\, it highlights its effectiveness in closing security gaps and equipping developers with tools to protect containers. The study emphasizes the need for proactive security measures and continuous vigilance in securing containerized systems.
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:9cfbe1ef1823bb8a62dd93246c279d05
URL:http://11tict4sd.sched.com/event/9cfbe1ef1823bb8a62dd93246c279d05
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:Factors Influencing Generation Z's Adoption of Digital Wallets as a Payment Method
DESCRIPTION:Authors - Chaithra S Raju\, Nimisha S\, Arathi A N Abstract - The Digital payment landscape in India has seen rapid progress\, driven by technological advancements\, government initiatives and increased smartphone penetration. Digital wallets are becoming a payment method as they are convenient\, secure and seamlessly integrate with financial services. Although Generation Z known for a Digital-first approach\, inconsistency in the adoption of Digital wallets can be observed among this segment. This research will look at the reasons why Generation Z may adopt or not adopt Digital wallets\, namely perceived ease of use\, perceived usefulness and perceived security.A cross-sectional survey was undertaken for the 220 Gen Z respondents using a structured questionnaire. The statistical analysis was done by using SPSS analysis of variance to examine the impact of these factors on adoption behaviour. . The findings highlight that while convenience and utility drive adoption\, security concerns remain a critical barrier.This study provides valuable insights for fintech companies\, policymakers\, and firms looking to bolster the digital payment infrastructure and build trust in Digital wallet services. Overcoming security concerns and improving the user experience can accelerate the transition towards a cashless economy.
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:dea97253baa00c90195c61dc5e626ca1
URL:http://11tict4sd.sched.com/event/dea97253baa00c90195c61dc5e626ca1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:HEALTH MONITORING SYSTEM FOR THE ELDERLY
DESCRIPTION:Authors - S.Asha\, Siddharth M Nair Abstract - According to a study\, one out of every 20 people above the age of 65 are suffering from Alzheimer's. People with such neurological conditions have poor navigation skills and often wander around without having knowledge of where and what they are doing. In such situations\, tracking them down is extremely important as it is life threatening to themselves and the people around them. It is also important to monitor elderly individuals' vitals like heart rate and steps along with detecting an impact (fall) so that necessary actions can be taken. Other than the strong personal motivation the current market needs a product through which people suffering from such neurological conditions can be supported. But not many are present in the current market and the ones that are\, require the patient to wear some dedicated device like a neck ring or other uncomfortable devices. Often\, people\, especially elderly individuals lose their lives because 'it was too late'. There is a major requirement in today's market for a system which would send alerts and concerned individuals in case of any abnormality in detected data so that it would not be 'too late' to act. The sensors that are incorporated within the Apple Watch provide an ocean of valuable data which can be harnessed by caretakers and other concerned individuals. Now-a-days\, people are too involved and busy with their work to stay at home and be there for elderly individuals at all times. Through this data\, people can take care of their loved ones even when they are not around. By receiving timely notifications in case of any emergencies\, the world would become a safer\, more reliable place for all elderly individuals\, especially those who suffer from Alzheimer’s and other neurological conditions.
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:7976e48e11efaaa31e3dbc374103ccf8
URL:http://11tict4sd.sched.com/event/7976e48e11efaaa31e3dbc374103ccf8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:Multi-Modal MRI Imaging and Deep Learning for Predicting MGMT Promoter Methylation in Gliomas
DESCRIPTION:Authors - Anitha D\, Swetanshu Agrawal\, Samudra Banerjee Abstract - Particularly affecting patient response to alkylating treatment\, the methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter is a well-established prognostic and predictive biomarker in gliomas. Conventional evaluation techniques are prone to limits including sampling mistakes and intratumoral heterogeneity and call for invasive tissue biopsies. In this work\, we present a non-invasive\, deep learning-based system for multi-modal magnetic resonance imaging (MRI) based MGMT promoter methylation prediction. The method combines improved preprocessing\, automated tumor segmentation\, and a customized EfficientNet-based classification architecture with structural MRI sequences including T1-weighted\, contrast-enhanced T1-weighted\, T2-weighted\, and FLAIR imaging. Our model achieves strong performance\, high accuracy and generalizability in methylation status prediction. Comparative study including current literature shows either better or equivalent prediction performance\, so highlighting the clinical possibilities of this technique. The suggested pipeline advances the function of virtual biopsy in neuro-oncology by providing a scalable\, dependable\, radiation-free substitute for MGMT methylation testing\, therefore enabling individualized therapy planning.
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:2f6031e52fd82e74324d2fcc7804e183
URL:http://11tict4sd.sched.com/event/2f6031e52fd82e74324d2fcc7804e183
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:Optimizing Phishing Detection: A Robust Feature Selection using Hybrid GA-PSO
DESCRIPTION:Authors - Richa Goenka\, Meenu Chawla\, Namita Tiwari Abstract - In recent years\, phishing attacks have emerged as a substantial hazard\, endangering online businesses and security by exploiting users to divulge sensitive financial information through fraudulent websites. Despite various proposed methods\, accurately distinguishing between legitimate and fraudulent sites in real-time remains challenging. This paper provides a new approach to identifying phishing URLs by employing a feature selection approach that integrates Genetic Algorithm and Particle Swarm optimization. This system optimizes feature selection through population initialisation\, fitness evaluation\, GA operations\, and PSO integration\, dynamically balancing exploration and exploitation. The objective is to identify significant features for supervised machine learning techniques\, enabling precise phishing URL detection. For classification\, multiple machine learning classifiers are employed among which XGBoost provided the best results. Experimental results using the hybrid feature selection prove that the machine learning classifier works much better than the prevailing feature selection approaches. This comprehensive approach provides a reliable method for detecting phishing URLs\, improving internet security\, and reducing the threats associated with phishing attacks.
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:0a96a369443d6fe35d0705e11b70352c
URL:http://11tict4sd.sched.com/event/0a96a369443d6fe35d0705e11b70352c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:Process of Imprecise Data using New Methods of Neutrosophic Set
DESCRIPTION:Authors - Soumitra De\, Jaydev Mishra Abstract - In this paper\, a new method is focused to handle indeterminacy part of an imprecise data using neutrosophic set to generate proper constructive message. This method is capable to handle imprecise part of a neutrosophic data. Earlier no uncertain data set was handled this indeterminacy part of any uncertain data. We have drawn an output using this new method of any patient related data set that has suffering from disease. Vague logic is unable to process indeterminacy part. So only neutrosophic set is handled indeterminacy part of a imprecise data to outcome.
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:1af49aa21c6fe1770849b27492f0f465
URL:http://11tict4sd.sched.com/event/1af49aa21c6fe1770849b27492f0f465
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:Smartness and Sustainability in Footwear
DESCRIPTION:Authors - D.K. Chaturvedi\, Nisha Verma Abstract - The technological intervention in our day-to-day life\, impacted our social\, physical\, psychological and spiritual domains. The shoes are not untouchable from the latest innovations. The footwear is an essential wear in present time. The technology is completely changed the footwear industry and the customer flavour. Now the customer is looking for customized\, smart footwear\, which is environment friendly. The present footwear is using polymer soles (i.e. PVC\, PU\, EVA or Rubber)\, chemical based adhesives and animal leather upper material\, which are not eco-friendly. A lot of research is going on to make sustainable and eco-friendly shoes with different biodegradable materials. The footwear industry is embracing both smartness and sustainability\, blending technological innovation with eco-conscious practices. The smart footwear uses many types of sensors/IoTs to include different features of smartness. This paper discusses some innovations in footwear technology\, important issues\, challenges and their remedies related to design and development of smart sustainable footwear.
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:e147f8f61b551aa1c5d7baf53b91f367
URL:http://11tict4sd.sched.com/event/e147f8f61b551aa1c5d7baf53b91f367
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:THE MOTHERHOOD BIAS: ANALYZING PROMOTIONS AND LEADERSHIP PROSPECTS FOR WOMEN POST-MATERNITY IN INDIA
DESCRIPTION:Authors - Kashinadh.S\, Dhanush Devaraj\, Yedukrishnan VS Abstract - Food safety and nutritional transparency are essential for public health\, particularly as diet-related illnesses like obesity and diabetes rise. The Food Safety and Standards Authority of India (FSSAI) introduced a menu labeling policy in 2020\, requiring restaurant chains to display calorie counts and nutritional information.The consumer awareness on the menu labelling is poor\, and compliance is still low among restaurants. FSSAI Food Safety Connect app\, which is designed to help with grievance redressal was having some negetive shades because of the complaints registered and reviews posted . This study employs stakeholder interviews\, compliance audits\, and sentiment analysis to evaluate how effective the policy is. The findings indicate that the compliance is lacking because of financial barriers and enforcement is also lacking. This study recommends implementing chatbot-driven grievance resolution\, using QR codes for digital menus\, and leveraging AI for compliance tracking as strategies to boost adherence. These solutions leads to the Sustainable Development Goals (SDGs 3 and 12) by enabling customers to make informed decisions while also promoting food safety. Menu labeling will become a more effective public health tool if we can improve digital enforcement in food industry.
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:a86266f19a6c1ad6588cd642a014aad8
URL:http://11tict4sd.sched.com/event/a86266f19a6c1ad6588cd642a014aad8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:Understanding Unsupervised Learning Using Hierarchical Clustering
DESCRIPTION:Authors - Vanishree Pabalkar\, Anuja Bokhare\, Reena (Mahapatra) Lenka\, Jaya Chitranshi Abstract - [1] Crime is one of the most worrying and widespread issues of our society. Criminal deterrence is essential for people safety. The overall crime inference when assessed\, does help to keep a record of crime and assist in avoiding adversities. The aim of the study is to examine patterns in data acquired over time. Criminal violations offend humanity\, and it should be prosecuted as soon as possible. Criminology is the scientific method of understanding crime and the motives behind the act. Criminology is an interdisciplinary area which gathers data and conducts further study into such offenses. While there is such a large amount of data on criminal activities\, identifying and preventing crimes is one of the most difficult tasks. It is imperative to develop approaches and procedures for predicting future crimes and taking appropriate preventative steps. Cluster analysis includes breaking down huge data to minute groups with similar or identical characteristics. We can evaluate and assess methods\, structures\, layouts and interactions that are present in the data using visualization tools\, so as to help uncover interesting areas and acceptable parameters for future analysis.
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:035443fbe7439146b5dd74c4cba22d4b
URL:http://11tict4sd.sched.com/event/035443fbe7439146b5dd74c4cba22d4b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:Benchmarking gRPC protocol on various virtualization technologies
DESCRIPTION:Authors - Akshar Sodankoor\, Avanish Shenoy\, B Monish Moger\, Mohnish Gowda\, Prafullata Kiran Auradkar\, Subramaniam Kalambur Abstract - Virtualization is essential for efficient resource utilization in cloud and development environments. With the growing adoption of gRPC as a Remote Procedure Call (RPC) framework\, evaluating its performance across different virtualization technologies has become crucial. This work benchmarks the performance of four gRPC call types: unary\, client-streaming\, server-streaming and bi-streaming\, across four lightweight virtualization technologies. Docker\, gVisor\, Firecracker and nanos unikernel. The analysis examines CPU utilization\, memory utilization and network capabilities to provide a comprehensive comparison. The results show that docker delivers the best performance across all metrics. Firecracker shows comparable latency performance to docker\, but consumes higher memory. Nanos unikernel exhibits CPU utilization similar to that of docker\, but has the highest latencies in all cases except unary gRPC call. gVisor exhibits the lowest CPU utilization under heavier workloads and also has the lowest latencies for client-streaming and server-streaming gRPC calls.
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:3e6c8e61cf0521ecc2ef8efe6bce40d0
URL:http://11tict4sd.sched.com/event/3e6c8e61cf0521ecc2ef8efe6bce40d0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:Deep Learning and Beyond: Innovations\, Limitations\, and the Road Ahead
DESCRIPTION:Authors - Aryan Goyat\, Aditya Maan\, Vimmi Malhotra Abstract - Deep learning has transformed artificial intelligence and enabled major breakthroughs in applications like computer vision\, natural language processing\, medicine\, cybersecurity\, and robotics. Through the use of deep neural networks\, it enables automatic feature learning and exceeds machine learning-based methods in accuracy and flexibility. Challenges including excessive computational expense\, uninterpretable nature\, and ethics are still major hurdles to its widespread application. This article discusses the development and applications of deep learning and presents new research directions that seek to overcome its limitations. Federated and decentralized learning methods improve security and privacy by enabling collaborative model training without raw data sharing. Explainable AI (XAI) techniques\, including SHAP and LIME\, enhance the interpretability of deep learning models\, making their decision-making more transparent. In addition\, energy-efficient deep learning methods\, such as model pruning\, quantization\, and neural architecture search (NAS)\, are being designed to minimize computational and environmental expenses. The emergence of self supervised learning further minimizes dependence on labeled data\, making deep learning more feasible across domains. Future developments will center on the fusion of deep learning with reinforcement learning\, symbolic AI\, and evolutionary algorithms to build more generalizable and efficient systems. These technologies will power the next wave of intelligent\, ethical\, and sustainable AI solutions.
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:852951fda16b8df6792d8f0257ba6150
URL:http://11tict4sd.sched.com/event/852951fda16b8df6792d8f0257ba6150
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:Design considerations while building a multi user multi access web app : A case study on automated weather stations
DESCRIPTION:Authors - Anita Agrawal\, Ruhi Panjwani\, Aditya Mallik Abstract - This paper explores the design and implementation of a multi-user\, multi-access web application tailored specifically for automated weather stations (AWS). By examining real-world scenarios and user interactions\, we identify key design considerations\, including system performance\, security\, and scalability. The study aims to provide practical insights for developers to create efficient and user-friendly web applications that effectively handle large-scale weather data and cater to various user access levels.
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:40d32de3f4ee957ecb605e26c09b08ed
URL:http://11tict4sd.sched.com/event/40d32de3f4ee957ecb605e26c09b08ed
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:DevTogether: A Real-Time Collaborative Coding Platform
DESCRIPTION:Authors - Krishna Shirsath\, Abdullah Ansari\, Riyaz Memon\, Phiroj Shaikh Abstract - Real-time collaboration is essential for modern software development\, enabling developers to work together seamlessly from different locations. This paper presents DevTogether\, a collaborative coding platform that facilitates efficient teamwork through live code editing\, customizable collaboration sessions\, integrated chat\, a collaborative drawing board for design prototyping\, and real-time video meetings powered by WebRTC. The platform incorporates an intelligent code assistant using the Gemini API\, along with an autosave mechanism to ensure workflow continuity. Built using the MERN stack\, DevTogether emphasizes scalability\, low latency\, and responsive performance while addressing synchronization conflicts and communication challenges. Experimental evaluations and simulated performance metrics underscore its effectiveness compared to similar platforms.
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:3006b43d7fa95f29b38446af8782589f
URL:http://11tict4sd.sched.com/event/3006b43d7fa95f29b38446af8782589f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:Empowering Communities Through Democratic Crowdfunding Decisions
DESCRIPTION:Authors - Arnav Shukla\, Subhashree Choudhury\, Logeshwaran R. Abstract - One of the key limitations of any crowdfunding platform that uses blockchain technology is the lack of transparency regarding fund usage by campaign creators after receiving donations. The current paper addresses this issue. For this investigation\, we conducted a detailed examination of the relevant literature on decentralized applications (DApps) and the use of smart contracts to automate administrative tasks. We identified a significant gap in existing platforms: the ambiguity surrounding post-donation and how fund management can be unfair\, which blockchain alone does not address effectively. Our findings highlight the strengths of blockchain security and automation capabilities\, which ensure a safe and trustworthy environment for users. Our proposed solution integrates a decentralized voting model and a collusion prevention algorithm called EigenTrust to empower donors with a participatory role in decision-making processes\, eliminating the need for centralized authority. In this study\, we implement and evaluate a decentralized voting model that uses specific techniques to prevent any attacks or chances of misuse of powers that would then empower donors to participate in critical campaign decisions\, enhancing trust and satisfaction by allowing them to verify the responsible use of their contributions. By reducing uncertainty around fund allocation\, our model increases a more engaging and end-to-end secured donor experience\, encouraging donations and supporting the long-term success of social crowdfunding projects. In all\, this paper presents a novel approach to increase crowdfunding platforms using blockchain technology with a voting model paired with collusion prevention to address the issue.
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:8e5b76a5552bf15ae621bdf0ce7e1d64
URL:http://11tict4sd.sched.com/event/8e5b76a5552bf15ae621bdf0ce7e1d64
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:Human Resources Blockchain Intelligence Recommendation System
DESCRIPTION:Authors - P S Sree Harsha\, Harshitha S\, Ayush Sisodiya\, M P Deepti\, Sarasvathi V Abstract - Concerning the Conventional recruitment methods\, which has many challenges such as the poor matching of the candidates to the right requirements\, issues of transparency and issues of privacy in dealing with sensitive information. This leads to the hiring of candidates who are not qualified to meet their requirements and compromise the organizational data. To solve these problems\, Human Resource Blockchain Intelligence Recommendation System (HRBIRS) is proposed to use Blockchain and Recommendation system technologies for getting an efficient recruitment process. Blockchain is evolving technology providing transparent and secure data. Privacy issues are resolved by decentralized architecture. The Hybrid recommendation system makes recruitment effective by determining the qualifications\, skills and experience of the job seekers relevant to certain job recruitments posted by the HR. One of the most attractive features of HRBIRS is the peer approval and endorsement systems within the organization without bias to a particular candidate. This paper provides detailed information on the architectural design\, implementation and its performance features and demonstrates how this system could be beneficial for recruitment processes throughout the organization by providing data security and enhancing the effectiveness of selecting the right candidate.
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:517d6f5132bfd3fd7231648e52fc0c54
URL:http://11tict4sd.sched.com/event/517d6f5132bfd3fd7231648e52fc0c54
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:Intelligent Communication Strategies for Digital Loyalty Program Participation: A Study on Promotional Efficiency in Fuel Retail Industry
DESCRIPTION:Authors - Arunangshu Giri\, Dipanwita Chakrabarty\, Manash Routray Abstract - The present study emphasized on how enrollment of loyalty programs at fuel retail outlets get enhanced through digital communication and intelligent promotional strategies. The study has evaluated the three major dimensions like participation intention and promotional efficiency to understand the efficacy of the loyalty programs organized by different fuel retail outlets. 454 Indian customers were interviewed over a three months period using a structured questionnaire. Cross-sectional descriptive research design was followed for the study. Both qualitative and quantitative analysis was done using NVivo and SPSS-28 software. The study revealed that customer participation in loyalty programs was highly influenced by flexible redemption options\, digital reward system and exclusive benefits. Again\, the study has shown how staff knowledge\, promotional policies\, customer loyalty and digital engagement influence promotional effectiveness. The study acknowledged the pivotal role of intelligent communication strategies in enrichment of customer adaptability towards digitized loyalty programs. Establishing a seamless communication between the customers and digital platforms along with effective staff training can be prudential for optimal customer engagement\, sustainability and loyalty.
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:4a9cc4603f8fb811a20e9143ed0901b5
URL:http://11tict4sd.sched.com/event/4a9cc4603f8fb811a20e9143ed0901b5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:Multimodal playlist recommendation system
DESCRIPTION:Authors - Lalithya Govardhan\, Shalini M S\, Gagan Deep P S Abstract - This work shows an extensive multimodal system of mood detection and customized playlist recommendation based on EEG\, GSR\, and face recognition. Brainwave activity for emotional evaluation is sensed by EEG electrodes\, while GSR sensors provide skin conductance\, heart rate variability\, and temperature values for physiological behavior. Facial behavior with emotional facial expressions is determined through facial landmarks. Preprocessing entails Butterworth filters for EEG frequency bands\, GSR data normalization\, and facial feature extraction from a pretrained model (FER) to track eyebrow position\, mouth curvature\, and eye openness. EEG features are examined using frequency domain analysis\, whereas GSR and facial features are classified using Random Forest. To increase precision\, a fusion model aggregates predictions by weighted averaging or majority voting\, with EEG assigned the greatest weight due to its high correlation with mood. After determining the emotional state\, a suitable playlist is suggested: energetic songs for happiness\, relaxing music for stress\, comforting songs for sadness\, and relaxing music for relaxation. This feature-based recommendation system enhances personalization through the use of features like tempo\, genre\, and mood to provide a dynamic and interactive listening experience for the user
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:c4c43b67bca026dc725c3084fcf2d4fa
URL:http://11tict4sd.sched.com/event/c4c43b67bca026dc725c3084fcf2d4fa
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:Speech Based Robust Telugu Grocery Items Identification Using PLP and GMM
DESCRIPTION:Authors - A. Revathi\, A. Sunidhar Reddy\, Geetika Alapati\, R. Pranay Abstract - This paper presents the performance of the grocery identification system concerning Telugu grocery items\, considering both native and non-native speakers. Speech recognition for Telugu groceries presents a unique challenge due to variations in pronunciation\, accent\, and noise conditions. This study explores the implementation of a Gaussian mixture model (GMM) classifier in conjunction with rasta-perceptual linear prediction (RASTA-PLP) features to enhance the accuracy of Telugu grocery identification. Rasta-PLP effectively captures robust speech features by suppressing unwanted spectral variations\, while GMM provides a probabilistic framework for classification. The proposed system is trained on a dataset comprising commonly used Telugu grocery names and evaluated under diverse acoustic environments. Experimental results demonstrate improved recognition performance\, showcasing the effectiveness of RASTA-PLP in feature extraction and GMM in classification. This work contributes to developing efficient speech-based interfaces for regional language applications\, facilitating voice-driven grocery identification systems. The recognition accuracy of the proposed system is approximately 99%\, ensuring high reliability in real-world applications. This technology benefits society by aiding visually impaired individuals and non-Telugu speakers in grocery identification\, enhancing accessibility and convenience. By enabling seamless voice-based interaction\, promotes inclusivity and improves social equity through technological advancement.
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:ac6141db948432a8603e961c8d9e27ba
URL:http://11tict4sd.sched.com/event/ac6141db948432a8603e961c8d9e27ba
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:Speech-based Robust Speaker Authentication Against voice conversion-based spoofing attacks
DESCRIPTION:Authors - A.Revathi\, Reethikaa Vallinayagam\, S. Sivaranjani\, A. Deepthi Abstract - This research work introduces a system for identifying genuine speech and recorded (replay) speech through Mel-Frequency Cepstral Coefficients extraction and uses k-means clustering for classification purposes. Speech features obtained from various speakers undergo normalization procedures before receiving cluster assignment during training sessions. During the testing phase speaker identification depends on measuring the distance between input features against cluster centroids. The confusion matrix indicates system performance by showing correct genuine speech detection through high diagonal values yet exhibiting lower off-diagonal values to indicate possible attacks based on recorded speech. Auto-correlation together with cross-correlation serve to evaluate the similarities between speakers. Strong recognition of the same speaker is indicated by high auto-correlation values but weak cross-correlation values demonstrate effective differentiation between different speakers. The AVSpoof dataset serves as the experimental foundation because it includes ten recording subjects who are distributed between five male and five female speakers. The acceptance and accuracy evaluation for the system happens through testing samples which proves its ability to recognize genuine speech from recordings as well as identify distinct speakers properly.
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:314afc0c9d08f0007a17fd91741dedaa
URL:http://11tict4sd.sched.com/event/314afc0c9d08f0007a17fd91741dedaa
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:A Novel Explainable Hybrid Framework for Temporal Risk Prediction and Anomaly Detection in Diabetic Disease using Multimodal Health Data
DESCRIPTION:Authors - K Shailaja\, Shirina Samreen Abstract - Diabetes mellitus is becoming an increasingly critical health issue across globe that demands early diagnosis and continuous monitoring. Although traditional machine learning models have been employed to predict diabetes\, they frequently lack clarity and have difficulty in identifying uncommon or rare patient cases. This research proposes a novel explainable hybrid framework that combines deep learning-based temporal modeling with classical machine learning and unsupervised anomaly detection. The framework utilizes multimodal data sources\, including static clinical features\, time-series Electronic Health Records (HER) and wearable sensor data\, for robust diabetic risk assessment. Explainability is achieved using SHAP (SHapley Additive exPlanations) and counterfactual reasoning to provide both global and local interpretability. In addition\, an autoencoder-based novelty detection module identifies patients whose health patterns deviate significantly from the normal. Experimental results on benchmark datasets demonstrate improved prediction accuracy\, better anomaly identification and enhanced interpretability making the model suitable for real-world clinical decision support systems.
CATEGORIES:VIRTUAL ROOM 7E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:b880769a567daaadceb61d775086354c
URL:http://11tict4sd.sched.com/event/b880769a567daaadceb61d775086354c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:Access Control Mechanism in Internet of Things: A Comprehensive Survey
DESCRIPTION:Authors - Vishal Ambhore\, Ketki Kshirsagar\, Parikshit Mahalle Abstract - This survey paper thoroughly examines the landscape of access control within the context of the Internet of Things (IoT). With the rapid expansion of IoT technologies\, ensuring secure access to resources and data has become a critical concern. We thus present a very wide-ranging review of existing literature\, doing an all-round analysis of current access control mechanisms in order to discuss their strengths\, weaknesses\, and applicability to IoT environments. The result of our investigation shows few major gaps and challenges\, such as issues of scalability\, interoperability concerns\, and a call for access policies that account for contexts. Taking this into account\, we introduce new approaches and improvements to positively address these shortcomings and boost the security and efficiency of access control in IoT systems. By integrating findings from multi study research endeavors\, we provide new points of view and approaches toward IoT security improvement. Furthermore\, we present potential future research directions and challenges to guide development for more resilient and adaptive access control solutions in IoT ecosystems. The paper is a ready reference for researchers\, practitioners\, and policymakers who want to bolster the security posture of IoT deployments and mitigate newly emerging cyber threats effectively.
CATEGORIES:VIRTUAL ROOM 7E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:5a836d93eeefe6078febcabd867fb36b
URL:http://11tict4sd.sched.com/event/5a836d93eeefe6078febcabd867fb36b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:E-Agriculture and Its Applications in ICT for Sustainable Agricultural Development
DESCRIPTION:Authors - Garima Ahuja\, Heena Hooda\, Vimmi Malhotra Abstract - The combination of agriculture and Information and Communication Technologies (ICT)\, known as e- Agriculture\, is changing farming practices\, especially in regions facing limited resources and climate challenges. e-Agriculture includes digital tools such as mobile-based advisory platforms\, Geographic Information Systems (GIS)\, Internet of Things (IoT)\, Artificial Intelligence (AI)\, and data analytics. These technologies provide timely information\, improve the use of inputs\, and increase access to markets and financial services [1]. Applications of ICT in farming across different regions show improvements in crop planning\, yield monitoring\, and risk reduction. Evidence from India\, Kenya\, and the Netherlands highlights positive results where digital tools are adapted to local needs. Improvements include better harvest management\, lower post-harvest losses\, and increased climate resilience [3]. Despite these benefits\, challenges such as poor rural connectivity\, low digital skills\, and limited policy support continue to affect the full use of such technologies. Scaling e-Agriculture requires affordable access\, local training\, and supportive ecosystems. Broader adoption can support sustainable farming systems and contribute to goals such as food security\, poverty reduction\, and environmental protection.
CATEGORIES:VIRTUAL ROOM 7E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:10e505ae581152df79401a8d4697f86e
URL:http://11tict4sd.sched.com/event/10e505ae581152df79401a8d4697f86e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:Enhancing Healthcare Data Security with Quantum-Safe Cryptographic Techniques: E91 Protocol and AES-CBC Integration
DESCRIPTION:Authors - Khloe Bhel\, Krutthika Hirebasur Krishnappa Abstract - Healthcare data growth at an exponential rate together with rising cyber threats require sophisticated cryptographic systems to protect Electronic Health Records (EHRs) from unauthorized access and data tampering. The research develops a cryptographic system which uses simulated quantum key distribution through the E91 protocol to generate symmetric encryption keys that is encrypted using Advanced Encryption Standard (AES) in Cipher Block Chaining (CBC) mode to protect healthcare data. The system operates by using IBM Qiskit’s AerSimulator backend to create entangled qubit pairs for deriving a quantum-safe key between two communicating parties. The entropy measurement of the generated key approaches the maximum value of randomness which provides effective protection against brute-force attacks. The AES encryption process using achieves rate of approximately 6.58 MB/sec during encryption operations and 9.34 MB/sec during decryption operations. The proposed method demonstrates efficient computation and deployment potential for healthcare applications with limited resources including edge-based IoT medical devices and federated learning systems. Security analyses show that this approach gives a good protection against both classical attackers and near-term quantum attackers. The research shows how post-quantum cryptographic methods can be practically used to protect future healthcare systems.
CATEGORIES:VIRTUAL ROOM 7E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:55f8f67f775f5aefdcf39fa9920146d8
URL:http://11tict4sd.sched.com/event/55f8f67f775f5aefdcf39fa9920146d8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:Exploratory Data Analysis of Alzheimer’s Disease: Risk Patterns and Regional Impact
DESCRIPTION:Authors - Suprit V. Hatti\, P. G. Sunitha Hiremath\, Manohar Madgi\, Neha Tarannum Pendari Abstract - This study presents a comprehensive analysis of the factors influencing cognitive decline in Alzheimer’s patients in the USA. The objective is to examine cognitive decline and dementia prevalence across various U.S. regions\, focusing on gender\, age\, and race/ethnicity disparities. Using data from the Behavioral Risk Factor Surveillance System (BRFSS)\, collected from 2015 to 2021\, 59 locations were categorized into Northeast\, Midwest\, Southeast\, Southwest\, and West regions. The original dataset contained 31 attributes. The analysis included year-wise trend examination\, gender-wise\, age-wise\, and race-wise distribution assessments\, and identification of key risk factors. The Midwest (21.98%) and West (21.83%) showed higher cognitive decline rates\, with significant disparities across demographics. The ‘Overall’ age group showed the highest prevalence at 8.54%. Females were most affected\, with diabetes\, asthma\, arthritis\, depression\, and cardiovascular diseases being the most correlated comorbidities.
CATEGORIES:VIRTUAL ROOM 7E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:0c17bb4a40ad30d3475a37d4cb40f7a6
URL:http://11tict4sd.sched.com/event/0c17bb4a40ad30d3475a37d4cb40f7a6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:Fake Review Detection using LSTM and BERT
DESCRIPTION:Authors - Reshma Y Totare\, Anushka Kurandale\, Sakshi Kuyte\, Kiran Mane\, Snehal Nale Abstract - With the increasing reliance on online reviews across various digital platforms\, user feedback has become a vital factor in influencing public perception and decision-making. Since users cannot physically verify products or services online\, they often depend on reviews to assess quality and credibility. This dependency has led to a rise in deceptive practices\, where fake reviews are used to mislead audiences—either by promoting certain offerings or undermining competitors. Detecting such fraudulent content presents a significant challenge in the field of natural language processing (NLP)\, due to the subtle and human-like nature of these reviews. In this project\, we present an approach for fake review detection using a deep learning model that combines Long Short-Term Memory (LSTM) networks with Bidirectional Encoder Representations from Transformers (BERT). Our model utilizes LSTM’s ability to capture long-range dependencies along with BERT’s contextual language understanding to enhance detection accuracy. To improve practicality and trustworthiness\, we incorporate several additional features plugin support for easy integration into various review-based platforms\, multilingual capability to handle reviews in different languages\, and LIME (Local Interpretable Model-agnostic Explanations) to provide word-level interpretability of predictions. We evaluate our model on publicly available datasets containing both real and fake reviews\, and the results demonstrate that our LSTM-BERT approach significantly outperforms traditional machine learning techniques. This work contributes to the growing efforts in combating misinformation and enhancing the credibility of online content across diverse platforms.
CATEGORIES:VIRTUAL ROOM 7E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:2a7f81d0525656da86675e9b7f150c7c
URL:http://11tict4sd.sched.com/event/2a7f81d0525656da86675e9b7f150c7c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:IoT-Based Crime Prevention System: A Modern Surveillance Approach
DESCRIPTION:Authors - Ajay Talele\, Siddhi Shingate\, Om Gaikwad\, Mahek Sayyad\, Sai More\, Pooja Nanaware\, Ashwini Borole\, Pratiksha Bile\, Bharat Bangar Abstract - Crime prevention is being transformed by the Internet of Things (IoT) through proactive\, smart solutions. Abstract This paper presents some techniques at the time of crime prevention by the use of IoT. Through the use of IoT driven surveillance systems\, integrated smart sensors\, real-time data aggregation and analytics\, and automated alerts to emergency services and law enforcement\, crime prevention\, identification\, and response are improved significantly. AI in Smart City: The study also calls out real-time examples such as smart cities establishing AI powered CCTV\, IoT-enabled access control\, predictive policing[1] The contemporary world faces the overwhelming challenge of crime. Criminal activity exists in almost every country and the statistics for certain nations are quite alarming. The advancement of technology has been one of the most significant factors in the development of crime control and prevention measures\, such as drone surveillance\, GPS tracking and tagging\, closed circuit television cameras\, etc. Technological advances such as the IoT (Internet of Things)\, Machine Learning\, and Edge Computing call for the attention of the scientific world in regards to the question: how can they utilize these innovations for the purpose of minimizing criminal activities globally? In conclusion\, the research results reflect on the employability of AI and IoT technologies in reinforcing security and obstructing offences against state and equally\, challenges and ethical issues that accompany their employments [2]
CATEGORIES:VIRTUAL ROOM 7E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:b6aa7f08b95d413cd9a95364bf732c21
URL:http://11tict4sd.sched.com/event/b6aa7f08b95d413cd9a95364bf732c21
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:On-Location ADR Recording: Advancements and Challenges in Real Time Dialogue Replacement
DESCRIPTION:Authors - Sambhram Pattanayak\, Archana Paswan Abstract - The evolving landscape of dialogue replacement in film and television production is examined\, focusing on advancements and challenges in on-location Automated Dialogue Replacement (ADR) recording and the emergence of real-time dialogue replacement technologies. Advancements in portable recording equipment\, microphone technology optimized for field use\, and specialized software solutions have significantly enhanced the feasibility and quality of ADR conducted outside traditional studio settings. However\, on-location ADR presents unique challenges related to acoustic control\, environmental noise\, actor availability\, logistical complexities\, and technical limitations. The paper also examines the current state and potential impact of real-time dialogue replacement technologies\, which offer immediate solutions and increased flexibility on set and in post-production. Case studies illustrate the practical applications of on-location ADR\, while the discussion of future trends highlights the anticipated integration of artificial intelligence\, advancements in hardware and software\, the influence of remote collaboration tools\, and the potential for incorporating virtual and augmented reality into dialogue replacement workflows. This analysis underscores the ongoing evolution of audio post-production techniques aimed at enhancing efficiency and creative possibilities in modern filmmaking. The research findings presented in this study underscore the importance of ADR in film production and introduce novel approaches and technologies. These advancements provide filmmakers with state-of-the-art tools and techniques\, revolutionizing their creative processes and enhancing the quality of their productions.
CATEGORIES:VIRTUAL ROOM 7E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:e3096299aa72294832dee36db7586429
URL:http://11tict4sd.sched.com/event/e3096299aa72294832dee36db7586429
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:Review of Routing Protocols to Improve Quality of Service in SDWSN
DESCRIPTION:Authors - Mario Castro Romero\, Carlos Ernesto Carrillo Arellano\, Leonardo Daniel Sánchez Martínez Abstract - Software-defined wireless sensor networks are emerging as a transformative technology for industrial\, research\, and IoT applications leveraging their control-data plane separation. This paper analyzes routing protocols\, emphasizing their advantages\, limitations\, and QoS-impacting factors (e.g.\, latency\, bandwidth\, among others). Through a systematic review\, we identify innovative solutions to enhance QoS in SDWSNs\, addressing challenges like energy efficiency and dynamic adaptability. Previous results demonstrate that centralized routing and machine learning-based algorithms significantly improve reliability in critical applications.
CATEGORIES:VIRTUAL ROOM 7E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:0a78e24e3f46a5c183c4d9198f087acb
URL:http://11tict4sd.sched.com/event/0a78e24e3f46a5c183c4d9198f087acb
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T040000Z
DTEND:20260826T060000Z
SUMMARY:Waste Classification with Convolutional Neural Networks: A Comparative Study of Various Models
DESCRIPTION:Authors - Manpreet Kour\, Naman Jain\, Neeraj Gupta\, Geetanjali Bhola Abstract - Waste recycling is important both in the global economy and in the global climate as a whole. As a result\, classification of recyclable waste has become a critical goal for humanity\, and deep learning models have important potential to fulfill this task. In this study\, six advanced folding network models of neural networks - EfficientNetV2L\, EfficientNetB1\, EfficientNetB0\, MobileNetV2\, ResNet50\, and VGG16\, were compared for the effects of the garbage classification task. The results show that EfficienctNetB0 gave better performance than the other models. Furthermore\, data augmentation techniques were used to improve classification accuracy\, as data records contained a limited number of samples. Notably\, MobileNetV2 not only achieved competitive accuracy\, but also became a green choice for its low carbon emissions.
CATEGORIES:VIRTUAL ROOM 7E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:56b1ae1e9cef677c033047396e537357
URL:http://11tict4sd.sched.com/event/56b1ae1e9cef677c033047396e537357
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T060000Z
DTEND:20260826T060200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:6458a57cc5ee0c67e883997428612fb6
URL:http://11tict4sd.sched.com/event/6458a57cc5ee0c67e883997428612fb6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T060000Z
DTEND:20260826T060200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:4e6bb77381181e03db605774770c4156
URL:http://11tict4sd.sched.com/event/4e6bb77381181e03db605774770c4156
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T060000Z
DTEND:20260826T060200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:563cfd341f8de952ecda3b5e98a875bc
URL:http://11tict4sd.sched.com/event/563cfd341f8de952ecda3b5e98a875bc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T060000Z
DTEND:20260826T060200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:8504bb5d200de806dcc6bdd0a101119d
URL:http://11tict4sd.sched.com/event/8504bb5d200de806dcc6bdd0a101119d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T060000Z
DTEND:20260826T060200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 7E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:0e33cf009eaef775d300f12f459a51d6
URL:http://11tict4sd.sched.com/event/0e33cf009eaef775d300f12f459a51d6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T060200Z
DTEND:20260826T060500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:97aac7f82f271971e3022fd2bbe73d5d
URL:http://11tict4sd.sched.com/event/97aac7f82f271971e3022fd2bbe73d5d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T060200Z
DTEND:20260826T060500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:f026ffd5aacf11ecc081f2fc183c3232
URL:http://11tict4sd.sched.com/event/f026ffd5aacf11ecc081f2fc183c3232
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T060200Z
DTEND:20260826T060500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:cd19438a387f97387f77d70406244e3d
URL:http://11tict4sd.sched.com/event/cd19438a387f97387f77d70406244e3d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T060200Z
DTEND:20260826T060500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:63ebb7a79b35b2ec4f3be1b7f5c7c67f
URL:http://11tict4sd.sched.com/event/63ebb7a79b35b2ec4f3be1b7f5c7c67f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T060200Z
DTEND:20260826T060500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 7E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:ce7adc1a81f3313bf153c0249fd97818
URL:http://11tict4sd.sched.com/event/ce7adc1a81f3313bf153c0249fd97818
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T065800Z
DTEND:20260826T070000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 8A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:3091be016753a865b20a0d1aa78e8db7
URL:http://11tict4sd.sched.com/event/3091be016753a865b20a0d1aa78e8db7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T065800Z
DTEND:20260826T070000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 8B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:275a6b84dfcfccf54558456d177c3d0b
URL:http://11tict4sd.sched.com/event/275a6b84dfcfccf54558456d177c3d0b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T065800Z
DTEND:20260826T070000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 8C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:abb345a2ddec88fe3416c97b1a6f7f6b
URL:http://11tict4sd.sched.com/event/abb345a2ddec88fe3416c97b1a6f7f6b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T065800Z
DTEND:20260826T070000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 8D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:b70a39ea10dbd8bf3bf7e3daa2f47d53
URL:http://11tict4sd.sched.com/event/b70a39ea10dbd8bf3bf7e3daa2f47d53
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T065800Z
DTEND:20260826T070000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 8E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:f6162af3b6cab27f03dbbb711d1cd29d
URL:http://11tict4sd.sched.com/event/f6162af3b6cab27f03dbbb711d1cd29d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:A Comprehensive Investigation and Implementation of Lossless Image Compression Techniques for Social Media Network
DESCRIPTION:Authors - Sanchit Prashant Joshi\, Parth Atul Gargate\, Yash Prabhakar Apotikar\, Rupesh C Jaiswal\, Mousami V. Munot Abstract - Social media platforms operate at top speeds when transferring image-based data. The shared and posted images and videos on WhatsApp and Instagram consume the majority of network resources. Lossless compression techniques were applied to images while maintaining image quality throughout data storage and transmission processes because this fundamental method produces perfect information reconstruction after decompression. The research evaluates Predictive Coding (DPCM) and Context-Based Coding and Arithmetic Coding and Dictionary-Based Techniques (LZW) and Block-Based Compression through analyses of their efficiency metrics and computational complexity and practical usage. New developments in JPEG2000 and LZW compression have led to increase speed and efficiency through Parallel Symbol Encoding in Arithmetic Coding and Compression Ratio Prediction. The Optimized Run-Length Encoding (ORLE) system uses dynamic compression approach adaptation according to image orientation to enhance its flexibility. The speed of real-time applications increases remarkably when using FPGA implementations. This survey examines trade-offs among compression ratio together with computational expense and suitable data sets to perform an evaluation between classical and modern methods. Future development in lossless data and image compression relies on emerging trends such as AI-driven compression models as well as hardware-accelerated algorithms and hybrid frameworksDeflate is the fastest compression technique taking about 0.043 seconds\, with a Maximum compression ratio of 26.66 given by WebP.
CATEGORIES:VIRTUAL ROOM 8A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:3371eb444c108af2074085918660d5d5
URL:http://11tict4sd.sched.com/event/3371eb444c108af2074085918660d5d5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:AI for Personalized Financial Advisory
DESCRIPTION:Authors - Anuj Sudhir Kulkarni\, Rama Gaikwad\, Prathamesh Zad\, Sai Lahane\, Shivam Shelke\, Saurav Jadhav Abstract - The rapid development of artificial intelligence (AI) is changing the financial landscape. It offers innovative solutions to optimize personal financial management and advisory services. This research focuses on developing an AI-based platform to improve financial decision-making by analyzing users' investments to provide insights into financial health. Key features include Portfolio Visualizer\, Risk Radar\, Fundamental Analyst\, Price Forecaster and Financial advisory services ensure a comprehensive view of financial planning\, emphasizing AI frameworks and interpretable applications. To build user trust and transparency\, challenges such as mitigating bias are explored. Real-time problem solving and fine-grained scalability with a commitment to accessibility and precision This research highlights the ability of AI to democratize financial advisory services. and overcome limitations in the current system. Future directions include real-time risk assessment. Advanced portfolio management and innovative AI integration for dynamic market simulation.
CATEGORIES:VIRTUAL ROOM 8A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:8b0813cebb07809f8dd305d8e571a3ff
URL:http://11tict4sd.sched.com/event/8b0813cebb07809f8dd305d8e571a3ff
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:Application of YOLO in Indian driving conditions
DESCRIPTION:Authors - Mohan S G\, Abhilash K Raj\, Nayana S A\, Pradhaan S\, Rajendra Bhat Abstract - This paper explores the application of the You Only Look Once (YOLO) v11 model for real-time object detection in Indian road conditions\, addressing challenges posed by unconventional objects like animals\, autorickshaws\, carts\, and tractors. A dataset from dashcam and mobile footage was annotated using the Computer Vision Annotation Tool (CVAT) tool and combined with COCO to train YOLO v11. The model significantly improved detection accuracy\, increasing classes from 30 to 108. Its high accuracy and real-time performance make it suitable for autonomous vehicles and traffic monitoring in India.
CATEGORIES:VIRTUAL ROOM 8A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:3684911b087815953fcd20f6e3d68957
URL:http://11tict4sd.sched.com/event/3684911b087815953fcd20f6e3d68957
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:Classification of Plant Species Based on Leaf Veins
DESCRIPTION:Authors - Vani E S\, Gourav Subnani\, Prajwal Gupta\, Shivee Jaiswal\, Mihir Sahu Abstract - Plant species classification accuracy is crucial for biodiversity conservation and ecosystem monitoring. Traditional taxonomy-based methods\, which rely heavily on expert analysis\, can be inefficient and prone to errors\, particularly when processing large datasets. This study leverages deep learning and machine learning techniques to automate plant species identification\, with a strong focus on leaf vein morphology analysis. The proposed approach begins with preprocessing leaf images by converting them to grayscale\, extracting significant structural features\, and skeletonizing vein patterns. Key morphological characteristics\, including vein distributions\, textures\, and geometric attributes\, are then used as input for classification models. They use both sophisticated deep learning models like Convolutional Neural Networks (CNN) and more traditional machine learning approaches like Random Forest (RF)\, k-Nearest Neighbours (kNN)\, and Support Vector Machines (SVM). The Xception architecture\, known for its depth wise separable convolutions\, is particularly effective in capturing intricate vein structures\, enhancing classification accuracy. This automated system reduces the dependency on manual identification efforts\, making it scalable for large-scale biodiversity research. By integrating deep learning-driven analysis\, the proposed framework provides a robust and efficient solution for plant species classification\, aiding conservation initiatives and ecological studies.
CATEGORIES:VIRTUAL ROOM 8A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:d39e72e74d2f6bbbe9d9e76c0eafb504
URL:http://11tict4sd.sched.com/event/d39e72e74d2f6bbbe9d9e76c0eafb504
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:Code Generation for Machine Learning Models on Diverse Data Formats
DESCRIPTION:Authors - Sangita Lade\, Muhammad Parkar\, Shreyas Nagarkar\, Om Shintre\, Shivam Padalkar Abstract - The rapid evolution of machine learning (ML) has transformed industries by enabling automation\, prediction\, and optimization for complex real-world problems. However\, developing ML pipelines involves repetitive tasks such as data preparation\, model building\, and evaluation\, which are time-consuming and prone to errors. This paper introduces an automated system for generating ML code using Jinja2 templating and supervised MLbased feature prediction. The system analyzes 5000 ML code templates to extract parameters like data type\, preprocessing techniques\, model architecture\, and hyperparameters. A supervised ML model predicts missing parameters based on partial user input\, enabling dynamic code generation. The framework supports diverse data formats (tabular\, image\, text) and ML tasks (classification\, regression). Experimental results demonstrate high accuracy in parameter prediction and significant time savings (70-80% reduction in setup time). The system simplifies ML development\, reduces errors\, and accelerates experimentation\, making it accessible to researchers\, developers\, and students.
CATEGORIES:VIRTUAL ROOM 8A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:dc002d692a174ddf1c961deffa6f7aab
URL:http://11tict4sd.sched.com/event/dc002d692a174ddf1c961deffa6f7aab
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:DevLaunch: A Simple and Efficient Platform for Seamless Web Deployment
DESCRIPTION:Authors - Rohini T.V\, Srikrishna Adiga G\, Tejas C\, Sunil Mashyale\, Sunil Kumar C Abstract - DevLaunch is a cloud-native deployment platform purpose-built for MERN stack apps\, using AWS Amplify to make hosting and configuration easy. The platform provides real-time monitoring\, auto-resource provisioning\, and a CDN-tuned Next.js frontend to abstract away deployment nuances. In addition\, DevLaunch increases developer efficiency by reducing the need for manual setup and cutting deployment and debug times by 40% and 30%\, respectively. The auto-scaling architecture and simplicity of the system make it a secure and highly scalable way to deploy contemporary web applications.
CATEGORIES:VIRTUAL ROOM 8A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:af2ee5085b81dc73b652759ea743f895
URL:http://11tict4sd.sched.com/event/af2ee5085b81dc73b652759ea743f895
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:Medicinal Plant Classification using Machine Learning
DESCRIPTION:Authors - Namrata Jangam\, Nipun Jadhav\, Riya Chavan\, Priya Chavan\, Rutuja Surve Abstract - Time-honoured treatment has long relied on pharmaceutical plants as genuine remedies due to their bioactive compounds. With increasing demand for natural products and sustainable healthcare\, accurately identifying and classifying these plants is crucial. However\, distinguishing species is challenging due to similar physical traits and varying environmental conditions. Machine learning (ML) and deep learning (DL) have shown substantial ability in medicinal plant detection and classification by analysing large datasets and extracting subtle features. Image recognition techniques\, particularly convolutional neural networks (CNNs)\, can identify morphological traits like leaf size\, shape\, and texture for classification. Studies have demonstrated that CNN models can achieve up to 90% accuracy in medicinal plant identification\, enhancing the process for novel drug discovery and therapeutic applications.
CATEGORIES:VIRTUAL ROOM 8A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:85fb2e418cd61a7654a6a8c0dadeec6d
URL:http://11tict4sd.sched.com/event/85fb2e418cd61a7654a6a8c0dadeec6d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:PoseNet : A Novel YOLO-Driven Framework for Badminton Posture Detection and Correction
DESCRIPTION:Authors - Ananya Kini\, Saranya Rubini Abstract - In recent times\, there have been several advancements in computer vision and image processing\, and when combined with machine learning models\, is very helpful in posture recognition applications. Posture detection is a useful tool in sports and fitness\, as it helps people avoid injuries caused by poor alignment and achieve optimal posture in order to stay healthy. This paper reports on ”PoseNet : A Novel YOLODriven Framework for Badminton Posture Detection and Correction”\, which is a Python-based application that utilizes Roboflow for dataset construction\, annotation and augmentation\, YOLOv5 for custom training the model on the dataset\, and MediaPipe for giving corrective suggestions to the user. The novelty of this framework lies in its dual-stage architecture\, combining YOLOv5 for classification and MediaPipe for real-time correction\, specifically tailored for badminton. Additionally\, it leverages a badminton-specific dataset\, ensuring domain relevance and precise analysis. It predicts the stance that the player is planning to achieve and then tells whether the stance is correct based on their key points. The model obtained a high classification accuracy\, with mAP50 value of 96.2% and mAP50-95 value of 81.1%.
CATEGORIES:VIRTUAL ROOM 8A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:2a3171cf2d670ae04a39ca41fe26c861
URL:http://11tict4sd.sched.com/event/2a3171cf2d670ae04a39ca41fe26c861
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:Real-Time Cyber Incident Monitoring for Critical Information Infrastructure (CII) using Machine Learning and ELK Stack.
DESCRIPTION:Authors - K. V. Deshpande\, Sanskruti Parkhe\, Varad Pawar\, Vaishnavi Thorat\, Rutuja Bagad\, Priti R. Kale Abstract - In today's digital world\, cyberattacks targeting critical infrastructure pose a significant threat to government agencies and organizations. These attacks can disrupt essential services and compromise national security\, making it crucial to identify and respond to them quickly. This survey paper discusses the challenges faced in monitoring cyber threats and presents a proposed solution: a real-time cyberattack monitoring tool. This tool uses machine learning and web scraping to gather data from various online sources\, storing it in a structured format for easy access. By visualizing the collected data through an interactive dashboard\, cybersecurity teams can quickly identify and understand the nature of ongoing attacks. Additionally\, the system includes an alert mechanism that notifies teams of high-frequency attack patterns\, enabling prompt action. Overall\, this solution aims to enhance the ability of organizations to protect their critical infrastructure by providing timely insights and effective incident response strategies.
CATEGORIES:VIRTUAL ROOM 8A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:ba5cedc98bdd44ef7724e3fe4fee51b3
URL:http://11tict4sd.sched.com/event/ba5cedc98bdd44ef7724e3fe4fee51b3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:Towards Green Blockchain: A Review of Energy Efficient Protocols in Mobile Cryptographic Applications
DESCRIPTION:Authors - Garima Ratra\, Akriti Kumari\, Vimmi Malhotra Abstract - Blockchain has been widely adopted across numerous industries and applications to improve privacy and security factors. However\, with the rapid expansion of this technology\, its significant energy consumption has become a growing concern\, particularly in mobile cryptographic applications. Traditional consensus mechanisms\, such as Proof-of-Work (PoW)\, require substantial computational power\, making them unsuitable for mobile environments. This paper reviews innovative blockchain protocols that prioritize energy efficiency while ensuring security and decentralization. By analyzing alternative consensus mechanisms including Proof-of-Stake (PoS)\, Delegated Proof-of-Stake (DPoS) and energy optimization strategies\, we assess their effectiveness in lowering power consumption. The study highlights the role of sustainable blockchain approaches in enhancing mobile application efficiency with minimized environmental impact. This will help in increasing the energy efficiency and understanding the impact and applicability of blockchain by switching to greener systems.
CATEGORIES:VIRTUAL ROOM 8A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:a9bc112d3aec42ad697f7493135492c2
URL:http://11tict4sd.sched.com/event/a9bc112d3aec42ad697f7493135492c2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:A Novel Approach to Mitigate Information Spread on Social Networks
DESCRIPTION:Authors - Jishnu Prakash k\, Rithish S V\, Liz Maria Liyons\, Vijval Srinivasan\, Deepthi L R Abstract - The rapid spread of misinformation and rumors on social media platforms\, particularly Twitter\, poses significant risks to public perception and decision-making. This study presents a comprehensive approach to analyzing and mitigating rumor propagation by identifying key influencers and optimizing propagation time within online communities. Our dataset consists of over 800\,000 nodes with interactions categorized as retweets\, mentions\, and replies\, each assigned an influence score to quantify user impact. Using the Infomap algorithm\, we initially detected 13\,500 communities and filtered them to retain 67 influential clusters with a higher number of nodes and stronger influence scores. To analyze the spread of rumors\, we developed an algorithm that tracks propagation within these communities\, leveraging top influencers as initial spreaders and computing the average propagation time. Furthermore\, we introduced a node deletion strategy to iteratively remove high-impact influencers\, reducing the overall propagation time and limiting misinformation spread. Finally\, we adjusted the propagation times by normalizing them with the earliest influencer timestamps to ensure precise measurement. Our findings highlight that targeted removal of key spreaders significantly disrupts rumor diffusion\, providing insights into optimizing influence-based network interventions for misinformation control.
CATEGORIES:VIRTUAL ROOM 8B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:2497ed72f67d68afc4cf868ac2793e0a
URL:http://11tict4sd.sched.com/event/2497ed72f67d68afc4cf868ac2793e0a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:AI IN FAKE NEWS DETECTION
DESCRIPTION:Authors - Yukta\, Nishant Yadav\, Sukrati Chaturvedi Abstract - The rapid expansion of social media platforms and online news consumption has led to an increased spread of fake news\, posing significant challenges to society by influencing public decision- making and causing severe consequences in domains such as politics and healthcare. Traditional methods for identifying fake news are often too slow to combat its swift dissemination. Therefore\, identifying and addressing misinformation is crucial for maintaining the accuracy and trustworthiness of information disseminated on social media. Natural language processing (NLP) and artificial intelligence (AI) play a vital role in this endeavor by facilitating the effective examination of extensive datasets to uncover patterns that are suggestive of misinformation. Machine learning models\, particularly transformer-based architectures\, enhance fake news detection by improving accuracy and interpretability. The integration of Explainable AI (XAI) methods further enhances trasparency and trust in these models
CATEGORIES:VIRTUAL ROOM 8B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:97329719bfcd6e64dc2dc53e25970110
URL:http://11tict4sd.sched.com/event/97329719bfcd6e64dc2dc53e25970110
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:AI-Enhanced Fashion Assistant Featuring Image Recommendation - Glambot
DESCRIPTION:Authors - Praful Sambhare\, Nitin Choudhary\, Abhay Rahangdale\, Atharva Rane\, Sahil Raina Abstract - The integration of artificial intelligence (AI) within the fashion industry is becoming increasingly prevalent\, with the aim of delivering a shopping experience that is personalized\, seamless\, and engaging. As an example of this trend\, GlamBot is an AI- driven fashion assistant\, a sophisticated fashion assistant that employs a range of advanced AI methodologies. These methodologies include Natural Language Processing (NLP)\, image- based similarity searches\, and voice recognition technologies\, all of which together transform the interactions that users have with fashion platforms. The functionality of GlamBot significantly improves the user experience by providing tailored fashion recommendations. This is achieved through the analysis of text inputs\, the execution of visual similarity searches\, and the processing of voice commands. Consequently\, fashion discovery is rendered more intuitive and accessible for users\, thereby facilitating a more engaging interaction with the fashion domain. . By analyzing and understanding user preferences\, GlamBot builds personalized profiles that evolve over time to deliver increasingly accurate recommendations . GlamBot’s image-based search feature allows users to upload pictures of fashion items they like. Using advanced ResNet50 image recognition models\, GlamBot analyzes these images and provides visually similar product recommendations\, bridging the gap between users’ visual preferences and available fashion products .
CATEGORIES:VIRTUAL ROOM 8B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:9c63e6ab2898c91d797837c50b9fde1c
URL:http://11tict4sd.sched.com/event/9c63e6ab2898c91d797837c50b9fde1c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:BLOCKCHAIN BASED EXAMINATION SYSTEM
DESCRIPTION:Authors - Merlin Priya Jacob\, Sukhada Aloni\, Hetal Rawat\, Shrishti Sakore\, Shreya Naik\, Shubham Pardhi Abstract - Using Ethereum\, IPFS\, and the MERN stack\, this project offers a transparent and safe blockchain-based examination system. The solution guarantees tamper-proof question paper management by utilising IPFS (via Pinata) for decentralised storage and Ethereum smart contracts. Instructors submit tests to IPFS\, ensuring data integrity by storing their cryptographic hashes on the Ethereum blockchain. While MetaMask allows for safe user interaction and authentication\, Hardhat makes it easier to design and deploy smart contracts on a testnet. A strong online application is powered by the MERN stack\, with Node.js managing database functions and instructor authentication. Exam papers are safely retrieved by authorised superintendents\, guaranteeing regulated access. Academic assessment integrity is improved by this decentralised method\, which reduces the possibility of paper leaks and unauthorised changes while offering a scalable and effective substitute for conventional test systems.
CATEGORIES:VIRTUAL ROOM 8B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:a893c90637b4c25940cecb2c1d089836
URL:http://11tict4sd.sched.com/event/a893c90637b4c25940cecb2c1d089836
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:Energy Consumption in Wireless Sensor Networks Using Fruit Fly and Ant Colony Optimization Algorithms in Heterogeneous Environments
DESCRIPTION:Authors - Sarbjit Kaur\, Jasmeen Gill Abstract - Wireless sensor networks (WSNs) play a vital role in sensing environmental conditions in far-flung areas. However\, their energy consumption remains a critical issue\, affecting the network's lifetime and coverage area. Clustering has emerged as an efficient strategy to prolong sensor network lifespan\, and the Fruit Fly Algorithm (FFA) and Ant Colony Optimization (ACO) are promising techniques for cluster formation and efficient path establishment\, respectively. In this study\, we propose an innovative approach that combines FFA for cluster formation and ACO for path establishment. This novel algorithm is implemented in MATLAB and evaluated in both homogeneous and heterogeneous environments. We compare our proposed algorithm with the Biogeography-Based Optimization Algorithm (BOA) and the Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm. Our results indicate that the proposed algorithm significantly outperforms both BOA and LEACH in terms of network lifetime and coverage area\, particularly in heterogeneous environments.
CATEGORIES:VIRTUAL ROOM 8B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:4d5272119a454791bcde19312af6b522
URL:http://11tict4sd.sched.com/event/4d5272119a454791bcde19312af6b522
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:Eyes as Interfaces: A Novel Eye-Tracking Mouse Cursor System
DESCRIPTION:Authors - M Jayaram\, Kodari Madhavi\, Amboth Anil Kumar\, Pachipala Naveen\, Gajula Rithvik Abstract - Human-Computer Interaction prioritizes universally accessible systems\, crucial for individuals with physical disabilities. This study introduces an Eyes as Interfaces: A Novel Eye-Tracking Mouse Cursor System\, a hands-free solution enabling seamless digital environment interaction. For people those with paralysis\, muscular dystrophy\, or spinal injuries\, this technology provides independent computing access\, eliminating dependency on external assistance. A Convolutional Neural Network (CNN) is the system's backbone that provides real-time pupil detection\, mapping eye gaze to exact cursor movement. Advanced image processing like this guarantees smooth operation regardless of changing lighting and user conditions. By accurately mapping eye movements to cursor actions\, users can navigate and communicate with computer interfaces\, opening avenues for information access\, communication\, and work participation. This encourages independence and enables users to access the web on their own\, performing tasks like document creation and web navigation. Beyond personal benefits\, this technology promotes inclusivity by bridging the digital divide\, allowing for real-time\, unrestricted participation in learning\, employment\, and social activities. Its smooth integration in widespread digital platforms ensures that it carries the highest level of potential in changing lives of people with mobility disabilities Worldwide.
CATEGORIES:VIRTUAL ROOM 8B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:c4d67aae652c271c0a081436617e3c09
URL:http://11tict4sd.sched.com/event/c4d67aae652c271c0a081436617e3c09
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:Heart Failure Prediction with Explainable Artificial Intelligence towards Trusted Approach: A Comparative Analysis of Black-Box and Transparent Models
DESCRIPTION:Authors - Kailash Agarwal\, Parikshit N. Mahalle\, Bhagwan D. Thorat Abstract - Heart failure is a serious medical condition that affects millions worldwide\, and early prediction is essential for timely intervention and better patient outcomes. While machine learning models have demonstrated strong predictive capabilities in healthcare\, many high-performing models\, such as Support Vector Machines (SVM)\, function as black boxes\, making them difficult to interpret in clinical settings. This study examines how Explainable AI (XAI) techniques can enhance transparency in heart failure prediction.Using a publicly available dataset from Kaggle\, we preprocess the data with label encoding\, feature scaling and Hyperparameter tuning before training various machine learning models for binary classification. Our results indicate that the Support Vector Classifier (SVC) with a Linear kernel achieves the highest predictive accuracy. However\, to improve interpretability\, we compare its performance with explainable models like Decision Trees and apply post-hoc explanation techniques such as SHAP (SHapley Additive Explanations) and Permutation Importance.Through this comparative analysis\, we highlight the trade-off between model accuracy and interpretability\, offering insights into the feasibility of XAI-driven models in real-world clinical decision-making. Our findings reinforce the importance of developing AI systems that not only perform well but also provide understandable and trustworthy insights for medical professionals.
CATEGORIES:VIRTUAL ROOM 8B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:b46897b3d4d6e066f75907a0b93d9a90
URL:http://11tict4sd.sched.com/event/b46897b3d4d6e066f75907a0b93d9a90
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:Mental Health Management with Emotion Detection using OpenCV
DESCRIPTION:Authors - Bhagwan Thorat\, Omkar More\, Prathamesh Medage\, Aditi Mali\, Janhavi Maske\, Sanika Maind Abstract - Traditional healthcare systems have primarily focused on physical health\, often overlooking mental well-being. With the rapid advancement of technology\, integrating AI-driven emotion detection into mental health management can offer valuable insights. This paper presents a comprehensive mental health management system that utilizes Haar Cascade classifiers and a Keras deep learning model for real-time emotion recognition via OpenCV. A Flask-based web interface\, built using HTML\, CSS\, and Python\, enables users to monitor their emotional states and facilitates therapist booking and automated receipt generation. By leveraging facial expression analysis\, the system provides a data-driven approach to mental health assessment\, enabling early intervention. The platform also ensures accessibility and efficiency\, reducing the burden on healthcare providers. Experimental evaluations demonstrate the system’s effectiveness in accurately detecting emotions and its potential in AI-assisted psychological support. Future enhancements will focus on multi-modal emotion detection\, incorporating natural language processing (NLP) and IoT-based physiological monitoring for a more holistic approach to mental health assessment. This research contributes to the growing field of AI-powered mental health solutions\, bridging the gap between technology and psychological well-being while promoting early detection and accessible care.
CATEGORIES:VIRTUAL ROOM 8B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:76782e43eadf01a19f84f85ebe62cf51
URL:http://11tict4sd.sched.com/event/76782e43eadf01a19f84f85ebe62cf51
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:Sleep-Driven Mental Health Prediction A Multi-Channel CNN Approach Using Wearable Sensor Data
DESCRIPTION:Authors - Sonali Patil\, Siddhesh Arun Patil\, Ayush Patil\, Piyush Pawar\, Siddhesh Sandeep Patil Abstract - Around 970 million people face mental health disorders across the world and depression affects 75% of these people because of their sleep disturbances. The connection between persistent sleep problems and depression emerges when affected individuals become twice as likely to develop depression thus establishing sleep as a major sign for mental health forecasting. Current approaches to this problem deal with three key issues which are dataset biases\, small available sample sizes along with the reliance on self-reported symptoms instead of actual physiological signals. Our deep learning solution relies on multi-channel Convolutional Neural Networks (CNNs) to analyze wearable sensor data because it tackles existing analysis limitations. DreamT-150 contains heart rate (HR)\, blood volume pulse (BVP) and electrothermal activity (EDA) measurements from 150 sleep patients. Three models including MultiChannelCNN and MultiChannelEfficientNet and MultiChannelResNet analyzed the signals which appeared as time-series graphs. The best model proved to be EfficientNet-B0 because it demonstrated superior generalization. The pre-trained layers from EfficientNet adjusted the vulnerability of training loss which led to stable model performance. The research demonstrates sleep-derived physiological signals' usefulness for non-invasive mental health predictions which can lead to real-time monitoring systems. The upcoming research aims to boost both dataset range and better models for clinical adoption requirements.
CATEGORIES:VIRTUAL ROOM 8B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:4c5d2ed6c1f15af68b35e4bb9c3a7f14
URL:http://11tict4sd.sched.com/event/4c5d2ed6c1f15af68b35e4bb9c3a7f14
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:Stress Detection using HRV as a Biological Marker: A Research Study based on Machine Learning Techniques
DESCRIPTION:Authors - Bhoomi C. Parikh\, Zankhana Shah Abstract - Stress is any type of mental imbalance that can lead to mental disorders ranging from low to high severities which can be classified as acute and chronic stress conditions. Chronic stress leads to hyperactivation of the sympathetic nervous system\, resulting in physical\, psychological\, and behavioural problems. Currently\, there is no recognised standard for stress assessment. Thus depressive disorders leading to stress are a flight or fight response to the stimulus generated by human nervous system caused due to any unacceptable behaviour or circumstance. Throughout this response adrenaline hormones are secreted that leads to increased respiration and heart rates\, along with increased muscle activity. Such type of biological alterations prime the organism for a physical response that affects human body mechanisms in terms of sleep abnormalities\, digestive disorders or work imbalances in routine lives. Thus WESAD is a multimodal wearable dataset which combines both affective states(baseline\, depression and happy) and other sensor modalities such as blood pressure\, ECG\, skin conductivity \, EMG\, breathing\, and three-axis acceleration. There are also other classification parameters based on physiological changes which are also found in WESAD dataset and by using different types of Machine Learning Classifiers analysis is done . The algorithms with the highest accuracy can be used for developing a novel and a hybrid model which can categorise stress based on Heartrate Variability and stating HRV as a biomarker for stress detection. Both characteristics related to time and frequency of heart rate are categorized in the research study. In the context of the three-class classification based on three affect states \, baseline\, stress\, and amusement result up to 99% was obtained. Use of two affective states like stress and amusement gave an accuracy up to 84% using DT classifier.
CATEGORIES:VIRTUAL ROOM 8B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:b6235533145bd22649aa5bfdfedfd00f
URL:http://11tict4sd.sched.com/event/b6235533145bd22649aa5bfdfedfd00f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:A Comparative Analysis of ETF Performance Using Machine Learning Algorithms and Traditional Models
DESCRIPTION:Authors - Ajay J\, Kavitha R\, V S Ashwin\, Dhanya M Abstract - The exchange-traded funds have seen greater condition as an entertainment choice in modern financial markets as they were designed for providing diversified exposure in equities as well as bonds and commodities types of asset classes. Despite the steadily increasing attractiveness and usage of such products\, there still exists a gaping research gap in predicting their performance relative to sectors\, especially in a comparatively emerging market such as India. This work intends to fill that gap by comparing Machine Learning models such as Random Forest and SVM with traditional models for sector-wise performance forecasting like ARIMA and Holt-Winters. Based on data available in investing.com\, the work analyzes daily ETF prices across seven key sectors—Pharmaceuticals\, FMCG\, Banking\, IT\, Infrastructure\, Consumption\, and Healthcare—from 2021 to 2024. Performance of the model is evaluated by overall fit criterion: R² (coefficient of determination)\, Mean Absolute Error (MAE)\, Difference in Square Errors (DSE). Machine learning techniques have been found to considerably out-perform classical statical models in capturing complicated market activities\, especially in volatile sectors like Infrastructure and Banking. Hill-Winters and ARIMA models reliably forecast stable sectors\, such as Pharmaceuticals and Healthcare\, while their kings fade away in overly dynamic markets. These research observations offer information to assist investors\, portfolio managers\, and policymakers as an illustration of the possibilities that exist for machine learning applications in financial forecasting. The integration of machine learning approaches should thus be magnified to improve on ETF price forecasting and investment strategies.
CATEGORIES:VIRTUAL ROOM 8C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:e488af33fc0591bb3e58eed409a57e8b
URL:http://11tict4sd.sched.com/event/e488af33fc0591bb3e58eed409a57e8b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:A Novel Deep Transfer Learning Model for IoT Botnet Attack Identificationn
DESCRIPTION:Authors - S.Prince Samuel\, R.kiruba\, P.Kingston Stanley\, R.Karthick Abstract - The Internet of Things (IoT) has seen an increase in cyber attacks\, especially botnet attacks\, mainly brought on by weak security on networks. As a result of the rise in IoT users\, the requirement for electronic data interchange\, and the desire for virtual services\, the frequency of cyberattacks to gain access to private data has increased in recent years. As a result\, industry and researchers have given the security of IoT applications and particular data attention. A botnet is a formally organized group of infected\, internet-connected devices managed by cybercriminals. Attacks from botnets\, which spread spam and viruses and are no longer under the control of authorized users\, can damage IoT devices. To effectively detect botnet attacks\, proposed a botnet attacks detection system based on Transfer Learning (TL). The transfer learning (TL) model is built upon convolutional neural networks (CNNs)\, which are widely used for their effectiveness in feature extraction and pattern recognition in complex datasets. For the existing model achieved 91.93%\, the proposed botnet attacks detection model performed with over 99.54% accuracy on two well-known public benchmark IoT security datasets: CICIDS2017 and UNSW-NB 15. This shows the proposed model’s effectiveness in predicting botnet attacks in an IoT environment.
CATEGORIES:VIRTUAL ROOM 8C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:aae6e4ced11bf0a29dceec0356490a7a
URL:http://11tict4sd.sched.com/event/aae6e4ced11bf0a29dceec0356490a7a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:Advancing Educational Inclusion: Integrating Indian Sign Language with Spoken Language through LSTM Neural Networks
DESCRIPTION:Authors - Aaron Mendonca\, Arya Gawde\, Nikki Mehta\, Nilay Koul\, Rohit Parmar\, Nikita Raichada Abstract - Communication barriers have a major influence on the deaf and mute society in India\, resulting in social isolation and restricted access to education\, employment\, and everyday interactions. Indian Sign Language (ISL) is the primary mode of communication\, but its lack of widespread understanding restricts integration with the larger society. This study presents a real-time ISL recognition and translation system that integrates deep learning\, spatio-temporal analysis\, and natural language processing (NLP) to overcome this communication barrier. This study proposes a real-time ISL gesture recognition and translation system utilizing Long Short-Term Memory (LSTM) networks\, which are ideal for sequential gesture recognition so that accurate mapping of static and dynamic ISL gestures into text and speech can be done. A spatiotemporal feature extraction pipeline is incorporated using MediaPipe-based skeletal keypoint detection to guarantee strong recognition through capturing hand\, facial\, and body landmarks. The dataset\, created with deaf and mute people’s inputs\, provides regional gesture diversity and sign diversity. It has been engineered to operate effectively in real-world environments\, with adaptations to lighting changes\, background noise\, and the complexity of gestures. This work contributes to assistive technology\, accessibility\, and human computer interaction\, fostering social inclusion through facilitating effective communication between the hearing and non-hearing populations. This paper is a step towards a more inclusive digital communication environment\, empowering the deaf community in various aspects of life.
CATEGORIES:VIRTUAL ROOM 8C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:c61fec94ad99890408e745c8e7f5012d
URL:http://11tict4sd.sched.com/event/c61fec94ad99890408e745c8e7f5012d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:AGROTECH NAVIGATOR : ML Model for Projecting Demand as well as Supply for Agricultural Commodities
DESCRIPTION:Authors - Mohit Matte\, Sandeep M.Chaware\, Pratik Dahagaonkar\, Anurag Deotale\, Laukik Pagar\, Jayesh Sarwade Abstract - Agriculture\, in particular\, has drawn a lot of attention lately due to the introduction of innovations like machine learning and smart computing. It is becoming increasingly challenging for farmers to effectively manage land and optimize profit in a particular terrain due to the changing economics of agri-produce. Crop yield forecast is heavily reliant on environmental parameters such soil composition\, rainfall\, humidity\, and cultivable area\, among other crucial indicators. Because they don't adequately account for a variety of environmental factors\, traditional Crop Yield Prediction approaches like historical averages frequently don't yield reliable results. Furthermore\, farmers find it challenging to choose crops and cultivate them effectively due to shifting market patterns in supply and demand. While a shortage of a certain crop could result in lost profit chances\, a surplus production could result in reduced market pricing. Thus\, combining yield prediction models with demand and supply research can assist farmers in improving crop planning for increased profitability. These challenges are addressed and accurate forecasts are generated using a machine learning-based approach. Crop prediction is done with classification models\, whereas yield prediction is done with regression models trained on both historical and present data. To identify best course actions\, these models examine a number of performance indicators. For practical use\, the top-performing model is integrated into the backend. With a MAE of .64 \, an R-squared mark of .96\, Random Forest Regression outperforms the other models employed for yield prediction. At 99.39%\, the Naïve Bayes classifier has the best accuracy for crop prediction. Predictions are further improved by adding market data to these models\, such as price swings\, customer demand\, and past sales patterns. Farmers can improve profitability and minimize waste by matching their agricultural techniques with market demands through the integration of demand and supply analytics. This study demonstrates how machine learning may transform crop management by assisting farmers in making data-driven decisions to match their output with supply and demand in the market\, as well as by optimizing resource allocation and raising total yield.
CATEGORIES:VIRTUAL ROOM 8C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:4eb4a984a23709c00abe62a56152671e
URL:http://11tict4sd.sched.com/event/4eb4a984a23709c00abe62a56152671e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:Exploring Emerging Trends & Market Potential of Barrier Coating Chemicals in Sustainable Paper Packaging
DESCRIPTION:Authors - Vanishree Pabalkar\, Reena Lenka\, Jaya Chitranshi\, Kalpesh Bhave Abstract - Barrier coating refers to a type of coating applied to the surface of a material\, such as paper\, cardboard\, or plastic\, to create a protective layer that prevents the penetration of liquids\, gases\, oils\, or other substances. The primary purpose of barrier coatings is to enhance the material's resistance to moisture\, oxygen\, grease\, and other environmental factors\, thereby improving its functionality and extending its durability. In the context of the paper industry\, barrier coatings are often used to make paper and paperboard suitable for packaging applications\, particularly for food products\, where protection from moisture and grease is essential. These coatings can be made from a variety of materials\, including polymers\, waxes\, biopolymers\, and even certain types of natural and sustainable compounds\, depending on the desired properties and environmental considerations. Barrier coatings are crucial in the development of sustainable packaging solutions\, as they allow paper-based materials to replace plastics and other non-renewable materials in various packaging applications. End Use of barrier chemical coated paper: Pizza Boxes\, Pet food Bags/Boxes\, Ice cream Frozen food\, Fish Trays\, Meat Packaging\, Paper Cups & Plates\, Cakes / Cookies\, Wet Vegetables.
CATEGORIES:VIRTUAL ROOM 8C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:af56c1b55d9809cdb093e5adfc8492b1
URL:http://11tict4sd.sched.com/event/af56c1b55d9809cdb093e5adfc8492b1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:FBCA-IoMT: A Federated Binary Contrastive Autoencoder Framework for Anomaly Detection
DESCRIPTION:Authors - Archita Bhattacharyya\, Ayan Bhaumik\, Mrinal Kanti Deb Barma Abstract - The rapid expansion of the Internet of Medical Things (IoMT)\, a healthcare-driven subset of the Internet of Things (IoT)\, has introduced significant cybersecurity threats\, underscoring the need for effective and privacy-preserving anomaly detection systems. In this study\, we present an anomaly detection framework for IoMT data using autoencoder-based reconstruction loss analysis and feature space visualization. The reconstruction loss distribution enables the identification of anomalous samples using a predefined threshold. In addition\, anomaly scores plotted against sample indices help visualize deviations in model behavior\, distinguishing normal from suspicious activities. To better understand the latent feature space\, the t-SNE visualization provides clear clustering of encoded representations\, highlighting the separation between normal and anomalous patterns. This integrated approach offers an interpretable and effective means of detecting anomalies in IoMT environments.
CATEGORIES:VIRTUAL ROOM 8C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:53312eab4c60352eea0e78441772e214
URL:http://11tict4sd.sched.com/event/53312eab4c60352eea0e78441772e214
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:Handwriting Digit Recognition Using CNN
DESCRIPTION:Authors - Naman Yadav\, Preety Sharma\, Ayush Singh\, Atharva Deshmukh\, Aditya Thakur\, Akshat Gora Abstract - This paper studies handwritten digit recognition methods with Convolutional Neural Networks (CNN) while performing a performance comparison with EfficientNetV2. The investigators applied the EMNIST dataset for model education and performance testing before using it to examine the model generalization characteristics through HASYv2 dataset analyses. The research examines key obstacles in handwritten digit recognition through multiple aspects such as different writing styles and diverse dataset characteristics as well as inefficient computing capabilities. The research evaluates enhanced accuracy through preprocessing methods along with model optimization methods. The research data reveals CNN provides excellent performance on EMNIST although it falls short on HASYv2 whereas EfficientNetV2 extracts superior features yet requires more computation power. The evaluation reveals the effective features and challenging aspects of both models so researchers can focus on developing hybrid structures and growing datasets for actual handwriting recognition systems in OCR applications and banking and automated document processing fields.
CATEGORIES:VIRTUAL ROOM 8C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:f4a12161962e22b7fec89976da810817
URL:http://11tict4sd.sched.com/event/f4a12161962e22b7fec89976da810817
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:Hedonic and Utilitarian motivations to use AI powered parenting apps among young Indian parents- A pilot study
DESCRIPTION:Authors - Anupama K\, Kalyani Suresh Abstract - As digital tools become integral to parenting\, understanding the psychological and practical factors influencing app adoption is crucial. Parenting apps are leaning progressively more towards integrating AI for personalization and societal benefits\, which is an emerging area of study in the Indian context. While research points towards parental attitudes being significantly affected by AI mediated technologies\, AI research culture is poised to draw on the experience and theory related to parenting. Drawing from Human-AI interaction theories\, this study explores the hedonic and utilitarian motivations driving the use of AI-powered parenting apps among young Indian parents. The study uses a quantitative approach\, to assess the extent to which young parents are motivated to use the AI-driven apps within the different levels of family support scenarios. Cluster analysis revealed the presence of four clusters based on their levels of hedonic or utilitarian motivations. Findings suggest that young Indian parents who use AI powered parenting apps are mostly Beta users – moderately engaging with selective feature usage - with both hedonic and utilitarian motivations playing crucial roles. Family support is found to improve hedonic and utilitarian motivations to use AI driven parenting apps. This study provides initial insights into the complex interplay between pleasure and practicality in technology adoption\, setting the stage for larger-scale research on the impact of AI in parenting practices in India.
CATEGORIES:VIRTUAL ROOM 8C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:f8204893734bd4d6b45b1755a4800d67
URL:http://11tict4sd.sched.com/event/f8204893734bd4d6b45b1755a4800d67
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:Off-Line Signature Verification Using Region-Based Ge-ometric Feature Matching with Adaptive Similarity Scoring
DESCRIPTION:Authors - Prabira Kumar Sethy\, Sachin Sharma\, Ajit Behera\, Satyaprakash Barik\, Amresh Bhuyan Abstract - Signature verification constitutes a fundamental component of biometric authentication methods used in financial and legal identity verification systems. The research presents an offline signature verification method that examines geometric and morphological region-based features to authenticate test signatures. The methodology analyzes binarized signature images to extract important attributes such as area\, perimeter\, centroid\, eccentricity\, solidity\, extent\, major and minor axis lengths\, orientation\, convex area\, Euler number\, and equivalent diameter. After analyzing the reference signature collection\, the most prominent image region gets processed for feature extraction. The test signature is evaluated through feature-wise similarity calculations while undergoing pre-processing identical to reference images. The normalization process for each feature difference allows comparison against specific thresholds to determine cumulative similarity scores. Authentication confirmation for a signature occurs when its score level exceeds the 95% predetermined acceptance benchmark. Experimental results demonstrate that our method achieves optimal computational efficiency while providing high verification accuracy and clear distinction between real signatures and forgeries. The framework merges reliable performance with simple operation and quick processing abilities making it ideal for lightweight biometric systems.
CATEGORIES:VIRTUAL ROOM 8C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:c3c1d03dfa9d9c1fea79fea2be3c3dfd
URL:http://11tict4sd.sched.com/event/c3c1d03dfa9d9c1fea79fea2be3c3dfd
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:Review on Security Schemes in Modern IoT Integrated Cloud Systems
DESCRIPTION:Authors - Atul Kumar\, Devendra Kumar\, Niranjan Kumar Abstract - The Internet of Things (IoT) has transformed the digital environment\, but its fast expansion raises substantial cybersecurity concerns. IoT devices are naturally vulnerable to a variety of assaults\, and the data they manage can be used by malevolent or unauthorized service providers. The introduction of IoT into cloud-based systems creates new security vulnerabilities. Cloud-based IoT solutions provide flexibility and scalability\, but they also increase security vulnerabilities. The complicated interconnections between these traditional devices and systems demand strong measures to ensure privacy and integrity. This article tackles important security problems in IoT adoption by strategies to suggest in bridging present gaps and prepare for future difficulties. Its goal is to improve service security systems and device and strengthen IoT ecosystems through proactive approaches.
CATEGORIES:VIRTUAL ROOM 8C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:8e7f8cad3d20842cb530b8bda2b343f2
URL:http://11tict4sd.sched.com/event/8e7f8cad3d20842cb530b8bda2b343f2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:A Hybrid Deep Learning Approach for Cyberbullying Detection: Enhancing Performance & Interpretability with Attention Mechanisms
DESCRIPTION:Authors - Moushmee Milind Kuri\, Ganesh Pathak Abstract - Cyberbullying is a growing concern across social media platforms\, necessitating advanced detection mechanisms to mitigate its impact. Traditional machine learning models often struggle with understanding contextual dependencies and ensuring model interpretability. This paper proposes a hybrid deep learning approach that combines BERT and RoBERTA for feature extraction and Bidirectional Long Short-Term Memory (BiLSTM) networks for sequential dependency learning. To enhance interpretability\, attention mechanisms such as Self-Attention and Bahdanau Attention are integrated\, allowing the model to focus on crucial words contributing to classification. The proposed system aims to improve accuracy\, scalability\, and explainability while addressing key challenges in cyberbullying detection. This research lays the groundwork for developing more transparent and effective AI-driven moderation systems for online safety.
CATEGORIES:VIRTUAL ROOM 8D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:8ecd85e72cb9d6da1579415010ec35e3
URL:http://11tict4sd.sched.com/event/8ecd85e72cb9d6da1579415010ec35e3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:Assessing the Effectiveness of Deductions and Exemptions in Income Tax for Promoting Savings and Investments
DESCRIPTION:Authors - Devi V S\, Durgalashmi C V Abstract - This study assesses the effectiveness of income tax deductions and exemptions in promoting savings and investments in India. The Indian government has implemented various tax incentives to encourage individuals to save and invest\, including provisions under sections 80C\, 80D\, and others. These deductions and exemptions are designed to stimulate economic growth by fostering long-term financial planning among individuals. The research examines the impact of these provisions on individual taxpayers' behavior and their overall influence on savings and investment patterns. Through a comprehensive analysis of available data\, the study identifies the key tax incentives that have led to increased savings in instruments such as Provident Funds\, National Savings Certificates\, and insurance products. Additionally\, the research evaluates the extent to which these tax benefits contribute to fostering a culture of investment and financial security. The study concludes that while tax deductions and exemptions have provided some incentives for savings\, their effectiveness is often limited by lack of awareness and financial literacy. To further promote savings and investments\, the study recommends improvements in policy communication\, accessibility\, and the alignment of tax incentives with broader economic goals.
CATEGORIES:VIRTUAL ROOM 8D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:2c9f56773000ee4bfef2da699128edf7
URL:http://11tict4sd.sched.com/event/2c9f56773000ee4bfef2da699128edf7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:Design and Verification of AHB to APB Bridge
DESCRIPTION:Authors - Akash Tibeli\, Saroja V Siddamal\, Suneeta V Budihal Abstract - The AHB to APB Bridge is crucial component in System-on-Chip (SoC) designs\, Achieving efficient communication between the pipelined AHB bus and the non-pipelined APB bus. In the proposed work a AHB to APB bridge is built using a bridge architecture which enables to translate pipelined\, burst-oriented\, high speed AHB transactions into sequential\, low-power APB transactions by maintaining synchronization and data integrity. It was developed with a FSM to manage transactions and pipelining to maintain efficiency. Verification was performed using a Universal Verification Methodology testbench environment through direct and random testcases of burst\, single\, sequential\, non-sequential transactions. 80 testcases were tested to obtain a functional coverage of 88%.
CATEGORIES:VIRTUAL ROOM 8D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:5d966f86c9d7918b732c2e0b5d98c52d
URL:http://11tict4sd.sched.com/event/5d966f86c9d7918b732c2e0b5d98c52d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:Fixation-Guided Recognition and Categorization of Handwritten Characters
DESCRIPTION:Authors - Judy K George\, Elizabeth Sherly Abstract - Convolutional Neural Networks are extensively employed in critical domains such as computer vision\, medical imaging\, and autonomous systems. Enhancing model interpretability by providing users with concise and context-relevant explanations of CNN decision making such as visualizing feature maps or saliency regions\, enables a deeper understanding of the model’s internal representations and inference process. The proposed work presents a deep learning framework integrating a ResNet-based U-Net architecture with a Fixation Point Generator (FPG) to perform classification and saliency aware reconstruction on the hand-written dataset. The model leverages transfer learning by employing a pre-trained ResNet-18 as the encoder backbone\, enabling robust feature extraction. A custom decoder reconstructs input images while a classification head predicts digit labels. To enhance model interpretability\, a Fixation Point Generator predicts spatial attention maps (saliency maps) from high-level global features\, highlighting regions of interest that influence model decisions. This implementation aims to bridge the gap between classification performance and model explainability\, offering insights into the model’s focus areas through learned attention. The model got an accuracy of 98.44 on the Malayalam handwritten dataset\, 97.81 on English handwritten dataset\, and 99.56 on the MNIST dataset.
CATEGORIES:VIRTUAL ROOM 8D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:bab90b0bf6afb3bf40f6a0094df31f74
URL:http://11tict4sd.sched.com/event/bab90b0bf6afb3bf40f6a0094df31f74
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:Fraudlens: Deepfake Intelligence with ML
DESCRIPTION:Authors - Meghali Kalyankar\, Om Pratap Gajra\, Prathamesh Vilas Sagvekar\, Mehul lalit Sharma\, Zoheir Shahid Shaikh Abstract - Deepfakes are an emerging threat to digital authenticity and security\, hence a proper detection technique needs to be created in order to establish public study confidence. A thorough roadmap to the development of deepfake detection software has been provided in this paper\, reviewing the state-of-the-art algorithms\, such as XceptionNet\, EfficientNet\, and hybrid models integrating spatial and temporal analysis. It provides methodologies for implementation\, data preprocessing\, and software pipeline development\, serving as a practical guide to researchers and developers. Theoretical study to application-oriented practice closes the gap in terms of bottom line development and adaptive detection systems addressing the growing menace of deepfake media.
CATEGORIES:VIRTUAL ROOM 8D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:bd1c4b53110679cc3c95883dd87dec9f
URL:http://11tict4sd.sched.com/event/bd1c4b53110679cc3c95883dd87dec9f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:Graph Neural Networks for Music Recommendation
DESCRIPTION:Authors - PRANAY SAMAL\, K R LOKESH KUMAR\, CHAKILELA SAIRAJ\, G VIDYA SRI\, SUSHAMA RANI DUTTA\, SUKLA SATAPATHY Abstract - Music plays a significant role in our day-to-day life\, and selecting appropriate songs can enhance the experience. This paper describes an intelligent music recommendation system that applies machine learning to recognize what users prefer and recommend music that they will like. It incorporates various approaches\, including considering user decisions and music attributes\, to enhance suggestions. The system adapts based on user actions and refines recommendations over time. Findings indicate that this method provides easier and more precise music discovery. This paper emphasizes how technology can assist in providing a higher quality and better personalized music experience.
CATEGORIES:VIRTUAL ROOM 8D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:2ef195bbbeabb476f9b68d79045c8ed9
URL:http://11tict4sd.sched.com/event/2ef195bbbeabb476f9b68d79045c8ed9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:Incorporating Cryptoprocessor on RISC-V Architecture
DESCRIPTION:Authors - Arya Tripathi\, Akash Mecwan Abstract - In recent years\, the RISC-V architecture has emerged as a promising platform for embedded systems\, offering flexibility and open-source accessibility. Consequently\, the demand for secure communication in embedded devices\, particularly within the Internet of Things (IoT) ecosystem\, has driven the adoption of cryptographic algorithms. Integrating cryptographic functionalities into RISC-V architecture presents unique challenges\, requiring innovative solutions to optimize performance and security. In response\, the proposed design introduces an approach to address these challenges by incorporating a dedicated cryptoprocessor module into the RISC- V architecture specifically designed to handle encryption and decryption tasks efficiently. The cryptoprocessor module employs the Blowfish-64 algorithm to ensure robust security while lowering the computational overhead. Blowfish is a well-established symmetric-key block cipher known for its simplicity and efficiency. The compact design of the cryptoprocessor module significantly reduces resource utilization and execution time compared to existing implementations while preserving security and functionality. The design emphasizes low resource utilization\, achieving a utilization rate of 34% (11\,340 out of 33\,216 available units) with an execution time of 160 ns. The implementation is carried out using Verilog HDL for the Cyclone II EP2C35F672C6 based FPGA.
CATEGORIES:VIRTUAL ROOM 8D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:9d3f0d59c8b4d87d7e196085b1659198
URL:http://11tict4sd.sched.com/event/9d3f0d59c8b4d87d7e196085b1659198
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:Optimizing Shortest Path Selection in Weighted Graphs: A Hybrid Approach Using BFS and Machine Learning Models
DESCRIPTION:Authors - Kirti Karande\, Sujata Kadu\, Deven Shah Abstract - Breadth-First Search (BFS) is a foundational graph traversal algorithm\, it’s systematic layer-by-layer exploration of nodes makes it invaluable for a variety of domains\, including transportation networks\, social network analysis\, and artificial intelligence. However\, traditional BFS implementations face challenges when dealing with large-scale graphs due to memory limitations and inefficiencies in handling massive datasets. This project addresses these challenges by integrating BFS with a CSV-based data storage system\, enabling efficient traversal of large graphs without relying on a traditional SQL database or requiring the entire graph to be loaded into memory. The graph data\, comprising nodes and edges\, is stored in CSV files\, which act as lightweight and accessible storage. The implementation is memory-efficient due to the use of Pandas DataFrames for handling CSV data and NetworkX graphs for traversal. Additionally\, the integration of a machine learning model from Scikit-learn\, a memory-efficient library\, ensures effective prioritization of edges without excessive computational overhead. In this project\, we address a key limitation of the traditional Breadth-First Search (BFS) algorithm: its inability to consider edge weights during traversal. It is unsuitable for scenarios where varying edge weights significantly impact the traversal outcome\, such as in shortest-path calculations for weighted graphs. To overcome this drawback\, our project integrates a machine learning (ML) model to analyze and prioritize edges based on their weights\, effectively augmenting BFS for weighted graphs. By leveraging CSV-based storage and combining it with an ML-driven edge prioritization mechanism. this project offers a scalable solution for managing and analyzing large\, weighted graphs. This combination ensures that the navigation system not only computes the shortest path but also suggests the most practical and efficient routes tailored to user preferences or constraints.
CATEGORIES:VIRTUAL ROOM 8D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:018c0c821c7c3847d2805ae9d960b4c7
URL:http://11tict4sd.sched.com/event/018c0c821c7c3847d2805ae9d960b4c7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:Satellite Image Analytics for Tree enumeration for diversion of Forest Land
DESCRIPTION:Authors - Dipak Ligade\, Chiranjit Das\, Rupali Parte\, Masira Kulkarni\, Shivraj Jadhav\, Abhishek Mohite Abstract - —This project aims to automate tree counting and forest land diversion assessment through satellite image combined with advanced computing techniques. The treatment of forest re sources needs accurate monitoring because growing environmental challenges such as deforestation\, biodiversity loss\, and climate change require it for sustainable land management. The research uses satellite imagery along with machine learning and deep learning tools\, specifically convolutional neural networks (CNNs)\, to precisely detect and count trees across expansive territories. The study demonstrates how satellite analytics technologies will enhance forestry applications with their capabilities for better tree enumeration at higher efficiency and greater accuracy.
CATEGORIES:VIRTUAL ROOM 8D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:29582b6c52ae8b2fff23daebf234fa22
URL:http://11tict4sd.sched.com/event/29582b6c52ae8b2fff23daebf234fa22
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:Secure Authentication Using Biometric and Behavioral Analysis
DESCRIPTION:Authors - Suchanta Ravan\, Prashant Dhotre Abstract - Conventional identification techniques that depend on privacy concerns and credentials are becoming more vulnerable to web-based risks like hacking and data breaches. The need for sophisticated authentication techniques has grown dramatically because of identity theft\, cyberthreats\, and illegal access. Traditional security methods\, such as PINs and passwords\, are insufficient for high-security applications since they are vulnerable to phishing\, brute-force assaults\, and credential breaches. To improve safety and tackle problems like privacy threats\, spoofing\, and accessibility problems\, this study suggests a strong adaptive authentication mechanism that integrates biometric along with behavioral assessment. The multimodal authentication framework guarantees a smooth and easy verification process while also enhancing security. This structure guarantees a smooth and safe authenticating process by utilizing cutting-edge security methods like encryption\, machine learning\, and multifaceted biometrics in conjunction with a user-centric architecture. By combining behavioral biometrics with conventional authentication techniques\, total authentication reliability is increased\, and cyber risk is mitigated. The effectiveness of the suggested approach in lowering susceptibility to cyberattacks while preserving superior usability and consumer satisfaction is demonstrated by experimental findings\, Highlighting the importance of two-way authentication.
CATEGORIES:VIRTUAL ROOM 8D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:a0df5f03c0b93c944b17d95e53f5cba9
URL:http://11tict4sd.sched.com/event/a0df5f03c0b93c944b17d95e53f5cba9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:Anviksa: A Machine Learning Model for Passive Bot Detection
DESCRIPTION:Authors - Manasi Golesar\, Priti Jagtap\, Kamlesh Khatod\, Kshitij Malode\, Vaishali Pawar Abstract - This research presents a novel approach to bot detection in web applications using behavioral biometrics and machine learning. Our system leverages a Flask based web application with a registration form as a testbed to distinguish between human and automated users. The implementation collects multidimensional behavioral data including mouse movements\, typing patterns\, form fill speed\, and browser fingerprinting to build a comprehensive user profile.Two machine learning models\, Random Forest and XGBoost\, are dynamically compared for performance\, with the superior model being automatically selected for deployment. The system incorporates a honeypot field as a simple yet effective first pass filter and implements progressive model learning through a database backed training pipeline that continually improves detection accuracy.Key innovations include the real time behavioral analysis during form completion\, automated weekly model retraining\, and an administrative interface that allows for manual labeling of edge cases to enhance the training dataset. Our approach achieves high detection accuracy while maintaining a low false positive rate\, effectively balancing security with user experience.This research demonstrates that integrating behavioral biometrics with adaptive machine learning provides a robust defense against increasingly sophisticated bot attacks without requiring traditional CAPTCHA challenges that often degrade user experience.
CATEGORIES:VIRTUAL ROOM 8E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:a2c796ad68d6843be7c7a8ffd87a4205
URL:http://11tict4sd.sched.com/event/a2c796ad68d6843be7c7a8ffd87a4205
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:Decoding user sentiments towards ai-powered fitness applications: a sentiment analysis of user reviews
DESCRIPTION:Authors - Devarsh Damodaran\, Krishna Bharathi V\, Dhanya M Abstract - This study explores user sentiments towards AI-powered fitness applications by analyzing user reviews from platforms like Google Play Store. With the increasing adoption of digital health solutions\, understanding user satisfaction\, trust\, and key concerns is crucial. Using Natural Language Processing (NLP) techniques\, sentiment analysis was conducted to classify user feedback into positive and negative sentiments. Machine learning algorithms like Logistic Regression and Support Vector Machine (SVM) were utilized for classification. Findings are prominent drivers of satisfaction\, where usability\, effectiveness\, and personalization are essential drivers\, while cost\, technology glitches\, and unrealized expectations drive dissatisfaction. These findings give interesting insights for fitness-tech business companies and app developers to drive engagement and better experience for their users.
CATEGORIES:VIRTUAL ROOM 8E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:4751e6138ed5e26447e67202dccffa86
URL:http://11tict4sd.sched.com/event/4751e6138ed5e26447e67202dccffa86
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:Driving Business Sustainability through Social Media: Exploring Digital Women Entrepreneurial Ventures
DESCRIPTION:Authors - Silpa Raj R\, Durgalashmi C V Abstract - The widespread adoption of social media enables female entrepreneurs to leverage innovative tools and strategies\, fostering the development of sustainable business practices. The present study analyses how the female entrepreneurs in Kerala utilize social media (SM) in promoting sustainable innovations in their business activities. Research investigates how social media affects sustainable business practices among women entrepreneurs in Kerala\, with the focus of four key variables: idea generation\, customer connectivity\, collaboration\, and sustainable outcomes. This study aims to fills the gap by exploring how women use social media for entrepreneurial practices and adoption of sustainable outcomes. This study used a structured questionnaire to collects data from women entrepreneurs in Kerala. The variables including frequency of idea generation through social media\, customer or stakeholders’ collaborations and the adoption of sustainable practices influenced by digital platforms are observed. To ensure the participation of entrepreneurs actively using social media for innovation\, purposive sampling techniques were employed. The hypotheses were tested using the statistical tools like chi-square\, correlation and regression analysis\, providing empirical evidence on impact of SM usage among Kerala’s women entrepreneurs. The study emphasizes the significance of social networking platforms in encouraging innovative methods that contribute to sustainability via three major variables. The findings suggest practical implications for policyholders\, entrepreneurs and researchers. The research adds existing corpuses of research on digital entrepreneurship and sustainability focusing on how social media improve sustainable practices.
CATEGORIES:VIRTUAL ROOM 8E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:ae942d45f2cf4b141d746aa6658180ee
URL:http://11tict4sd.sched.com/event/ae942d45f2cf4b141d746aa6658180ee
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:Face Recognition Based Attendance System (FRAS)
DESCRIPTION:Authors - Vineet Wagh\, Srushti Chopade\, Sneha Patil\, Vighnesh Padwal\, Sarika Kuhikar Abstract - In Institutions and schools\, attendance management is a crucial task for faculty to monitor class strength. Traditional methods such as manual entry\, biometrics\, and RFID-based systems are commonly used\, but they are time-consuming and\, in the case of biometrics\, potentially unhygienic. This paper presents an automated face recognition-based attendance system that utilizes preinstalled CCTV cameras to monitor student presence in real-time. The system employs RetinaFace for face detection and the face_recognition library for face encoding and matching. Known face images are preprocessed to generate face encodings\, which are then compared with detected faces in each frame to determine attendance. The proposed system offers accuracy\, efficiency\, automation\, and contactless operation while seamlessly integrating with existing infrastructure. A web interface allows users to start and stop attendance tracking\, remove duplicate records\, and download attendance logs in CSV format. The system demonstrates its applicability in educational environments by providing a scalable\, non-intrusive\, and secure solution for automated attendance management.
CATEGORIES:VIRTUAL ROOM 8E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:ef8775803b3ea64eb13f88ce7164d3a6
URL:http://11tict4sd.sched.com/event/ef8775803b3ea64eb13f88ce7164d3a6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:FedCloud:A Dyanamic Trust Management Framework for Federated Environments
DESCRIPTION:Authors - Madhumati Shinde\, Premanand Ghadekar Abstract - Cloud computing is a dynamic part of today's high-tech framework\, given that frequent welfares such as cost-effectiveness\, scalability\, convenience\, novelty\, and safety. Its impact is multifaceted\, transforming competition and corporate operations in the digital age. To improve speed\, optimize resource usage\, and support sophisticated applications\, cloud computing makes use of a variety of learning strategies. A learning technique's effectiveness in the field of cloud security depends on its ability to recognize\, stop\, and handle security threats. In order to identify and reduce security threats\, machine learning particularly anomaly detection using supervised and unsupervised learning is crucial with advancement of federated learning. Deep learning models like RNNs and CNNs process extensive datasets to uncover intricate attack patterns\, while federated learning improves privacy by training models on decentralized data sources. Reinforcement learning facilitates adaptive security strategies\, continually enhancing threat responses. Security is paramount in cloud computing as it safeguards sensitive data\, applications\, and services hosted on cloud platforms from unauthorized access\, breaches\, and cyber threats.This paper highlights the security concerns in cloud environment with framework to improve the performance matrix to recognize federated cloud trust.
CATEGORIES:VIRTUAL ROOM 8E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:cdec8b90503cab77044c66fc2a1e6ee7
URL:http://11tict4sd.sched.com/event/cdec8b90503cab77044c66fc2a1e6ee7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:Multi LLM Framework with Dynamic Prompting
DESCRIPTION:Authors - Ajuram. P\, E. Grace Mary Kanaga Abstract - Large Language Models have demonstrated great effectiveness in generating text and images. However they can become even more efficient by perfecting the prompt given to them. This paper proposes a multi LLM framework that dynamically orchestrizes several specialized LLM models in accordance with complex user prompts. First\, a primary LLM analyzes the user prompt and breaks it down into multiple sub tasks. Then\, for each identified sub task with respect to its type (text to text\, text to image\, or image to text)\, a suitable LLM is assigned. The context\, instructions\, and the output format is also provided by the primary LLM for each chosen model. The sub tasks are executed either in parallel or in sequential order. This approach automates the workflow\, optimizes model utilization\, and improves response relevance\, making it suitable for applications requiring multi modal collaboration and processing.
CATEGORIES:VIRTUAL ROOM 8E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:86e7a9e03329102e4162fd98198a65f3
URL:http://11tict4sd.sched.com/event/86e7a9e03329102e4162fd98198a65f3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:Real-time Over-steering Detection of Vehicle using Machine Learning and Embedded System Integration
DESCRIPTION:Authors - Devika Vijapur\, Nidhi Desai\, Aishwarya Naik\, Smita Ganur\, Supriya Katwe Abstract - Road accidents are one of the global safety concerns leading to loss of millions of lives every year. One of the factor leading to this is over-steering. Oversteering is phenomenon that occurs when the rear wheels of the vehicle lose grip which causes the vehicle to turn more than expected. The detection of oversteering in real-time is crucial for the improvement of vehicle safety to prevent accidents as well as for advanced driving assistance systems(ADAS). This paper presents a holistic approach to over-steering detection using a decision tree algorithm. The proposed system analyzes various vehicle dynamics parameters such as lateral acceleration\, yaw rate and steering angle to identify the patterns that cause over-steering. The system incorporates collection of real-time data from Inertial Measurement Unit (IMU) sensors that enhances reliability of oversteering detection under various conditions. The model is trained from the data obtained\, using decision tree algorithm and obtained accuracy of 96.08%. The hardware implementation is done by placing ESP-32 integrated with MPU 6050 and Arduino Nano 33 BLE sense accordingly in the vehicle. Based on thresholds of the parameters mentioned in the paper\, oversteering is detected.
CATEGORIES:VIRTUAL ROOM 8E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:c28461b3b8335bb0551887b1d8d58298
URL:http://11tict4sd.sched.com/event/c28461b3b8335bb0551887b1d8d58298
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:Smart Irrigation System Using Raspberry Pi
DESCRIPTION:Authors - Sneha S. Temgire\, Y.S. Angal\, Ashwini V. Waghmare\, Chetana Sharma\, Ashwini Gajre Abstract - Agriculture is essential for food security and economic growth\, but traditional farming faces challenges such as plant diseases\, inefficient irrigation\, and labour-intensive monitoring. This project focuses on automated and manual irrigation in addition with plant disease detection and growth monitoring using image processing on a Raspberry Pi 3B+. By leveraging TensorFlow Lite and OpenCV\, the system can analyze plant health and trigger appropriate irrigation actions. The aim is to design accurate agriculture system by reducing water wastage and improving crop monitoring. A key feature of this system is web-based monitoring\, where the Raspberry Pi transmits real-time plant health data and sensor readings to an HTML-based webpage. Users can remotely access this data via a web interface\, enabling continuous monitoring of plant conditions\, disease status\, and irrigation control from any location. By combining machine learning\, image processing\, IoT automation\, and real-time web-based monitoring\, this system reduces manual labour\, optimizes water usage\, and ensures early disease detection. The web interface enhances accessibility\, allowing farmers and researchers to track plant health remotely and make informed decisions.
CATEGORIES:VIRTUAL ROOM 8E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:6358c92096df7074fa4593b533208c5c
URL:http://11tict4sd.sched.com/event/6358c92096df7074fa4593b533208c5c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:Sustainability of Avian Monitoring Near Mobile Base Stations Using Drones: A Case Study in Arambagh Municipality\, Hooghly\, West Bengal\, India
DESCRIPTION:Authors - Sauvik Bose\, Rina Bhattacharya\, Rajeshwari Roy Abstract - Avian monitoring is a crucial component of biodiversity conservation\, providing insights into population trends\, habitat changes\, and environmental stressors. The fast growth of mobile telephony has raised issues regarding its potential upon the avian population\, their behaviors and breeding\, predominantly due to electromagnetic radiation exposure. This study investigates the feasibility of using drones for avian monitoring near mobile towers in Arambagh Municipality (22.8838° N\, 87.7819° E)\, Hooghly\, West Bengal\, India\, which is a semi-urban landscape with rich avian diversity and has undergone a significant growth in mobile tower installation over the last few decades. Drones offer a non-invasive\, scalable\, and high-resolution method for ecological monitoring\, surpassing traditional survey techniques in terms of not only efficiency and data accuracy but also consuming less time and effort. A drone (model: DJI MAVIC MINI) equipped with a high-resolution camera is deployed at selected base station sites within the study area. The study pattern included regulated flight patterns\, periodic monitoring. Findings disclosed noticeable behavioral variations in birds near mobile base stations. The repulsion of smaller birds to the high EMR zone has been distinctly observed along with anomalies in roosting and breeding habits. A correlation was observed between radiation levels and avian health oddities\, underscoring the need for further research. In the future\, research ought to be performed on in-depth monitoring efforts in urban and semi-urban areas along the different geographical landscapes. Improving drone technology for ecological studies and exploring alternative communication infrastructures with reduced environmental impact is much needed.
CATEGORIES:VIRTUAL ROOM 8E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:91f6ecbb8ff225bf5606295ad68bda90
URL:http://11tict4sd.sched.com/event/91f6ecbb8ff225bf5606295ad68bda90
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T070000Z
DTEND:20260826T090000Z
SUMMARY:The Chain reaction: How Cliffhangers and Binge-watching lead to Self-regulatory depletion\, Binge Eating\, and Impact Mental Wellbeing
DESCRIPTION:Authors - Dhanyashree S\, Keshav S\, Deepak Gupta\, Shobhana Palat Madhavan Abstract - This study investigates a chain reaction triggered by cliffhangers in media consumption\, focusing on their role in driving binge-watching\, self-regulatory depletion\, binge-eating\, and reduced mental well-being. Grounded in Self-Regulatory Depletion Theory\, a sequential mediation model is proposed and analyzed through serial mediation regression. Data from 170 Indian respondents revealed that cliffhangers significantly predicted binge-watching\, which in turn increased self-regulatory depletion. Depletion heightened binge-eating tendencies\, and binge-eating negatively impacted mental well-being. Bootstrapped mediation confirmed an indirect pathway from cliffhangers to reduced mental well-being via binge-watching and self-regulatory depletion. These findings underscore the ethical responsibility of streaming platforms to mitigate compulsive viewing and highlight interventions for mindful consumption.
CATEGORIES:VIRTUAL ROOM 8E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:3304a01b27a6ff58ac47f4e570aeaf78
URL:http://11tict4sd.sched.com/event/3304a01b27a6ff58ac47f4e570aeaf78
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T090000Z
DTEND:20260826T090200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 8A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:d3f4fb697301d93c06eb551de9a3eb54
URL:http://11tict4sd.sched.com/event/d3f4fb697301d93c06eb551de9a3eb54
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T090000Z
DTEND:20260826T090200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 8B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:e40f55081091187b247a5d82902124fe
URL:http://11tict4sd.sched.com/event/e40f55081091187b247a5d82902124fe
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T090000Z
DTEND:20260826T090200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 8C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:9a9900a2b922024ac1c96562e7d6397c
URL:http://11tict4sd.sched.com/event/9a9900a2b922024ac1c96562e7d6397c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T090000Z
DTEND:20260826T090200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 8D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:00edaac5ad71861c2b647dd475927593
URL:http://11tict4sd.sched.com/event/00edaac5ad71861c2b647dd475927593
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T090000Z
DTEND:20260826T090200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 8E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:9054d09506b5b2bbecbcd902651c56be
URL:http://11tict4sd.sched.com/event/9054d09506b5b2bbecbcd902651c56be
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T090200Z
DTEND:20260826T090500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 8A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:26dbfa69f6849d855c50998abd872a41
URL:http://11tict4sd.sched.com/event/26dbfa69f6849d855c50998abd872a41
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T090200Z
DTEND:20260826T090500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 8B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:4fac7bdc656e984501ef7aa07dc9dc4b
URL:http://11tict4sd.sched.com/event/4fac7bdc656e984501ef7aa07dc9dc4b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T090200Z
DTEND:20260826T090500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 8C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:8346225c810d0ce1c142239018a38113
URL:http://11tict4sd.sched.com/event/8346225c810d0ce1c142239018a38113
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T090200Z
DTEND:20260826T090500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 8D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:baa5bc95e230b4167799a62646a7aa74
URL:http://11tict4sd.sched.com/event/baa5bc95e230b4167799a62646a7aa74
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T090200Z
DTEND:20260826T090500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 8E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:14001a077200410a39f60ae7dd68c897
URL:http://11tict4sd.sched.com/event/14001a077200410a39f60ae7dd68c897
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T095800Z
DTEND:20260826T100000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 9A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:759a83c9bd2e8147c310a11cf30b74e2
URL:http://11tict4sd.sched.com/event/759a83c9bd2e8147c310a11cf30b74e2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T095800Z
DTEND:20260826T100000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 9B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:dc629bb7f6d26366510ee9baf46fa487
URL:http://11tict4sd.sched.com/event/dc629bb7f6d26366510ee9baf46fa487
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T095800Z
DTEND:20260826T100000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 9C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:c32145a507839b52cecc102845fd51ec
URL:http://11tict4sd.sched.com/event/c32145a507839b52cecc102845fd51ec
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T095800Z
DTEND:20260826T100000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 9D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:17f0b88aefd8c3ddcc5fd5d9d2fc1090
URL:http://11tict4sd.sched.com/event/17f0b88aefd8c3ddcc5fd5d9d2fc1090
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T095800Z
DTEND:20260826T100000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 9E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:3d35179c0fee37f122b6073f36a9e16c
URL:http://11tict4sd.sched.com/event/3d35179c0fee37f122b6073f36a9e16c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:A Comparative GIS-Based Remote Sensing Framework for Surface Water Quality Monitoring
DESCRIPTION:Authors - Kavya Soni\, Sujal Rajput\, Babita Tiwari\, Chirag Joshi\, Gaurav Kumawat Abstract - Surface water quality is essential for ecological stability and mortal health\, but it faces growing pitfalls from urbanization\, industrialization\, and husbandry. Traditional in-situ monitoring styles are essential yet limited in their spatial and temporal compass. This paper aims to provide a comparative analysis of different techniques available for surface water quality analysis. We have analysed studies grounded on freely available satellite data from Landsat\, Sentinel- 2\, and MERIS to determine crucial water quality parameters similar to chlorophyll- at attention\, turbidity\, and dangerous algal blooms. The review demonstrates the effectiveness of various methods to use spectral imaging to predict parameters such as BOD\, chlorophyll content in water. Further to this multi-sensor data integration within the pall calculating platform Google Earth Engine aids in dynamic water quality assessments. Results indicate these technologies indeed give scalable low-cost observers of submarine ecosystems and implicit means of filling gaps between in- situ measures and comprehensive water resource operation. The study identifies implicit in the integration of a Civilians approach grounded on remote seeing in climate modelling\, monitoring of ecosystem health\, and sustainable water governance.
CATEGORIES:VIRTUAL ROOM 9A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:42d1ac11048d00fb5bac1e67ccecce2b
URL:http://11tict4sd.sched.com/event/42d1ac11048d00fb5bac1e67ccecce2b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:AI and AR Based Integrated Solution for Optimal Sericulture Management
DESCRIPTION:Authors - G. Indhumathi\, G. Saranya\, S. Riju Sundar\, S. Paul Joseph Abstract - Sericulture or silkworm breeding for silk is faced with the challenges of maintaining the ideal environmental conditions\, feeding patterns\, and disease recognition. Manual and improper monitoring lead to compromised production and quality. This project introduces the implementation of an Augmented Reality (AR)-based real-time system for sericulture management using the intersection of IoT and AI. The system keeps tracks of temperature\, humidity\, and feeding patterns and presents real-time visualization of data in an interactive AR platform. An AI subsystem identifies diseased silkworms via image processing\, annotates them in AR\, and recommends treatment. Predictive analysis also maximizes environmental conditions and feeding patterns for maximum production effectiveness. The uniqueness of the system is its interconnection of AR\, AI\, and IoT that provides easy monitoring\, automatic detection of diseases\, and data- in-formed decision-making. The utilization of the system enhances the quantity of silk yield\, product quality\, and saves labor\, and its disruptive contribution to sericulture management is evident through innovative technologies.
CATEGORIES:VIRTUAL ROOM 9A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:7140d661bf96cd3de8f20e33201fda32
URL:http://11tict4sd.sched.com/event/7140d661bf96cd3de8f20e33201fda32
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:An Iterative Statistical Analytical Review of Blockchain-Based Federated Learning Consensus Mechanisms for Real Time Deployments
DESCRIPTION:Authors - Geetanjali Popat Rokade\, Sonali Patil Abstract - The pressing need for secure\, private\, decentralized frameworks for machine learning in healthcare has been fueled by the increasingly popularization of Federated Learning (FL). In conventional FL\, the aggregation is centralized\, allowing potential data leakages or model-poisoning attacks against a central point of failure. A possible solution to these aforementioned impediments is Blockchain-Based Federated Learning (BDFL)\, as such a setup can utilize the immutability\, transparency\, and distributed consensus of the blockchain to enhance security and achieve better performance. Nevertheless\, the existing review articles have not offered a thorough investigation of BDFL consensus algorithms\, their specific applications to the healthcare sector\, and an iteratively empirical performance evaluation of their efficiency\, scalability\, and robustness. This paper provides a systematic and empirical review of state-of-the-art BDFL consensus programs in their application to health care\; it analyzes these programs' performances based on consensus efficiency\, incentive mechanisms\, privacy-preserving capabilities\, and computational scalability. Key approaches examined in this study include Proof-of-Contribution (PoC) [2\,3]\, Byzantine Fault Tolerance (BFT) [5]\, DAG-based Blockchain FL [4\,13]\, Multi-center Federated Learning (MCFL) [24]\, and Proof-of-Accuracy (PoAcc) [20]. The reason for this focus is that these methods best integrate security\, efficiency\, and fairness in the context of decentralized health data cooperation. The results indicate that MCFL models would optimize institution-wise healthcare cooperation\, PoAcc would optimize the accuracy of medical diagnosis\, and the DAG-based blockchain would guarantee high throughput scalability for FL. This review sets out an extensive framework for selecting the best models in BDFL\, which will encourage developments in AI-nurtured healthcare data analysis\, clinical decision support\, and secure EHR management. This study's findings will propel future advancement in federated learning security\, quantum-safe consensus mechanisms\, and hierarchical blockchain architectures for global health applications.
CATEGORIES:VIRTUAL ROOM 9A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:526768b17c2c1aa7e3885f95b730b6b2
URL:http://11tict4sd.sched.com/event/526768b17c2c1aa7e3885f95b730b6b2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:Analyzing DEI Initiatives in IT/ITES Organizations: A Comparative Study of Organizational Disclosures and Employee Perspectives
DESCRIPTION:Authors - Parvathi NB Panicker\, Bhadra R\, PR Mahadevan\, Vandana Madhavan Abstract - Diversity\, Equity\, and Inclusion have integrated into organizations through incorporations in their Strategic Plans. The presence of a globally dispersed workforce in the IT/ITES sector implies that these strategies are particularly vital in those organizations. Many organizations made pronouncements of publicly declaring their DEI initiatives\; however\, usually a difference exists between such declarations and the experiences of the employees. This study investigates the given DEI initiatives in IT/ITES organizations through two lenses: namely\, by organizational disclosures as well as employee perception. The qualitative research methods involved the gathering of data with corporate DEI reports\, sustainability statements\, and employee-generated reviews through semi-structured interviews with employees. Thematic analysis reveals leading gaps of representation of leadership\, equity in progression of careers\, and inclusion incidences in the workplace. Diversity is preached at entry-level but drops off in representation at leadership levels. Promotion and pay equity remain as sticking issues: underrepresented groups tend to progress in their careers at slower rates. Employees considered organizational DEI commitments as more aspirational than actual\, with workplace inclusion and psychological safety differing in various organizations. Employees expressed skepticism because many DEI efforts do not set measurable success metrics. The study also underscores that organizations should go beyond performative DEI efforts by incorporating employee feedback\, installing structured mentorship programs\, and adopting outcome-based DEI evaluation systems
CATEGORIES:VIRTUAL ROOM 9A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:313ac49180b801d7e451a6c2347c5a1d
URL:http://11tict4sd.sched.com/event/313ac49180b801d7e451a6c2347c5a1d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:Bridging Career Gaps Using AI-Driven Career Pathways and Engaging Augmented Reality Simulations
DESCRIPTION:Authors - Sohana R\, Niharika R\, Khushi Shah\, Tanya Singh\, M Shahina Parveen Abstract - The project majorly includes a methodology to create an AI - driven career counselling platform that can be used to recommend various career options for students (focusing on starting to give them more exposure from a younger age. So that they can incorporate the necessary skills required or in general know what is in it for them in every career option available) based on every individual's profile and varied interests. We utilize artificial intelligence to make sure we can provide personalization of suggestions. The platform takes factors like the interests of students\, their strengths and what kind of work environments they would want to work in\, and then evaluates a list of suitable options. There are also prevailing recent studies that indicate that such systems powered by AI have enhanced the accuracy and reliability of career counselling services by a great extent especially by analyzing extensive behavioral and educational data. Upon this our platform utilizes augmented reality for simulating real- world career environments\, making sure that students get a chance to explore their potential career paths by interactively taking part in the simulations. There has also been extensive research that has demonstrated that Augmented reality-based tools on the whole improve and provide enhancement in immersion\, hands on experiences and helps with better exploration for various professions. Therefore\, we want to merge AI and AR to arrive at best of both worlds and hence approach this problem by providing with an innovative platform that fosters informed decision making and comprehensive career exploration among students. Ultimately our platform's mission is to spread awareness and to align the aspirations that students have with their career paths and to lead to the overall improved educational and career outcomes and job satisfaction.
CATEGORIES:VIRTUAL ROOM 9A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:77cde7ecc20cb5a690acbdf0c576c7b7
URL:http://11tict4sd.sched.com/event/77cde7ecc20cb5a690acbdf0c576c7b7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:Deep Learning-Based Classification of Spine X-Ray Images Using Attention Mechanisms
DESCRIPTION:Authors - Janwale Asaram Pandurang\, Minal Dutta\, Savita Mohurle\, Vaduguru Venkata Ramya Abstract - This study investigates the classification of images of spine X-ray into three groups: Normal\, Scoliosis\, and Spondylolisthesis\, deep learning models improves with attention mechanisms. A labelled dataset of X-ray images was working\, addressed with imbalances class through oversampling techniques. Pretrained convolutional neural network (CNN) models\, including Xception\, InceptionV3\, and DenseNet\, were fine-tuned for this categorised task. The combination of attention mechanisms enhanced interpretability of model and precision score. Working with the models\, InceptionV3 achieved perfect accuracy\, outperforming Xception and DenseNet. The findings insides the efficacy of attention-based deep learning approaches with potential applications in clinical diagnostics\, in medical image classification\, for spinal conditions.
CATEGORIES:VIRTUAL ROOM 9A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:bc373a95d488f27dc7e4659bad4673c6
URL:http://11tict4sd.sched.com/event/bc373a95d488f27dc7e4659bad4673c6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:Empowering EV Sustainability in Decentralized Energy Environment
DESCRIPTION:Authors - Yogesh K. Sable\, Rajesh Kumar Kashyap\, Sagar Satpute Abstract - Microgrids have emerged as cutting-edge and game-changing energy solutions\, providing a plethora of benefits in the search for a robust and sustainable energy future. In-depth examination of the many facets of microgrids is provided in this review\, with specific consideration paid to their capability in the mix of sustainable power sources\, support for charge and e-portability\, and contribution in a debacle readiness and flexibility. The topic of conversation is the arrangement of limited energy frameworks by means of microgrids\, which might work both autonomously and related to the essential electrical network. They successfully consolidate environmentally friendly power assets\, like sunlight powered chargers and wind turbines\, and advance the development of electric vehicles through wise accusing and connection of the framework. Additionally\, because of their intrinsic resilience\, they may keep operating in the face of grid failures and natural disasters\, supplying crucial backup power to crucial facilities. Case studies highlight the real-world uses of microgrids in various contexts and highlight their potential effects on environmental sustainability\, cost savings\, and energy efficiency. The improvement of microgrids is expected to assume a significant part in making versatile and maintainable energy framework as the globe faces rising environment related concerns.
CATEGORIES:VIRTUAL ROOM 9A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:405e7679105912b2ac8ab71577b48445
URL:http://11tict4sd.sched.com/event/405e7679105912b2ac8ab71577b48445
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:Enhanced Slice-Aware Energy Optimization in 5G Networks Using Simplicial Homology: A Comprehensive Framework
DESCRIPTION:Authors - Jaden Ekbote\, Sheshank K Patil\, Ramakrishna S\, Nalini C Iyer Abstract - In the era of 5G\, the dual imperatives of high performance and energy efficiency have led to the development of sophisticated network management techniques. This paper introduces an innovative slice-aware energy optimization framework that leverages simplicial homology to model and analyze network coverage. By representing base stations as vertices in a simplicial complex and encoding overlapping coverage as higher-dimensional simplices\, the approach captures connectivity and potential coverage gaps through homological invariants. An optimization algorithm is then formulated to minimize overall power consumption while fulfilling stringent slice-specific quality-of-service (QoS) constraints for enhanced Mobile Broadband (eMBB)\, Ultra-Reliable Low-Latency Communications (URLLC)\, and massive Machine-Type Communications (mMTC). Extensive simulations in MATLAB demonstrate the viability of the proposed method\, showing significant power reductions over baseline uniform allocation schemes without compromising slice performance. This work underscores the potential of topological methods in addressing the energy challenges inherent in next-generation network deployments.
CATEGORIES:VIRTUAL ROOM 9A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:06d6da608c08634de7823cecf6a0ef5e
URL:http://11tict4sd.sched.com/event/06d6da608c08634de7823cecf6a0ef5e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:Smart Agriculture and Next-Gen Sustainability: Harnessing Big Data and Machine Learning for Carbon Sequestration Prediction with Blockchain-Powered Carbon Credit Trading
DESCRIPTION:Authors - Aditya Poddar\, Soham Sarkar\, Ananya Hegde\, Shravya Reddy\, Animesh Giri Abstract - As climate change accelerates\, there is an urgent need for solutions that balance ecological responsibility with economic incentives. While capping carbon emissions is widely recognized as essential\, it remains a challenging task to quantify carbon sequestration correctly and ensure complete transparency in carbon credit markets. The increasing demand for effective carbon sequestration measurement and transparent carbon credit trading demands an innovative approach using advanced technologies. This research focuses on applying big data using Kafka for parallel data streaming in a distributed environment\, together with machine learning models to optimize the prediction of carbon capture\, integrating blockchain technology which provides security and transparency in transactions involving the carbon credit market. Through our research\, we aim to provide an interdisciplinary framework that will improve the accuracy and scalability of carbon sequestration predictions\, building trust and accountability in carbon trading to support a more sustainable and economically viable future.
CATEGORIES:VIRTUAL ROOM 9A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:103544e7a020c55d8a5b216c54924868
URL:http://11tict4sd.sched.com/event/103544e7a020c55d8a5b216c54924868
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:Smart Safety Surveillance: Deep Learning-Based Detection of Drowning and Slipping
DESCRIPTION:Authors - S. T. Patil\, Gaurav Sulsule\, Urmila Kakarwal\, Sanika Kolawale\, Prathmesh Deshmukh Abstract - This paper suggests a deep learning-based solution for real-time detection of drowning and slipping accidents through computer vision. The system\, which is grounded on the YOLOv8 (You Only Look Once) model\, offers effective and efficient detection by analyzing video streams in real-time to detect dangerous incidents in settings such as swimming pools\, building sites\, and home homes. The system has a web-based user interface\, real-time alerting capabilities\, and SQLite database for storing data. The model was trained and tested with a large set of labeled images with an emphasis on balancing detection performance on frequent and infrequent incident classes. The results include robust detection performance with few false negatives and positives\, fast response times\, and effective processing of multiple video feeds. Despite problems with dataset imbalance and integration complexities\, the system offers a cost-effective solution for enhancing safety\, minimizing human error\, and enhancing real-time monitoring capability. The research suggests the viability of AI-based solutions for safety-critical domains\, with advantages of automated incident detection over conventional surveillance techniques.
CATEGORIES:VIRTUAL ROOM 9A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:6fc9f8c53a4f49497ed556ef80e6b816
URL:http://11tict4sd.sched.com/event/6fc9f8c53a4f49497ed556ef80e6b816
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:Addressing Consumer Resistance to Sustainable Marketing: A Policy and Business Framework Using ISM and Integrated Theoretical Insights
DESCRIPTION:Authors - Payel Das\, Siri Kethineedi Abstract - This study explores the elements of consumer resistance to sustainable marketing with an integrated theoretical approach adopting cognitive dissonance theory\, institutional theory\, and theory of planned behaviour. Although awareness of sustainability is increasing\, consumers frequently do not accept sustainable products because of psychological discomfort\, institutional barriers\, and perceived behavioural limits. Using interpretive structural modelling (ISM)\, this study elucidates the hierarchy relationships among the main barriers\, such as greenwashing\, lack of transparency\, price sensitivity\, norm conformity\, and instantaneous gratification. The most impactful of these drivers were identified as greenwashing and transparency deficits\, both of which contribute to distrust and ultimately erode consumer confidence. Weak regulations and social norms that perpetuate these problems are demonstrated by Institutional Theory\, while price premiums and limited access reduce perceived behavioural control and are described in the theory of planned behaviour. This study proposes a multi-tiered effort for policymakers and businesses to address resistance. Transparency will be enforced through independent certifications and stringent sustainability standards regulated by the regulatory frameworks. To regain consumer trust\, companies must embrace true sustainability and communicate honestly. Price premiums can also be lowered through innovation\, subsidies\, and supply chain efficiencies to help play a role in them become more affordable. Understanding these barriers allows businesses to understand how they can build consumer trust\, policymakers to enact effective regulations\, and society as a whole to begin moving toward more sustainable consumption habits.
CATEGORIES:VIRTUAL ROOM 9B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:7449c3b968e830225c346702eaeb2ba7
URL:http://11tict4sd.sched.com/event/7449c3b968e830225c346702eaeb2ba7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:Analyzing Enablers of Omnichannel Retailing Success Using ISM and MICMAC: A Research-Driven Approach
DESCRIPTION:Authors - Payel Das\, Sonali Bolisetty\, Digumarthi Iswarya Abstract - This study seeks to identify the success enablers of omnichannel retailing by using Interpretive Structural Modeling (ISM) for building a hierarchical framework. From findings in a consumer survey with 108 consumers and expert evaluations\, the research uncovers user enablers such as technological infrastructure\, data analytics capability\, personalization\, mobile optimization\, and seamless integration. The study is based on Service-Dominant Logic (SDL) and the Technology Acceptance Model (TAM) to investigate theory around foundational\, operational\, and experiential issues that result in customer engagement and brand loyalty. The findings underscore the critical importance of strong technological infrastructure and the use of real-time data in helping with friction reduction between digital and physical touchpoints. Using AI-powered analytics\, it can improve personalization\, which affects perceived system usefulness and thus\, customer satisfaction. In addition\, the study emphasizes the need for brand consistency and proper employee training to provide trouble-free service experiences. We also explore privacy and security concerns and their impact on consumer trust and omnichannel adoption. To policymakers\, this research calls for prescriptive regulations that will protect data privacy and grow the space of technological innovation. For practitioners\, it provides actionable insights to maximize omnichannel universality\, improve customer pursuits\, and develop sustainable total brand loyalty. In doing so\, with the introduction of SDL and TAM\, the study contributes to theoretical knowledge and proposes a comprehensive framework for the businesses who are dealing with the complexities of omnichannel retailing.
CATEGORIES:VIRTUAL ROOM 9B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:5fc505dcbd905dd989a2f685226bdccc
URL:http://11tict4sd.sched.com/event/5fc505dcbd905dd989a2f685226bdccc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:Automated Temperature and Humidity Controller for Grain Storage
DESCRIPTION:Authors - Ketki Kshrisagar\, Chinmay Kalbhor\, Sudarshan Chitte\, Atharva Chivate\, Pragati Chopade\, Sanika Chougule Abstract - This paper describes the design and implementation of an automated control system for grain storage temperature and humidity. Operations begin using a microcontroller\, Arduino Uno\, and DHT11 or thermocouple sensors\, for real-time environmental conditions\, whereas the temperature and humidity are controlled through a Peltier module and a USB spray humidifier\, to give the ideal storage conditions. Another complementing feature is an I2C LCD\, which visualizes real-time parameters for the users locally to monitor environmental conditions. Besides\, the system also includes a Blynk app\, which allows the users to monitor and control it through a phone interface from a remote location. The main function of this is to act as a standalone and inexpensive system\, which is primarily aimed at reducing grain spoilage and ensuring quality. Test results have confirmed that it was able to provide applicable environmental control for various storage scenarios
CATEGORIES:VIRTUAL ROOM 9B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:607efca6156772337a90752e3d047b2d
URL:http://11tict4sd.sched.com/event/607efca6156772337a90752e3d047b2d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:Comparative Analysis of Thresholding and GMM-based Methods for Mixed Pixel Identification in Thermal Images
DESCRIPTION:Authors - Fathima Mariya A K\, Sarath S\, Jyothisha J Nair\, Sunitha E V Abstract - Thermal images often hide mixed signals\, making accurate analysis challenging. However\, segmentation and analysis are significantly compromised with the task of mixed pixels (a pixel containing the signals from several endmember sources). This study proposes a hybrid approach combining gradient-based thresholding (80 percentile and 85 percentile) and different clustering techniques (K-Means\, Variational Bayesian GMM\, Dirichlet Process GMM and Constrained GMM) to boost precision in mixed pixel identification. Results show that the gradient threshold has a positive effect on detection error (20.77 percentile)\, closely matching the values of K-Means (20.82 percentile) and Constrained GMM (20.69 percentile). The deviation from those methods to VBGMM and DP GMM is more moderate by 13.60 percentile. This study confirms the usefulness of an integrated approach for a more accurate interpretation of thermal images. Deep learning and multi-spectral will be researched to boost segmentation accuracy in the future.
CATEGORIES:VIRTUAL ROOM 9B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:27b8572d7cfc7f60e77c65115c9c945c
URL:http://11tict4sd.sched.com/event/27b8572d7cfc7f60e77c65115c9c945c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:E-Voting System Using Blockchain App
DESCRIPTION:Authors - Rashmi S. Bhumbare\, Pallavi S. Gaikwad\, Anjali M. Gutte\, Araju M. Shaikh\, Gayatri K. Chaudhari Abstract - In this paper\, ensuring secure\, transparent\, and tamper-proof elections is a critical challenge in modern democratic processes. Traditional voting systems\, including paper ballots and electronic voting machines (EVMs)\, suffer from issues such as fraud\, lack of transparency\, and centralized control. This project presents a Blockchain-Based Voting System\, implemented as an Android application using Java/XML\, with SHA- 256 encryption ensuring vote security and Firebase Realtime Database handling user authentication and data management. The system leverages blockchain technology to record votes in an immutable and decentralized ledger\, preventing manipulation and unauthorized access. The implementation includes secure voter authentication\, encrypted vote submission\, blockchain-based integrity verification\, and real-time result compilation. This approach eliminates traditional vulnerabilities such as vote tampering\, duplicate voting\, and unauthorized system access. Furthermore\, the decentralized nature of blockchain ensures transparency\, allowing voters to independently verify their votes while maintaining anonymity.
CATEGORIES:VIRTUAL ROOM 9B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:c9ba6b9e29ed0d9af2c9a7b9c517eb88
URL:http://11tict4sd.sched.com/event/c9ba6b9e29ed0d9af2c9a7b9c517eb88
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:Optimizing CPU Power Consumption for Sustainable Cloud Operations
DESCRIPTION:Authors - Beena B.M\, Devika Madhusoodanan\, Nithin Sagar\, Vismaya R\, Hridyalakshmi Santhosh Abstract - Energy conservation in cloud data centers remains one of the biggest research challenges today. Energy efficiency has become an important concern in the management of contemporary data centers due to the rapidly growing computational needs and the environmental impact of power consumption. This study examines various power management techniques\, including Dynamic Voltage and Frequency Scaling (DVFS)\, Dynamic Power Management (DPM)\, and Adaptive Voltage Scaling (AVS)\, to optimize CPU power consumption. Using frequency data from historical and current CPU usage\, these algorithms control CPU frequency settings and assess their impact on energy consumption and performance. The results indicate that DVFS reduces power consumption by 25-30%\, DPM achieves energy savings of 28-35%\, and AVS provides savings of 35-40% by dynamically adjusting both voltage and frequency. A performance matrix evaluates the power savings and utilization efficiency of these strategies to determine the most suitable approach. AVS was found to be 5-10% more energy efficient than DVFS and DPM\, demonstrating its advantage in real-world applications. Furthermore\, AVS exhibited the highest precision (96%) to adapt to workload fluctuations\, compared to 95% for DVFS and 92% for DPM. This study focuses on adaptive power management and provides key findings on algorithmic solutions for energy efficiency in software-defined cloud infrastructures. The findings contribute to reducing data center energy consumption while maintaining performance\, aligning with the UN Sustainable Development Goals by promoting sustainable and eco-friendly cloud operations.
CATEGORIES:VIRTUAL ROOM 9B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:4b3ebb18e7400f90f77c2986044cefb2
URL:http://11tict4sd.sched.com/event/4b3ebb18e7400f90f77c2986044cefb2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:Thematic Analysis to Assess Business Continuity Intentions among Women-led Micro Enterprises in Kerala
DESCRIPTION:Authors - Ajay Menon\, Anjali Sivan\, Navya S\, Sandhya G\, Astha Santhosh T Abstract - The micro\, small and medium enterprise sector plays a crucial role in Kerala’s rural economy and makes a substantial contribution to socio-economic development and job creation. This study explores business continuity intentions among women-led micro enterprises in rural Kerala\, using thematic analysis of in-depth interviews with six units from agro-processing\, dairy and fisheries sectors. Drawing insights from qualitative data\, this study uses the Theory of Planned Behaviour (TPB) to show that continuity intentions are strongly influenced by perceived behavioural control\, strong family support\, and positive attitudes. However\, institutional inefficiencies and financial limitations create significant obstacles. This study also introduces ‘Team-led resilience’ and ‘Gendered leadership dynamics’ as critical factors\, highlighting collaborative support and autonomous female leadership. These findings highlight the importance of financial literacy\, access to credit\, and supportive government policies\, which will also help to expand the traditional TPB framework\, emphasizing the importance of social and financial resilience.
CATEGORIES:VIRTUAL ROOM 9B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:38a0a355d33579348ac1057a0358228b
URL:http://11tict4sd.sched.com/event/38a0a355d33579348ac1057a0358228b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:Trends in Teaching Entrepreneurship Research: A Bibliometric Exploration
DESCRIPTION:Authors - M. Suresh\, T. A. Alka\, Aswathy Sreenivasan Abstract - The main purpose of this study is to theoretically explore the evolution of trends in teaching entrepreneurship through a Bibliometric analysis. The final number of documents selected is 1375\, which are analysed through the Biblioshiny package under R programming. The results show that there are technology-related and non-technology-related trends that have evolved in teaching entrepreneurship. Major trends are happening in teaching methods\, learning\, courses\, global reach\, teamwork\, and the emergence of technology trends such as artificial intelligence\, virtual reality\, etc. Bibliometric results draw that the major themes evolved in this domain are related to innovation trends in teaching entrepreneurship for shaping entrepreneurs for tomorrow\, transformation\, learning culture\, technology trends\, academic entrepreneurship in the covid-19 pandemic\, learning types\, concepts\, skills required\, sustainability and teaching entrepreneurship\, entrepreneurialism and thinking in teaching entrepreneurship. The major future research avenues are\; entrepreneurial intention\; effectuation\; entrepreneurship\, business model innovation\; innovation\; digital transformation\, and entrepreneurial university\; academic entrepreneurship\; innovation. The limitations of the research are\; the Scopus database is only used for the search. Only the documents in the English language and final publication stage papers were selected. The inherent drawbacks of the bibliometric methodology may influence the results. The study offers theoretical implications for future research work including Sci-Val future research topics and practical implications by offering insights to entrepreneurs\, investors\, researchers\, academicians\, policymakers\, etc. The novelty and the originality of the study are underlying in the in-depth theoretical exploration through a comprehensive literature review of the past thirty years.
CATEGORIES:VIRTUAL ROOM 9B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:7f46dbe7d610cd6aeaf0f2417fd868ee
URL:http://11tict4sd.sched.com/event/7f46dbe7d610cd6aeaf0f2417fd868ee
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:Understanding Intent to Use Robo-Advisory Services Among Gen-Z Investors
DESCRIPTION:Authors - Ganga S\, Nitharshana P\, Varun Madhusoodan\, Rojalin Patri Abstract - Advancement of financial technology has resulted in the emergence of automated investment solutions\, such as robo-advisors. While Gen-Z investors are typically receptive to digital innovations\, their adoption of robo-advisory services remains an underexplored area. This study investigates the primary factors affecting Gen-Z's inclination to use robo-advisors\, applying the Technology Acceptance Model (TAM). A quantitative methodology was utilized\, with data collected from 161 respondents and analyzed through multiple regression techniques. Findings indicate that trust and attitude have a significant impact on the adoption intent of robo-advisory services in investment decisions made by Gen-Z investors. The results suggest that fostering trust and shaping positive perceptions of robo-advisors are more crucial for adoption than enhancing usability. This study contributes to fintech literature and offers insights for financial institutions and policymakers aiming to increase robo-advisory adoption among young investors.
CATEGORIES:VIRTUAL ROOM 9B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:32f2088273719c1d811a661c3e2a48ef
URL:http://11tict4sd.sched.com/event/32f2088273719c1d811a661c3e2a48ef
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:Understanding the Factors Influencing Online Classes in General Education
DESCRIPTION:Authors - Apolinar P. Datu\, Annaliza C. Sinfuego\, Garry C. Bayran\, Dominic T. Urgelles\, Julius R. Beltran\, Rossana B. Liray\, Janina Odette S. Vidallon\, Erwin Joel B. Layug Abstract - The rapid transition to online learning\, catalyzed by the global pandemic\, has necessitated a critical examination of its implications within the context of general education. This study investigates the multifaceted factors influencing the implementation\, delivery\, and reception of online classes in general education programs across selected higher education institutions. Employing a mixed-methods research design\, quantitative data were gathered through structured surveys while qualitative insights were obtained via in-depth interviews with students and faculty members. Results indicate that technological accessibility\, digital competency\, instructional quality\, learner motivation\, and institutional support are central determinants of effective online learning. The research highlights disparities in students’ digital readiness and access to conducive learning environments\, which significantly affect their academic engagement and performance. Moreover\, pedagogical adaptability and the integration of interactive tools were found to be critical in maintaining student interest and participation in virtual settings. The findings underscore the necessity for higher education institutions to invest in sustainable digital infrastructures\, provide continuous faculty development programs\, and adopt inclusive\, student-centered online learning strategies. This study contributes to the growing body of literature on e-learning by offering empirical evidence on the challenges and enablers of online education in general education curricula. It also presents actionable recommendations aimed at enhancing the quality and equity of online instruction. In doing so\, the research supports the advancement of resilient and adaptive educational systems capable of meeting the evolving demands of 21st-century learners.
CATEGORIES:VIRTUAL ROOM 9B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:29fa410d76ef7ecb21c807bec16fc5f7
URL:http://11tict4sd.sched.com/event/29fa410d76ef7ecb21c807bec16fc5f7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:A STUDY ON SPENDING BEHAVIOR OF CREDIT CARD USERS WITH REFERENCE TO WARDHA CITY
DESCRIPTION:Authors - Nisha Fulzele\, Chetan Parlikar Abstract - The development of financial instruments has greatly changed consumer expenditure patterns\, and credit cards have been central in contemporary economies This paper analyzes the expenditure behavior of credit card customers in Wardha City\, with reference to priority drivers of expenditure patterns. Employing a descriptive research method\, primary data were gathered from 140 participants using a systematic questionnaire. Analysis proves that young professional salaried individuals constitute the maximum segment of credit card customers\, who prefer online payment and high-end transactions. Whereas convenience and payment flexibility come with credit cards\, their use in everyday consumption is still limited. Correlation analysis indicates that rewards\, cashback\, impulse buying\, and financial security drive spending most\, compared to peer influence and promotional offers\, which have lesser impacts. The research indicates that credit card use in Wardha City is increasing\, driven mostly by electronic payment behavior and financial stability. By comprehending these behavior patterns\, financial institutions can make strategies to encourage prudent use of credit and financial literacy among consumers.
CATEGORIES:VIRTUAL ROOM 9C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:8295090cba67888f02f41c2b1a3fce87
URL:http://11tict4sd.sched.com/event/8295090cba67888f02f41c2b1a3fce87
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:An Intelligent System for Dynamic Indian Sign Language Recognition
DESCRIPTION:Authors - Radhika V. Kulkarni\, Vaibhav Aher\, Harsh Ukey\, Sujal Dubey\, Aarya Labhshetwar\, Manjiri Kulkarni Abstract - The majority of community in globe use sign language as the most basic way of interaction with Deaf and speech-impaired people. In most instances\, a person finds it difficult to learn sign language for communicating with deaf and dump people\, which leads to isolation among those individuals. Most people are unaware of the interpretations made in sign language. Hence\, this paper presents an intelligent sign recognition system for translation of dynamic sign language for easy communication among people with hearing and speech impairments. The intelligent system takes advantage of advanced computer vision and deep learning techniques to identify dynamic hand signs accurately. This approach includes video data capture\, preprocessing\, feature extraction\, and real-time gesture recognition. Hand movements are captured from webcam video streams\, and the MediaPipe library is used to capture key points over the hand. A sequential model based on deep learning maps the relationships in hand gestures\, which ensures high recognition accuracy. Extensive testing on different hand gesture recognition datasets shows that they perform efficiently and reliably in real-world situations. This technology facilitates greater accessibility through the ability to quickly and accurately translate sign language\, thereby helping create inclusive communication technologies.
CATEGORIES:VIRTUAL ROOM 9C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:a144d5c5ab9ab69838ff6202b7e8aafa
URL:http://11tict4sd.sched.com/event/a144d5c5ab9ab69838ff6202b7e8aafa
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:Blockchain Based Voting System Using Smart Contracts
DESCRIPTION:Authors - Molly Goel\, Prince Kumar Sharma\, Nainshi Singh\, Madhvi Gaur Abstract - A secure and dignified electronic voting system is needed to provide the security and decency of a traditional one. While still allowing for flexibility and accuracy\, this system has been tested for a long time. The use of blockchain technology can be utilized to actualize distributed voting structures. Despite the technological advancements that have occurred in the past few years\, the traditional balloting system still remains unsuited for the modern era. There are numerous issues that prevent the integrity of the elections\, such as the lack of transparency and the use of bribes. Besides these\, the time it takes to check the vote's integrity is also very long. Current technology has to be used to improve the voting system. One of the most important factors that needs to be considered is the development of blockchain technology. This type of innovation eliminates the character flaw in the voting process and ensures that the correct votes are sent out. The development of blockchain technology is carried out through a stable set of rules that are designed to solve the problems related to the voting process. This type of innovation will help to ensure that the public can easily remember the individuals who participated in the process. The development of a voting poll programming application can help the political selection executives and citizens get the most out of it. However\, it can also expose them to various risks. For instance\, e-voting can lead to political race safety issues and fraud. Despite the advantages of this type of innovation\, it is still not ideal for the people who are interested in maintaining a transparent and honest political selection process.
CATEGORIES:VIRTUAL ROOM 9C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:300fef4f1ad5fa7341bea5b18210953f
URL:http://11tict4sd.sched.com/event/300fef4f1ad5fa7341bea5b18210953f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:Enhanced UAV Human Detection Using Multimodal Sensor Fusion
DESCRIPTION:Authors - Manisha Mane\, Saurav Bedse\, Vikrant Patil\, Pruthviraj Dhande\, Om Darekar Abstract - The fast advances in deep learning and computer vision have dramatically improved the ability to detect objects\, with applications in surveillance\, driverless cars\, and smart traffic management. The current paper describes an implementation of the YOLOv8 model for real-time object detection on different categories such as persons\, cars\, and bicycles. We trained the model on a customized dataset of annotated images\, fine-tuning it through extensive hyperparameter tuning and multiple training epochs. Our training setup consisted of 75 epochs\, utilizing a Tesla T4 GPU for computation. The model recorded a mean Average Precision (mAP@50) of 76.5% over all classes\, with class performance highlighting high precision and recall rates for classes like cars (98.2%) and bicycles (87.8%). To further improve accuracy\, we utilized data augmentation methods\, batch normalization\, and optimizer tuning. After training\, the model was subjected to extensive validation\, with an inference speed of 8.5ms per image\, making it viable for real-time performance. We also incorporated the model into a realistic deployment pipeline\, showcasing its efficacy in real-world applications. This paper presents a thorough analysis of the trained model\, such as performance metrics\, comparison with other versions of YOLO\, and discussion of future improvements. Our results emphasize the model’s ability to achieve speed and accuracy balance\, rendering it an appropriate choice for object detection in real-time applications. Future research will investigate additional optimizations such as light-weight model variants and domain-specific dataset adaptation.
CATEGORIES:VIRTUAL ROOM 9C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:560826bf0aba412f0dd61f6450f87818
URL:http://11tict4sd.sched.com/event/560826bf0aba412f0dd61f6450f87818
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:Improving Solar Panel Efficiency Through Passive Solar Tracking Solutions
DESCRIPTION:Authors - Abhay Shinde\, Ketal Patil\, Nirmitee Chaudhari\, Samrudhi Bachhav\, Kavita Moholkar Abstract - The energy that never goes out of style is solar energy that is readily available and produces no pollution\; its use has increased over the years. It is an endless supply of energy. Optimising solar radiation absorption for power generation is still a major challenge. A solar panel's best position for collecting sunlight is orthogonal to the trajectory of the sun's rays\, but throughout time\, the sun's rays direction varies. Even though a solar tracking system does a good job of recording the sun's motion during the day\, it suffers when adverse weather conditions cause the sun's intensity to decrease. A passive tracking system\, which can handle such circumstances and yield better results\, can therefore be employed to overcome them. The design and functionality of a solar tracking system are the topics of this research. By aligning the solar panel with the sun's position\, which is grounded on a fluid medium\, the suggested outcome offers the best possible conversion of solar energy into electrical power.
CATEGORIES:VIRTUAL ROOM 9C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:8017bc27bf85cdad997f55d14b8f6a28
URL:http://11tict4sd.sched.com/event/8017bc27bf85cdad997f55d14b8f6a28
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:Plant Disease Detection Techniques: An Automated Approach
DESCRIPTION:Authors - Trupti Chetan Kherde\, Dhiraj Jitendra Marathe\, Prathamesh Shivaji Kadam\, Sanskar Dipak Shinde\, Chetan Balaji Phulmante Abstract - Agriculture is one of the fundamental pillars of human civilization. In addition to providing food\, it boosts the economy. Crops and plant leaves are susceptible to several diseases during agricultural production. Diseases prevent each species from growing. Early and accurate plant leaves disease diagnosis helps to minimize major damages to plants. Plant leaves disease classification and detection has grown to be major issues. Failure to promptly identify and categorize plant diseases could lead to agricultural plant loss and a sharp decrease in product. Utilizing digital image processing techniques in their fields can help farmers enhance output and decrease losses. Various techniques have been developed and implemented to identify and classify plant diseases. Over the years\, considerable advancements have been made in finding different disease by exploring and applying different methodologies. However\, because of new developments\, and conversations\, improvements are needed. Globally\, crop production can be greatly increased with the application of technology. Conventional techniques\, such as laboratory-based diagnostics and manual inspection\, are still dependable but time-consuming and labor-intensive. Emerging technologies\, such as Machine learning (ML) and deep learning (DL) techniques have revolutionized automated disease detection\, offering robust solutions for analyzing complex patterns in plant images. This survey highlights recent advancements in these areas.
CATEGORIES:VIRTUAL ROOM 9C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:f8a25f95316cfe475bf6eee884304356
URL:http://11tict4sd.sched.com/event/f8a25f95316cfe475bf6eee884304356
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:Reimagining Dalkhai: A Study of Gender Performativity and Digital Evolution
DESCRIPTION:Authors - Jayasmita Kuanr\, Deepanjali Mishra Abstract - Dalkhai is conventionally a female-centric folk tradition\; nonetheless\, patriarchal frameworks have frequently influenced its performance and distribution. Grounded in Judith Butler's theory of gender performativity\, which analyses how Dalkhai's lyrical narratives and physical expressions formulate\, contest\, and navigate gender identities. The emergence of digital media has allowed Dalkhai to explore new avenues of representation\, enhancing reinterpretations of old themes and promoting wider interaction. Digital media and technology-enhanced performances have elevated female voices\, but they may also commodify or alter traditional expressions to conform to modern cultural norms. This study contends that although digital technology provides opportunities for transformation and inclusivity\, it also requires critical awareness about the recontextualization of traditional folk narratives in virtual environments. The study indicates that the convergence of gender performativity and digital media is transforming Dalkhai’s cultural relevance\, establishing a dynamic arena for both continuity and transformation. The technology integration and folk traditions such as Dalkhai can transform while preserving their artistic integrity\, providing novel opportunities for female representation in the digital era. Therefore\, the study examines the changing performance of Odisha’s Dalkhai folk music via the perspectives of gender performativity and digital transformation. It proposes a critical textual and performative examination of Dalkhai's lyrics\, gestures\, and vocal expressions to elucidate how the folk tradition both reinforces and subverts gender stereotypes.
CATEGORIES:VIRTUAL ROOM 9C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:171107a065f10b5869ccb0f9b936a368
URL:http://11tict4sd.sched.com/event/171107a065f10b5869ccb0f9b936a368
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:Seamless Handovers in 5G Networks: WLAN to LTE
DESCRIPTION:Authors - G.B.Sambare\, Prajwal Solase\, Raj Lokhande\, Chaitanya Shinde\, Sujit Aher Abstract - Heterogeneous wireless networks face challenges in ensuring smooth mobility between WLAN and LTE\, as traditional handover decisions based on signal strength often degrade service quality. A more advanced approach incorporates multiple network parameters like signal power\, link speed\, system delay\, and user mobility for optimized vertical handover. Real-time throughput calculations and dynamic network ranking enhance selection\, while MCDA techniques improve transfer continuity\, reduce delays\, and minimize packet loss. Simulation results confirm that this strategy outperforms conventional methods by reducing handover failures and improving network selection. Additionally\, advanced techniques like FSHO and SSHO are explored for seamless multimedia services in 5G networks.
CATEGORIES:VIRTUAL ROOM 9C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:c2f2eb4481aaee8c0194036f7d518944
URL:http://11tict4sd.sched.com/event/c2f2eb4481aaee8c0194036f7d518944
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:Sentiment Analysis of Textual Data: A Comparative Study of SVM\, Logistic Regression\, and Naive Bayes
DESCRIPTION:Authors - Khushi Ingalalli\, Vanshika Kavi\, Sainath Walthati\, Satish Chikkamath\, Suneeta Budihal\, Sujata Kotabagi Abstract - With the millions of tweets per day\, Twitter is a rich and large database of information on public sentiment on a wide range of issues\, including events\, products\, politics\, and social issues. The purpose of this research is to create an automated system that can analyze tweet sentiments to determine attitudes as positive or negative. Through Natural Language Processing (NLP) methods and machine learning algorithms\, the system efficiently handles high quantities of unstructured data\, making sentiment classification possible in real time. The model begins the analysis by gathering various tweets from various sources\, such as hashtags\, user mentions\, and trends. The tweets are then subjected to preprocessing techniques like removing stop words and treating misspellings\, emojis\, and special characters. Various classification models\, like Naive Bayes\, Support Vector Machines (SVM)\, Logistic Regression (LR) were experimented with to see which was most efficient in sentiment classification. Of these\, Logistic Regression (LR) showed the best performance with an F1 score of 0.833 and accuracy of 83%. The efficiency of various feature extraction methods\, such as Term Frequency- Inverse Document Frequency (TF-IDF) and word embeddings\, was also examined to try and improve model performance. This work emphasizes the increasing importance of Twitter Sentiment Analysis across different fields\, such as market research\, event tracking\, and social research. Sentiment analysis is employed by companies to know customer views and enhance services\, whereas policymakers utilize it for measuring public reaction. By combining NLP and machine learning\, the suggested system provides better and scalable method for sentiment analysis[1].
CATEGORIES:VIRTUAL ROOM 9C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:14ec672bdb4d4bad95348ddaf1209835
URL:http://11tict4sd.sched.com/event/14ec672bdb4d4bad95348ddaf1209835
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:Toxic Hinglish Comment Detection
DESCRIPTION:Authors - Gopal D. Upadhye\, Deepak T. Mane\, Devang Gentyal\, Chetan Channa\, Shubham Landge\, Radhika Gadewar Abstract - Toxic comment identification in Hinglish (a combination of Hindi and English) is a difficult task because of code-switching\, transliteration\, and class imbalance. This paper suggests a machine learning based method for identifying toxic Hinglish comments based on TF-IDF feature extraction along with an ensemble model. In order to mitigate class imbalance\, Random Oversampling was utilized\, and model interpretability was facilitated using SHAP (Shapley Additive Explanations). The suggested model was trained on publicly released datasets\, with 90.0% accuracy compared to individual classifiers. This work contributes to content moderation system for code-mixed languages and offer an extensible solution for social media toxicity detection.
CATEGORIES:VIRTUAL ROOM 9C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:6b310afd2791f1b47ae6a39b246e6cd7
URL:http://11tict4sd.sched.com/event/6b310afd2791f1b47ae6a39b246e6cd7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:A Comprehensive Framework for LiDAR–Camera Calibration and Temporal Synchronization Using Target-Based Method
DESCRIPTION:Authors - S M Boomika\, C M Tulasi\, Sharvani V Nagur\, Bhagyashri Badakali\, Nalini C Iyer\, Preeti Pillai\, Ujwala Patil Abstract - LiDAR and cameras play a vital role in autonomous vehicles by providing complementary data for object detection and environmental perception. However\, achieving seamless data integration from these sensors depends on partial and temporal synchronization. Unlike conventional methods that depend on pre-calibrated datasets\, our methodology utilizes a custom-acquired multimodal dataset comprising both image and video data from a monocular camera and point cloud data from a VLP-16 Velodyne LiDAR sensor. In this paper\, we proposed a comprehensive framework for LiDAR and camera calibration and temporal synchronization of real time data\, synthesized and validated in a controlled lab environment. Calibration of the raw data was performed using a checkerboard as the target to ensure accurate spatial alignment between heterogeneous sensor systems.The collected corpus is further timestamped\, synchronized\, and validated.The accuracy of the proposed methodology is evaluated by projecting LiDAR points onto image frames\, enabling qualitative verification of spatial and temporal consistency. The proposed method integrates target-based calibration with software-level timestamp synchronization to create a reproducible and scalable calibration pipeline. Results demonstrate accurate alignment across modalities\, validating the effectiveness of our approach. This 1 work provides a practical contribution to multi-sensor fusion research\, especially for applications requiring custom datasets or operating in constrained environments.
CATEGORIES:VIRTUAL ROOM 9D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:890908444b85b2b4ae5ceb2f2894fa5b
URL:http://11tict4sd.sched.com/event/890908444b85b2b4ae5ceb2f2894fa5b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:A Study on Use of Wearable Sensors to Empower Personalized Healthcare
DESCRIPTION:Authors - Wendrila Biswas\, Arunangshu Giri\, Dipanwita Chakrabarty\, Dibyendu Rath Abstract - The study has examined the effect of user engagement (UE)\, perceived benefit (PB)\, and perceived risk (PR) of wearable sensor-based healthcare devices adoption. User empowerment (UEM) in IOT-enabled healthcare has been explored on the basis of two established theories\, Technology Acceptance Model (TAM) and Behavioral Reasoning Theory (BRT). A cross-sectional online survey was conducted from November 2024 to January 2025 involving 361 valid Indian respondents and the collected responses were analyzed through NVivo software for qualitative analysis. SEM (structural equation modeling) was done for quantitative analysis and hypothesis testing. The findings have shown a positive association between UE and PB and between UE and PR. Again\, the study has revealed that PB and PR positively influenced UEM. The study contributes both to existing literatures and making managerial decisions by establishing how benefits from wearable sensor-based healthcare devices can be explored by avoiding the perceived risk of the consumers and how they can get empowered with the same.
CATEGORIES:VIRTUAL ROOM 9D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:306f8b4ad672ec6734018a5f2f0ee844
URL:http://11tict4sd.sched.com/event/306f8b4ad672ec6734018a5f2f0ee844
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:Customer Retention Prediction
DESCRIPTION:Authors - Vaishali Langote\, Siddhesh Kulkarni\, Aaditya Ghorpade\, Aditya Songirkar\, Aditya Chincholkar Abstract - Identifying customer retention is essential for decreasing lost revenues as well as maintaining an established base of loyal customers. By reviewing historical data that includes customer demographics\, purchasing habits and behaviours\, businesses will be able to determine which customers are going to discontinue using their services or products. In generating models that can identify customers at risk\, this process includes machine learning models such as decision trees\, logistic regression and neural networks. It is important that predictive retention can work provided the right algorithms are selected\, and reliable data is sourced. Continual updates and improved models will enhance accuracy\, giving firms the opportunity to keep up with changes in how consumers behave. The models will also give businesses the ability to produce more targeted retention marketing plans since they will not only identify at-risk customers but also give clear data on what they are doing to create customer churn.
CATEGORIES:VIRTUAL ROOM 9D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:1a3cf9f6decc058ca94fc843739ba29a
URL:http://11tict4sd.sched.com/event/1a3cf9f6decc058ca94fc843739ba29a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:Harnessing AI and Biomimicry for Resource Recovery: Advancing Circularity in Smart Infrastructure Systems
DESCRIPTION:Authors - Sharon Koshy\, Padmadas Sundaram Abstract - The intensifying depletion of natural resources\, fueled by world population growth and unsustainable consumption\, poses severe threats to global sustainability. Specifically\, the ICT and smart infrastructure industries make substantial contributions to resource inefficiencies through growing e-waste\, inefficient material recovery\, and unsustainable construction methods. Forecasts suggest that by 2050\, with a projected 9.8 billion world population\, resource use will surpass planetary limits\, urging rapid interventions in resource management and the transition to circular economies. Despite growing recognition\, inefficiencies in recycling infrastructure\, defective waste-to-energy technologies\, and inadequate water management persist to drive global resource insecurity and further environmental degradation. Solutions must be backed by evidence-based policy design\, technological development\, and systemic change. In this context\, the combination of Artificial Intelligence and biomimicry offers a new way to increase sustainability and resilience in systems. AI-based models improve resource efficiency\, reduce environmental footprint\, optimize waste management\, facilitate predictive maintenance\, and enhance material recovery\, while biomimicry offers nature-inspired solutions for sustainable design\, energy efficiency\, and waste reduction. These technologies not only foster resource recovery but also set the stage for the creation of wiser\, more sustainable industries and cities. In conclusion\, this study high- lights the revolutionary power of ICT that AI and biomimicry make possible to create closed-loop\, self-sustaining models that boost urban resilience\, sustainability\, and efficiency\, maximize recovery of resources\, minimize waste\, and maximize value for a truly circular future.
CATEGORIES:VIRTUAL ROOM 9D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:a2765aacf321215dbc81a0f2d85f232f
URL:http://11tict4sd.sched.com/event/a2765aacf321215dbc81a0f2d85f232f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:Opinion Mining of YouTube Video Comments using Machine Learning and Deep Learning
DESCRIPTION:Authors - Priya Surana\, Sushma Vispute\, Madhura Kalbhor\, Shubhangi Vairagar\, Pragati Ugale\, Imtiyaz Syeda\, Mahek Yakumsha\, Ashish Suryawanshi Abstract - This research presents a YouTube Comments Analyzer that leverages machine learning and deep learning algorithms to examine and classify user comments. A large volume of comments is processed by the system\, enabling it to detect key patterns\, including sentiment classification and emotion detection. Using natural language processing and machine learning techniques\, the tool provides meaningful insights to content creators for understanding their audience and to moderators for identifying problematic content. Researchers can also benefit by studying online commentary at scale. Our team collected video comments from various genres to train and develop the models\, followed by evaluation using multiple performance metrics. The analysis tool achieves 96% accuracy in sentiment detection and 90% accuracy in emotion detection\, successfully identifying complex patterns that manual evaluation often misses. To demonstrate the practical applicability of our models\, we further developed a web-based application that integrates the analysis pipeline\, providing an accessible platform for real-time comment analysis. This research highlights the effectiveness of automated text analysis in social media environments and demonstrates real-world applications for YouTube content management and audience engagement strategies.
CATEGORIES:VIRTUAL ROOM 9D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:797df497e706acf8147bf5f9dbe65bd6
URL:http://11tict4sd.sched.com/event/797df497e706acf8147bf5f9dbe65bd6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:Optimized Bounding Box Fitting Method for Object Detection
DESCRIPTION:Authors - Omkar Kalantre\, Jyoti Joglekar Abstract - Optimized Bounding box fitting around an object is necessary for accurate localization of the Region of Interest (ROI)\, so that features extracted from the ROI are useful for many computer vision applications. Current methods tend to be inefficient\, imprecise\, and with high computational complexity. In this work a novel algorithm is presented that is designed for fitting a bounding box around an object that covers maximum part of the object as ROI\,. The improvement in inserting bounding box enhances the process of recognizing\, tracking\, and classifying objects\, which is highly valuable for applications such as surveillance\, autonomous driving\, and security. In this work we are proposing a novel algorithm for fitting a bounding box around an object to maximize the object area covering and for minimizing the background clutter as a part of ROI.
CATEGORIES:VIRTUAL ROOM 9D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:7d50eaacbb08db24dff5489f7ae7dc86
URL:http://11tict4sd.sched.com/event/7d50eaacbb08db24dff5489f7ae7dc86
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:Optimizing Road And Pothole Segmentation on Indian Traffic Data Using Pretrained computer vision Models
DESCRIPTION:Authors - Mohan Sellappa Gounder\, Rohan Mahantesh Kamatgi\, Sharath Prabhu T M\, Sanya Gupta\, Seema Abstract - This research investigates the application of the DINO (Distillation with No Labels) framework\, a self-supervised learning approach\, for efficient road and pothole segmentation. By integrating a DINO-enhanced ResNet-50 backbone with a U-Net model\, this study addresses segmentation challenges in dynamic environments. The framework employs momentum encoders\, multi-crop training\, and stability mechanisms to facilitate robust feature extraction without requiring labeled datasets. Through strategic fine-tuning\, the model achieves precise segmentation of road surfaces and potholes\, making it a promising approach for real-world applications in autonomous systems and infrastructure assessment. This study further discusses model evaluation\, comparison with state-of-the-art approaches\, and its implications for transportation infrastructure.
CATEGORIES:VIRTUAL ROOM 9D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:ddad18411f31bb896561abb0ba25970c
URL:http://11tict4sd.sched.com/event/ddad18411f31bb896561abb0ba25970c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:Precision Agriculture: Enhancing Crop Selection and Yield Forecasting with ML
DESCRIPTION:Authors - Gopal D. Upadhye\, Ranjana Jadhav\, Aryan Pungale\, Ashish Shadija\, Nikita Rajput\, Pranav Pendse Abstract - A data-informed system is described for generating crop recommendations and crop yield forecast based on a variety of data sources of farmer-level soil characteristics\, historical crop yield records\, and meteorological variable data. In the proposed system\, crop recommendations based on a classification algorithm and crop yield estimates based on a regression algorithm are provided to farmers. The data-driven crop recommendations and crop yield forecasts will improve decision-making by providing the farmer with data-based recommendations providing the productivity isolation. The data-informed system will utilize machine learning algorithms to process the data and analyze the complex interaction of the various farming agri-parameters in the farm operation. Composition of soil nutrient values\, weather patterns\, and historical productivity variable data will be a key ingredient in the model to provide farmers with singularly specific crop selections. Ability to yield prediction gives farmers anticipate yield of the crops\, improve resource planning. The validation tests demonstrate better accuracy than traditional heuristics\, improving farmer overall risk reliability and increasing efficiency\, sustainability. The results shows us that the transformative role of machine learning in agriculture and the associated movement toward precision farming practices
CATEGORIES:VIRTUAL ROOM 9D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:561785fcea68a547824ce64daf990498
URL:http://11tict4sd.sched.com/event/561785fcea68a547824ce64daf990498
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:SEMICONDUCTOR WAFER FAULT DETECTION USING MACHINE LEARNING
DESCRIPTION:Authors - Vanshika R Kavi\, Sujata Kotabagi Abstract - Semiconductor production demands high-quality control to detect faulty wafers early on in the production process. Manual inspection and rule-based systems are conventional methods that are time consuming and error-prone. This research investigates machine learning (ML) based wafer detection on a dataset of 590 sensor readings per wafer\, with wafers being labeled as good (+1) or faulty (-1). Several traditional ML models\, such as Logistic Regression (LR)\, Support Vector Machines (SVM)\, K-Nearest Neighbors (KNN)\, and Random Forest\, are tested for defect classification effectiveness. The processing of data includes handling missing values by dropping features with high missing data and using median imputation. Feature selection is done through SHAP (Shapely Additive Explanations) analysis and correlation filtering to select only the most important sensor readings. Feature scaling is done to maintain consistency in data distribution. For handling the class imbalance in the dataset\, SMOTE (Synthetic Minority Over-sampling Technique) is employed to create synthetic samples for the minority class to enhance model learning. Once trained\, the models are evaluated on the basis of accuracy\, precision\, recall\, F1-score\, confusion matrix\, and SHAP-based explainability analysis. SVM and Random Forest perform better compared to other models with 97-99% accuracy\, and KNN does not perform well because of high dimensionality. The research showcases how ML is able to automate defect detection\, increase production efficiency\, and minimize human inspection errors. Work for the future encompasses ensemble learning optimization\, real-time deployment\, and semi-supervised learning optimization for enhanced defect classification in the semiconductor industry.
CATEGORIES:VIRTUAL ROOM 9D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:6e25ad49c43753ea0eb57671a0a0a1a3
URL:http://11tict4sd.sched.com/event/6e25ad49c43753ea0eb57671a0a0a1a3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:Smart Hostel Security System Using Face Recognition for Enhanced Access Control
DESCRIPTION:Authors - Piyusha S. Shetgar\, Asha V. Thalange\, Rohini R. Mergu\, Aishwarya Khobare Abstract - Throughout the world\, the number of educational institutions has significantly increased in recent decades. But the majority of recently established universities continue to manage their resources\, including their hostels\, using traditional methods. These conventional methods are frequently hindered by innate restrictions that negatively impact the organization's overall effectiveness. This study suggests an automated hostel lodging management system that is made with Microsoft Access as the underlying database and Visual Basic as the programming language to handle these issues. To stop unwanted access\, the system has an integrated authentication algorithm. The system that has been built leverages face recognition technology to address the shortcomings of conventional approaches. It provides a graphical user interface\, dependability\, efficiency\, and improved security by implementing access control mechanisms.
CATEGORIES:VIRTUAL ROOM 9D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:191a618fe886870f81446ecca45fd326
URL:http://11tict4sd.sched.com/event/191a618fe886870f81446ecca45fd326
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:Adaptive Control Strategy for Seamless Bidirectional Power Flow in Three-Phase Dual Active Bridge Converters for EV Fast Charging Applications
DESCRIPTION:Authors - Manasa S\, Rupam Bhaduri\, Pramod Kumar Naik\, Gangadhar T G\, Bharath Kumar S Abstract - This paper introduces an adaptive control strategy for a three-phase Dual Active Bridge (DAB) converter\, designed to facilitate efficient bidirectional power flow in electric vehicle (EV) fast-charging stations. The proposed control method effectively manages real-time fluctuations in grid conditions and the state-of-charge (SOC) of batteries\, ensuring stable operation in both Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) modes. Utilizing a dq-reference frame-based decoupled controller with SOC feedback\, the solution is rigorously validated through MATLAB/Simulink simulations. The design encompasses LCL filter modeling\, DAB phase shift modulation\, and battery interfacing under diverse loading scenarios. Simulation results reveal significant improvements in performance\, highlighting the system's ability to maintain high efficiency during both charging and discharging phases. By enhancing the responsive-ness and stability of power exchange between EVs and the grid\, this research aims to contribute to the development of advanced fast-charging infrastructure capable of supporting increasing EV adoption while optimizing overall electric grid performance. The findings underscore the potential of adaptive control strategies in ensuring reliable and efficient energy management within smart grid environments.
CATEGORIES:VIRTUAL ROOM 9E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:96668d259d39c04ba7eb9cf1a2751673
URL:http://11tict4sd.sched.com/event/96668d259d39c04ba7eb9cf1a2751673
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:An Indoor Navigation System for the Visually Impaired
DESCRIPTION:Authors - Saraswati Patil\, Kalyani Rathod\, Adarsh Jayfale\, Wasim Pathan\, Adesh Bhore Abstract - This paper looks at how object detection technology can help blind and visually impaired people. Visually challenged individuals struggle to comprehend their surroundings\, especially in outdoor settings where objects constantly shift and move. Object detection solutions can help visually impaired individuals overcome difficulties in daily life. The object detecting system aims to provide a simple\, user-friendly\, convenient\, and cost-effective solution for visually impaired individuals. This yolov11 model has the frame process rate 45 FPS on CPU and 100 – 150 FPS on GPU .The system was tested with different objects and in various environments to see how well it works. Key factors like how accurate it was\, how quickly it responded\, and how satisfied users were measured. The results showed that the system was good at detecting objects and giving clear instructions to the user in real time.
CATEGORIES:VIRTUAL ROOM 9E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:39b2764ab0532e1ec12fd6a952fceb9b
URL:http://11tict4sd.sched.com/event/39b2764ab0532e1ec12fd6a952fceb9b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:AN INVESTIGATION INTO HEALTHCARE AND IT PROFESSIONALS' CONFIDENCE LEVELS REGARDING THE USE OF AI IN THE HEALTHCARE INDUSTRY
DESCRIPTION:Authors - Aranya G\, Durgalashmi C V\, Nidheesh Melethadathil Abstract - This study examines the confidence levels of healthcare and IT professionals regarding the execution of artificial intelligence (AI) in the healthcare sector. Focus on understanding the perceived impacts of AI on patient safety\, quality of care\, and the ethical and legal implications involved\, the research employed an analysis through a detailed questionnaire\, gathering responses from 50 healthcare professionals and 50 IT professionals in Kerala using judgmental sampling. Survey model and percentage analysis were used in this study. The findings indicate a mixed sentiment: a substantial proportion of respondents acknowledge the potential of AI to enhance healthcare delivery and patient outcomes\, yet there remains significant apprehension concerning data privacy\, potential biases\, and the need for human oversight. While IT professionals generally display greater confidence and familiarity in AI technologies\, healthcare professionals are more cautious\, emphasizing the importance of ethical considerations and human involvement in clinical decision-making. The study suggests that bridging the gap between these professional groups through targeted education\, hands-on experience\, and robust governance frameworks can enhance confidence and facilitate the effective integration of AI in healthcare. Recommendations include ongoing training and clear communication about AI's capabilities and limitations to ensure both ethical application and improved healthcare outcomes.
CATEGORIES:VIRTUAL ROOM 9E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:9b0d4bcec1dbcc8796017eecf4b5b256
URL:http://11tict4sd.sched.com/event/9b0d4bcec1dbcc8796017eecf4b5b256
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:Automatic Irrigation and Tank Water Monitoring System
DESCRIPTION:Authors - Ritu Ramesh Vernekar\, Vijeta D Chitragar\, Laxmi Koutanali\, Prajwal Sangalad\, Hemantaraj M Kelagadi\, Suhas B Shirol Abstract - The ESP32 microcontroller and the Blynk IoT application are integrated in a novel system for automatic irrigation and tank water level management. Sensors for water levels\, rainfall\, temperature\, and soil moisture track real-time environmental parameters. Temperature readings ranged from 25°C to 31°C over the 8-day research\, but soil moisture was continuously kept within ideal ranges. Water waste was reduced and timely refills were ensured by the water tank level sensor mechanism\, which successfully maintained a threshold of 15 cm. Based on sensor data\, intelligent algorithms control irrigation\, minimize waterlogging\, and maximize water usage. Convenience and operational efficiency are increased via remote management via the Blynk app. This intelligent irrigation system provides a sustainable and effective answer to contemporary agriculture by preserving water\, improving crop health\, and facilitating data- driven farming methods.
CATEGORIES:VIRTUAL ROOM 9E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:955fccac2e1ab3b8925e373a8472448e
URL:http://11tict4sd.sched.com/event/955fccac2e1ab3b8925e373a8472448e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:Blockchain-Enhanced Federated Learning for Adaptive IoT Network Security
DESCRIPTION:Authors - Yash Prajapati\, Ketul Patel\, Nidhi Acharya\, Nidhi Dubey\, Nisarg Patel Abstract - The Internet of Things (IoT) is progressively changing and offers IoT ecosystems integrated network security challenges that require sophisticated security solutions. In this paper\, we discuss the hybrid model that combines Federated Learning (FL) with Random Forest (RF) algorithms along with the validation of Blockchain to provide adaptive network security within IoT frameworks. The proposed architecture merges Blockchain’s protection against unauthorized access with the automatic updates and data processing of FL\, decentralizing the security measures within the IoT ecosystems while increasing detection accuracy and safeguarding sensitive infor-mation. This framework overcomes the constraints imposed by centralized machine learning intrusion detection techniques\, providing solutions to real world IoT security issues.
CATEGORIES:VIRTUAL ROOM 9E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:df5f869f43c3a19f889f362ca9ee4020
URL:http://11tict4sd.sched.com/event/df5f869f43c3a19f889f362ca9ee4020
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:CNN-LSTM Hybrid Network for Blind Recognition of Channel Encoders
DESCRIPTION:Authors - Harsh Raj\, Kanishk Tewatia\, Sumeet Gupta Abstract - Channel encoding plays a vital role in modern communication systems by maintaining data integrity and reducing the impact of noise. In this paper\, we propose a hybrid model that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to classify various channel encoders. This approach aims to improve feature extraction and classification performance compared to traditional CNN architectures. In typical scenarios\, receivers are aware of the encoder’s type and configuration. However\, in non-cooperative environments such as military communications\, surveillance\, and cognitive radio systems\, this information is often limited or unavailable. To address this\, we explore a deep learning-based method to identify four types of encoders: block\, convolutional\, Bose–Chaudhuri–Hocquenghem (BCH)\, and polar encoders. By integrating CNN and LSTM layers\, our proposed model achieves up to 98% classification accuracy and demonstrates strong generalization. Comparative analysis reveals that the hybrid model outperforms conventional CNN-based methods in terms of accuracy and robustness.
CATEGORIES:VIRTUAL ROOM 9E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:f4b6ec8f97171234926c7301a2ee00de
URL:http://11tict4sd.sched.com/event/f4b6ec8f97171234926c7301a2ee00de
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:Cyber-Security Awareness and Sustainable Human Development in India: A Capability Approach
DESCRIPTION:Authors - Arunkumar V N\, Agna.S. Nath\, Aswathi.K. B Abstract - This study examines the disconnect between India’s cybersecurity policies and their real-world implementation\, revealing systemic barriers to digital empowerment. Through qualitative analysis\, the research identifies four critical challenges: inadequate awareness programs\, urban-rural security divides\, gender-based vulnerabilities\, and educational gaps in cyber-literacy. Findings show urban users exhibit risky digital behaviours despite high connectivity\, while rural populations avoid online services due to security fears. Women face compounded risks\, with many dependent on male relatives for digital access. The education system largely fails to equip students with basic cybersecurity knowledge. However\, community-led initiatives demonstrate promising alternatives. Localized\, vernacular training programs have successfully enhanced digital safety awareness and reduced fraud incidents. These models highlight the importance of contextual\, participatory approaches to cybersecurity education. The study argues for rethinking cybersecurity as an essential dimension of human development rather than just technical infrastructure. It proposes shifting from compliance-focused governance to capability-building frameworks that prioritize protective freedoms for all citizens. Key recommendations include integrating cybersecurity into school curricula\, developing gender-responsive digital safety programs\, and creating community-based "digital mitra" networks. By bridging policy intentions with ground realities\, this research offers pathways to make India's digital growth truly inclusive and secure.
CATEGORIES:VIRTUAL ROOM 9E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:7d93e9ce341da71b516ffdff202bcf84
URL:http://11tict4sd.sched.com/event/7d93e9ce341da71b516ffdff202bcf84
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:Enhanced Human Presence Detection in Restricted Zones Using mmWave Technology and Deep Learning
DESCRIPTION:Authors - Pratyush Jaishankar\, Ayman Aftab\, Divyanshu Vyas\, Dhanashree G Bhate Abstract - The research proposes a distinctive method to identify unauthorized people who enter restricted areas through a combination of KLD7 millimeter wave radar systems and deep learning algorithms. Gait patterns obtained from Doppler and micro-Doppler signals are analyzed by the system which offers both privacy preservation and non intrusiveness as opposed to conventional methods like CCTV surveillance. The Random Forest Classifier shows excellence by accurately identifying authorized or unauthorized individuals at a rate of 82% while maintaining its capabilities during various challenging environmental situations. The solution provides high practicality when used for real-time monitoring deployments. Future development efforts will direct their attention to growing the dataset while making the solution work efficiently on edge computing devices.
CATEGORIES:VIRTUAL ROOM 9E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:beca6e2f4ddc65ed9f35ab30a5d3524b
URL:http://11tict4sd.sched.com/event/beca6e2f4ddc65ed9f35ab30a5d3524b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:Event Detection from News Articles Using Lexical and Contextual Ranking Models in IR Systems
DESCRIPTION:Authors - Shreya Kapadia\, Payal D Joshi Abstract - In the era of IR\, event detection has moved beyond simple keyword searches to utilize advanced techniques to extract relevant events from massive news article datasets. The rapid growth of news highlights the need for efficient information retrieval techniques to capture the most relevant events. Traditional lexical-based retrieval methods\, such as Whoosh and BM25\, are effective in keyword matching\; however\, they have some limitations in understanding the semantic events from the indexed text. To enhance this limitation\, this study introduces a Transformer-based deep learning model for Natural Language Processing (NLP)\, such as BERT\, capable of capturing contextual relationships and improving the relevance of data. This research also explores an optimized approach that seamlessly integrates Whoosh for efficient indexing\, BM25 for probabilistic ranking\, and BERT for neural re-ranking\, designed to improve event detection performance. Additionally\, Named Entity Recognition (NER) significantly enhances event extraction by accurately identifying real-world entities like individuals\, locations\, organizations.The results of this research indicate that the integration of lexical models(Whoosh and BM25) with neural ranking models(BERT) significantly enhances precision\, recall\, and relevance\, thereby exceeding the performance of traditional retrieval techniques. In our experiments BERT achieved a relevance score of 62% \,outperforming BM25 \, which scored 55%. This demonstrates superior ability to capture contextual and semantic relationship in text. In conclusion\, this study articulates prospective directions for future research within the realm of event detection\, improving the efficacy of information retrieval in rapidly evolving news environments.
CATEGORIES:VIRTUAL ROOM 9E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:c34c636a3b78c1fbc7d6691378b5a896
URL:http://11tict4sd.sched.com/event/c34c636a3b78c1fbc7d6691378b5a896
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:Optimized Control Circuit Design for Single-Phase Inverter with Enhanced Efficiency
DESCRIPTION:Authors - Sarika Kuhikar\, Kashish Mishra\, Tejas Dabholkar\, Tejal Narvekar\, Siddharth Suyal Abstract - This paper presents the design of a control circuit for a single-phase inverter capable of generating a pure sine wave output that is accurately aligned with the desired voltage amplitude and frequency. With the global shift toward renewable energy sources\, the need for efficient and reliable power conversion systems has become more critical than ever. The proposed design utilizes advanced microcontroller technology along with modulation techniques such as Sinusoidal Pulse Width Modulation (SPWM) and Selective Harmonic Elimination (SHE). These techniques help achieve higher efficiency\, significantly reduce harmonic distortion\, and enhance the overall reliability of the inverter. This innovative approach contributes to improved energy efficiency and supports the development of smarter\, more environmentally friendly power systems. The inverter is highly suitable for integration into solar energy systems\, offering a stable and clean AC power supply for both residential and commercial applications. Its modular architecture also allows easy scalability to meet varying load demands and future upgrades.
CATEGORIES:VIRTUAL ROOM 9E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:c3dc878641207ba78cfc947343362821
URL:http://11tict4sd.sched.com/event/c3dc878641207ba78cfc947343362821
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T100000Z
DTEND:20260826T120000Z
SUMMARY:ParkSense: An IoT-Driven Smart Parking Solution
DESCRIPTION:Authors - Aditya Waradkar\, Anagha Galagali\, Niha Solkar\, Shloka Suvarna\, Aparna Bannore Abstract - Urbanization has resulted in a high rise in the use of vehicles\, thus increasing parking problems like extended search times\, fuel consumption\, traffic congestion\, and user frustration. To counter these problems\, this paper introduces ParkSense\, an IoT-based smart parking system that combines hardware and software elements for real-time parking space monitoring and management. It uses NodeMCU microcontrollers and IR sensors for car presence detection and an LCD display for real-time on-site updates. It has connectivity with ThingSpeak cloud to provide remote data access and visualization. The frontend is built with the MERN stack (MongoDB\, Express\, React\, Node.js)\, and the Tailwind CSS provides a user-friendly and responsive interface on devices. ParkSense functionalities include real-time slot monitoring\, access to historical data\, administrative dashboards\, and secure online payments. The system has proven to be highly efficient\, reliable\, scalable\, and easy to use during testing and implementation. It saves considerable parking search time and fuel consumption\, thus helping to create a more sustainable city environment. Future developments involve AI-based predictive analytics\, dynamic pricing\, personalized recommendations\, and integration with EV charging stations.
CATEGORIES:VIRTUAL ROOM 9E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:d10b78e9a0e0837c9ea130a31d421e38
URL:http://11tict4sd.sched.com/event/d10b78e9a0e0837c9ea130a31d421e38
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T120000Z
DTEND:20260826T120200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 9A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:ea54d8df8c249adf6cc386d99ccc965f
URL:http://11tict4sd.sched.com/event/ea54d8df8c249adf6cc386d99ccc965f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T120000Z
DTEND:20260826T120200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 9B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:6c5b2b954fb9873811af0f34f6987697
URL:http://11tict4sd.sched.com/event/6c5b2b954fb9873811af0f34f6987697
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T120000Z
DTEND:20260826T120200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 9C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:1deb442a34422d3cd05e4af1847b00ef
URL:http://11tict4sd.sched.com/event/1deb442a34422d3cd05e4af1847b00ef
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T120000Z
DTEND:20260826T120200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 9D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:3db888401b450d45ba5cfaed86b1e1b5
URL:http://11tict4sd.sched.com/event/3db888401b450d45ba5cfaed86b1e1b5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T120000Z
DTEND:20260826T120200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 9E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:a09151128fb93807e656c4128746223e
URL:http://11tict4sd.sched.com/event/a09151128fb93807e656c4128746223e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T120200Z
DTEND:20260826T120500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 9A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:13ac5a809683b417735ec98e2af417f3
URL:http://11tict4sd.sched.com/event/13ac5a809683b417735ec98e2af417f3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T120200Z
DTEND:20260826T120500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 9B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:268099e004be2760e09f92efec5ce397
URL:http://11tict4sd.sched.com/event/268099e004be2760e09f92efec5ce397
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T120200Z
DTEND:20260826T120500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 9C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:93f00ba9787ab7bcb81cf16dfab6fad2
URL:http://11tict4sd.sched.com/event/93f00ba9787ab7bcb81cf16dfab6fad2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T120200Z
DTEND:20260826T120500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 9D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:1dd778d569f789da9bcd719b3c56f4f3
URL:http://11tict4sd.sched.com/event/1dd778d569f789da9bcd719b3c56f4f3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260826T120200Z
DTEND:20260826T120500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM 9E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:b48e94d88f656cabe78514b66471339d
URL:http://11tict4sd.sched.com/event/b48e94d88f656cabe78514b66471339d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T035800Z
DTEND:20260827T040000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_10A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:fb80e2efaab9c3583c09be6c07484e05
URL:http://11tict4sd.sched.com/event/fb80e2efaab9c3583c09be6c07484e05
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T035800Z
DTEND:20260827T040000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_10B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:670cb13950f0c31f3356320f7dbf5687
URL:http://11tict4sd.sched.com/event/670cb13950f0c31f3356320f7dbf5687
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T035800Z
DTEND:20260827T040000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_10C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:16f042e9e2575a8b2e8d00c81d771cc5
URL:http://11tict4sd.sched.com/event/16f042e9e2575a8b2e8d00c81d771cc5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T035800Z
DTEND:20260827T040000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_10D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:29725585cf2621bdcc5385f38472a78f
URL:http://11tict4sd.sched.com/event/29725585cf2621bdcc5385f38472a78f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T035800Z
DTEND:20260827T040000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_10E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:75bce67e7c813c181c5bd405a1adeadf
URL:http://11tict4sd.sched.com/event/75bce67e7c813c181c5bd405a1adeadf
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:Early Detection of Kidney Disease Using Ensemble Learning and Feature Engineering Techniques
DESCRIPTION:Authors - Nita Dakhare\, Shailesh Gahane Abstract - Kidney disease poses a significant global health challenge\, necessitating innovative approaches for early detection and intervention. This study delves into the realm of predictive analytics through the utilization of machine learning algorithms to enhance kidney disease risk assessment. The research employs a comprehensive dataset comprising clinical and demographic variables\, fostering a robust analysis of potential risk factors. The initial phase involves a systematic exploration of the dataset\, employing statistical methods to identify correlations and patterns within the data. Subsequently\, a comparative analysis of various machine learning algorithms\, including but not limited to support vector machines\, decision trees\, and ensemble methods\, is undertaken. Development of hybrid algorithm for kidney disease prediction using machine learning involves combining different techniques to improve accuracy\, robustness or efficiency in predicting this condition. This evaluation aims to pinpoint the most effective model in terms of accuracy\, sensitivity\, and specificity in predicting kidney disease onset. The model development phase focuses on the implementation of the chosen machine learning model\, incorporating features that contribute significantly to predictive accuracy. The model undergoes rigorous validation using distinct datasets to ensure its generalizability and reliability. Additionally\, interpretability and transparency are prioritized to enhance the model's clinical applicability and acceptance. The study's findings provide valuable insights into the identification and understanding of key predictors of kidney disease\, offering a potential tool for early diagnosis and intervention. The integration of machine learning in kidney disease prediction not only aids healthcare professionals in risk stratification but also contributes to the broader landscape of predictive analytics in preventive healthcare. The implications of this research extend to improving patient outcomes\, reducing healthcare costs\, and fostering a proactive approach to managing kidney disease on a global scale.
CATEGORIES:VIRTUAL ROOM_10A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:0b36debfefc0e0a25d9c6e07982df1cc
URL:http://11tict4sd.sched.com/event/0b36debfefc0e0a25d9c6e07982df1cc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:Enhancing Efficiency\, Security and Patient Safety for NFC Card Based Pharmaceutical Inventory Management
DESCRIPTION:Authors - Vedant Vaidya\, Shailesh Gahane\, Prachi Mandade\, Deepak S. Sharma\, Pankajkumar Anawade Abstract - Pharmaceutical storage management is an important aspect of the health care system It makes sure medicines are on hand and stops fake drugs from spreading\, while boosting overall operations. Old ways of tracking stock\, like counting by hand or using barcodes\, face many issues. These methods tend to be slow\, prone to mistakes\, and need lots of manual work. This leads to high running costs and inefficiencies. This study looks at how NFC card tech might solve these problems in drug inventory control. We focus on key areas such as accelerating inventory checks\, reducing expenses\, preventing counterfeit medications\, protecting patients\, and streamlining the supply chain. NFC cards help stop fake drugs by giving each item a secure tamper-proof ID. NFC cards aid in the fight against fake medicine. This guarantees that genuine medications pass through the supply chain. Additionally\, patients are safer when utilizing NFC cards. It reduces drug mix-ups\, provides reliable data on drug usage\, and enables accurate prescription tracking. We also demonstrate how NFC technology improves supply chain efficiency. It streamlines the entire process of sending medications where they need to go by enabling real-time updates and reducing stock management delays. Besides\, NFC calling card boost patient safety. They allow exact prescription monitoring thin down on medicinal drug mistakes\, and propose trustworthy datum on drug usage. We too highlight how NFC tech further supply chemical chain productiveness. It activate live updates and cutting off delays in stock management making the whole drug distribution appendage smoother. Our research wraps up by showing that NFC placard tech offers a growth-friendly\, budget-friendly fix for the crowing topic in drug inventory control. It impart major gains in precision\, f number\, costs\, and safety. This spend a penny it a hopeful answer to bring drug supply Chain up to date.
CATEGORIES:VIRTUAL ROOM_10A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:c5e5485f39001ec1908f356fbda22ea0
URL:http://11tict4sd.sched.com/event/c5e5485f39001ec1908f356fbda22ea0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:Holistic Solution for Student Relocation Challenges for Housing\, Social and Financial Integration
DESCRIPTION:Authors - Vanshika Landge\, Shailesh Gahane\, Deepak S. Sharma\, Pankajkumar Anawade Abstract - The relocation to a new city poses significant challenges to the students\, especially with the search for safe and relatively affordable accommodation\, food service\, and transportation. Stress associated with academic demands tends to be amplified in light of these difficulties\, indicating the need for a fully integrated solution that would correspond to the needs of a student. This paper explores a web application aimed to help students during their relocation period to new urban environments. Key services include housing listings\, food delivery options\, community engagement tools\, and transportation services while incorporating budgeting features that enable financial responsibility. The application is user-centric and makes relocation easier for students and fosters a sense of community among them. The research indicates that there are critical gaps in the literature. It shows that current digital solutions miss the specific needs of students\, especially with regard to affordability\, safety\, and ease of access to essential services. The methodology includes requirement analysis\, exhaustive literature reviews\, development in iterations\, and rigid testing to ensure that this application will meet the expectations of the users. Utilizing contemporary web technologies and real-time data integration\, this project addresses both the logistical problems and emotional support to facilitate students in informed decision making. Ultimately\, this research shall contribute to a better understanding of the student experience while alleviating the stress involved in moving to unknown environments and enables the students to focus on their academic pursuit while becoming an integral part of the new community. It is\, therefore\, an important step toward a comprehensive solution to the multifaceted problems students face from relocation.
CATEGORIES:VIRTUAL ROOM_10A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:7f298659c5b8511f1336c173f654b59e
URL:http://11tict4sd.sched.com/event/7f298659c5b8511f1336c173f654b59e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:Integrating HR Pratices with Business Analytics to Drive Organizational Performance at Varron Autokast LTD Nagpur
DESCRIPTION:Authors - Reena Bhagat\, Smita Urkunde\, Payal Khode\, Shailesh Gahane Abstract - The dynamic interplay between Human Resource (HR) practices and business analytics has emerged as a pivotal factor in driving organizational performance. This research investigates the integration of HR practices with business analytics to enhance operational efficiency and strategic decision-making at Varron Autokast LTD.\, Nagpur. It also explores the impact of HR Analytics and Performance Management Systems on organizational outcomes at Wipro Limited\, Pune. Employing a mixed-methods approach\, the study delves into how HR analytics tools and data-driven strategies optimize talent management\, improve workforce productivity\, and align HR objectives with organizational goals. The research emphasizes the role of advanced analytics in identifying key performance indicators\, fostering employee engagement\, and enabling predictive insights for proactive HR interventions. Key findings aim to provide actionable frameworks for leveraging HR analytics in diverse corporate contexts\, ensuring scalable\, adaptive\, and measurable improvements in HR processes. This study contributes to the broader understanding of HR analytics as a transformative tool for achieving sustainable competitive advantage in a rapidly evolving business landscape.
CATEGORIES:VIRTUAL ROOM_10A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:8584b897a0d686de1f844f6021bd0704
URL:http://11tict4sd.sched.com/event/8584b897a0d686de1f844f6021bd0704
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:Inventory Management Challenges and Solutions for Essential Medicines in Rural Healthcare Facilities
DESCRIPTION:Authors - Shrinivas Patwardhan\, Shailesh Gahane\, Pankajkumar Anawade\, Vanshika Landge\, Prachi Mandade Abstract - The provision of essential medicines in rural health facilities is a complex issue\, primarily influenced by frequent stock repletion\, drug wastage\, and poor record-keeping. Most of these problems are as a result of limited resources\, old organizational systems\, and poor infrastructure that characterizes most rural settings. This study evaluates the possible applicability of advanced technologies\, like Radio Frequency Identification (RFID)\, the Internet of Things (IoT)\, and cloud computing\, in meeting the above-mentioned requirements and to better inventory management of rural health facilities. It shall be considered with a mixed-methods approach based on survey and interview methodologies and case studies as well as cost-benefit analysis for testing feasibility\, benefits\, and drawback regarding the introduction of these technologies into low resource environments. The findings of this study indicate that the implementation of RFID\, IoT\, and cloud computing technologies possesses the capacity to significantly reduce drug wastage\, enhance operational efficiency\, and increase inventory accuracy. The primary obstacles to the adoption of these technologies include insufficient internet connectivity\, constrained financial resources\, and the necessity for specialized training. This study supports stepwise implementation\, with key attention to pilot testing\, financial assessment\, and scalable approaches to these technological innovations. Finally\, the investigation determines that\, despite the considerable promise these technologies hold in transforming rural healthcare systems\, there exists an urgent requirement to address technical\, logistical\, and financial obstacles to render them feasible and appropriate for application in resource-constrained environments.
CATEGORIES:VIRTUAL ROOM_10A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:886eb4699519363021ae44625514a580
URL:http://11tict4sd.sched.com/event/886eb4699519363021ae44625514a580
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:Issues and Challenges of MRI Based Brain Tumor Detection using Deep Learning
DESCRIPTION:Authors - Ritika Tiwari\, Shailesh Gahanae Abstract - This research work suggested brain tumor detection and the use of a combination of deep learning and reinforcement studying techniques applied to magnetic resonance imaging (MRI) records. The mixing of deep mastering models\, specifically convolutional neural networks (CNN) and reinforcement gaining knowledge of algorithms\, aims to enhance the accuracy and performance of brain tumor detection structures. A comprehensive assessment of machine overall performance is carried out using standards such as sensitivity\, specificity\, accuracy\, and computational performance. Early treatment for mind tumors is critical. The only way to identify a tumor is by biopsy\, which requires mind surgical treatment. Medical doctors can locate and classify brain tumors with the help of equipment primarily based on Computational algorithms. To help medical doctors perceive early Tumor with high ac-curacy\, we are able to suggest deep gaining knowledge of and diverse system studying strategies using magnetic resonance imaging mind and enable the prognosis of numerous varieties of tumors as well as healthy tumors. Massive image files need to be processed and this may be a completely time-eating undertaking. due to the fact brain tumors and normal tissues have similar findings\, it is able to be tough to differentiate nearby tumors. Consequently\, there's a want for a rather sensitive automatic tumor detection technique. Experimental effects demonstrate the effectiveness of our technique\, with vast improvements in accuracy\, sensitivity\, and specificity in comparison to conventional strategies. Moreover\, we discuss the consequences of our findings for scientific practice\, highlighting the capacity of deep getting to know-based strategies to beautify the performance and reliability of brain tumor detection. Standard\, this research contributes to advancing the sector of clinical photo evaluation and underscores the importance of leveraging deep mastering and MRI within the combat in opposition to mind tumors.
CATEGORIES:VIRTUAL ROOM_10A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:952ca1ea782d97ca85a77f738c506dd9
URL:http://11tict4sd.sched.com/event/952ca1ea782d97ca85a77f738c506dd9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:Pharmaceutical Inventory Management and Access to Essential Medicines in Rural Healthcare
DESCRIPTION:Authors - Shrinivas Patwardhan\, Shailesh Gahane\, Pankajkumar Anawade\, Prachi Mandade\, Vedant Vaidya Abstract - Pharmaceutical inventory management in health care settings is important to ensure accessibility\, access and ability to essential medicines. However\, the challenges in rural areas include limited infrastructure\, insufficient storage systems\, disabled tracking methods\, poor visibility in the supply chain and lack of monitoring of real-time portfolio. These factors cause frequent warehouses\, drugs and disruption in the patient's care\, affecting health results in signed areas. This paper examines the current status of pharmaceutical inventory management in rural health systems\, including both manual and automatic systems to determine the efficiency\, efficiency and scalability of these approaches. It then examines the effect of poor inventory management on medicines\, patient safety and general lack of health care. In addition\, the study in existing research and training\, especially in the environment with low resources\, where cost effective\, technology -driven solutions are necessary\, intervals within.
CATEGORIES:VIRTUAL ROOM_10A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:095a591d5698703145b9356fc411ba16
URL:http://11tict4sd.sched.com/event/095a591d5698703145b9356fc411ba16
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:The Impact of HR Analytics and Performance Management Systems in Wipro Limited Pune
DESCRIPTION:Authors - Reena Bhagat\, Smita Urkunde\, Payal Khode\, Shailesh Gahane Abstract - Data driven strategy is already on the rise for better performance of Human Resource in its decision making\, thus helping to attract business in the current global market. The escalating growth\, hands in glove with human resources\, is the transformation of HR analytics within performance management systems\, motivating organizations to consolidate the objectives and performance of individuals. The current research is about the integration and impacts of HR Analytics made in Wipro Limited\, Pune and aims to identify the role of HR Analytics toward improvement in the performance of the workforce\, aligning their goals\, and mean to enhance the overall productivity of the organization. This research would cover both the methods: quantitative and qualitative analyses to establish the use and effectiveness of HR Analytics when it introduces quantitative data analysis along with the instrument with qualitative data. Some commonly faced challenges where HR analytics could be used are: silos in data\, lack of technological infrastructure\, employee resistance\, and so on. This research will also embody certain benefits of the HR analytics among some of which: it helps in decision-making\, talent management\, and allocation of resources in a better manner. It further gives strategic recommendations to organizations for optimum integration of HR analytics and brings out actionable insights to better guarantee performance and subsequent organizational growth. The new findings contribute to HR Analytics and HRM Literature Growth\, which can serve as praxis toward the solution for HR professionals and organizational leaders or policymakers.
CATEGORIES:VIRTUAL ROOM_10A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:f3d5274941cfc0af6598b7b0fe594746
URL:http://11tict4sd.sched.com/event/f3d5274941cfc0af6598b7b0fe594746
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:The Role of Cloud Computing in Enhancing Collaborative Learning in Higher Education
DESCRIPTION:Authors - Lal Mohan kumar\, Shailesh Gahane\, Chandan Kumar\, Deepak S. Sharma\, Pankajkumar Anawade Abstract - This paper does go into the roles cloud computing has in changing the face of online education\, but this time\, it focuses on its advantages and the flip-side of it all. Advantages reaped from using cloud computing in the education sector include resource access to scalable\, flexible\, and accessible learning\, where students are able to learn through various personalized learning experiences with collaborative learning environments from which the students and their educators interact and share insights in real time. Most importantly\, this paper discovers that cloud-based platforms offer many benefits\, such as improving access to educational resources and data analytics to achieve personalized learning support for diversity in learning styles. However\, despite the widespread benefits\, this study also considers inevitable critical challenges that may limit equal access to education\, such as creating considerable difficulties related to data privacy issues\, digital literacy\, and the digital divide. Therefore\, research needs to be con-ducted to apply cloud computing solutions in education to improve understanding of its benefits and limitations. Such recognition would lead to better incorporation of cloud computing solutions to facilitate learner engagement\, improve educational outcomes\, and support inclusive educational ecosystems in those institutions. Thus\, this paper suggests more empirical research be conducted to understand the long-term impact of cloud computing on student performance\, engagement\, and retention in different educational contexts.
CATEGORIES:VIRTUAL ROOM_10A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:e0423ecdf701209e4f7cbca2ee99fa24
URL:http://11tict4sd.sched.com/event/e0423ecdf701209e4f7cbca2ee99fa24
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:Transforming Public Transport Through RFID & NFC: An Approach For Security\, Scalability and User Centricity
DESCRIPTION:Authors - Vanshika Landge\, Shailesh Gahane\, Deepak S. Sharma\, Pankajkumar Anawade Abstract - Public transportation systems face rising pressure to provide services that are secure\, efficient\, and accessible to users\, while still having a major segment dependent on outdated infrastructure which cannot fulfill the demands of modern-day commuters. Some key challenges include inefficient fare-collection mechanisms\, rigid travel routes and poor provision of real-time information. This paper covers the adoption of Radio Frequency Identification (RFID) and Near Field Communication (NFC) technologies within the public transportation system as one of the comprehensive approaches. The proposed solution integrates safe and contactless fare collection along with dynamic travel flexibility through real-time GPS updates with help of smart cards as well as mobile applications. Its multi-phase research approach toward requirement analysis\, prototype building\, pilot testing\, and scaling up ensures the robustness as well as practicality in the system. Modular architectures for scalability\, safe use of advanced encryption\, as well as intuitive interfaces towards users are integrated into this proposed solution. Pilot implementations show considerable improvements in operational efficiency\, transaction accuracy\, passenger satisfaction\, and system reliability. The results show that RFID and NFC technologies are promising innovations to trans-form public transportation to address essential weaknesses in security\, adaptability\, and user convenience. This work lays a foundation for introducing innovative\, integrated solutions to urban mobility in a manner that promotes sustainable\, adaptable\, and commuter-centered transit systems.
CATEGORIES:VIRTUAL ROOM_10A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:a3c81162eccab7f40a5577db2b59518c
URL:http://11tict4sd.sched.com/event/a3c81162eccab7f40a5577db2b59518c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:A Comprehensive Review and Future Directions on Hybrid Detection Models for Phishing Websites
DESCRIPTION:Authors - Deepti Maheshwari\, Shailesh Gahane Abstract - Website phishing poses a massive security threat that continues to increase in prevalence. Internet scammers exploit human faith by running imitation websites which aim to obtain confidential user information. An extensive review of multiple phishing detection techniques and hybrid detection models appears in this paper which brings together different detection methods to speed up and increase the accuracy of breaking down phishing-related websites. The paper investigates how machine learning (ML)\, artificial intelligence (AI) and heuristic-based approaches and anomaly detection should be implemented within hybrid systems which detect phishing behavior. Multiple studies from the literature receive analysis through which we identify their research approaches as well as their outcomes together with their limitations along with their contributions to the field. The evaluation will demonstrate how hybrid models can boost the detection of phished emails while detailing methods to strengthen model performance and flexible design and growing capability.
CATEGORIES:VIRTUAL ROOM_10B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:5fead4f6f82bbd14357d04809edfb695
URL:http://11tict4sd.sched.com/event/5fead4f6f82bbd14357d04809edfb695
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:An External Validation Framework for Machine Learning based Early Detection and Prediction of Surgical Site Infections
DESCRIPTION:Authors - Rinkle Solanki\, Shailesh Gahane Abstract - Surgical site infections (SSIs) represent a major concern for the healthcare industry\, highlighting the need for timely intervention and effective predictive strategies.This paper presents an external validation framework for machine learning algorithms designed to identify and forecast SSIs in advance. We use sophisticated algorithms to create accurate predictive models using a variety of variables\, including clinical features\, microbiological data\, and patient demographics. Across a range of patient demographics and therapeutic circumstances\, thorough external validation is carried out. Our results demonstrate how effective this strategy is at precisely identifying SSIs\, enabling prompt interventions\, and improving patient outcomes. Surgical care procedures could be improved and medical expenses could be decreased by using validated models.
CATEGORIES:VIRTUAL ROOM_10B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:09fb4a04a63679e2fbf0d6b73e56d95f
URL:http://11tict4sd.sched.com/event/09fb4a04a63679e2fbf0d6b73e56d95f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:APTSum: AI-Powered Timestamp Based Summarization
DESCRIPTION:Authors - G Jeyashaathvee\, Anish Pranav\, Sindhu Chandra Sekharan\, Jesline D\, Ajanthaa Lakkshmanan Abstract - The challenge of extracting useful insights from unstructured data in the presence of big digital information is still present today. AI Intensive Timestamp based Summarization proposes a novel framework to automatically extract and summarize pivotal events\, along with their corresponding timestamps from different data sets. Making use of Natural Language Processing and deep learning machine learning approaches\, the system checks text data for temporal markers\, extracts salient events and produces verbose summaries. The method proposed shall make the historical analysis easier and trend detection along with automated reporting of large dataset summaries more concise. Technique is implemented using Named Entity Recognition for date extraction and transformer models in case of summarization Experiments show that AI-based timestamp summarization is efficient in enhancing IR results and autodoc reliability. Making This Research Unique and Contributing to the much Expanding AI-driven text analysis filed\, a Scalable in nature for timestamp extraction on domain wise basis.
CATEGORIES:VIRTUAL ROOM_10B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:df523f59dee52dad43dff867603ed0f4
URL:http://11tict4sd.sched.com/event/df523f59dee52dad43dff867603ed0f4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:Bipolar Disorder Detection using CNN and LSTM: A Comparative Study of Twitter and Questionnaire Datasets
DESCRIPTION:Authors - Ankita Mehta\, Shailesh Gahane Abstract - This paper discusses about\, we display a profound learning-based approach bipolar clutter discovery utilizing Convolutional- Neural Systems (CNN) and Long Short-Term Memory (LSTM) systems. To assess the model’s performance\, two particular datasets Twitter information and survey data were analyzed. Preprocessing steps\, counting information enlargement\, normalization\, and the application of the Adam optimizer\, were joined to upgrade the model’s adequacy. The model’s exactness and misfortune were measured for both datasets\, and it was watched that the survey dataset given superior execution\, yielding higher precision and lower misfortune compared to the Twitter dataset. These discoveries propose that the questionnaire-based information may be more reasonable for solid bipolar disorder location within the given show. The inquire about illustrates the potential of combining CNN and LSTM for mental wellbeing examination\, highlighting the significance of information determination in accomplishing ideal comes about.
CATEGORIES:VIRTUAL ROOM_10B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:2b9140d71ea6dbcf52e426cf1ea151e9
URL:http://11tict4sd.sched.com/event/2b9140d71ea6dbcf52e426cf1ea151e9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:Brain Tumor Recognition and Classification based on MRI Images using deep learning
DESCRIPTION:Authors - Sarika G. Songire\, Deepa S. Deshpande Abstract - Deep learning algorithms have completely transformed medical diagnostics by enabling accurate and efficient identification of brain tumors. Brain disorders often arise due to increased in excessive and improper cell counts\, which can damage neural structures and\, in severe cases\, lead to malignant brain cancer. The reducing mortality rates requires prompt intervention and early discovery. This research work presents an architecture of deep neural network specifically designed for the purpose of detecting brain tumors from MRI images. The proposed model is evaluated against existing research using a similar dataset to assess its effectiveness. For comparison\, performance parameters including area under the curve (AUC)\, recall\, accuracy\, precision\, and loss are used. According to experimental results\, the suggested CNN model accomplishes 98.61% accuracy\, 99.70% AUC\, 99% of both precision and recall\, and 0.41 loss in a data set of 3\,264 MRI images. These findings indicate that the suggested model surpasses current models and provides a dependable and effective technique for prompt brain tumor diagnosis.
CATEGORIES:VIRTUAL ROOM_10B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:28405ae47b9045d6085e689db282808a
URL:http://11tict4sd.sched.com/event/28405ae47b9045d6085e689db282808a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:Brick Manufacturing Using Embedded System
DESCRIPTION:Authors - Ayush Itkhede\, Aryan Fulsunge\, Sarthak Bomanwar\, Sujal khobragade\, D.M.Shinde Abstract - The integration of embedded systems into brick manufacturing has led to significant improvements in efficiency\, quality control\, and resource management. This paper presents an embedded system designed to optimize brick production by monitoring critical parameters such as temperature\, humidity\, and material composition in real-time. The system achieved a 32% reduction in production cycle time\, from 6.5 hours to 4.4 hours per batch\, and improved material handling speed by 41%\, from 120 kg/hour to 169 kg/hour. Quality control metrics showed a 78% reduction in defect rates\, from 5.2% to 1.15%\, while energy consumption decreased by 29.3%\, from 17.4 kWh to 12.3 kWh per 1\,000 bricks. The system also reduced waste by 64%\, from 8.9% to 3.2%\, and improved material utilization rates from 83.5% to 94.8%. These results demonstrate the potential of embedded systems to revolutionize brick manufacturing\, making it more sustainable and cost-effective.
CATEGORIES:VIRTUAL ROOM_10B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:f3d7006a9b05e499a7bca0864bef1334
URL:http://11tict4sd.sched.com/event/f3d7006a9b05e499a7bca0864bef1334
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:Diabetes Disease Using Deep Learning Classification in ANN
DESCRIPTION:Authors - Kamini Solanki\, Rahul Vaghela\, Jay Panchal\, Anjali Mahavar\, Jaimin Undavia\, Nilay Vaidya Abstract - Diabetes is a chronic illness that affects the body's ability to metabolize glucose\, which is the sugar that provides energy to the body. Type 1 and type 2 diabetes are the two main types of the disease. The immune system of a person with type 1 diabetes attacks and destroys the pancreatic cells that make insulin\, which causes blood sugar levels to rise. Type 1 diabetes symptoms include excessive thirst\, frequent urination\, extreme hunger\, weight loss\, fatigue\, impaired vision\, delayed healing\, tingling or numbness in the hands and feet\, and recurring infections. AI algorithms may be used to collect and analyze medical data to support early diagnosis and treatment. Typically developing in childhood or adolescence\, type 1 diabetes can be managed with the injection of insulin or an insulin pump. A person with type 2 diabetes either develops an inability to use insulin or ceases making enough of it to regulate blood sugar levels.
CATEGORIES:VIRTUAL ROOM_10B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:a4b14454d1cfac56b2206a512b17ab62
URL:http://11tict4sd.sched.com/event/a4b14454d1cfac56b2206a512b17ab62
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:Recent Advances in Deep Learning for Oral Cancer Identification and Classification
DESCRIPTION:Authors - Dipali Wankhade\, Shailesh Gahane\, Mrunal Meshram Abstract - Oral cancer is one of the fatal diseases present in society. Its late-stage diagnosis puts it under the list of diseases with high mortality rates. Deep learning has conceptualized the revolution in the field of medical imaging with impressive depth and accuracy in diagnosis and early detection. This review highlights the advancements in deep-learning-based methodologies for oral cancer detection and classification\, including convolutional neural networks (CNNs)\, recurrent neural networks (RNNs)\, deep reinforcement learning (DRL). Multi-modal learning and hybrid models combining histopathological and radiological data have increased the precision of tumor segmentation and subtype classification. The observational data will help provide insights into the factors responsible for explaining the model's decisions and the associated risks\, which are most relevant for potential applications. The development of transfer learning and self-supervised learning also seems to have significantly solved some of the most serious challenges regarding the volume of clinical data. Future studies can harness standardized practices for data collection\, deploy technically sound explainable AI frameworks\, and conduct clinical validations as the closing gap between tremendous strides made in deep learning and practical real-world implementation. This review provides a wide-ranging overview of such AI-driven methodologies\, focused on the critical challenges and future directions for improvements in early oral cancer detection and reduced mortality rates.
CATEGORIES:VIRTUAL ROOM_10B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:832cc146cc130810067aa4c0d7f5e57f
URL:http://11tict4sd.sched.com/event/832cc146cc130810067aa4c0d7f5e57f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:Survey on Prediction of Bipolar Disorder using CNN and LSTM
DESCRIPTION:Authors - Ankita Mehta\, Shailesh Gahane Abstract - In this research\, we focus specifically on mental disorder which is bipolar disorder using machine learning techniques\, utilizing a simple dataset from Kaggle for training and evaluation. The study involves applying various ML models\, including R. Forest\, XG-Boost\, and S. Vector Machines (SVM)\, on the dataset. We assess the performance of these models using evaluation MSE (how far actual value to predicted value)\, Precision\, and Fl Score to determine their effectiveness in predicting bipolar disorder. However\, through extensive experimentation\, we found that the combination of (CNN) and (LSTM) networks outperformed the other algorithms\, achieving an overall accuracy of 95%.
CATEGORIES:VIRTUAL ROOM_10B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:0f71407a2f11d44200a0912f4f082328
URL:http://11tict4sd.sched.com/event/0f71407a2f11d44200a0912f4f082328
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:Voice OTP Authentication: An Advanced Speaker Verification System
DESCRIPTION:Authors - Vijeta V Shettar\, SR Nirmala\, Satish Chikkamath\, Suneeta V Budihal Abstract - This study investigates the performance of various speaker recognition models\, including SpeakerNet\, TiTANet Small\, and TiTANet Large\, using the IITG-MV dataset for speaker verification tasks. The preprocessing steps involved resampling\, noise reduction\, segmentation\, and volume normalization to prepare the audio data for input into the models. The models were evaluated based on their ability to correctly verify whether two audio samples belong to the same speaker or not. The evaluation metrics\, derived from the confusion matrix\, revealed that SpeakerNet outperformed both TiTANet variants\, achieving an accuracy of 85%\, while TiTANet Small and TiTANet Large achieved accuracies of 75% and 80%\, respectively. Despite lacking graphical visualizations\, the confusion matrix provided a comprehensive view of the models’ performance\, showing how each model handled correct and incorrect speaker match predictions. The results highlight that SpeakerNet is the most effective model for speaker recognition in this setup\, demonstrating superior accuracy and robustness in identifying speaker-specific features. These findings can guide future research in optimizing speaker recognition models for real-world applications involving speaker verification.
CATEGORIES:VIRTUAL ROOM_10B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:b6d51c380e7f331195d0a4bb730e8d69
URL:http://11tict4sd.sched.com/event/b6d51c380e7f331195d0a4bb730e8d69
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:A Systematic Review of Denial-of-Service Attack Resilience in IEEE 802.11 Networks
DESCRIPTION:Authors - Aoudumber Londhe\, Ravindra Apare\, Parikshit Mahalle\, Bhagwati Galande Abstract - Wireless communication technologies\, particularly those based on IEEE 802.11\, have significantly improved connectivity but remain highly vulnerable to Denial-of-Service (DoS) attacks. These attacks\, which exploit protocol weaknesses and resource limitations\, can severely disrupt network availability\, particularly in mission-critical applications such as healthcare\, financial services\, and industrial control systems. In this research\, we investigate various DoS attack techniques targeting IEEE 802.11 networks\, including deauthentication flooding\, disassociation attacks\, authentication request flooding (AuthRF)\, association request flooding (AssRF)\, and cascading DoS attacks.To mitigate these threats\, we analyze IEEE 802.11w\, which provides management frame protection (MFP)\, and evaluate its effectiveness under different attack scenarios. The model integrates supervised learning for attack classification\, unsupervised learning for detecting novel threats\, and reinforcement learning for adaptive mitigation strategies. Additionally\, the system incorporates IEEE 802.11w security enhancements and anomaly-based behavior analysis to strengthen network resilience. This study provides a comprehensive review of existing DoS attack mechanisms\, explores recent mitigation techniques\, and introduces an advanced IDS framework to safeguard IEEE 802.11 networks against sophisticated cyber threats. Finally\, the analysis is organized through a survey that evaluates the articles based on publication year\, research techniques\, performance metrics\, toolset and utilized database.
CATEGORIES:VIRTUAL ROOM_10C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:b38a3fe94708c43f01689da5ddba981b
URL:http://11tict4sd.sched.com/event/b38a3fe94708c43f01689da5ddba981b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:Blockchain in Education and Lifelong Learning’s: Challenges\, Solutions\, and Future Directions
DESCRIPTION:Authors - Vedant Patel\, Vidisha Pradhan\, Akshita Kadam Abstract - Blockchain technology is transforming the education sector by offering decentralized\, secure\, and tamper-proof solutions to many of the inefficiencies in traditional educational systems. This paper explores the role of blockchain in lifelong learning\, focusing on how it addresses key challenges such as learner autonomy\, credential verification\, and the creation of secure\, decentralized education ecosystems. Through an examination of current developments and case studies—including Blockcerts\, Sony Global Education\, and Woolf University—the paper highlights blockchain’s applications in academic data storage\, personalized learning pathways\, and digital credentialing. Additionally\, this study discusses the opportunities blockchain provides for improving transparency and trust in the verification of academic credentials across borders. While the technology presents promising solutions\, significant challenges remain\, including issues of interoperability\, privacy\, scalability\, and legal frameworks. The paper concludes by outlining unanswered questions and future directions for research\, emphasizing the need for standardization\, privacy-preserving technologies\, and scalable implementations to fully harness the potential of blockchain in lifelong learning.
CATEGORIES:VIRTUAL ROOM_10C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:a10c4b2a2d191b983a581800855f70c2
URL:http://11tict4sd.sched.com/event/a10c4b2a2d191b983a581800855f70c2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:Community Startup Management System: A Blockchain Approach
DESCRIPTION:Authors - Umesh Kumar Pandey\, Mamta Santosh Nair\, Shikha Gupta Abstract - Start-ups are buzzing word around the world. These start-ups need funding in their early stage with a high risk of failure and violation of the innovator's intellectual property. Any system's prime responsibility is to ensure the fund availability to the start-up and save the innovator's intellectual property since blockchain has become the chief technology in digital crypto-currencies. Bitcoin. Blockchain has become popular in finance\, the health sector\, social services and many more areas where transactions are recorded among the parties\, known or unknown—the critical features of block Chainz. Decentralisation\, distribution\, immutability\, transparency and audit-ability enrich the usability of this technology and increase the trust to use it. Therefore\, a system is proposed here to manage start-ups utilising blockchain features. The proposed system ensures that parties to the contract have more confidence and feel safe to grow start-ups in the community and prevent unnecessary conflicts.
CATEGORIES:VIRTUAL ROOM_10C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:fc377ab43c2ed64ccf33f4a3cfd04815
URL:http://11tict4sd.sched.com/event/fc377ab43c2ed64ccf33f4a3cfd04815
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:Comparative design of Antenna for Hexagonal and Triangular structure for 5G and beyond
DESCRIPTION:Authors - Smrity Dwivedi Abstract - This manuscript has oriented to new generation and new technology used for required resources and terms\, which gives wide bandwidth for each and everyone’s perception. For this reason\, microwave frequency area eight to 10 GHz has been explored for 5G and also 7 to 20 GHz is being explored for beyond 5G. This is why both possibilities were taken right here. First assessment among hexagonal and triangular structure complete floor were designed with CST microwave studio. Results obtained from those designs are -23.35dB for 7.77dBi benefit and -29.103dB for 7.85dBi gain for hexagonal and triangular systems respectively. For enhancing the advantage\, a partial ground has been used for triangular structure and 10.5dBi has been completed at -38.65dB S11 and for 9.0988 GHz frequency. Bandwidth is increased from 0.29 GHz to 0.32 GHz. Everything is simulated and analysed by simulation software. Novelty is the simple structure gives beyond 5G applications.
CATEGORIES:VIRTUAL ROOM_10C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:8d83e12e5fbcda68fc6b2a00c1259901
URL:http://11tict4sd.sched.com/event/8d83e12e5fbcda68fc6b2a00c1259901
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:Detecting Malicious Dark Pattern Codes Using SHAP (Shapley Additive Explanations) Feature Engineering
DESCRIPTION:Authors - A.Punidha\, E.Arul\, E.Yuvarani\, S.Rajasakaran Abstract - Understanding how dark patterns influence user sentiment is crucial for developing ethical and user-friendly digital experiences. This study evaluates the performance of XGBoost and Random Forest in predicting sentiment (negative\, neutral\, or positive) based on user interactions. The models were assessed using accuracy\, precision\, recall\, and F1-score\, with results indicating that XGBoost outperforms Random Forest\, achieving an accuracy of 88.4% compared to 85.9%. To enhance interpretability\, SHAP (Shapley Additive Explanations) was used to break down model predictions and identify the most in-fluential features. The analysis revealed that "Number of Clicks" and "Time Spent on Page" were the strongest indicators of user sentiment\, particularly in detecting frustration associated with dark patterns. The results provide valuable insights into how machine learning models interpret user engagement and emphasize the importance of transparent AI-driven sentiment analysis. By leveraging explainable AI techniques like SHAP\, this research contributes to improving trust in sentiment classification models and guiding the development of more user-centric digital interfaces..
CATEGORIES:VIRTUAL ROOM_10C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:af135e7fd1d14d0ce6d8050637ab708f
URL:http://11tict4sd.sched.com/event/af135e7fd1d14d0ce6d8050637ab708f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:Enhanced Skin Disease Classification using Deep Learning
DESCRIPTION:Authors - J. Jeslin Shanthamalar\, Prateesh Kumar S\, Abishin J\, Sindhu Chandra Sekharan\, Malar Selvi G Abstract - There is a growing necessity for noninvasive and sophisticated diagnostic capabilities with the ability to very early prediction of skin conditions from the patient. Timely diagnosis is a powerful influence on patient care out comes\, access to dermatologists is not\, especially in rural. We propose an AI and deep learning model for improving classification of skin diseases in a highly accurate advantageous manner. This model\, which follows the architecture of Convolutional Neural Network\, trained on a multi-class skin disease images dataset where every image has a label per lesion. Through hyperparameter fine- tuning\, the model is optimized to achieve performance from metrics that include accuracy and trade-off accuracy vs. precision/recall. With user-friendly access in mind\, the model runs into the app (web or mobile) that supports a friendly diagnostic user interface. Advanced security floor work is taken within designed to reduce the effect of adversarial attacks. Multimodal processing (text\, image and speech inputs) improves classification substantially resulting in accurate and robust diagnosis. The platform has been built based on healthcare professionals and patients' input to provide a easy-to-use diagnostic tool. Research to edit the ai applications in dermatology through fewer dataset bias\, more human like NLP explainable models\, as well as ongoing work for improved security. In the end\, this system is what makes skin disease detection accessible and fast via AI- determined aids an inclusivity in healthcare.
CATEGORIES:VIRTUAL ROOM_10C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:78e67d08ac69a3184bacb1788c6f7209
URL:http://11tict4sd.sched.com/event/78e67d08ac69a3184bacb1788c6f7209
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:EVALUATING UNIFIED MOBILE APPLICATION FOR NEW-AGE GOVERNANCE (UMANG) COMPLIANCE WITH WEB CONTENT ACCESSIBILITY GUIDELINES (WCAG) 2.2: A STUDY ON WEB ACCESSIBILITY
DESCRIPTION:Authors - Aravind A R\, Archa A S\, Gouri S Krishna Abstract - With India rapidly embracing digital transformation\, initiatives like UMANG by the government are the means to achieve online public services. Although UMANG offers over 1\,750 services of many departments\, it has some critical accessibility concerns\, particularly for differently-abled citizens. In this study\, we evaluate the UMANG website for WCAG 2.2 compliance using a two-stage method: automated checking using AccessibilityChecker.org and user review analysis using Appbot. Findings identify prominent issues of poor ARIA labeling\, flawed heading order\, inadequate color contrast\, and improper focus order as hindrances for assistive technology users. Sentiment analysis also indicates frustration with usability\, login failure\, and performance. For the improvement of accessibility\, the present study has some suggestions that make UMANG equivalent to international standards. By making inclusive design central to e-governance\, India can enjoy equal access to fundamental digital services\, creating an inclusive digital space.
CATEGORIES:VIRTUAL ROOM_10C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:e0c492bd0529367e0591651eac27decc
URL:http://11tict4sd.sched.com/event/e0c492bd0529367e0591651eac27decc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:Kubernetes Scaling: A Comprehensive Review of Scalability in Kubernetes
DESCRIPTION:Authors - Thisura S. Wijesekera\, Dinuka R. Wijendra Abstract - Kubernetes has become the leading container orchestration platform due to its powerful scalability features\, enabling dynamic resource management and efficient workload handling in cloud-native environments. This review examines Kubernetes scaling mechanisms at both the application and cluster levels\, focusing on Horizontal Pod Autoscaler (HPA)\, Vertical Pod Autoscaler (VPA)\, and event-driven scaling with KEDA for adaptive application scaling. At the cluster level\, Cluster Autoscaler (CA)\, Karpenter\, Cluster Proportional Autoscaler (CPA)\, and Cluster Proportional Vertical Autoscaler (CPVA) optimize node provisioning and resource allocation. Despite these advancements\, challenges persist\, including reactive scaling delays\, resource fragmentation\, inconsistent scaling decisions across multiple autoscalers\, and security vulnerabilities like Economic Denial of Sustainability (EDoS) attacks. To address these issues\, emerging trends in AI-driven observability\, predictive analytics\, and unified autoscaling frameworks offer proactive scaling\, anomaly detection\, and self-healing capabilities. This review synthesizes academic research and industry practices to highlight the current state\, challenges\, and future directions of Kubernetes scalability\, emphasizing the need for intelligent\, adaptive\, and secure scaling solutions.
CATEGORIES:VIRTUAL ROOM_10C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:290fe02bbcf6026684217726ddcaf3d1
URL:http://11tict4sd.sched.com/event/290fe02bbcf6026684217726ddcaf3d1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:SUSTAINABLE LEADERSHIP PRACTICES ADOPTED BY CORPORATE AND DEFENCE
DESCRIPTION:Authors - Suruchi Pandey\, Hemlata Gaikwad\, Yograj Ingale\, Neha Sharma Abstract - This article looks at how organisational culture\, resource allocation\, and decision-making procedures reflect sustainable leadership principles in different settings. A comparative investigation shows that military commanders place a higher priority on mission success and national security than do business executives\, who place more emphasis on profitability and shareholder value. Nonetheless\, there are similarities between the two fields\, including the value of making moral decisions\, flexibility in the face of change\, and an emphasis on long-term goals. The role of innovation in sustainable leadership is also examined in this article\, with particular attention paid to how strategy development and technology support organisational resilience. Additionally\, it looks at how sustainable leadership affects worker engagement\, emphasising how crucial it is to develop a feeling of dedication and purpose. This article seeks to provide a more comprehensive knowledge of successful leadership techniques by exploring the subtleties of sustainable leadership in business and defence environments. Regardless of the particular difficulties they encounter\, executives looking to im-prove the sustainability and resilience of their organisations may gain a great deal of insight from identifying the parallels and variations across these industries.
CATEGORIES:VIRTUAL ROOM_10C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:fe3bd26db3b70839e7171e6109d87058
URL:http://11tict4sd.sched.com/event/fe3bd26db3b70839e7171e6109d87058
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:Vastra Kalpana: AI-Driven Generative Model for Saree Design Creation
DESCRIPTION:Authors - G. Ram Sundar\, Sindhu Chandra Sekharan\, Taruni Mamidipaka\, Yoga Sreedhar Reddy Kakanuru\, Priyadharshini M Abstract - The traditional sarees in India represent a rich history both culturally and artistically\, as their patterns are created from regional influences along with modern fashion. Making sarees requires detailed skill\, and traditional techniques are highly laborious and time-consuming. In this research\, we have developed Vastra Kalpana\, an AI-driven saree design generator that uses generative deep learning models to automate textile pattern creation. Users can now provide voice commands\, and they are converted to text prompts by our integrated OpenAI Whisper speech-to-text software\, which are then transformed into structured textual descriptions. These descriptions serve as instructions for the Stable Diffusion's high-resolution saree design generator. Our research results suggest that this automated saree design generator is both solution oriented and efficient\, proving the generative techniques offer a novel approach for saree design while tackling the challenges of overreliance on handmade designs. This research mainly contributes to the field of fashion design and establishes a framework for future advancements in automated textile pattern generation.
CATEGORIES:VIRTUAL ROOM_10C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:d29ce081dc81b5d48198e6a7f13a6d88
URL:http://11tict4sd.sched.com/event/d29ce081dc81b5d48198e6a7f13a6d88
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:A Hybrid Ensemble Approach for Time Series Prediction in Industrial IoT: The EERA Model
DESCRIPTION:Authors - Ajit Patil\, Amol Potgantwar Abstract - In the era of Industry 4.0\, accurate time series prediction is crucial for extracting valuable insights from high-frequency sensor data in Industrial Internet of Things (IIoT) applications. This paper presents EERA: A Hybrid Ensemble Regression Model designed to improve predictive accuracy for time series data in IIoT environments. EERA combines the strengths of multiple base models\, including REPTree\, SMOreg\, and Multi-Layer Perceptron (MLP)\, through a weighted ensemble approach to achieve better overall performance. The model was tested using a real-world dataset that captures heat index data (temperature and humidity)\, which has diverse applications in areas such as agriculture\, weather forecasting\, and enterprise maintenance. Comparative analysis shows that EERA outperforms individual models\, achieving a Mean Squared Error (MSE) of 4.150960 & R-squared value of 0.872540\, demonstrating high predictive accuracy. These findings suggest that EERA is a dependable &1 effective solution for time series prediction in fast-paced IIoT data environments.
CATEGORIES:VIRTUAL ROOM_10D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:ee6fd9da3d2524568b9f91a5591e6740
URL:http://11tict4sd.sched.com/event/ee6fd9da3d2524568b9f91a5591e6740
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:AutoForest PlantBot: Autonomous Tree Plantation and Path Optimization for Sustainable Reforestation
DESCRIPTION:Authors - Manikrao Dhore\, Parth Mahajan\, Pratik Meshram\, Ashish Nikam\, Samarth Otari Abstract - Deforestation and improper plantation of trees are the key issues in realizing sustainable environmental management. The AutoForest PlantBot\, an autonomous robot system\, is introduced in this paper\, which makes use of advanced image processing\, path optimization\, and real-time navigation for efficient tree plantation. The system employs the Deep Forest Package for 92% accurate tree detection and uses Dijkstra's algorithm to find optimal routes\, cutting tree removal by 40% as compared to traditional straight-path approaches. The hardware system consists of an Arduino-controlled rover with BO motors\, a GPS module\, ultrasonic sensors\, and an automated drill mechanism\, providing accurate plantation with an accuracy of ±2 cm. The outcomes validate the system's potential for large-scale reforestation applications. Future developments will emphasize integrating reinforcement learning for adaptive path optimization and using renewable energy sources for sustainable operation.
CATEGORIES:VIRTUAL ROOM_10D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:58af7c26b6462062992998bd8fac36ac
URL:http://11tict4sd.sched.com/event/58af7c26b6462062992998bd8fac36ac
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:Dr. BOT: Developing a Chatbot for Multilingual Healthcare Environments - A Novel Approach to Breaking Language Barriers in Healthcare Communication
DESCRIPTION:Authors - S. Rahul\, Anusha Preetham\, Aniketh Patil\, Abhishek Nimbal\, Sahana Meti Abstract - This paper introduces Dr. BOT\, a comprehensive web-based healthcare application designed to overcome language barriers in medical communication across diverse linguistic environments. While initially trained to predict several diseases including diabetes\, heart disease\, kidney disease\, liver disease\, and breast cancer\, the system's architecture enables expansion to detect and interpret a wide range of medical conditions. Dr. BOT employs robust machine learning algorithms (Random Forest\, Support Vector Machine\, and Logistic Regression) trained on validated datasets\, with special emphasis on multilingual functionality through a hybrid approach combining NLP with neural machine translation models specifically finetuned for medical terminology. The platform operates effectively in low-connectivity environments through innovative offline capabilities\, offering preventive healthcare guidance and localized medical resource information in users' native languages\, thereby supporting both individuals and healthcare providers in improving health outcomes globally.
CATEGORIES:VIRTUAL ROOM_10D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:0ab44b6ec739d81d86dcdf9c4a862ef1
URL:http://11tict4sd.sched.com/event/0ab44b6ec739d81d86dcdf9c4a862ef1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:Improving Agricultural Productivity Through Data-Driven Pattern Classification and Machine Learning-Based Fertility Detection
DESCRIPTION:Authors - Prasad Chaudhari\, Ritesh V. Patil\, Parikshit N. Mahalle Abstract - The Agricultural Productivity Enhancement System leverages data-driven pattern classification and machine learning-based fertility detection to improve farming efficiency. The architecture integrates IoT sensors\, satellite imagery\, and soil analysis to collect crucial agricultural data. A preprocessing module ensures data cleaning and feature extraction\, storing refined data in an agricultural repository for further analysis. Machine learning models\, including pattern classification and fertility detection\, process this data to assess crop health and soil fertility. A decision support system then provides real-time recommendations to farmers\, enhancing precision agriculture. Researchers and data analysts contribute to model refinement\, ensuring scalability and adaptability. This system optimizes resource allocation\, reduces wastage\, and increases crop yield by enabling real-time\, AI-driven decision-making.
CATEGORIES:VIRTUAL ROOM_10D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:efda3270e650a93c69d1a3698152754d
URL:http://11tict4sd.sched.com/event/efda3270e650a93c69d1a3698152754d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:Improving Lung Cancer Prognosis Through Data Science
DESCRIPTION:Authors - Shiva Jyoti\, Samriddhi Ganguly\, B Sri Soumya\, Nachiyappan S Abstract - This paper presents a comprehensive study on the Clinical Readiness Score (CRS)\, a structured evaluation metric for assessing AI models used in lung cancer diagnosis. The CRS incorporates multiple criteria such as interpretability\, efficiency\, clinical validation\, and accuracy\, employ- ing the Analytic Hierarchy Process (AHP) for weight assignments. This study discusses the methodology behind CRS\, validates its consistency\, and explores its practical implications. Additionally\, graphical represen- tations of AHP weight distribution\, sensitivity analysis\, and CRS factor contributions are provided for better comprehension.
CATEGORIES:VIRTUAL ROOM_10D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:caec07384a0873e47ecda6843ac96258
URL:http://11tict4sd.sched.com/event/caec07384a0873e47ecda6843ac96258
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:META FOR PRE CONSTRUCTION SALES
DESCRIPTION:Authors - Harjas Singh Bajwa\, Lokesh Jayakar\, Abhiyanshu Singh\, Prafulla Bafna\, Mukta Deshpande Abstract - Builders often open sales of their property in India as soon as they purchase out land\, they do this in order to secure funds to carry out their construction operations\, for this they often make rendered photos and videos of concept property. Traditional property marketing techniques like images and videos lack interactivity and fail to provide a 360-degree view of properties. This research work explores the role of metaverse-driven\, gamified\, interactive property tours in enhancing pre-construction sales. Unlike previous studies focusing on metaverse real estate as an investment platform\, this research emphasizes its ability to engage buyers\, boost confidence\, and aid decision-making. By combining insights from virtual real estate\, Augmented Reality (AR) \, virtual Reality VR\, gamification\, and Artificial intelligence (AI) customization\, a metaverse-based property visualization framework is proposed. The study highlights how interactive walkthroughs\, real-time customization\, and immersive storytelling increase trust and engagement. Gamification elements\, such as virtual staging\, achievement systems\, and AI-led personalization\, deepen buyers’ connection with properties.
CATEGORIES:VIRTUAL ROOM_10D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:b8cde71e36fdbd8baaf463445bc75f2d
URL:http://11tict4sd.sched.com/event/b8cde71e36fdbd8baaf463445bc75f2d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:Multi-Label Emotion Classification from Text data based on AI Techniques
DESCRIPTION:Authors - Ajay V\, Sharon P S\, Philomina Simon\, Ambily George\, Mehanas Shahul Abstract - This work delineates an inquiry into how artificial intelligence identifies multiple emotions in texts. Unlike mere sentiment analysis\, which is a simple positive\, negative\, or neutral classification of text\, multilabel emotion classification requires a more intense understanding of the text. The paper examines various challenges in multi-label emotion classification\, where emotions often overlap (e.g.\, joy and surprise) and have varying frequencies in datasets. Traditional machine learning and deep learning based models such as BERT and other transformer-based models\, show sufficiently strong performance in capturing nuances of emotional expression in text. Moreover\, it addresses the issue of how this task can be distorted by linguistic and contextual diversity and diversity and therefore how such systems should be evaluated with respect to these variables.
CATEGORIES:VIRTUAL ROOM_10D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:30d0792d80d8c15d920a0b28c4a1212f
URL:http://11tict4sd.sched.com/event/30d0792d80d8c15d920a0b28c4a1212f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:Optimized Feature-Based Machine Learning Models for Breast Cancer Detection
DESCRIPTION:Authors - Gowri Shaju\, Lekha S Nair Abstract - Histopathology refers to the study of a disease at a cellular level which stands as a golden method of predicting breast cancer. In this paper\, a comparative study of the performance of machine learning models trained using optimized feature sets is done. The experiments are conducted using two datasets. The first is the Wisconsin Breast Cancer dataset\, which contains 30 extracted features of cell nuclei. The second is the MITOS-ATYPIA 14 dataset\, consisting of histopathology images\, from which hand-crafted features have been extracted. Population based metaheuristic optimization algorithms are used to optimize and choose the key features from the available feature set to increase the efficacy of the model. Support vector machines\, logistic regression model and other classification models are tested using this optimized feature set. To evaluate the impact of feature optimization\, accuracy\, precision\, recall\, and F1 score are assessed using both the full feature set and the optimized subset from two datasets. The results demonstrate how model performance varies with different feature sets\, underscoring the significance of optimization techniques in enhancing machine learning-based breast cancer diagnosis in medical imaging.
CATEGORIES:VIRTUAL ROOM_10D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:d9bb9e11b22d4d868b098eae90342b38
URL:http://11tict4sd.sched.com/event/d9bb9e11b22d4d868b098eae90342b38
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:Real-Time Stock Forecasting and User Verification using Azure AI Services
DESCRIPTION:Authors - Kalyanasundaram V\, Keerthi AJ\, Krishnaa RK\, Thirumurugan A\, Joshua Sunder David Reddipogu Abstract - The volatility of the stock market offers a big challenge to traders depending on timely and correct information for informed decision-making. Security risks\, including fraudulent practices and theft of identity\, threaten online trading platforms. The present paper presents an AI-based stock trading app that tackles these issues by using predictive analytics with robust security features. The system also employs Azure AutoML to work through historical stock data\, identify market trends\, and generate livestock predictions to enable traders to respond proactively to fluctuations. For security reasons\, the app employs Azure Document Intelligence for live Know Your Customer (KYC) verification to ensure that only valid users have access. Additionally\, the platform automates document processing using AI-powered text extraction\, minimizing errors from manual input and increasing efficiency. Developed with Flutter for smooth cross-platform use and backed by Azure cloud infrastructure for scalability and dependability\, this software solution offers an intelligent\, secure\, and user-friendly trading experience. Through the integration of AI-based forecasting with robust security measures\, this work helps develop more efficient\, reliable\, and technologically sophisticated stock trading platforms.
CATEGORIES:VIRTUAL ROOM_10D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:25ceef816e739c118374648add5dc0b1
URL:http://11tict4sd.sched.com/event/25ceef816e739c118374648add5dc0b1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:Sensor-Integrated Smart Pump for Deep Vein Thrombosis Prevention
DESCRIPTION:Authors - George Sebastian\, K. Ananda Krishnan Menon\, Migheal Newton\, Sonal Shaju\, Haneesh K. M Abstract - Deep Vein Thrombosis (DVT) is the formation of blood clots in the lower limb because of prolonged immobility. Such critical medical conditions can be avoided by regularly using compression cuffs on the limbs\; however\, traditional compression devices lack adaptability\, cause patient discomfort\, and have inconsistent pressure application. This study presents a smart wearable and portable compression system integrated with sensors to receive real-time feedback. A DC motor\, controlled by an H-bridge converter\, inflates the system’s inflatable sleeves. The DC motor speed controls the pumping pressure\, and a solenoid valve controls the inflation rate. Pressure\, temperature\, and moisture sensors are embedded in the inner part of the cuff to monitor the physiological parameters. An Arduino-based control system was used to control the inflation rate\, air pressure\, and duration of compression\, optimally ensuring patient comfort. The designed pump was tested and shown adaptability when the sensor data changes. An AI-based control framework is also proposed in this work to enhance the performance and to make the pump autonomous and user-friendly. The response of the proposed AI-based control was validated through simulations of the model developed from fundamentals. The simulation results suggest that the AI-based DVT pump is more adaptable to the physiological parameter variations\, even when the parameters change rapidly. The AI-driven model provides faster and more precise control of inflation and deflation patterns\, preventing overheating\, over-compression\, and sweating. This study highlights the feasibility of a smart\, wearable DVT pump that can adapt to the compression requirements while ensuring safety and comfort.
CATEGORIES:VIRTUAL ROOM_10D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:18a0f1fbd9ced5930c67bf7b47646e86
URL:http://11tict4sd.sched.com/event/18a0f1fbd9ced5930c67bf7b47646e86
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:6G for Resilience: A Framework for Real-Time Disaster Management
DESCRIPTION:Authors - Rachit Chetankumar Mehwala\, Angshuman Kishore Mahato\, Patil Sujit Maruti\, Surendra Solanki\, Gaurav Kumawat\, Ravindra Kumar Soni Abstract - Disaster whether it is natural or man-made most of time led to significant challenges to society\, often resulting in loss of life\, economic instability\, infrastructure damage. Effective "Disaster Management" requires seamless communication and good connectivity under extreme conditions. 6G technology with its capabilities such as ultra-low latency\, large bandwidth\, and terahertz frequencies offers an evolutionary approach to real-time disaster management. This paper explores how 6G technology can be harnessed to build reliable system capable of alleviating the impacts of disasters. By using 6G’s unparalleled communication capabilities we propose framework that ensure strong connectivity and efficient resource allocation in real-time and monitoring during disasters.
CATEGORIES:VIRTUAL ROOM_10E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:b160816c014acda1b482c0954324a9c4
URL:http://11tict4sd.sched.com/event/b160816c014acda1b482c0954324a9c4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:A Systematic Review of AI-Driven Personalized Mental Health Interventions
DESCRIPTION:Authors - Kumkum Saxena\, Akshay Rathod\, Shagun Gupta\, Archie Shah\, Deep Prajapati Abstract - Professional scarcity\, the absence of individualized treatments\, and access to mental health assistance only in limited regions creates problems in mental health care. These issues can fundamentally be solved with the introduction of AI. AI has powerful applications in the realm of mental health care\, including systematic classification and analysis of data\, as well as facilitating tracking and predictive treatment that leads to personalized medicine. Chatbots\, predictive analysis and cognitive computing try to give precise diagnosis by facing the unsolved challenges in the empiric domain of cognitive sciences. This review attempts to highlight the need for multidisciplinary collaboration and more research so that mental health care AI systems are more inclusive.
CATEGORIES:VIRTUAL ROOM_10E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:c3c4fb5c6a1b29f66693802c49cbf1a7
URL:http://11tict4sd.sched.com/event/c3c4fb5c6a1b29f66693802c49cbf1a7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:Asana Master—Intelligent Yoga Mat
DESCRIPTION:Authors - Amruta Amuna\, Hardik Rokde\, Anuj Gosavi\, Gaurang Gulhane\, Ishaan Chepurwar\, Arnav Jadhav\, Vinayak Musale Abstract - In today’s digital age\, most of the work has become sedentary. Whatever you want to do is now available at the tip of your fingers\, reducing your physical activity and causing individuals to suffer from chronic health issues\, such as back pain and postural disorders. Yoga has been publicised throughout the world\, and now all of us are aware\, and many of us even perform yoga regularly. But doing yoga is not enough. We must do it efficiently & with the correct posture to get the benefits of yoga. It has been observed that more than 40% of people doing yoga do it incorrectly. This motivated us to develop a smart\, Iot-enabled solution that addresses this problem during yoga practice. The system integrates Force Sensing Resistors (FSRs) to measure the force applied on the sensor\, LED indicators to guide the correct position for palms and feet for every yoga pose\, and a buzzer to give auditory feedback for misalignment. By combining all of these along with the Arduino Uno microcontroller board\, this solution bridges the gap between self-guided yoga practice and expert supervision\, making each yoga pose more efficient\, easier\, and more effective for the user’s body.
CATEGORIES:VIRTUAL ROOM_10E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:97898bc725a60a43f272908fc47184ea
URL:http://11tict4sd.sched.com/event/97898bc725a60a43f272908fc47184ea
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:EVALUATING POTHOLE DETECTION PERFORMANCE ACROSS DIFFERENT YOLO MODELS
DESCRIPTION:Authors - Sahana S\, Umabharati H\, Rakshita.G\, Vaishanvi.K\, Nikita Patil Abstract - In a populous nation like India\, one fundamental need is travel so travel encompasses road\, rail and water. Road transport is most utilized and also the prime reason people get to lead simple lives. Even after studying the present situation\, we found a major problem on the roads: potholes. These potholes have become a source of harm to the condition of the roads and an additional threat of accidents on the roads. The detection of potholes is vital for safety on roads. The best method for pothole detection is using the real-time accurate efficient YOLOv10 model. A Raspberry Pi Camera Module can record real-time video and images of the road. Further\, the Raspberry Pi can be integrated with a GPS module to find the precise coordinates of any potholes. The data generated by the GPS module is helpful in making repairs and guiding drivers in choosing routes. The system relies on a Convolution Neural Network (CNN) model\, which assists in pothole detection using YOLO models.
CATEGORIES:VIRTUAL ROOM_10E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:9b91f4c1f13040562fdb0da6980354be
URL:http://11tict4sd.sched.com/event/9b91f4c1f13040562fdb0da6980354be
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:Generating Workplace Insights From Employee Reviews Using Aspect-Based Sentiment Analysis
DESCRIPTION:Authors - Jai Ramani\, Tanay Kelkar\, Darshil Shah\, Divanshu Maheshwari\, Archana Nanade Abstract - Anonymized employee reviews on platforms like Ambition-Box offer insights into workplace experiences such as salary\, work culture\, working hours\, and management quality. However\, manually analyzing large volumes of reviews is challenging and time-consuming. To overcome this limitation\, an automated system is proposed to collect\, process\, and present employee sentiment in a structured and meaningful way. Using the technique of Aspect-Based Sentiment Analysis (ABSA)\, the system classifies reviews as positive or negative while identifying sentiment across key concerns such as salary\, work-life balance\, career growth\, management quality\, etc. To identify the keywords and their corresponding sentiment\, this study utilizes the T5 model that is fine-tuned using the InstructABSA framework. Data is gathered through web scraping\, ensuring coverage of employee opinions from multiple platforms. The resulting analysis highlights areas where companies excel or need improvement\, providing actionable insights to enhance the workplace.
CATEGORIES:VIRTUAL ROOM_10E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:4e227223fc0811f3e9844ea4985b4fe5
URL:http://11tict4sd.sched.com/event/4e227223fc0811f3e9844ea4985b4fe5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:Impact of Hardware Trojan on Cache Replacement Policy
DESCRIPTION:Authors - Dhaval Shah\, Shivani D. Anjaria\, Bhupendra Fataniya Abstract - Hardware Security is the key aspect of the integrated circuit’s life cycle\; Any malicious modification in the system design at the foundry is a significant concern for hardware threats\, known as a Hardware Trojan attack. These Trojans are very difficult to detect in the real world\, even during manufacturing and testing. In this article\, the impact of Hardware Trojan on the performance of the cache memory is presented. Insertion of Trojan demonstrated in the cache replacement policy\, which replaces the original cache replacement (least recently used\, first in first out\, and least frequently used) policies with another replacement policy (most recently used). The performance was analyzed in the gem5 simulator after inserting a Trojan. It was clearly evident that Trojan insertion degrades cache performance and affects overall processor performance. It is observed that the impact on the Trojan was a savior on LFU compared to other cache replacement policies\, since LFU persists in its counter-based memory.
CATEGORIES:VIRTUAL ROOM_10E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:f2ee843d21f9aac4ab4d953f23f575d2
URL:http://11tict4sd.sched.com/event/f2ee843d21f9aac4ab4d953f23f575d2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:IOT BASED SMART CAR PARKING SYSTEM
DESCRIPTION:Authors - Ajay Talele\, Revati More\, Satej Patil\, Shreya Bedre\, Gokarn Nemade\, Harshwardhan Vanmore\, Dipak Parvate\, Sakshi Dhumale\, Varun Deshmane\, Vedika Dange Abstract - An Advancement in Effective Parking Solutions: The Smart Car Parking system. In cities\, parking congestion results in wasted time\, fuel\, and irritated drivers. By offering real-time parking availability updates\, expediting the procedure\, and lowering traffic in parking lots\, the Smart Car Parking System provides an answer. The system\, which was constructed with an Arduino Uno microprocessor\, uses infrared (IR) sensors to identify whether a car is in each slot. To assist drivers in making educated judgments\, a linked LCD shows real-time data on available and occupied spaces. Entry and exit barriers are controlled by servo motors\, which grant only permitted access. The technology automatically updates the slot status when cars enter or exit. This improves traffic flow\, lowers pollutants\, saves fuel\, and lessens the need for manual supervision. The system is perfect for public lots\, workplaces\, malls\, and residential areas. It can be improved with AI for space optimization and IoT-based apps for slot reservations\, increasing the sustainability and efficiency of urban parking. [5]
CATEGORIES:VIRTUAL ROOM_10E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:63b5bf9246bacd0be9da62d8b33a7bb6
URL:http://11tict4sd.sched.com/event/63b5bf9246bacd0be9da62d8b33a7bb6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:Kalaahithaa: Design intervention for Bharatanatyam dancers to help with compositions and choreographies
DESCRIPTION:Authors - P Sanjana\, Smrthi Harits\, Divyadarshan C.S Abstract - This project aims to solve the issues faced by Bharatanatyam dancers in accessing the translations and interpretations of compositions used for Bharatanatyam performances. Understanding these compositions is crucial in depicting an accurate vision of the composition. Through structured user interviews\, it was discovered that due to the non-preservation of these translations and limited access to scholars\, accurate translations\, and music resources\, dancers face significant issues. This hinders their creative process and restricts their creative freedom. Compared to experienced dancers with access to vast resources\, the upcoming artists have very few such connections and guidance\, making them more vulnerable to this problem. This necessitates a solution to reduce the accessibility issue faced by the upcoming dancers. Various design methodologies were employed to tackle the issues faced by Bharatanatyam dancers. To bridge this gap\, Kalaahithaa\, a digital service platform\, was designed to provide a repository of verified translations\, connect dancers to scholars\, musicians and teachers to help them understand the compositions\, create new compositions and choreographies\, and foster collaborative opportunities. This solution was further developed using a service design blueprint\, user flows\, site maps\, and low-fidelity and high-fidelity prototypes. The platform supports personalised interactions\, enabling dancers to seek expert guidance\, upload and access translations\, and engage in meaningful exchanges that enhance their understanding of compositions while promoting monetary ethical practices. By centralising resources and fostering connections with a usercentred approach\, Kalaahithaa serves as a vital tool for dancers to refine their art while preserving the integrity of Bharatanatyam.
CATEGORIES:VIRTUAL ROOM_10E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:22bd3323b765be3bf1c7682c06ca02db
URL:http://11tict4sd.sched.com/event/22bd3323b765be3bf1c7682c06ca02db
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:Smart Healthcare Using NLP for Advanced Chatbots and Root Cause Analysis
DESCRIPTION:Authors - Sonali Antad\, Kalyani Ghuge\, Prakash Sharma\, Vaishnavi Chirawande\, Aditi Gade\, Shweta Ahire\, Sharvari Jadhav Abstract - This study describes an AI healthcare chatbot that automatically assesses patients’ medical needs\, conducts inter- views and performs thorough health analyses. Using several state-of-the-art natural language processing (NLP) models\, including sentence transformers and Llama-based large language models\, the system analyzes user symptoms and classifies them into specific medical domains like diabetes\, blood pressure\, skin and stomach disorders. It uses Gemini API’s generative AI. This dynamically creates several questions and suggests some medical diagnoses. The platform’s improved fishbone diagram considerably aids root cause analysis by visually showing how potential causes relate to user-reported symptoms. Our project creates efficient\, interactive healthcare assistants that improve access to preliminary medical advice.
CATEGORIES:VIRTUAL ROOM_10E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:b1da7de23af148983b73b9d7222be05f
URL:http://11tict4sd.sched.com/event/b1da7de23af148983b73b9d7222be05f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T040000Z
DTEND:20260827T060000Z
SUMMARY:Vision Based Real Time Indian Sign Language (ISL) Detection
DESCRIPTION:Authors - Rhucha Deodhar\, Tanya Gadwal\, Ananya Bhat\, Aditi Hinge\, Shilpa Pant Abstract - This paper presents a real-time\, vision-based system for Indian Sign Language (ISL) recognition and translation\, aimed at enhancing communication between the deaf community and non-signers. The system combines a CNN-LSTM architecture for static gesture recognition\, achieving an accuracy of 98.47% and introduces GestureNet\, a bidirectional LSTM model trained on a custom dynamic gesture dataset\, which attains 96.83% recognition accuracy. Ad-ditionally\, a Generative AI framework is integrated to convert recognized ges-tures into semantically coherent and contextually appropriate sentences. By em-phasizing real-world applicability and high recognition performance\, the pro-posed system advances sustainable and accessible communication technologies\, with potential impact in education\, public services\, and digital inclusion\, partic-ularly in developing regions.
CATEGORIES:VIRTUAL ROOM_10E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:8ac81c22fde4cf1c4bad0032cbceec1b
URL:http://11tict4sd.sched.com/event/8ac81c22fde4cf1c4bad0032cbceec1b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T060000Z
DTEND:20260827T060200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_10A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:1f03746fb163013e4d23f08f3950d07d
URL:http://11tict4sd.sched.com/event/1f03746fb163013e4d23f08f3950d07d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T060000Z
DTEND:20260827T060200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_10B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:572c6565553a1563191d0b900e0c49ab
URL:http://11tict4sd.sched.com/event/572c6565553a1563191d0b900e0c49ab
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T060000Z
DTEND:20260827T060200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_10C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:57a0f2f4909821a067e78554627ca647
URL:http://11tict4sd.sched.com/event/57a0f2f4909821a067e78554627ca647
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T060000Z
DTEND:20260827T060200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_10D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:73bd5e54a7a02bc19b77817a6cdfef6f
URL:http://11tict4sd.sched.com/event/73bd5e54a7a02bc19b77817a6cdfef6f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T060000Z
DTEND:20260827T060200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_10E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:ed3e745839f7a1d87141a105d312b71b
URL:http://11tict4sd.sched.com/event/ed3e745839f7a1d87141a105d312b71b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T060200Z
DTEND:20260827T060500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_10A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:94bab6d24f5bafe6464a02f1441fd49d
URL:http://11tict4sd.sched.com/event/94bab6d24f5bafe6464a02f1441fd49d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T060200Z
DTEND:20260827T060500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_10B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:97792bfa6b572c554e15a76287329baa
URL:http://11tict4sd.sched.com/event/97792bfa6b572c554e15a76287329baa
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T060200Z
DTEND:20260827T060500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_10C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:cc5a68c0b5f7b28c8ec4afd0f44e61cb
URL:http://11tict4sd.sched.com/event/cc5a68c0b5f7b28c8ec4afd0f44e61cb
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T060200Z
DTEND:20260827T060500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_10D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:eb1f40f57c6e0b50ec3bf0f0039d051f
URL:http://11tict4sd.sched.com/event/eb1f40f57c6e0b50ec3bf0f0039d051f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T060200Z
DTEND:20260827T060500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_10E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:4d6117d050b7369c7446f1780e99b451
URL:http://11tict4sd.sched.com/event/4d6117d050b7369c7446f1780e99b451
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T065800Z
DTEND:20260827T070000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_11A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:4f941e8302dd54ad64e811fa4cbde54d
URL:http://11tict4sd.sched.com/event/4f941e8302dd54ad64e811fa4cbde54d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T065800Z
DTEND:20260827T070000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_11B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:75ad914e94b5bd2dbdc0ab1fb7ce662d
URL:http://11tict4sd.sched.com/event/75ad914e94b5bd2dbdc0ab1fb7ce662d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T065800Z
DTEND:20260827T070000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_11C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:0b809b626f554d96772371aab72a1a0e
URL:http://11tict4sd.sched.com/event/0b809b626f554d96772371aab72a1a0e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T065800Z
DTEND:20260827T070000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_11D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:3706b916f84968211775f740e6e1ea43
URL:http://11tict4sd.sched.com/event/3706b916f84968211775f740e6e1ea43
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T065800Z
DTEND:20260827T070000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_11E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:cd44908c8d06bac450100ab39c997240
URL:http://11tict4sd.sched.com/event/cd44908c8d06bac450100ab39c997240
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:Analysis of Disguised Face Recognition on Indian Faces
DESCRIPTION:Authors - Darshan L.M\, Nagasundara K.B Abstract - Nowadays\, most of the people alters/or conceals their true facial appearance intentionally and/or unintentionally by wearing various disguise accessories such as sunglasses\, artificial beard and moustache\, face make-up\, and many more fancy items. Since\, these accessories obscures the prominent facial features\, the traditional face recognition systems have not shown a notable recognition performance and thus its performance is challengeable in the various applications fields such as immigration and border control\, national security\, surveillance\, and many more. In literature\, IIIT-DDFD\, IMFDB\, and FDB are disguise datasets are available for Indian ethnicity. Our analysis indicates that\, these datasets are not sufficient with enough facial samples to meet the current trends. Therefore\, we have introduced an Indian celebrity disguise face dataset (ICDFD)\, which includes the samples with wide range of complex disguise variations combined with pose\, illumination\, and expression. Initially\, we analyze the performance of these datasets using holistic approaches and followed by deep learning models. From the experimental analysis\, it reveals that the deep learning models have shown an optimal performance over holistic approaches. It is observed that\, the disguised faces are continue to pose wide open challenges for the researchers in the area of computer vision.
CATEGORIES:VIRTUAL ROOM_11A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:5da26e83112f9ba8fc930a7d5dfb504b
URL:http://11tict4sd.sched.com/event/5da26e83112f9ba8fc930a7d5dfb504b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:ASSURANCE SAVING BLOCKCHAIN STRUCTURE FOR MEDICAL THE EXECUTIVES WITH RESPECT TO THE BOARD
DESCRIPTION:Authors - Harini S\, Shamila Ebenezer A Abstract - The document introduces a protected blockchain application for patient care that improves system visibility and operational speed. The application allows patients to book appointments which hospital administrators check before authorization. Following permission by administrators\, patients may evaluate their appointment schedule and doctors analyze medical records for medical assessments. Specialist approval must authorize the download of prescription reports. Through blockchain technology physicians can perform decentralized transactions as well as track assets and exchange secure data which leads to lower operational costs and better trust and platform collaboration between patients and specialists and administrators.
CATEGORIES:VIRTUAL ROOM_11A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:39d753713483a7f7f8b472e37feb5f99
URL:http://11tict4sd.sched.com/event/39d753713483a7f7f8b472e37feb5f99
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:Augmented Reality Software for Design and Architecture
DESCRIPTION:Authors - Vishwesh Kumbhre\, M. L. Dhore\, Pradhynan Lohar\, Ajinkya Lende\, Om Popade\, Shivkumar Padalwar Abstract - Augmented Reality has contributed in many important fields like Education and other fields like Medical and Military\, which needs 100% accuracy and focus while performing certain operations. These tasks can be perfected if you have a thorough practice of every situation and gain knowledge about everything being used in that instance. This requirement is fulfilled by Augmented Reality where it creates virtual objects on a real-world background which gives us a real time experience\, as if we are actually performing these tasks using solid objects. And the responses are as legit as they would be\, in real experiments. Using these features\, we can make different software which guides the students and cadets in real life situations.
CATEGORIES:VIRTUAL ROOM_11A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:0f1f1c4fda9d49801732fd72eb7e5a6b
URL:http://11tict4sd.sched.com/event/0f1f1c4fda9d49801732fd72eb7e5a6b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:Carcinogenic Assessment of Novel Imidazo[1\,2\,a]pyridine ligands using in silico molecular toxicity prediction tool
DESCRIPTION:Authors - Anjali Mahavar\, Atul Patel\, Ashish Patel Abstract - When given to the human body\, different substances and products might present varying health hazards. Concerns regarding toxicity have caused fewer new medicines to access the market throughout the years via the conventional drug development path. The choice of lead compounds and ADMET research depends much on the use of in silico toxicity prediction techniques as ethics\, time\, money\, and other resources often limit in vitro and in vivo approaches. In this regard\, we propose a variety of toxicity tools that use structural and physicochemical- based characteristics in the form of molecular descriptors and fingerprints to assess the carcinogenicity of five distinct imidazo[1\,2\,a]pyridine ligands\, including Protox-3\, VenomPred\, PkCSm\, etc. According to the results of the in silico toxicity tool\, ligand-1 (IP-1) has a low likelihood of carcinogenicity (0.56%) and excellent accuracy (67.38%) when compared to other ligands. As a consequence\, it can be the first choice for medication development in the treatment of cancer.
CATEGORIES:VIRTUAL ROOM_11A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:3cbcd9f134507d47d87bc651b1a8edbb
URL:http://11tict4sd.sched.com/event/3cbcd9f134507d47d87bc651b1a8edbb
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:Centralized Application Context Aware Firewall
DESCRIPTION:Authors - Atharv Khunte\, Vishakha Dhotare\, Divya Pawar\, Nikita Dandgavhal\, V.M Kokane Abstract - For increasing cyberattacks\, web applications require robust and scalable security mechanisms. We suggest a centralized firewall structure that effectively detects and blocks attacks on multiple hosts simultaneously. The system is a command center that collects information about attacks from clients not yet attacked. Information collected is transmitted to the afflicted client only after receiving all necessary details. The system works by detecting and neutralizing various forms of cyberattacks such as SQL injection (SQLi)\, cross-site scripting (XSS)\, and distributed denial-of-service (DDoS) attacks. As soon as malicious activity is detected\, the system will automatically block the attacking IP address\, preventing further intrusion. This process enhances real-time protection\, limiting the likelihood of repeated cyberattacks on interconnected web applications. By employing a single defense method\, this centralized firewall maximizes threat intelligence sharing\, rendering all the connected clients secure. Unlike standalone firewalls in the past\, this approach consolidates security policies and enhances cybersecurity resilience on various platforms. Further\, this system not only secures web applications against emerging threats but also ensures that organizations meet cybersecurity compliance requirements by hosting a neat and responsive security mechanism. The centralized structure of the fire-wall provides early attack detection\, largely reducing downtime\, data loss\, and monetary loss.
CATEGORIES:VIRTUAL ROOM_11A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:d3083edcc37600b51faf841d898f3c47
URL:http://11tict4sd.sched.com/event/d3083edcc37600b51faf841d898f3c47
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:Communication Theory: Understanding Communication Theory in Healthcare
DESCRIPTION:Authors - Prajakta Prasad Kohale\, Michael Savariapitchai Abstract - Communication is fundamental for quality healthcare. It is the bridge between the patients and the provider. The basis of any group teamwork and an important factor in an efficient healthcare system is communication. The objective of this paper is to trace the history of communication’s evolution from basic\, traditional models to the complex systems that are present in modern-day healthcare. Effective communication helps in establishing trust and confidence which motivates both the patients and the care teams to work together and take accountability for their health. The most favorable health outcomes are shown in patients who feel most sincerely cared for.
CATEGORIES:VIRTUAL ROOM_11A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:1c600b601a3f2eb07fdbe243587e0fdf
URL:http://11tict4sd.sched.com/event/1c600b601a3f2eb07fdbe243587e0fdf
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:Graph Neural Networks for Device Driver Malware Detection: A Feature Engineering-Based Approach to Predict Malicious Attacks
DESCRIPTION:Authors - A.Punidha\, E.Arul\, E.Yuvarani\, S.Rajasakaran Abstract - Device drivers are a critical component of modern computing but are increasingly targeted by attackers to gain unauthorized access\, execute malicious code\, or escalate privileges. Traditional malware detection techniques\, such as signature-based and heuristic methods\, struggle against advanced threats that employ evasion tactics. To address this\, we propose a Graph Neural Network (GNN)-based framework that leverages feature engineering and graph-based learning to detect malicious drivers with high accuracy.By modeling system execution as a graph\, our approach captures complex dependencies between API calls\, memory accesses\, and kernel interactions. We employ Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) to analyze these relationships\, enabling detection of even stealthy and obfuscated malware.Experiments on Windows\, Linux\, and Android driver datasets demonstrate that our model achieves a 95.8% accuracy\, outperforming Random Forest\, XGBoost\, LSTM\, and CNNs. The model is also robust against adversarial evasion techniques\, making it a scalable and effective solution for endpoint security\, malware sandboxing\, and kernel protection.
CATEGORIES:VIRTUAL ROOM_11A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:8ce428d3966e256c79eca6441b56ce59
URL:http://11tict4sd.sched.com/event/8ce428d3966e256c79eca6441b56ce59
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:MCALHA: Multimodal Conversational AI for Lung Health Assessment
DESCRIPTION:Authors - R Saiprithvi\, Sindhu Chandra Sekharan\, Summia Parveen\, Ajanthaa Lakkshmanan\, Jesline D Abstract - Diseases of lungs like asthma\, Chronic obstructive pulmonary disease\, lung cancer are among the leading causes of death across the world. Ensuring better outcomes for patients with any medical condition requires an early diagnosis which is\, unfortunately\, technologically impossible in many regions. This work presents a multimodal Conversational Artificial Intelligence approach based on X-ray imaging\, respiratory sound processing\, and conversational interfaces that can help with the early and easy diagnosis of lung health. A Convolution Neural Network Processes X-ray images and reliably captures abnormalities. A Random Forest classifier examines MFCC features of lung sounds to confirm presence of asthma\, bronchitis\, and other diseases. The last method involves using a conversational AI chatbot that makes the collection of symptoms more convenient and provides the user with further information. with the capture of volumetric imaging\, sound\, and text\, healthcare accessibility\, efficiency\, and diagnostics can be achieved using Artificial intelligence in imaging technology. Lung health is of primary importance\, but millions of people worldwide live with easily preventable respiratory disease. EWHO alone estimates that more than 300 million people around the globe suffer from chronic lung disorders\, including asthma\, Lung
CATEGORIES:VIRTUAL ROOM_11A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:e1c721b0a8b8039263edbc481035577c
URL:http://11tict4sd.sched.com/event/e1c721b0a8b8039263edbc481035577c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:The Influence of Finfluencers on Student Investment Decisions
DESCRIPTION:Authors - Job Joseph\, Shivaprakash S\, Rahul\, Rojalin Patri Abstract - This research investigates the impact of financial influencers (finfluencers) on investment decisions of students using trust\, perceived risk\, and ethical concerns. Correlation and regression analysis reveal strong inter-linkages among them. The implications are drawn noting students' increasing utilization of social media as a source of personal finance information and both its benefits and risks. This work informs financial literacy scholarship and provides recommendations for policy change to contain the influence of finfluencers. Additionally\, findings from current research show that finfluencers are not only educators but also business entities that act with self-interest\, influencing market behaviour and investment decision-making among retail investors.
CATEGORIES:VIRTUAL ROOM_11A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:379e64d7cfcb41d88996cf65c5e74cc2
URL:http://11tict4sd.sched.com/event/379e64d7cfcb41d88996cf65c5e74cc2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:Wavelet based Representation of Images in Latent Space
DESCRIPTION:Authors - Dheeraj Hegde\, Aishwarya Kalatippi\, Prajwal Shiggavi\, Satish Chikkamath\, Nirmala S.R Abstract - This study presents a novel approach to image representation\, utilizing wavelet transforms to compress image information into a compact latent space. Wavelet transforms offer a multi-resolution analysis\, decomposing images into different frequency components at different resolution scales\, emphasizing the spatial and frequency attributes. By leveraging the hierarchical structure of wavelet coefficients\, we construct a latent space representation preserving essential features while reducing dimensionality. Experimental evaluations on benchmark datasets demonstrate competitive performance in tasks such as compression and classification compared to traditional deep learning approaches. Waveletbased representation offers promise for addressing challenges in highdimensional data while retaining crucial image information for diverse processing tasks.
CATEGORIES:VIRTUAL ROOM_11A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:0bfd1746374fe06264c01e1f32581d3e
URL:http://11tict4sd.sched.com/event/0bfd1746374fe06264c01e1f32581d3e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:A New Composite-structured CSRR Loaded Multi-Resonating Sensor for Mining based Material Characterization
DESCRIPTION:Authors - Madan Kumar Sharma\, Nadir Kamal Salih Idries\, Abdullah Said Alkalbani\, Satyanarayana Degala\, Gopal Rathinam\, Ankit Sharma Abstract - Multi-resonance (MR) Microwave sensors have emerged as a promising sensing device for mineral-based materials characterization due to their high sensitivity and precision. This research presents a novel microwave sensor for mineral-based material characterization. The sensor's resonating structure comprises a 3 × 3 array of circular-shaped complementary split-ring resonators (CSRR) coupled with four rectangular defected structures etched around the CSRR array. This unique configuration enhances electromagnetic interaction with the material under test (MUT)\, leading to precise characterization based on S-parameter analysis. To validate the sensor’s efficacy\, simulation-based investigations were conducted on the mining-based materials\, including chrome\, copper\, and quartz. The obtained results demonstrate distinct resonance shifts and attenuation variations corresponding to each mineral\, highlighting the sensor’s capability to differentiate and analyze their dielectric properties. The proposed MR-sensor design provides a robust and efficient method for non-destructive material characterization\, offering potential applications in the mining industry\, quality control\, and geophysical exploration.
CATEGORIES:VIRTUAL ROOM_11B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:e54a2ea56d6262602f2fb2c85e72c822
URL:http://11tict4sd.sched.com/event/e54a2ea56d6262602f2fb2c85e72c822
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:An approach for lung cancer detection using Optimization enabled Squeeze-Inception V3 model
DESCRIPTION:Authors - Geethu Lakshmi G\, P. Nagaraj\, P. Chinnasamy Abstract - Lung cancer detection constitutes a paramount process in the diagnosis and management of one of the predominant contributors of cancer-induced humanity worldwide. The significance of early screening is underscored by its essential role in enhancing survival rates through the identification of disease during a stage amenable to treatment. Diagnostic methodologies\, including imaging modalities\, are routinely utilized for diagnosis. Furthermore\, advancements in the realm of molecular biology have facilitated the emergence of biomarkers and genetic examines\, thereby enabling a more accurate identification of lung cancer. The prompt and defined detection of lung cancer facilitates timely therapeutic interventions\, which significantly influence both the efficacy of treatment and overall outcomes for patients. The primary objective of this research is to develop an optimization-based hybrid deep learning methodology for the detection of lung cancer. The initial phase involves pre-processing of input images through techniques such as color space transformation\, data augmentation\, resizing\, and normalization. Subsequently\, features derived from Slime Mould Algorithm-based Convolutional Neural Network (SMA-CNN) are employed for the detection of lung cancer\, with CNN being trained utilizing SMA\, extracted from pre-processed images. Finally\, the Squeeze-Inception V3 model\, which integrates SqueezeNet and Inception V3\, leverages SMA to train the classifier. Consequently\, the proposed SMA-based hybrid SqueezeNet-Inception V3 is utilized to classify instances as normal or abnormal. Empirical results designate that SMA-based hybrid SqueezeNet-Inception V3 attained an accuracy of 97.3%\, a specificity of 96.1%\, and a sensitivity of 98%\, thereby underscoring its efficacy in the detection of lung cancer.
CATEGORIES:VIRTUAL ROOM_11B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:78db3c3e685c4b00a9a16e8ea1fe0b7b
URL:http://11tict4sd.sched.com/event/78db3c3e685c4b00a9a16e8ea1fe0b7b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:Dynamic Stock Price Forecasting using Machine Learning and Real-Time Data Integration
DESCRIPTION:Authors - Narayan Gupta\, Pawan Kumar\, Prince Kumar Singh\, Priyabart Kumar\, Parampreet Kaur Abstract - For investors\, accurately predicting stock market prices is a critical part of financial analysis. This work studies a wide variety of machine learning algorithms specifically designed for the task of predicting stock prices using a variety of techniques and the latest technologies available as of the time of study. The study critically compares a suite of algorithms\, including Support Vector Regression (SVR)\, Random Forests\, Decision Tree models\, and Long Short-Term Memory (LSTM)\, each with differing strengths. Moreover\, it investigates a variety of approaches that are focused on understanding the complex connections found in the past price data. The dataset used for this study includes extremely long-term stock price representatives over a time range from 2010 to 2024 for Tata Consultancy services (TCS)\, containing a wide range of information\, including opening and closing trading prices\, trading volumes\, and a variety of calculated indicators reflecting market behaviour. To understand the first experiment carried out for this study\, it can be seen that in combination mode\, for the best-performing model\, Random Forest has the best interpretability. In addition\, both Support Vector Regression (SVR) and Decision Tree algorithms deliver both impressive short-term prediction results and clear explanations behind their decisions.
CATEGORIES:VIRTUAL ROOM_11B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:82af01c392b8b9003d8a10d094f20e75
URL:http://11tict4sd.sched.com/event/82af01c392b8b9003d8a10d094f20e75
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:Efficient Information Extraction from Large PDFs Using Retrieval-Augmented Generation and Large Language Models
DESCRIPTION:Authors - Sandeep Shinde\, Samarveer Moray\, Aditya Sakhare\, Prathamesh Salokhe\, Kedar Sathe Abstract - The exponential growth of digital documents\, particularly PDFs\, presents significant challenges in efficient information retrieval and extraction. Traditional methods often struggle with the complexity and variability inherent in large PDF documents. Recent advancements in Natural Language Processing (NLP)\, especially Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG)\, offer promising solutions. This paper presents a comprehensive system for efficient information extraction from large PDFs using RAG and LLMs. We propose a robust and scalable pipeline addressing challenges such as document segmentation\, dynamic retrieval\, and response contextualization. Through extensive experiments across multiple domains—including legal analysis\, technical documentation\, and scientific literature—we demonstrate that the proposed methodology significantly outperforms existing approaches in terms of accuracy\, scalability\, and efficiency. Our research lays the groundwork for integrating RAG and LLMs in various domains\, offering a valuable tool for extracting knowledge from complex documents.
CATEGORIES:VIRTUAL ROOM_11B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:5b14932292c21e7fe97df991115ba0d6
URL:http://11tict4sd.sched.com/event/5b14932292c21e7fe97df991115ba0d6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:Enhancing the Performance of Wireless Sensor Networks through AODV Protocol in Wireless Mesh Network
DESCRIPTION:Authors - Rahul Pethe\, Parag Puranik\, Abhay Kasetwar Abstract - Wireless Sensor Networks (WSNs) have been designed and developed by numerous researchers over time\, evolving based on emerging demands and environmental conditions. Over the years\, various enhancements and improvements have been proposed\, with multiple protocols introduced to address design challenges. In parallel\, Mobile Ad Hoc Networks (MANETs) have witnessed tremendous growth in this wireless era. While many researchers have focused on protocols like DSR and hybrid approaches for energy-efficient clustering\, these solutions have often proven to be time-bound and context-specific. To overcome these limitations and enhance the performance and lifetime of WSNs\, we propose a new scheme based on the AODV (Ad hoc On-Demand Distance Vector) protocol within a mesh networking framework. Our approach achieves optimal results\, including 100% throughput\, minimal jitter\, and significant improvements in network life-time.
CATEGORIES:VIRTUAL ROOM_11B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:f5b8dbad7026562de91bb931854ee18d
URL:http://11tict4sd.sched.com/event/f5b8dbad7026562de91bb931854ee18d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:Forecasting Stock Market Prices with Long Short-Term Memory (LSTM) Networks
DESCRIPTION:Authors - Pranav Mittal\, Prerna\, Dhruv Bansal\, Nikhil Panwar\, Krish Tyagi Abstract - Stock market prediction is an intricate process in financial analysis\, as its main aim is to predict the price trends and thus help to make trading strategies. But the stock market is so unpredictable with various factors affecting it\, and thus predicting whether the value will rise or not becomes a difficult task. This study aims to predict the stock market prices with historical data that will be achieved via Deep Learning techniques in particular LSTM networks. Due to its strength in learning long-term dependencies and preserving the sequence information\, LSTM (one of the variants of RNNs) is ideal for time-series data. Our approach leverages a dataset of daily stock prices from various financial indices over multiple years. The data is preprocessed using normalization techniques to improve model accuracy. The LSTM model is then compared to a traditional feed-forward neural network to demonstrate the superiority of LSTM in predicting shortterm stock trends. The models are optimized using the Adam optimizer\, The results indicate that LSTM significantly outperforms the conventional models in forecasting accuracy. Moreover\, this research introduced a hybrid LSTM-CNN method to extract features and prediction firmly. This study will contribute to financial forecasting by utilizing deep learning techniques and real trading scenarios. The research work was carried out using the programming language known as Python\, deep learning tools such as TensorFlow and Keras\, data management libraries such as Pandas and NumPy\, and data representation software such as Matplotlib. It was found that the LSTM model has a much higher level of success when time series patterns are approximate than any other type of neural network\, hence stock price predictions are more accurate. This study helps in how LSTM networks can be useful for forecasting in finance hence would be helpful to traders and market analysts.
CATEGORIES:VIRTUAL ROOM_11B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:93e6ab46eab9c384dbf585432864343b
URL:http://11tict4sd.sched.com/event/93e6ab46eab9c384dbf585432864343b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:KnowledgePilot: Pioneering First 1-Bit LLM for University Student Support
DESCRIPTION:Authors - Sheetal Phatangare\, Komal Potdar\, Yash Mahajan\, Mandar Pandagale\, Vivek Nikam Abstract - University students frequently encounter challenges in retrieving relevant academic information due to the limitations of traditional search engines. This research introduces KnowledgePilot\, the first 1-bit Large Language Model (LLM) specifically designed to support university students. Leveraging the BitNet b1.58 architecture\, which employs ternary parameterization (-1\, 0\, 1)\, KnowledgePilot achieves high performance with reduced computational costs\, making it both resource-efficient and fast. The system integrates Retrieval Augmented Generation (RAG) pipelines\, enabling it to access external academic data sources\, thus minimizing hallucination issues common in LLMs and providing accurate\, context-specific responses. The research also encompasses the development of tools for file conversion\, dataset creation\, model pretraining and fine tuning. Comprehensive evaluations will measure the system’s performance and user satisfaction\, demonstrating its potential to significantly enhance student access to academic resources\, while setting the stage for future advancements in low-bit AI technologies for education.
CATEGORIES:VIRTUAL ROOM_11B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:310e39a5586a2bd2fe1f6019fccde3db
URL:http://11tict4sd.sched.com/event/310e39a5586a2bd2fe1f6019fccde3db
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:NutriScan: A Python-Based Barcode Scanner for Ingredient Analysis and Personalized Health Warnings
DESCRIPTION:Authors - Yash Chavan\, Arnav Sonawane\, Arpit Pattiwar\, Aditya Nagdive\, Kaushalya Thopate Abstract - In today's fast-moving world\, consumers rely on packaged foods. This is extremely important for easy access to detailed and personalized nutritional information. This project focuses on developing mobile applications for barcode scanning. This includes extensive food details\, including ingredients\, nutritional value\, allergen warnings\, and personalized consumption recommendations based on a person's health. Applications written with Python and Kivy provide a seamless user experience\, allowing individuals to scan barcodes and upload images of ingredients to extract and analyze related information. Additionally\, it includes optical character detection (OCR) using Tesseract\, which extracts text from photos to allow users to analyze the ingredient list and nutritional name\, even if barcode scans are not possible. By taking into account user nutritional limitations or illnesses such as diabetes\, lactose intolerance\, or gluten sensitivity\, this application provides tailor-made health advice and helps individuals make found food decisions appropriately. A secure user authentication system improves the experience by storing your preferences and receiving recommendations created by tailors. The main goal of this project is to enable consumers to choose food in real time and promote healthier consumption habits. The combination of barcode scanning\, OCR\, and a structured database causes applications to close the gap between the complexity of food indicators and user understanding. Future improvements include mechanically learning-based ingredients\, integration into real-time product databases\, and expansion of several platforms beyond Android. This initiative represents an important step in using technology to improve consumer health awareness and ensure safer and sounder decisions for food consumption.
CATEGORIES:VIRTUAL ROOM_11B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:615db35e0c247d2a8f33468e3125364d
URL:http://11tict4sd.sched.com/event/615db35e0c247d2a8f33468e3125364d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:Pedestrian Trajectory Prediction Using Deep Learning: A CNN-LSTM Approach
DESCRIPTION:Authors - Evangeline R C\, Krupa Nirmal\, Laasya P\, Aishwarya K Abstract - Real-time pedestrian trajectory prediction is essential for enhancing safety and urban mobility\, particularly in dense and dynamic environments. This paper introduces a video data processing system that accurately predicts pedestrian movement by analyzing sequences of video frames in real time. The system effectively handles challenges such as overlapping individuals\, partial occlusions\, and diverse walking behaviors\, making it suitable for real-world deployment. The architecture is designed to be both modular and scalable\, allowing for seamless integration into various applications such as traffic management\, urban planning\, and pedestrian safety enhancement. A user-friendly interface provides real-time visualization of the predicted trajectories\, enabling accessibility for both technical and non-technical stakeholders\, including urban planners and public safety officials. Extensive experiments conducted on multiple diverse datasets demonstrate the system’s reliability and accuracy across various conditions\, including crowded scenes and irregular pedestrian movement. The system successfully captures complex behavior patterns and provides predictive insights that can help reduce pedestrian-related accidents. This research contributes significantly to the field of intelligent pedestrian monitoring systems. By combining real-time responsiveness with accurate trajectory prediction\, the proposed system supports the development of smarter and safer urban infrastructure\, fostering proactive decision-making and improved pedestrian safety in modern cities.
CATEGORIES:VIRTUAL ROOM_11B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:4616bce924c77ae0103536fc4c572df7
URL:http://11tict4sd.sched.com/event/4616bce924c77ae0103536fc4c572df7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:ROBOTIC FACIAL PROFILE Classification and recognition using Conventional Neuro Network
DESCRIPTION:Authors - Nisha Dubey\, Randeep Singh Abstract - Facial profile classification and acknowledgment have different applications in security\, perception\, and identity affirmation. This paper proposes a novel approach utilizing Convolutional Neural Frameworks (CNNs) to classify and recognize facial profiles. The proposed system utilizes a significant CNN designing to remove solid highlights from facial profiles\, taken after by a classification layer to recognize profile classes (e.g.\, cleared out\, right\, frontal). The illustrate is ready on a tremendous dataset of facial profiles and finishes tall accuracy in classification (95.2%) and affirmation (92.5%) errands. Test comes around outline the system's quality to assortments in lighting\, pose\, and expression. Besides\, the proposed system outflanks existing techniques in facial profile classification and affirmation. This work contributes to the movement of facial examination development\, engaging its course of action in real-world applications such as identity affirmation\, get to control\, and perception. In particular\, facial profiles provide an interesting challenge due to the distinctive variety of lighting\, attitude\, expression and disorders. Furthermore\, large data records and accessibility of arithmetic violations have made it possible to prepare violent .CNN models for facial profile classification and detection
CATEGORIES:VIRTUAL ROOM_11B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:ab0bdeea2dea30076380b4a41d8b495e
URL:http://11tict4sd.sched.com/event/ab0bdeea2dea30076380b4a41d8b495e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:A Comprehensive Survey on Recommendation Frameworks: Techniques\, Challenges\, and Future Directions
DESCRIPTION:Authors - Prranjali Jadhav\, Varsha H Patil Abstract - Recommendation systems play a crucial role in personalized content delivery across various domains such as e-commerce\, streaming platforms\, and healthcare. This survey presents a comprehensive analysis of recommendation frameworks\, emphasizing their architectures\, methodologies\, challenges\, and future directions. The provided framework integrates hybrid models\, deep learning\, collaborative filtering\, and content-based filtering\, processed through a multi-layered architecture. The data processing layer handles preprocessing\, feature extraction\, and data collection\, while the model selection layer chooses an appropriate recommendation technique. The recommendation engine ranks and scores predictions before delivering final recommendations. A critical component is the user feedback & continuous learning module\, incorporating explicit and implicit feedback to dynamically update the model. Challenges such as scalability\, data sparsity\, and real-time adaptation are explored\, along with emerging advancements like knowledge graphs and reinforcement learning. The paper highlights future research opportunities to enhance recommendation accuracy and user experience.
CATEGORIES:VIRTUAL ROOM_11C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:9d422fe7c972470ceba4211386b6b42a
URL:http://11tict4sd.sched.com/event/9d422fe7c972470ceba4211386b6b42a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:Achieving Excellence: AI-Driven Mock Interviews for Career Advancement
DESCRIPTION:Authors - Amit Budhodkar\, Rupali Umbare\, Nihar Ranjan\, Shubham Udgirkar\, Sakshi Suryawanshi\, Pradnya Aher Abstract - As mock interviews are essential for job interview preparation\, the resources available cannot evaluate both technical and non-technical skills. This paper outlines the AI Mock Interview Platform which interfaces with learners and simulates truthful interviews by assessing non-technical competencies such as body language\, confidence\, emotional expression\, and technical knowledge. The platform is capable of dynamically generating interview questions relevant to a candidate's particular role using AI technologies. In addition\, AI technologies enable real-time feedback provision. With regard to feedback generation\, the system utilizes AI technologies to consider specific features unique to the candidates’ voices\, movement\, and gaze direction. It utilizes Dlib’s human body posture detection library and video sentiment analysis for facial expression recognition with AffectNet dataset for Convolutional Neural Network faces as well as videos. With the Courses feature\, learners can focus on varied topics and the platform automatically selects suitable instructional content consistent with the candidates’ preferred method of learning
CATEGORIES:VIRTUAL ROOM_11C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:44a2b4532f44b3a645919e762e0c2e23
URL:http://11tict4sd.sched.com/event/44a2b4532f44b3a645919e762e0c2e23
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:Autism Spectrum Disorder Early Detection and Support Platform with OpenCV\, VGG16 Deep learning model and NLP concepts
DESCRIPTION:Authors - SHALINI S\, C NANDINI\, LAKSHMI MR\, KOUSTAV BISWAS\, L DIVYASHREE\, MITAYI AJAY KUMAR\, MONIKA V Abstract - This research work uses Artificial Intelligence for early detection and targeted intervention in the case of Autism Spectrum Disorder (ASD). Through sophisticated language analysis and pattern identification of interactions\, the platform detects signs of autism to facilitate early intervention. Relying on these findings\, the platform tailors developmental programs in pivotal areas of communication\, daily living\, and adaptive learning through fun\, interactive modules. An integrated chatbot powered by AI improves user experience through conversational assistance\, responding to questions\, and assisting individuals with autism\, as well as their caregivers. Ongoing interaction develops a greater familiarity with the resources available on the platform and encourages active involvement in skill development exercises. Structured with users from every age group\, the platform places strong emphasis on ethical use of AI and protecting data\, offering a secure and reliable environment. Through its fit to the singular developmental path of each user\, it fosters autonomy\, skills development\, and social integration. The platform is an integrated system of care and empowerment for the autism community. It seeks to respond to the broad range of individual needs on a universal\, adaptable\, and empathetic level\, facilitating personal development and increased autonomy for individuals on the spectrum.
CATEGORIES:VIRTUAL ROOM_11C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:55f72653306a33ec930275d05c393cf7
URL:http://11tict4sd.sched.com/event/55f72653306a33ec930275d05c393cf7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:Breaking Barriers: SignLingo as a Two-Way Communication Aid for the Deaf and Mutes
DESCRIPTION:Authors - Abhay Pratap Singh\, Aanya Mittal\, Ashmit Tyagi\, Kanan Agrawal\, Avdhesh Gupta Abstract - Communication is one of the major attributes of human life[1]. The system discussed in this research paper focuses on developing a novel and efficient way of communicating between a deaf-mute person and any other person who is normal (does not have deaf or dumb handicaps). Advanced technologies used in the design will support the conversion from voice to Indian Sign Language using Natural Language Processing Machine learning algorithms and computer vision techniques and vice versa. It translates audio messages into sign language images with text in real-time\, trying to basically eliminate the conventional dependency on interpreters as a means of communication for every person. The main idea of the research is the solution of urgent problems connected with communication of the deaf-mute people and\, at the same time\, to be able to solve this task with the use of modern technologies\, keeping in mind the principles of inclusiveness and independence. The design\, implementation\, and potential of the system to improve the living standards of deaf-mute people by bringing them closer to society are discussed. The aim is to plead for inclusion by raising the awareness of educators\, policymakers\, and the public at large regarding the demand for communication resources addressed specifically to the deaf and mute community. The consciousness of the demand for ISL interpreters and the promotion of video datasets will be helpful in bridging the gap in communication\, as seen in the research on the lack of certified ISL interpreters and the demand for automated sign recognition systems.
CATEGORIES:VIRTUAL ROOM_11C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:76083050b684a35e7679457000a5c32a
URL:http://11tict4sd.sched.com/event/76083050b684a35e7679457000a5c32a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:Domain and AI-Based Watermark Techniques for Intelligent Digital Image Forensics
DESCRIPTION:Authors - Debabala Swain\, Monalisa Swain\, Sharmistha Roy\, Debabrata Swain\, Jayanta Mondal\, Prachee Dewangan Abstract - The information stored or transmitted digitally is vulnerable to unauthorized access. The authentication of digital images is a critical issue in the era of digital advancements\, given the ease with which any image can be altered. Consequently\, methods for verifying the credibility of images are gaining widespread recognition due to their relevance in various societal domains\, such as government\, military\, forensics\, and electronic commerce. The significance of protecting images from manipulation has escalated\, recognizing that even a minor tampering incident could lead to severe consequences. Hence\, safeguarding images from alterations has become increasingly essential. Literature has seen the development of numerous approaches to ensure the genuineness and integrity of digital images. This study offers a comprehensive overview of both domain-based and AI-based watermark techniques for authenticating images\, providing the capability to detect tampering and pinpoint the specific manipulated areas within an image.
CATEGORIES:VIRTUAL ROOM_11C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:40feb2844590fd667adc6bfdb571e0e0
URL:http://11tict4sd.sched.com/event/40feb2844590fd667adc6bfdb571e0e0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:Object Detection and Pursuit: Recent Advancements in Algorithmic Developments and Emerging Challenges
DESCRIPTION:Authors - Pooja Singh Chaudhary\, Nirav Bhatt\, Purvi Prajapati Abstract - Object Detection and the Object Pursuit are the fundamental and the emerging tasks in the Machine learning and in the computer vision to detect the object and then too track the object in all the real and the dynamic environments. The latest trends which are emerged in this area\, highlighting the embedding of deep learning techniques has transformed the field of object detection and tracking. Methods like Convolutional Neural Networks\, Deep SORT\, You Only Look Once and Region-Based Convolutional Neural Networks have significantly improved accuracy and efficiency. We examine the shift towards more robust and measurable and the scalable solutions\, with particular focus on multi-object tracking\, real-time processing\, and handling challenging Challenges like occlusion\, variations in scale\, and varying in illumination. The survey also addresses key challenges that remain\, including computational efficiency\, accuracy in complex scenarios\, and the development of algorithms. Furthermore\, we discuss the applications of object detection and pursuit across industries like autonomous driving\, robotics\, surveillance\, and augmented reality\, while offering insights into future research directions that may overcome existing limitations and drive the field forward. These recent advancements\, combined with the evolution of tracking algorithms\, have made it possible to detect and track objects in real-time with high precision.
CATEGORIES:VIRTUAL ROOM_11C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:fda7bb0265d502e35482d2e007baed2d
URL:http://11tict4sd.sched.com/event/fda7bb0265d502e35482d2e007baed2d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:Performing Cryptojacking in Decentralized Networks
DESCRIPTION:Authors - Akhil K J\, Saurabh Shrivastava\, Harish R Abstract - The increasing concerns over online privacy and the growing prevalence of internet censorship have driven many users to seek greater anonymity through tools like proxies and virtual private networks (VPNs). While peer-to-peer (P2P) networks provide a decentralized way for users to communicate securely across multiple nodes\, they are not immune to security threats. One of the major vulnerabilities in P2P networks is the risk of man-in-the-middle (MITM) attacks\, where malicious actors intercept communication between nodes. In these attacks\, attackers can manipulate\, inject\, or even remove data being transmitted\, compromising the integrity of the information. Another rising threat within these networks is cryptojacking—a tactic where attackers surreptitiously use a website’s resources to mine cryptocurrency\, often without the knowledge or consent of the website visitors. This malicious practice has gained attention due to its increasing prevalence on popular sites. In the context of P2P networks\, the exploitation of exit nodes poses a significant risk\, as attackers can inject mining scripts into the HTTP responses sent from these nodes. These risks highlight the need for robust security protocols to safeguard decentralized networks and prevent malicious interference\, ensuring the security\, privacy\, and integrity of online communication systems. Effective measures are vital to protecting users and maintaining trust in these technologies.
CATEGORIES:VIRTUAL ROOM_11C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:b91812b1683ccfab94f2d71d2110a4dd
URL:http://11tict4sd.sched.com/event/b91812b1683ccfab94f2d71d2110a4dd
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:Review of Sentiment Analysis: Techniques\, Applications\, and Challenges
DESCRIPTION:Authors - Kamini Solanki\, Nilay Vaidya\, Jaimin Undavia\, Krishna Kant\, Jay Panchal\, Anjali Mahavar Abstract - The rapid growth of internet-based applications\, such as social media platforms and blogs\, has led to an increase in comments and reviews about everyday activities. Sentiment analysis involves collecting and analysing people's opinions\, thoughts\, and perceptions on various topics\, products\, services\, and subjects. These opinions can provide valuable insights for businesses\, governments\, and individuals in making informed decisions. However\, the process of sentiment analysis faces several challenges that make it difficult to accurately interpret sentiments and determine the correct sentiment polarity. Sentiment analysis extracts subjective information from text using natural language processing (NLP) and text mining techniques. This article provides an in-depth overview of the methods used to perform sentiment analysis\, along with its applications. It also evaluates and compares different approaches\, discussing their advantages and limitations. Finally\, the article examines the challenges in sentiment analysis and proposes future directions for the field. Sentiment analysis\, also referred to as opinion mining\, is a vital area of research in natural language processing (NLP) that focuses on identifying the sentiment expressed in text. This paper reviews various sentiment analysis techniques\, explores its broad range of applications\, and discusses the challenges within the field. The goal is to provide a thorough understanding of the current state of sentiment analysis and its potential future developments.
CATEGORIES:VIRTUAL ROOM_11C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:10bcaed121b74647991c8c2c4c453b50
URL:http://11tict4sd.sched.com/event/10bcaed121b74647991c8c2c4c453b50
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:Revolutionizing SDN Security: An Intelligent Intrusion Detection System
DESCRIPTION:Authors - Botcha Divya\, Yelavarti Kalyan Chakravarti\, V. Esther Jyothi\, A. Satya Kranthi Abstract - Software-Defined Networking (SDN) has transformed contemporary network topology by separating the control plane from the data plane\, allowing the network to be centrally and dynamically managed. Its central design\, however\, also presents enormous security threats that must be mitigated using efficient Intrusion Detection Systems (IDS). This paper proposes an intelligent IDS framework for SDN networks utilizing machine learning algorithms. The proposed method employs the UNSW-NB15 dataset\, preprocessing with advanced methods\, SMOTE-Tomek resampling\, and multi-class classification by XGBoost for attack detection and classification of different attacks. Interactive Streamlit-based dashboards and packet simulation allow real-time observation\, filtering of attacks\, and visualization of anomalies in detail. Experimental results demonstrate enhanced detection accuracy of 84% using the top 20 features selected that outperform conventional classifiers in precision and responsiveness. The addition of real-time prediction counters\, attack distribution graphs\, and downloading capability allows for tremendous flexibility when used in live SDN contexts. The project tries to minimize the theoretical/practical implementation gap found among existing IDS models and live deployments with its suggested scalable\, interpretable\, and effective intrusion detection solution.
CATEGORIES:VIRTUAL ROOM_11C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:8d78211a23c59884ca6d6e79dbd89795
URL:http://11tict4sd.sched.com/event/8d78211a23c59884ca6d6e79dbd89795
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:Sleep Quality and Body Strain Assessment through 3D Pressure Mapping using Deep Learning
DESCRIPTION:Authors -&nbsp\;Deepesh Sudhan Arunachalam\, Dennis Andrew\, K. S. Gayathri\, A. Shahina\, V. Durgadevi\, A. Saravanan\nAbstract -&nbsp\;This work introduces a deep learning-based framework for 3D pressure mapping to assess sleep quality and body strain. 2D pressure maps suffer from loss of depth information\, poor spatial context\, posture misclassification errors\, and limited accuracy in capturing regional pressure variations. To overcome these limitations\, the framework constructs 3D pressure maps that enable precise region-wise pressure estimation with anatomical landmarks to analyze body strain. Sleep quality is monitored by tracking frequent posture changes with converting pressure maps to point clouds achieved 99.26% accuracy with PointNet and 99.49% with PointCNN.
CATEGORIES:VIRTUAL ROOM_11C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:80a44537ad478c9a99bd8d56cd75f477
URL:http://11tict4sd.sched.com/event/80a44537ad478c9a99bd8d56cd75f477
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:A Mechanism for Data Security and User Authentication in Cloud Storage
DESCRIPTION:Authors - Gunjan Tewari\, Divyanshi Verma\, Simran Negi\, Richa Jain Abstract - Cloud computing has completely revolutionized the way of data storage and management in a scalable environment with cost efficiency and scalability. However\, the shift from physical to cloud infrastructure raises significant concerns related to security. The major security issues in cloud storage are: data breaches\, unauthorized access\, hacking of data\, and insider threats\, various types of cyber-attacks\, etc. Many advancements have been made in cloud security but despite that several challenges still exist. Many security frameworks rely on third-party providers creating potential risks of data exposure. This paper addresses these challenges by proposing an approach for secure file storage in the cloud having multiple layers of security. The key derivation for encryption is done uniquely and then AES-256 is used for encryption and decryption. At the time of decryption\, OTP authentication is done using RSA signing which provides multi-factor authentication. The method can also be used to encrypt all multimedia data. To provide security\, the file password and OTP information are not stored in any database. The proposed work provides a very secure data storage solution that protects the data from any kind of brute-force attacks and other cryptanalysis attacks.
CATEGORIES:VIRTUAL ROOM_11D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:3b2f63ec689bf981f1ca2a77853f45e7
URL:http://11tict4sd.sched.com/event/3b2f63ec689bf981f1ca2a77853f45e7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:Advancing Crop Cultivation Estimation with Aerial Imaging and Artificial Intelligence: A Comprehensive Review
DESCRIPTION:Authors - Jalindar Nivrutti Ekatpure\, Dinesh Bhagwan Hanchate Abstract - This review paper gives a thorough look at all the current methods and uses of AI in crop prediction with aerial images. With the development of drone technology and high-resolution satellite imagery\, gathering data on farming has been easier. This paper completely analyses the use full uses of Artificial Intelligence techniques in agricultural functions. The real-word ex-ample shows that how artificial intelligence techniques used in aerial imagery technology it may be accurately applied in different agricultural fields. These examples shows capability to develop observing\, expect yields\, and assist farmers with correct decisions. The next research enterprises have been suggested to address current difficulties and increasing artificial intelligence application in the crop cultivation techniques. This paper aims to train farmers experts\, educators\, and those who are to know how to use artificial intelligence in the precision farming.
CATEGORIES:VIRTUAL ROOM_11D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:68cc821258ca8d49477430bb3bc03dc2
URL:http://11tict4sd.sched.com/event/68cc821258ca8d49477430bb3bc03dc2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:AI Driven-Virtual Mouse Using Hand Gestures
DESCRIPTION:Authors - Rohit Bajirao Khedkar\, Bhakti Dudile\, Laksh Rupesh Khobragade\, Pawan Babanrao Avhad\, Santosh Kumar Abstract - This paper presents a comprehensive study on the development and implementation of a virtual mouse system using hand gestures. With the rapid advancement of human-computer interaction (HCI) technologies\, touchless interfaces have gained immense popularity. The proposed system eliminates the need for physical input devices by leveraging computer vision and machine learning techniques to interpret hand movements\, translating them into cursor control and command execution. The research explores various methodologies\, including hand tracking\, gesture recognition\, system integration\, and deployment strategies\, highlighting advancements\, challenges\, and future directions in the field. The objective of this study is to design an intuitive user interface\, develop a robust gesture recognition model\, and ensure seamless deployment across various platforms to enhance accessibility and usability.
CATEGORIES:VIRTUAL ROOM_11D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:b5fe575c7eebf016fad741099eaf203c
URL:http://11tict4sd.sched.com/event/b5fe575c7eebf016fad741099eaf203c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:Application of Laser Engraving and Cutting for Customized University Information Signage Creation
DESCRIPTION:Authors - Mariela Todorova\, Tihomir Dovramadjiev\, Darina Dobreva\, Tsena Murzova\, Mariana Murzova\, Iliya Iliev\, Ventsislav Markov Abstract - The pursuit of enhancing the quality and precision of final design models has become increasingly vital in modern production processes\, particularly in the realm of university information signage. This research explores the application of advanced laser engraving and cutting technologies to achieve maximum accuracy in geometric shapes\, fine details\, and textual elements. A systematic methodology has been developed and implemented\, focusing on the production of custom-designed metal information signs tailored to university environments. The article presents a comprehensive overview of the production stages\, including optimized workflows for managing digital data\, selection of appropriate file formats\, and precise laser machine settings. By integrating digital design tools with laser technology\, the study demonstrates how to streamline processes while maintaining exceptional quality and durability of signage. The research outcomes not only emphasize the technological advantages of laser systems—such as high-speed production\, cost efficiency\, and unparalleled precision— but also highlight their potential to transform design practices in educational settings. This study aims to contribute to the scientific and practical development of digital fabrication methods\, inspiring wider adoption of laser-based innovations across design disciplines.
CATEGORIES:VIRTUAL ROOM_11D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:533d2f57858ad4fb2b1cc658812700b2
URL:http://11tict4sd.sched.com/event/533d2f57858ad4fb2b1cc658812700b2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:HelioHarvest : Automated Building Footprint Extraction and Rooftop Solar Potential Estimation
DESCRIPTION:Authors - Devashish Sanjay Gaikwad\, Aadit Kisanrao Palande\, Prasad Padmakar Joshi\, Aditya Atul Kode\, Rachana Yogesh Patil Abstract - Assessing the solar potential of rooftops is crucial for optimizing photovoltaic (PV) installations and promoting renewable energy adoption. This study presents a methodology for estimating rooftop solar potential using advanced geospatial and machine learning techniques. The proposed framework integrates Mapbox GL for spatial visualization\, PVGIS for solar radiation data\, and Scikit-Learn for predictive modeling. A web-based application is developed using React.js\, HTML\, and TailwindCSS for the frontend\, with Node.js and Express.js handling backend processes. The system allows users to input rooftop data\, analyze solar potential\, and generate estimations of energy output based on historical and real-time solar radiation data. By leveraging machine learning algorithms\, the model enhances prediction accuracy and enables better decision-making for solar energy investments. The results demonstrate the feasibility and effectiveness of this approach in providing precise and user-friendly solar potential assessments. This research contributes to the growing field of smart energy solutions and supports the transition to sustainable energy sources.
CATEGORIES:VIRTUAL ROOM_11D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:2535ba66b29931e22ee7c0db748867a4
URL:http://11tict4sd.sched.com/event/2535ba66b29931e22ee7c0db748867a4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:Intelligent Spectrum Utilization: Challenges and Opportunities in Cognitive Radio Networks
DESCRIPTION:Authors - Shaveta Thakral\, JyotiVerma\, Suchita Ganage\, Dharmendra Ganage\, Monika\, Shankar Amalraj Abstract - Cognitive Radio (CR) is a revolutionary technology aimed at optimizing the utilization of the electromagnetic spectrum\, a limited and valuable resource. Despite its promising potential\, the deployment and widespread adoption of CR face several technical\, regulatory\, and practical challenges. This paper presents a comprehensive study of the key research challenges in Cognitive Radio Networks (CRNs). We begin with a chronological literature survey\, highlighting significant advancements and ongoing research efforts. Subsequently\, we delve into the current research challenges\, including spectrum sensing\, dynamic spectrum access\, security\, energy efficiency\, interoperability\, and regulatory issues. We also explore potential research opportunities that could address these challenges\, thereby paving the way for more robust and efficient CRNs. This review aims to serve as a foundational reference for researchers and practitioners in the field\, offering insights into future research directions.
CATEGORIES:VIRTUAL ROOM_11D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:b092075d48ff9850e76cc768c0caea69
URL:http://11tict4sd.sched.com/event/b092075d48ff9850e76cc768c0caea69
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:NFC based Smart Attendance System using Yolo Algorithm
DESCRIPTION:Authors - Sheela Chinchmalatpure\, Atharva Bondarde\, Atharva Joshi\, Archit Bagad\, Samyak Dawle\, Rajeshwar Chintawar Abstract - Proper waste management is essential for public health and urban sanitation. Conventional garbage collection systems tend to be absent of verification checks to confirm the emptying of waste bins and instead depend on manual records. To improve monitoring of waste collection\, this research suggests a smart\, technology-based solution that combines Near Field Communication (NFC) with computer vision through a TensorFlow Lite-based YOLO model. The system includes a mobile app that scans NFC tags on trash bins\, logging the time and staff member who serviced the bin. The app also includes a lightweight YOLO model in TensorFlow Lite format to check if a bin is full or not using real-time image processing. NFC scanning is only allowed after successful model verification to avoid fraudulent reporting and ensure accountability of sanitation workers. This two-in-one system serves as both a real-time waste collection monitoring system and an automated worker attendance tracking system. Evaluated on a bin image dataset\, the solution showed encouraging accuracy in empty and full bin detection. Through the use of AI and IoT-based tracking\, this system promotes accountability\, transparency\, and effectiveness in waste collection\, and makes it a scalable and affordable model for smart cities.
CATEGORIES:VIRTUAL ROOM_11D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:3625d76747d8c7fd5cefcd2e3b4ccee0
URL:http://11tict4sd.sched.com/event/3625d76747d8c7fd5cefcd2e3b4ccee0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:Predicting Human Personality Through Behavioral Data Using GMM and KNN Models
DESCRIPTION:Authors - Vishal V. Mahale\, Sanket R. Malode\, Sudarshan M. Pagare\, Punit Chaudhari Abstract - Personality prediction plays a key role in understanding human behavior\, decision-making\, and social interactions. The OCEAN model—comprising Openness\, Conscientiousness\, Extraversion\, Agreeableness\, and Neuroticism is widely used for assessing personality traits. With the rise of machine learning\, predicting personality using this model has become a growing interdisciplinary field. This survey paper reviews existing machine learning approaches\, such as K-Means and Gaussian Mixture Models\, used to analyze personality traits from questionnaire data. It also highlights the limitations of past studies\, including lower prediction accuracy and challenges in model interpretation. The aim is to provide a clear overview of current methods and explore how machine learning can improve personality prediction and reveal deeper links between personality traits and behavior.
CATEGORIES:VIRTUAL ROOM_11D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:675cb58f1ca95c909832bd5193d0d629
URL:http://11tict4sd.sched.com/event/675cb58f1ca95c909832bd5193d0d629
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:SKYASSIST: CONVERSATIONAL AI-DRIVEN REVENUE MANAGEMENT SYSTEM FOR AIRLINES
DESCRIPTION:Authors - Poonkuzhali S\, Shreya Sai Prabakar\, Sriram Venkat P Abstract - The airline industry is characterized by fluctuating demand\, intricate pricing\, and inventory management. In that regard\, this study is done regarding AI-driven revenue management systems\, where many authors engage quite heavily with techniques of demand forecasting\, pricing optimization\, and inventory control. With Random Forest Regression\, Catboost and LightGBM passenger demand can be predicted using fare class\, lead time\, and seasonality\, optimizes seat allocation by fare category so as to maximize revenue. Evaluation is done using several datasets regarding booking patterns and market behavior in order to critically assess the accuracy\, efficiency\, and adaptability of the model. This study will demonstrate the strengths and trade-offs of AI techniques\, indicating to the airline the power of using data for real- time decisions. Its strength lies in the improvement of demand forecasting combined with dynamic pricing and inventory management\, maximized profitability\, efficiency\, and customer satisfaction.
CATEGORIES:VIRTUAL ROOM_11D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:2bdfd3fad552d06b707c24a4f7010b20
URL:http://11tict4sd.sched.com/event/2bdfd3fad552d06b707c24a4f7010b20
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:Understanding Postmenopausal Osteoporosis: A Review of Bone Fragility and Fracture Risk Evaluation
DESCRIPTION:Authors - Vijayalakshmi B\, Jayasheela C S Abstract - Postmenopausal women (PW) are at a significantly increased risk of fractures\, largely due to estrogen deficiency leading to osteoporosis and altered bone quality. Fracture risk assessment is critical for early intervention and prevention strategies. This review explores advancements in fracture risk evaluation\, highlighting experimental methodologies\, clinical applications and emerging technologies. Key advanced imaging approaches include the use of dual-energy X-ray absorptiometry (DEXA) for bone mineral density (BMD) measurement\, high-resolution peripheral quantitative computed tomography (HR-pQCT) for micro-architectural assessment and biochemical markers like C-terminal telopeptide (CTX) and procollagen type I N-terminal propeptide (PINP) for monitoring bone mass density. Fracture risk assessment (FRA) tools such as FRAX and the Garvan calculator provide practical frameworks for estimating fracture probability by integrating clinical risk factors and BMD data. However\, challenges remain. including limited access to advanced imaging\, variability in biochemical marker and under representation of diverse populations in validation studies. Future directions emphasize integrating artificial intelligence\, expanding population specific validations and combining imaging with dynamic bone mass density data. This comprehensive review emphasizes the significance of a multidisciplinary approach in FRA\, aiming to enhance precision\, accessibility and clinical outcomes in postmenopausal women.
CATEGORIES:VIRTUAL ROOM_11D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:33846854ff00bf357f047f113e6d4e2b
URL:http://11tict4sd.sched.com/event/33846854ff00bf357f047f113e6d4e2b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:An Automated Interview Question Generation Framework: GenAI Agent
DESCRIPTION:Authors - Arokiaraj S\, Amudha T\, Swamynathan R Abstract - Technology has become the driving force of progress and development all around the world. The recent development of Generative Artificial Intelligence has led to a revolution in the field of Education\, Employment and Human Resources. Securing a dream job or building a suitable career is the goal of every student. Likewise finding the right candidate for the job is the goal of every employer. LLMs (Large Language Model) comes to the rescue\, through dynamic question content creation for a selected topic and test the candidate for that set of skills. A candidate’s unique set of skills and abilities are understood and tested by the Generative AI where conventional methods fall short to meet this criterion. This paper proposes an automated question generation framework built using LangChain\, LLM model -GPT Turbo 3.5 from OpenAI API and Streamlit application development tool. This application successfully tests the skills of the candidates by asking customized multiple-choice\, true/false and open-ended questions based on their chosen topic\, knowledge level\, number of questions and time limit. Results indicate that this framework can create a challenging environment for the contenders thereby facilitating the interview process and selection of highly suitable candidates.
CATEGORIES:VIRTUAL ROOM_11E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:8e5ae0d136e3de09041d50eff3d78040
URL:http://11tict4sd.sched.com/event/8e5ae0d136e3de09041d50eff3d78040
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:Anomaly Detection in Lungs Using Deep Learning
DESCRIPTION:Authors - Shailaja Uke\, Mohit Garg\, Suyash Chandolikar\, Swayam Chandak\, Shriraj Nelekar Abstract - Chest X-rays are the most common tool for diagnosing various thoracic diseases. However\, manual interpretation is time-consuming and prone to human error. This paper presents a deep learning approach for automated pathology detection in CXRs using the Customized DenseNet-121 model. The model performs binary classification to identify 14 pathologies\, including cardiomegaly\, pneumothorax\, mass\, and edema. To address class imbalance in medical imaging datasets\, weight normalization is applied. Additionally\, the visualization technique of Grad-CAM enhances interpretability by pointing out the most critical regions influencing the model's decisions\, which helps healthcare practitioners assess. Toward further refinement of segmentation and improvement in precision of localization\, we incorporate a customized U-Net model to enhance better delineation of regions of interest. Our model achieves an overall AUC of 87%\, showing the highest accuracy. The customized U-Net integration improves seg-mentation performance\, reducing localization error by 15%. This approach not only enhances diagnostic accuracy but also provides transparent decision-making\, making it a valuable tool for medical professionals.
CATEGORIES:VIRTUAL ROOM_11E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:13dbaf996133d6ce547e44c05fcc873c
URL:http://11tict4sd.sched.com/event/13dbaf996133d6ce547e44c05fcc873c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:Automated IoT-Based Multi-Level Parking Systems: A Technological Solution for Efficient Parking Management
DESCRIPTION:Authors - Aditya Gaura\, Mandeep Kaur\, Kimmi Verma\, Monali Gulhane\, Nitin Rakesh Abstract - This cutting-edge research paper introduces a paradigm shift in parking management\, underpinned by an intricate network of technology and user-centric design. The system's hallmark feature is its advanced slot allocation mechanism. Users can make reservations via the mobile or web application\, with the system autonomously assigning slots based on a holistic evaluation of user characteristics\, such as vehicle type and duration of stay. Leveraging IoT integration\, the system employs a sophisticated array of sensors and cameras to monitor parking slot occupancy in real-time\, resulting in a fluid entry and exit process. The user experience is paramount in this system. It offers a tailored approach based on user type\, streamlining the process for faculty\, students\, and visitors. Notably\, the reduction in the time spent hunting for parking spots has the potential to mitigate the perennial issue of urban traffic congestion. This\, in turn\, aligns with environmental conservation efforts\, as the system indirectly lowers emissions and the carbon footprint associated with circling for parking spaces. Moreover\, this system's role as a data aggregator is invaluable. It collects and processes a wealth of data\, offering parking operators unprecedented insights into daily usage patterns\, peak periods\, and favored slots. This data-driven approach empowers operators to make informed decisions about slot management\, maintenance\, and resource allocation.
CATEGORIES:VIRTUAL ROOM_11E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:c4ca0985900f97fb349a3153607804e1
URL:http://11tict4sd.sched.com/event/c4ca0985900f97fb349a3153607804e1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:From Swarms to Speech: Nature-Inspired Algorithms in Automatic Speech Recognition
DESCRIPTION:Authors - Balwinder Kaur\, Jaswinder Singh\, Deepika Abstract - Nature-inspired optimization algorithms constitute a class of computational techniques that derive their underlying mechanisms from biological\, ecological\, and physical systems. By emulating processes such as evolutionary adaptation\, collective swarm behavior\, and decentralized decision-making\, these algorithms offer robust solutions to complex optimization challenges across engineering and computational domains. Notable methodologies include Genetic Algorithms\, Particle Swarm Optimization\, and Ant Colony Optimization\, each demonstrating efficacy in handling both single and multi-objective optimization problems\, including those involving high-dimensional search spaces and non-linear constraints. Within the field of Automatic Speech Recognition (ASR)\, nature-inspired optimization techniques are instrumental in refining critical system components. Their application spans feature selection\, acoustic model training\, language model optimization\, and efficient decoding strategies. By leveraging adaptive search mechanisms\, these algorithms enhance model accuracy\, reduce computational overhead\, and improve generalization in ASR systems. This research study presents a systematic examination of nature-inspired optimization methods\, focusing on their theoretical foundations and practical implementations in ASR. Furthermore\, it critically evaluates existing challenges\, such as sensitivity to hyperparameter tuning\, computational scalability with large-scale datasets\, and the absence of comprehensive convergence guarantees. Addressing these limitations is essential for advancing the applicability of nature-inspired optimization in next-generation speech recognition systems and related domains.
CATEGORIES:VIRTUAL ROOM_11E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:58164af1438fc490bf2e7a5d00eefcea
URL:http://11tict4sd.sched.com/event/58164af1438fc490bf2e7a5d00eefcea
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:More Than Skills: How Digital Competence and Partner Attitudes Shape Financial Resilience in Couples
DESCRIPTION:Authors - Anchal Saini\, Nitin Kulshrestha Abstract - In the rapidly digitizing financial landscape\, the ability to effectively use digital tools has become essential for financial well-being. This study examines the impact of digital competence on financial resilience within dual-income married couples\, adopting a dyadic perspective. Drawing on the Actor-Partner Interdependence Moderation Model(APIMoM)\, it studies both actor and partner effects of digital competence. As well as the moderating role of each partner’s attitude toward FinTech. Data was collected from 107(214 individuals) working couples in Gurgaon\, India. Covariance-Based Structural Equation Modeling using SmartPLS revealed that digital competence significantly influences both individuals' and their partners’ financial resilience. Moreover\, attitudes toward FinTech were found to moderate these relationships\, strengthening the positive effects of digital competence. Notably\, the husband’s attitude had a stronger moderating impact on the wife’s resilience than vice versa\, indicating potential gender-based dynamics. The study marks the importance of addressing both digital skills and relational attributes in aiding household financial resilience. Practical implications suggest that digital literacy programs should consider couple-based interventions that target both digital competence and attitude change.
CATEGORIES:VIRTUAL ROOM_11E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:34eaa2076b897e1104e0a3b7088e9314
URL:http://11tict4sd.sched.com/event/34eaa2076b897e1104e0a3b7088e9314
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:Multilingual Automated Essay Scoring with Transformer Models
DESCRIPTION:Authors - Rasika Ransing\, Kaushik Sakre\, Neha Kudu\, Shivam Shinde\, Siddhi Talkar Abstract - The introduction of Automated Essay Scoring systems brought better assessment methods into education through standardized scoring systems that operate at scale while being time efficient. The current AES models function exclusively with English content while neglecting multilingual evaluation\, particularly in the Hindi and Marathi languages. A multilingual AES framework has been developed using transformer models XLM-RoBERTa\, MuRIL\, DistilBERT\, and mBERT for conducting context-based essay assessments throughout English\, Hindi\, and Marathi texts. Through multilingual embeddings combined with fine-tuned models\, the system maintains cohesive and coherent\, and argumentative quality in essays. The assessment by QWK and RMSE metrics demonstrates both high accuracy and reliability of the system. The highest performance emerged from XLM-RoBERTa and Google MuRIL at 0.78 QWK and 0.77 QWK\, respectively.
CATEGORIES:VIRTUAL ROOM_11E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:52f4a6dd913d64e0b446eb086c3f187a
URL:http://11tict4sd.sched.com/event/52f4a6dd913d64e0b446eb086c3f187a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:Network Anomaly Detection Using Graph Embedding
DESCRIPTION:Authors - Sarvesh Shinde\, Tarun Kurakula\, Venugopal Murugan\, Tanmay Patil\, Aparna Bannore Abstract - Network security is a difficult topic these days\, with threats appearing quickly and everywhere. According to the study "Network Security Using Graph Embedding\," connections are visible when jumbled network data is transformed into transparent graphs. First-order graphs have direct node links\, while second-order graphs have nodes that share neighbours. DeepWalk\, Node2Vec\, and sense-making tools. Node2Vec\, choice-based\, tight groups or large network view\, and modified random walks. DeepWalk is a straightforward\, sequential structure mapping method. Both embeddings are feasible in terms of network layout. A graph as opposed to the outdated equal-link techniques\, Attention Network\, GAT\, and anomaly hunt use attention tricks for important connections. fresh activity in the dataset\, labeled data\, normal versus odd markers\, and real-time data. Strange spikes\, unusual nodes\, rules established\, and threats identified. In continuous networks\, not data crunch for kicks\, quick catch\, hackers\, or weak spots.
CATEGORIES:VIRTUAL ROOM_11E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:ade63e3fa9a77e6377a7e79b7744aeac
URL:http://11tict4sd.sched.com/event/ade63e3fa9a77e6377a7e79b7744aeac
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:RECOMMENDATION SYSTEM FOR INR BONDS
DESCRIPTION:Authors - Atharva Shirbhate\, Jay Sutar\, Om Tathed\, Bhupal Shelke\, Geeta S. Navale Abstract - The development of Artificial Intelligence (AI) has led to significant advancements across numerous domains\, including finance\, healthcare\, and customer service. Recent progress\, particularly in the field of Natural Language Processing (NLP)\, has been driven by the emergence of Large Language Models (LLMs). These models utilize transformer architectures and vast datasets to perform a wide range of tasks\, such as language translation\, text generation\, and complex data analysis. As AI technology continues to evolve\, it offers the potential to streamline decision-making processes\, enhance data management\, and provide personalized recommendations. This study focuses on leveraging AI to address specific challenges in the financial sector. The objective of this paper is twofold: firstly\, to develop a system capable of recommending bonds based on user-specific requirements\, thus aiding investors in making informed decisions\; and secondly\, to collect bond data from sellers and integrate it seamlessly into an existing database. Through a systematic review of recent AI advancements and prompt engineering techniques\, this paper aims to provide insights into how these technologies can be harnessed to improve financial data integration and recommendation systems
CATEGORIES:VIRTUAL ROOM_11E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:9b0e35d02e042b5518613f2a686c6761
URL:http://11tict4sd.sched.com/event/9b0e35d02e042b5518613f2a686c6761
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:SHADE: A Blockchain based Anonymous Tip-Off System for Secure and Verifiable Reporting
DESCRIPTION:Authors - Kumkum Saxena\, Ayesha Nagdawala\, Esha Nemani\, Jatin Mawa\, Mamta Gupta Abstract - Even in the modern days of digital era\, reporting crimes like those of corruption or misconduct is still difficult owing to the fear of retaliation. Some traditional reporting mechanisms are available\, but they often do not allow enough anonymity or security\, preventing tipsters from reporting. Many whistleblowers face serious consequences\, including job loss\, legal action\, or even physical threats\, making them reluctant to report wrongdoing. SHADE is a blockchain-based solution that seeks to overcome these challenges by providing a decentralized and tamper-resistant medium for anonymous tip-offs. SHADE stands apart from conventional systems\, which store centralized databases vulnerable to breach\, providing full anonymity and data integrity with encryption. This paper explores SHADE’s architecture\, which integrates blockchain for immutable data storage\, cryptographic encryption for secure communication\, and smart contracts for automated processing.
CATEGORIES:VIRTUAL ROOM_11E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:d88fec9f0398392551c778aa2aef3920
URL:http://11tict4sd.sched.com/event/d88fec9f0398392551c778aa2aef3920
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T070000Z
DTEND:20260827T090000Z
SUMMARY:Statistical and Machine Learning Approaches for Time Series Forecasting in Industrial Edge Computing Environments
DESCRIPTION:Authors - Suhas Bhise\, Ketki Kshirsagar\, Vivek Deshpande Abstract - In the context of Industrial Edge Computing\, the growing deployment of IoT and mobile devices has resulted in an explosion of real-time\, high-velocity time series data. This paper investigates statistical and machine learning approaches for time series forecasting in such environments\, where latency\, bandwidth\, and computational efficiency are critical constraints. We evaluate traditional methods like Simple Moving Average (SMA)\, Holt-Winters Exponential Smoothing\, and ARIMA\, and contrast them with machine learning models such as Logistic Regression and XGBoost. Experiments conducted on the Microsoft Azure Predictive Maintenance dataset demonstrate that SMA and ARIMA offer comparable baseline accuracy\, while XGBoost outperforms them in terms of forecast quality for multivariate series. We also explore the effectiveness of SMOTE for improving failure prediction using logistic regression. The findings suggest that lightweight models like XGBoost with lag feature engineering can be viable for forecasting in edge environments.
CATEGORIES:VIRTUAL ROOM_11E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:2027ff6e479959fa936bd04121a5fd54
URL:http://11tict4sd.sched.com/event/2027ff6e479959fa936bd04121a5fd54
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T090000Z
DTEND:20260827T090200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_11A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:5df43c2c4b4bd50a221b7805d85b4a09
URL:http://11tict4sd.sched.com/event/5df43c2c4b4bd50a221b7805d85b4a09
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T090000Z
DTEND:20260827T090200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_11B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:83ec61b84c3659500170f946d50c5ac6
URL:http://11tict4sd.sched.com/event/83ec61b84c3659500170f946d50c5ac6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T090000Z
DTEND:20260827T090200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_11C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:85856a0720867713e7859b9199a8fd0f
URL:http://11tict4sd.sched.com/event/85856a0720867713e7859b9199a8fd0f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T090000Z
DTEND:20260827T090200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_11D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:735b0eaa8bf608a65833fef8ed0b65e3
URL:http://11tict4sd.sched.com/event/735b0eaa8bf608a65833fef8ed0b65e3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T090000Z
DTEND:20260827T090200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_11E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:a11f6f5d2e8a73d241569b948cf82124
URL:http://11tict4sd.sched.com/event/a11f6f5d2e8a73d241569b948cf82124
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T090200Z
DTEND:20260827T090500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_11A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:1d24ab5ad3d7e179eebdddf50da6d05b
URL:http://11tict4sd.sched.com/event/1d24ab5ad3d7e179eebdddf50da6d05b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T090200Z
DTEND:20260827T090500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_11B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:16f01015b7724bdb3d5da9e3968f65bd
URL:http://11tict4sd.sched.com/event/16f01015b7724bdb3d5da9e3968f65bd
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T090200Z
DTEND:20260827T090500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_11C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:5bf7bb4fb14940aaf77bf2a90da5580f
URL:http://11tict4sd.sched.com/event/5bf7bb4fb14940aaf77bf2a90da5580f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T090200Z
DTEND:20260827T090500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_11D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:a438ea63833fffbef7c9f025976a2f61
URL:http://11tict4sd.sched.com/event/a438ea63833fffbef7c9f025976a2f61
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T090200Z
DTEND:20260827T090500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_11E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:e782720590a4463b5002d4c67d3480f9
URL:http://11tict4sd.sched.com/event/e782720590a4463b5002d4c67d3480f9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T095800Z
DTEND:20260827T100000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_12A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:60c080e02fb44a68af8723d412998070
URL:http://11tict4sd.sched.com/event/60c080e02fb44a68af8723d412998070
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T095800Z
DTEND:20260827T100000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_12B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:6eeb178a55f6fd6cf937ed8510a4774e
URL:http://11tict4sd.sched.com/event/6eeb178a55f6fd6cf937ed8510a4774e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T095800Z
DTEND:20260827T100000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_12C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:05ac22218cd63255a5b7fef8a271e4e3
URL:http://11tict4sd.sched.com/event/05ac22218cd63255a5b7fef8a271e4e3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T095800Z
DTEND:20260827T100000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_12D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:a623b54667eff60ade3f0409676e69e1
URL:http://11tict4sd.sched.com/event/a623b54667eff60ade3f0409676e69e1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T095800Z
DTEND:20260827T100000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_12E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:5e8c9b71ffb16942bbdcf6d854e8d91a
URL:http://11tict4sd.sched.com/event/5e8c9b71ffb16942bbdcf6d854e8d91a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T095800Z
DTEND:20260827T100000Z
SUMMARY:Opening Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_12F
LOCATION:Virtual Room F\, GOA\, India
SEQUENCE:0
UID:d86ffddd09b675fd802ebb88d083ec53
URL:http://11tict4sd.sched.com/event/d86ffddd09b675fd802ebb88d083ec53
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:A GAN Approach for Energy Consumption Forecasting in Built Environment
DESCRIPTION:Authors - SnehalBalasaheb Salve\, Harsha Bhute Abstract - Developing a precise and strong model for forecasting energy utilization is importantaim for the management and functionality of smart buildings. The previous studies have researched different models for forecasting various load prediction schemes. The combined effects regarding data enrichment and machine learning approach in energy predictions have not been fully examined. This research proposes a novel approach\, an ensemble model enhanced by generative adversarial networks (GANs) for predicting the usage of energy in big buildings that are commercial. This combined system integrates various single models using ensemble method with stacking. Furthermore\, a GAN is utilized to capture the distribution of samples from the main dataset\, generating top-notch specimens to augment the dataset from the training data. This expanded dataset allows the model to train with a wider range of samples\, increasing its resilience. The experimental series evaluate the method that is proposed\, using three variants of GAN and assessing performance with metrics such as mean absolute error\, root mean square error\, and coefficient of variation of root mean square error. This proposed approach demonstrates practical results that develop a model for power utilization prediction in application of real world.
CATEGORIES:VIRTUAL ROOM_12A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:b72802ba856eb64811cc0859d0b48300
URL:http://11tict4sd.sched.com/event/b72802ba856eb64811cc0859d0b48300
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:An Adaptive Fault-Tolerant and control strategy Techniques for the Power Electronic Traction Transformer PETT
DESCRIPTION:Authors - G Roopa\, H.L.Suresh Abstract - The work outlined here provides a new method to handle fault of Power Electronic Traction Transformer (PETT) switch using reverse charging Cascaded H Bridge (CHB) and Dual Active Bridge (DAB) topologies. Precisely\, the main purpose is to improve the speed of detection\, determining the location\, and recovery of faults from the existing system using feature extraction and Machine Learning algorithms. The traditional approaches in achieving fault tolerance are defective in detecting faults in good time\, isolating faults inadequately\, and using backup hardware. In order to solve these problems\, the proposed methodology actively reassigns control signals to backup modules resulting in the exclusion of faulty elements while preserving a stable system performance with moderate loss in efficiency. The feasibility of the suggested approach is confirmed through simulation outcomes for fault detection precision\, which is increased to 98 percent\; the fault localization time of at most 5-10 ms\; and system throughput of 5-8 percent. Furthermore\, the work investigates how CHB and DAB function in fault conditions and enshrine a novel reverse charging method for maintaining the DC voltage of the redundant module. The startup process of the PETT system is also managed with optimization of voltage and transient\, which leads to enhance the general system initialization. Besides increasing the dependability and fault tolerance of PETT systems\, the above methodology also reduces the system’s embedded hardware duplication and elevates system performance and scalability \, which consequently leads to the decrease of the total system cost by 15 percent. These results point out that the proposed solution has potential for the development of the next generation of fault-tolerant power electronic systems.
CATEGORIES:VIRTUAL ROOM_12A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:c1346e8a685876833aa894d32dd17b20
URL:http://11tict4sd.sched.com/event/c1346e8a685876833aa894d32dd17b20
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:AutoHub: Integrated Vehicle Washing & Expense Management with AI & Blockchain
DESCRIPTION:Authors - Santushti Betgeri\, Rohit Rathod\, Sakshi Rathod\, Sanskar Raut\, Anisha Sadanshiv Abstract - AUTOHUB is an integrated solution for essential services regarding vehicles\, as well as vehicle expenses. This is a solution with both a native Android app and the response web interface that can easily integrate\, especially with dynamic time slot selection for service bookings and an extensive expense tracker for fuel\, repairs\, tolls/fines\, and others. It also has a cloud Online Document Manager for safe document storage\, automatic expiration reminders\, different dashboards for service providers\, and more. It is developed using Android Studio\, React\, Node.js with Express\, Firebase Firestore for real-time data sync\, and Razorpay for secure payment processing. The platform has a modular microservices architecture and is scalable and easy to maintain. This integrated solution not just tackles the current challenges posed by fragmented automotive service management\, but it also builds the base upon which future extensions can be realized\, placing AUTOHUB well ahead of its time as a platform for automotive care.
CATEGORIES:VIRTUAL ROOM_12A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:8ca1703820e871ea08b6e4e7a88d8e79
URL:http://11tict4sd.sched.com/event/8ca1703820e871ea08b6e4e7a88d8e79
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Brain Tumor Segmentation and Prognostication
DESCRIPTION:Authors - Ashwini Matange\, Harsha Talele\, Pratik Nagare\, Vineet Morankar\, Aniket Gavkare\, Moin Shaikh Abstract - Accurate segmentation and prognostication of brain tumors are critical for effective diagnosis\, treatment planning\, and patient management in glioma. In this work\, we present a unified framework built upon the BRATS2020 challenge data that integrates deep learning-based segmentation with radiomics and machine learning for overall survival prediction. First\, we employ a 3D-UNet architecture to perform robust segmentation of brain tumors from multi-modal MRI scans\, achieving a mean Intersection over Union (IOU) of 86%. This segmentation not only delineates tumor sub-regions effectively but also provides the basis for subsequent feature extraction. Leveraging the pre-trained 3D-UNet\, we extract deep features from the MRI scans\, and in parallel\, perform radiomics feature extraction on the corresponding tumor masks. These features are then combined with clinical and demographic data provided in the BRATS2020 challenge dataset. A random forest classifier is subsequently trained on this comprehensive feature set to predict overall patient survival\, achieving a classification accuracy of 70% in stratifying patients into survival categories. Our approach builds on recent advances in brain tumor segmentation—incorporating ideas such as ensemble learning\, multi-modal imaging\, and uncertainty quantification—to enhance both the segmentation accuracy and prognostication performance. The promising results demonstrate that the integration of deep learning segmentation with radiomics and traditional machine learning methods can serve as a robust tool for personalized treatment planning and risk stratification in glioma patients.
CATEGORIES:VIRTUAL ROOM_12A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:43fd8ac374ba05befa56f3b819a1ee0f
URL:http://11tict4sd.sched.com/event/43fd8ac374ba05befa56f3b819a1ee0f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Degradation-Agnostic Medicine Strip Data Enhancement via Residual Learning
DESCRIPTION:Authors - Vaishnavi Moorthy\, Jagadeesan Moorthy\, Shubhradip Saha\, Anshuman Kumar Abstract - In the medical field\, the readability of important information on medicine packs\, like the expiry date\, is of prime importance for maintaining patient safety. Yet\, a number of reasons like damage\, blurring\, and printing defects may hide this important information on medicine strips. To solve this problem\, we suggest a deep learning-based solution for medicine strip denoising and enhancement\, making important information such as expiry dates more legible. Our approach utilizes image denoising\, specifically designed to correct blurry or partially readable expiry dates on the packaging of medicines. This solution not only helps healthcare workers and patients validate medicines but also makes a contribution to the pharmaceutical industry.
CATEGORIES:VIRTUAL ROOM_12A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:0af312f02bba6316c040576c843a30a0
URL:http://11tict4sd.sched.com/event/0af312f02bba6316c040576c843a30a0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Fast Healthcare Interoperability Resources in Healthcare Sector for Transformation based Futuristic and Narrative Approach
DESCRIPTION:Authors - Shweta Kumar\, Saru Dhir\, Ashish Kumar Mourya Abstract - Healthcare informatics has many difficulties due to the complexity of varied medical data. Clinical notes\, imaging\, and genomic data are instances of unstructured data that is more flexible and has more depth than organized data\, such as digital records\, which are easier to use. Combining different healthcare data sources is difficult due to interoperability issues and semantic variability. Despite the emergence of standardization projects such as HL7 (Health Level 7) FHIR (Fast Healthcare Interoperability Resources) and SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms)\, inefficient processes and unreliable vocabulary continue to impede seamless communication of information. The dispensation of natural language\, or NLP (Natural language processing)\, methods enable the extraction of important information from uncontrolled health information. Furthermore\, instantaneous data analysis and scalability are enhanced by online computing\, and blockchain technology is being investigated as a safe\, independent method of sharing medical data. This study examines the challenges of managing a variety of healthcare information as well as the possible benefits of contemporary technologies. Future research focuses on improving interoperability frameworks\, developing AI-driven data analysis\, and ensuring confidentiality and security of data in order to provide effective and data-driven healthcare options.
CATEGORIES:VIRTUAL ROOM_12A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:b9789136569b0c5d9c9b5959d517a1c0
URL:http://11tict4sd.sched.com/event/b9789136569b0c5d9c9b5959d517a1c0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:FORECASTING THE STOCK PRICES OF GREEN ENERGY COMPANIES IN INDIA USING MACHINE LEARNING MODELS
DESCRIPTION:Authors - Arun N\, Rithika J Prabhu\, DHANYA M Abstract - Sustainability in India has been become a driving force behind the growth of the green energy sector and the economy's transition to cleaner energy. The research paper investigates the use of machine learning models to predict stock prices of green energy companies in India. It deliberates on the rapid growth of the green energy market and the potential for ever-advancing technologies making accurate prediction in finance for supporting the nation's sustainable development goals. Using machine learning\, it generates useful insight for stock performance for the benefit of investors and policy makers in arriving at decisions.
CATEGORIES:VIRTUAL ROOM_12A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:926c0d7bf3b6eafbcbc18ec3fa3682c3
URL:http://11tict4sd.sched.com/event/926c0d7bf3b6eafbcbc18ec3fa3682c3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Integrated Object Detection and Scene Analysis for Waste Classification Using YOLO and NLP Techniques
DESCRIPTION:Authors - M. Chaitanya Raju\, Maddu Reshma\, V. Anvesh\, Lekha S. Nair Abstract - Waste classification and management are important for healthier planet Earth. In this paper we are proposing an integrated approach for waste detection and classification using object detection along with natural language processing (NLP) techniques. which introduce a YOLO-based model to detect and classify waste in images by using Bootstrap Language-Image Pretraining (BLIP) for scene understanding and contextual analysis. The workflow involves\, feeding the waste images into a preprocessing stage (image)\, captioning image data with Natural Language Processing (NLP) to produce descriptive captions\, and analyzing the textual features of detected captions that exist in the waste (waste elements). The classification of the detected object is performed by a custom trained YOLOv8 model which is fine-tuned on a specific waste class dataset. Experiments show that the model recognizes garbage\, recyclables and litter with high accuracy. This system showcases the potential of combining visual and textual modalities to enhance waste detection accuracy\, offering a robust tool for automated environmental monitoring and management.
CATEGORIES:VIRTUAL ROOM_12A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:9c561bf7c52ded40684d168e5c890303
URL:http://11tict4sd.sched.com/event/9c561bf7c52ded40684d168e5c890303
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Leveraging Artificial Intelligence for Detection and Filtering of Inappropriate Social Media Content
DESCRIPTION:Authors - Juttiga Rohita\, B Teja Sree\, Ibrapatnam Anusha\, Mohammad Sharmila Begum\, Nirjogi Mahathi Abstract - Social media platforms have become increasingly vulnerable to online threats\, making safeguarding the internet an increasingly difficult task. Why? This project showcases an artificial intelligence-powered system that can detect and filter out inappropriate text and images in real-time. Machine learning and natural language processing (NLP) are utilized by the system to detect hate speech\, toxic terminology such as slang\, and explicit imagery while maintaining document integrity. TF-IDF\, LSA\, and Word Embeddings are utilized in text filtering to improve the understanding of context. In image filtering\, deep learning models using convolutional neural networks (CNNs) and pre-trained NSFW classifiers detect and remove explicit content. This balances scale with accuracy and provides a robust\, automated content moderation system that improves both safety and compliance on the Internet.
CATEGORIES:VIRTUAL ROOM_12A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:75a16ad2f2862e00f6faefe27d09bf55
URL:http://11tict4sd.sched.com/event/75a16ad2f2862e00f6faefe27d09bf55
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Robust Online Action Detection: Advancing Multi-Object Tracking in Surveillance Scenarios
DESCRIPTION:Authors - Shahedhadeennisa Shaik\, Abhinav R B\, Chaitra S P\, Sagari S M Abstract - Video traffic surveillance has become an essential tool for various applications\, including security\, transportation planning\, and traffic management. Recent advancements in deep learning have opened new possibilities for enhancing the performance of vehicle detection and tracking in these systems. This paper addresses the challenges of online action detection in surveillance scenarios by focusing on enhancing multi-object tracking (MOT) performance. Recognizing the limitations of current MOT methods in handling real-world surveillance complexities\, we propose a methodology that integrates appearance model extraction directly from the object detector\, adaptive adjustments of confidence thresholds and input resolutions\, and the incorporation of color information into ReID embeddings. We aim to bridge the gap between motion-based and ReID-based tracking methods\, improving both speed and accuracy. Our proposed techniques\, including scene-based and object-based adaptation through reinforcement learning\, and advanced feature fusion for ReID\, are designed to enhance robustness and efficiency. We evaluate our methodology using publicly available datasets\, focusing on surveillance-specific challenges. The enhancement in MOT performance is challenging and paving the way for more reliable and efficient surveillance system.
CATEGORIES:VIRTUAL ROOM_12A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:f94ee72678f25a66e2e875949bc1b4b5
URL:http://11tict4sd.sched.com/event/f94ee72678f25a66e2e875949bc1b4b5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:A Comprehensive Review of Techniques for Image Forgery Detection and Localization
DESCRIPTION:Authors - Meena Rani\, Randeep Singh Abstract - The exponential growth of smart devices and advanced image editing tools has made detecting and localizing image forgeries critical for ensuring digital content integrity. This paper focuses on developing a robust and scalable model for passive image forgery detection using convolutional neural networks (CNNs). Leveraging datasets like CASIA1 and MICC-F220\, the study aims to identify tampered regions in digital images by analysing noise patterns\, pixel-level anomalies\, and compression artefacts. The proposed methodology integrates preprocessing\, model training\, and validation using diverse datasets to enhance detection accuracy and scalability. Compared to traditional techniques\, the deep learning-based approach shows significant improvements in detecting complex forgeries\, including splicing and copy-move manipulations. Applications of this research extend to digital forensics\, media authentication\, and cybersecurity. The findings underscore Deep learning systems' show promise to tackle new issues in picture forgery detection and localization
CATEGORIES:VIRTUAL ROOM_12B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:528e5a33289225f3630694e1eda5fdf0
URL:http://11tict4sd.sched.com/event/528e5a33289225f3630694e1eda5fdf0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Blockchain In Public Distribution System
DESCRIPTION:Authors - Kritika Benjwal\, Rishika Agrawal\, Rashi Gupta\, Dinesh Kumar Saini Abstract - The ublic distribution system (PDS) plays a vital role in eradicating hunger and ensuring food security across the world[1]. However\, there are certain challenges like beneficiary identification\, inconsistent transaction\, diversion of grains during procurement at different stages to the open market\, ration shop owners selling subsidized goods at higher prices and most importantly the paper focusses on supply chain leakages.[2] This paper proposes a conceptual model for implementing blockchain in PDS and eradicating all the supply chain leakages and making PDS fair[3]. It first focusses on operations of PDS\, then assessing all the possible loopholes and implementing blockchain for removing these loopholes. It proposes the idea in which it leverages the benefits of using smart contract and consortium-based ecosystem that can bring efficiencies in PDS.
CATEGORIES:VIRTUAL ROOM_12B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:fd2b4116dede6fbc7cf727acda7a9b9b
URL:http://11tict4sd.sched.com/event/fd2b4116dede6fbc7cf727acda7a9b9b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Design and Analysis of smart IoT based system for fuel dispensing
DESCRIPTION:Authors - Sandhya Borkar\, Shital Patil Abstract - The rapid development of the Internet of Things (IoT) has made the provision for innovative solutions in various sectors\, including fuel management. The research presents the design and analysis of a smart IoT-based system for fuel dispensing\, aimed at improving the efficiency\, transparency and security of fuel distribution. The proposed system uses microcontrollers\, sensors and real-time data communication technologies to automate fuel dispensing\, monitor fuel levels and prevent theft or misuse. Key components include flow sensors to measure fuel output\, RFID modules for secure user authentication and cloud-based platforms for remote monitoring and control. Additionally\, mobile applications provide users with instant transaction records\, fueling history and alerts. The system undergoes performance evaluation to ensure precise fuel measurement and seamless data synchronization. The results demonstrate significant improvements in operational efficiency and customer satisfaction\, reducing manual errors\, fuel theft and fraud\, operational downtime\, high maintenance and labor cost. This smart fuel dispensing system offers a scalable and cost-effective solution suitable for fuel stations\, logistics companies and industrial applications\, contributing towards smarter resource management and enhanced energy distribution practices.
CATEGORIES:VIRTUAL ROOM_12B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:a1c24ef3ea5cc91fab719fe9eabba858
URL:http://11tict4sd.sched.com/event/a1c24ef3ea5cc91fab719fe9eabba858
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Enhanced Deepfake Detection Using Multi-Model Approaches: A Comprehensive Analysis
DESCRIPTION:Authors - Pravin Game\, Shubham Bhingardive Abstract - Deepfake technology\, which enables manipulation of images and videos\, poses serious threat to media integrity and cyber security. Existing detection models often struggle with accuracy due to data complexity and variability. This study introduces a hybrid deepfake detection model that combines MobileNetV2\, EfficientNetB7 and Vision Transformer (ViT) to enhance feature extraction and classification. ViT provides strong pattern recognition\, EfficientNetB7 offers scalable accuracy and MobileNetV2 ensures lightweight processing. The model is trained on a publicly available datastet from Yonsei University\, consisting of real and fake facial images. Techniques such as data augmentation and image resizing improve generalization. Experimental results show that the proposed model achieves 94.64% accuracy\, 93.55% precision\, 96.67% sensitivity\, 92.31% specificity and 95.08% F1 score outperforming precious methods. These improvements ase statistically significant (p < 0.05). The results highlight the effectiveness of multi-model fusion for robust deepfake detection offering a scalable and reliable solution for applications in digital forensics\, information verification and cybersecurity.
CATEGORIES:VIRTUAL ROOM_12B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:bf95c0742af16bad1b687de732baabb7
URL:http://11tict4sd.sched.com/event/bf95c0742af16bad1b687de732baabb7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Enhancing the accuracy of heart disease through Hippopotamus Optimization Algorithm: An Evaluation of Machine Learning Algorithms
DESCRIPTION:Authors - Pravin Game\, Shubham Bhingardive Abstract - The correct identification of heart disease is essential for successful treatment and management. In this work\, we assess different machine learning algorithms' predictive power for diagnosing heart disease. On the dataset\, we employed the method of principal component analysis (PCA) to choose features\, we got top 9 principal components out of 13 features. Then\, applied the hippopotamus optimization algorithm on that 9 principal components then trained and tested the model on eight different algorithms: Bagging\, Boosting\, Naive Bayes\, K - Nearest Neighbors (KNN)\, Random Forest\, Decision Tree\, Support Vector Machine (SVM)\, and Logistic Regression(LR). The algorithm’s accuracy ranged from 86.81% to 94.53%\, The most accurate methods were SVM\, KNN and random forest. These findings show that machine learning algorithms may be able to help with heart disease and focus on the need of choosing suitable algorithms for exact and trustworthy clinical decision-making. Future research will concentrate on using sophisticated on feature selection and ensemble learning strategies to further increase model accuracy.
CATEGORIES:VIRTUAL ROOM_12B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:008a6ffecbc7f378b94c8f7c07731ced
URL:http://11tict4sd.sched.com/event/008a6ffecbc7f378b94c8f7c07731ced
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Explainable AI for Data Leakage Detection: Enhancing Trust in Deep Learning-Based Security Systems
DESCRIPTION:Authors - Ganesh Shivaji Pise\, A D Londhe\, Hrushikesh Jaivant Joshi\, Bhagwan Dinkar Thorat\, Yashita Parikshit Mahalle\, Pankaj Chandre Abstract - Data leakage poses a significant threat to modern security systems\, leading to unauthorized access and privacy breaches. Deep learning models have shown promise in detecting such anomalies\; however\, their black-box nature raises concerns regarding trust and interpretability. This paper explores the role of Explainable AI (XAI) in enhancing transparency and trust in deep learning-based data leakage detection. Various explainability techniques\, including SHAP\, LIME\, and Grad-CAM\, are integrated into a security framework to provide interpretability while maintaining detection accuracy. The proposed architecture bridges the gap between AI-driven security solutions and human decision-making\, enabling security analysts and compliance officers to make informed assessments. Additionally\, the study evaluates different XAI approaches based on accuracy\, interpretability\, and scalability to identify optimal techniques for real-world security applications. The findings highlight the importance of balancing explainability with performance to ensure robust and trustworthy cybersecurity solutions.
CATEGORIES:VIRTUAL ROOM_12B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:44fdc09223225ee38efc1a8910f8ed09
URL:http://11tict4sd.sched.com/event/44fdc09223225ee38efc1a8910f8ed09
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Leveraging a combined Machine Learning (ML) and Deep Learning (DL) approach for Landslide Prediction
DESCRIPTION:Authors - Priya Surana\, Soham Jadhav\, Janhvi Jathot\, Arnav Joshi\, Roshani Kadam Abstract - Landslides are natural disasters posing great risks to life\, infrastructure\, and the environment. Timely and accurate predictions are highly beneficial to reduce such impact. The advent of machine learning (ML) and deep learning (DL) has significantly improved the state of landslide prediction models. The review outlines the various ML and DL techniques adopted for landslide prediction and gives a brief account of methodologies\, applications\, benefits\, and limitations. This is mainly the melding of ML and DL techniques\, such as Random Forest (RF)\, Support Vector Machines (SVM)\, Convolutional Neural Networks (CNN)\, and Long Short-Term Memory (LSTM) networks\, for the enhancement of the predictive ability of such models. Key challenges in landslide prediction\, such as data availability\, model interpretability\, and computational complexity\, alongside future directions\, will be discussed to contribute to the robustness of landslide prediction models. Finally\, inferences will be drawn as to the significance of hybrid ML-DL approaches in pushing forth landslide prediction models into better accuracies and reliability (Khuc\, T.D.\, et al\, 2023)(Wu\, X.\, et al\, 2023).
CATEGORIES:VIRTUAL ROOM_12B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:6ac6aa22047d5b8be628210ab9e79975
URL:http://11tict4sd.sched.com/event/6ac6aa22047d5b8be628210ab9e79975
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Smart Malware Detection in IoT Devices Using Optimized Feature Engineering and Deep Neural Networks
DESCRIPTION:Authors - A.Punidha\, E.Arul\, E.Yuvarani\, S.Rajasakaran Abstract - With the rapid expansion of Internet of Things (IoT) and smart device ecosystems\, security threats such as malware attacks have become a critical concern. Traditional signature-based malware detection methods struggle to detect evolving and polymorphic threats\, necessitating the development of intelligent\, data-driven cybersecurity mechanisms. This study proposes a novel malware detection framework that integrates optimized feature engineering and deep neural networks (DNNs) to classify malware in smart devices with high precision. The approach focuses on behavioral feature extraction\, including API call sequences\, network activity logs\, and application permissions\, followed by feature selection techniques to reduce dimensionality while retaining key discriminative attributes. A comparative analysis of various machine learning (ML) models\, including Random Forest\, Support Vector Machine (SVM)\, and Deep Learning models\, demonstrates that the proposed feature engineering-enhanced DNN model achieves 96.1% accuracy\, outperforming conventional methods. Extensive experimentation on a real-world dataset of 10\,000 smart device applications showcases the robustness and scalability of our framework. This research contributes to enhancing security in smart environments by providing an adaptive and computationally efficient malware detection system.
CATEGORIES:VIRTUAL ROOM_12B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:8a4af1dbb66b571acbd8d15866249868
URL:http://11tict4sd.sched.com/event/8a4af1dbb66b571acbd8d15866249868
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:The Influence of Artificial Intelligence on Higher Education in the Philippines
DESCRIPTION:Authors - Jolou Vincent M. Jala\, Everly A. Nacalaban\, Nenon Roy A. Sandinao\, Ryan Boyd D. Origines\, Randy Joy M. Ventayen\, Neilson D. Bation Abstract - Artificial intelligence (AI) has completely transformed enterprises and organizations all over the world due to its propensity to spur innovation and mimic operational efficiency (Jala\, J.V.M. et al.\, 2024). Through its capability to boost learning outcomes\, promote inclusion\, and streamline operations\, artificial intelligence (AI) is revolutionizing higher education. This study investigates the influence of artificial intelligence in higher education in the Philippines. The study specifically seeks to understand how artificial intelligence (AI) can be used in higher education institution in terms of personalized learning amidst large class sizes\, access to education in rural areas\, solving job skills mismatch\, modernizing administrative procedures in inadequate resources institutions\, artificial intelligence powered innovation and research\, challenges of embracing artificial intelligence such as digital literacy and infrastructure\, social and ethical implications and resistance to change and faculty development Moreover\, this work also examines the ethical considerations in employing arti-ficial intelligence in higher education in the Philippines\, precisely in terms of security and data privacy\, fairness and algorithmic bias\, digital divide\, human supervision and accountability\, autonomy and consent\, influence on staff and faculty roles and intellectual property and academic integrity. To realize this objective\, the proponents essentially examined 170 publications in the literature that were indexed by Scopus to look at artificial intelligence in the context of higher education. This finding highlights artificial intelligence’s essential role in embracing challenges and improving higher education in the Philippines while emphasizing ethical considerations such as fairness and data privacy. (Jala\, D.J.V.\, 2025).
CATEGORIES:VIRTUAL ROOM_12B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:46599728725bf884831bbd33d75351f2
URL:http://11tict4sd.sched.com/event/46599728725bf884831bbd33d75351f2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:The Role of Marketing Mix Elements & Personalization on the Brand Equity of Online Fashion Retailers
DESCRIPTION:Authors - Kishan Raj\, Samyuktha Vimal\, V Shini Abstract - In recent years\, the Indian online fashion e-commerce industry has experienced significant transformations due to fast-paced technology\, consumer behavior changes\, and a growing e-commerce landscape. Competition is fierce\, especially in the e-lifestyle market as its payoff will exceed $30 billion by 2025\, developing brand equity understandably becomes a key factor in maintaining long-term success. It is understood that brand equity is a significant factor as it directly affects consumers' trust\, buying decisions\, and consumer retention\, which are also vital elements in preserving brand equity in an industry where distinctions matter. This study's goal is to examine the impact of marketing mix variables 7Ps including Personalization\, which has just recently emerged as a particular critical factor in the e-commerce business\, on brand equity in the Indian online fashion e-commerce industry. Quantitative research methods were adopted to analyze data provided by consumers regarding the influence of such factors. The analyses indicate that Personalization and Place affect brand equity substantially\, whereas traditional elements such as Price and Promotion do not exert such influence in online fashion retailing. Indian consumers seem increasingly to make a shift toward a digital-first shopping experience\, the marketing strategies of brands must follow suit by creating engaging\, personalized\, and innovative interactions. These research findings will provide some strategic recommendations to online fashion retailers\, marketers\, and industry gurus seeking to consolidate their brand positioning in this ever-evolving and highly competitive marketplace.
CATEGORIES:VIRTUAL ROOM_12B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:0402d49e64161753e14d13f97d33c4d8
URL:http://11tict4sd.sched.com/event/0402d49e64161753e14d13f97d33c4d8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:AI-Driven Women Safety Analytics for Threat Detection
DESCRIPTION:Authors - Yash Tekade\, Mayur Shinde\, Bhumika Lipane\, Nikita Patil\, Suhasini Bhat Abstract - The paper presents the idea and methodology of development of a real-time threat detection system designed to enhance women's safety across various environments using AI technology and CCTV surveillance. The system consists of features like real-time person detection\, gender classification\, and SOS gesture recognition\, all connected to an alert system for law enforcement authorities. It effectively identifies potential threats\, including a lone woman at night or a woman surrounded by men\, enabling proactive actions before incidents escalate. Additionally\, the system maps hotspot areas where previous incidents have been recorded\, allowing authorities to allocate resources efficiently. It also alerts security personnel about low-light conditions in an area\, ensuring surveillance even in challenging environments. By combining these capabilities\, the system aims to create a safer atmosphere for women\, promoting proactive measures that can significantly reduce crime rates and contribute to enhance overall safety strategies.
CATEGORIES:VIRTUAL ROOM_12C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:77dea9bfa425644bf73f7f399d2a2e67
URL:http://11tict4sd.sched.com/event/77dea9bfa425644bf73f7f399d2a2e67
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:AI-Powered Sustainable Energy Tracking: Optimizing Efficiency for a Greener Future
DESCRIPTION:Authors - Ritveek Rana\, Manisha Manoj\,vAnitha Dhanasekaran Abstract - This research endeavors to apply artificial intelligence to estimate past energy statistics and forecast future energy consumption patterns in India. The research utilizes energy indicators such as access to electricity\, the share of renewable energy\, CO2 emissions\, and economic development to develop a model to forecast future energy needs and renewable energy share. The future energy consumption patterns and the share of renewable energy are forecast using regression analysis. The intention is to provide insights into energy transition required in order to ensure sustainability by reducing the reliance on fossil fuels and increasing renewable sources.
CATEGORIES:VIRTUAL ROOM_12C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:4138999b6690ce1040b0fafd54fd5d16
URL:http://11tict4sd.sched.com/event/4138999b6690ce1040b0fafd54fd5d16
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Blockchain-Enhanced KYC: A Secure and Decentralized Framework for Identity Verification
DESCRIPTION:Authors - G.B. Sambare\, Sankarsha Shelke\, Sahil Wawdhane\, Harshad Wable\, Abhinav Thube Abstract - The KYC Powered by Blockchain for decentralized\, secure\, and more efficient Know Your Customer (KYC) system using blockchain. This system solves the inherent inefficiencies of traditional KYC by allowing institutions to share validated customer data\, mitigating redundancy among KYC providers\, and reducing both costs and compliance time. Tamper-proof architecture of blockchain allows for strong data privacy\, security\, and compliance of AML and GDPR regulations. Customers gain full control over their personal data\, with the ability to grant and revoke access dynamically\, reducing risks of breaches and fraud. The framework integrates off-chain storage for sensitive data and combines advanced cryptographic methods like AES and ECC for encryption and security. Smart contracts automate data handling and permissions management\, ensuring secure\, transparent\, and immutable data sharing across institutions.
CATEGORIES:VIRTUAL ROOM_12C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:38a8c907be7ff830c971ad72ebe0b5d6
URL:http://11tict4sd.sched.com/event/38a8c907be7ff830c971ad72ebe0b5d6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Brain Tumor Detection using CNN
DESCRIPTION:Authors - Sakshi G. Wagh\, Snehal S. Shirsath\, Vaibhavi V. Pujari\, Shrirang A. Sonawane\, Milindkumar B. Vaidya Abstract - Diagnosing brain tumors is a complex task due to their intricate characteristics and variability in presentation. Timely and accurate detection plays a vital role in ensuring effective treatment and improving patient prognosis. This study presents the development of an automated system for brain tumor detection and segmentation using Convolutional Neural Networks (CNNs). The model is trained on annotated MRI datasets to distinguish between normal and tumorous brain tissues with high accuracy. The proposed approach involves a comprehensive pipeline that includes image preprocessing to enhance MRI quality\, training a CNN-based model for tumor recognition\, and applying post-processing techniques to refine the output. By automating the diagnostic process\, the system aims to support radiologists by increasing accuracy\, reducing diagnostic delays\, and minimizing manual interpretation errors. Furthermore\, the project incorporates various image processing techniques and data augmentation strategies to strengthen the model’s performance and generalizability across diverse imaging conditions. The result is an intelligent and accessible diagnostic tool intended to assist healthcare professionals in delivering more precise and efficient brain tumor diagnoses\, ultimately contributing to better clinical decision-making and patient care.
CATEGORIES:VIRTUAL ROOM_12C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:4864d4e05925aa2933debe8ede5c6abc
URL:http://11tict4sd.sched.com/event/4864d4e05925aa2933debe8ede5c6abc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Code Understanding Using Sherlock
DESCRIPTION:Authors - Monali P. Deshmukh\, Dhanashri Arjun Ghadage\, Mrunali Sunil Rangankar\, Prajakta Dattatray Supugade\, Deep Isane Abstract - This research paper presents an AI-assisted web-based coding platform\, Code Understanding using Sherlock\, that integrates a real-time compiler with an AI-powered chatbot. The chatbot provides contextual assistance based on selected code snippets or general programming queries. Users can toggle between a standard chatbot mode and a code-aware mode\, where the chatbot analyzes selected code portions to answer relevant questions. The system enhances the coding experience by providing explanations\, debugging help\, and execution functionalities. By leveraging AI and NLP techniques\, the chatbot can understand syntax\, logical structures\, and common programming errors\, offering detailed feedback and solutions. The platform streamlines the development process by reducing debugging time and enhancing code comprehension. Additionally\, the system provides a seamless file management experience\, enabling users to create\, edit\, and organize their projects efficiently. This integration fosters an interactive learning and development environment\, making it valuable for both beginners and experienced programmers.
CATEGORIES:VIRTUAL ROOM_12C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:e5815391029f857af94efa512db0e6b0
URL:http://11tict4sd.sched.com/event/e5815391029f857af94efa512db0e6b0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Digital Twins and Smart Supply Chains: Advancing Resilient and Intelligent Infrastructure Systems
DESCRIPTION:Authors - Anandhukrishna A S\, Santanu Mandal\, Raghu Raman Abstract - Digital Twin (DT) technology offers unprecedented capabilities that are transforming supply chain management (SCM)\, delivering a new level of system-wide resilience\, real-time insights and predictive analytics. Yet\, the nascent research still lacks in terms of cohesion\, with most studies being heavily centralised around technical solutions and missing strategic\, managerial and empirical perspectives. In light of this gap\, the current study provides a thorough bibliometric analysis of 99 peer-reviewed articles published from 2016 to 2024 selected from Scopus with analytical tools of Biblioshiny R package. The results clearly showed that there was a higher growth of DT-related SCM research after 2020\, indeed due to significant intercontinental disruptions and the demand of resilient\, sustainable\, and intelligent infrastructure systems. Resilience in the supply chain\, sustainability\, interoperability\, and AI-driven optimization are core themes. Importantly\, while China\, Germany\, and the USA dominate in terms of number of papers produced\, institutions such as The Hong Kong Polytechnic University are also leading in productivity metrics here. However\, the analysis reveals important gaps — notably a lack of cross-border cooperation and empirical case studies as well as longitudinal research. Less developed but rich prospects\, like the integration with blockchain\, extended reality and physical internet also emerge as compelling themes. This work offers actionable insights into the way forward for researchers\, policymakers\, and industry leaders\, calling for cross-disciplinary partnerships\, real-world pilots\, and frameworks for applying overarching compliance. This also advances the role of Digital Twins as a strategic enabler of a resilient and future-ready supply chain.
CATEGORIES:VIRTUAL ROOM_12C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:eb0b7d358fa382f8be9ae3d23c98b155
URL:http://11tict4sd.sched.com/event/eb0b7d358fa382f8be9ae3d23c98b155
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:DRIVER DROWSINESS DETECTION SYSTEM
DESCRIPTION:Authors - Jhalak Bansal\, Janvi Jain\, Sukti Jain\, Harsh Chaudhary\, Vikas Srivastava Abstract - Traffic accidents\, a leading cause of death worldwide with nearly one million fatalities annually (WHO)\, are often driven by fatigue-related drowsiness. Our project introduces a real-time drowsiness detection system leveraging technologies like OpenCV\, Python\, and machine learning to enhance safety and accuracy. Using a camera\, the system monitors facial features and eye movements\, Using facial landmark detection to identify 68 key points\, the system calculates the Eye Aspect Ratio (EAR). Extended periods of eye closure activate an alert\, and GPS-enabled location tracking enhances response by sending automated emails with the vehicle’s real-time location to pre-registered contacts. The methodology integrates image processing\, real-time facial landmark detection\, and a dynamic scoring system to evaluate drowsiness. With an accuracy target of over 85%\, the system addresses the limitations of existing solutions while introducing innovative location-based intervention. Results highlight its potential to reduce drowsy driving incidents\, ensuring safer roads.
CATEGORIES:VIRTUAL ROOM_12C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:02d4698c8d5fd840bf547daabb2c8380
URL:http://11tict4sd.sched.com/event/02d4698c8d5fd840bf547daabb2c8380
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Pragmatic augmentation in Aqua Status Prediction using hybrid learning techniques & Optimization
DESCRIPTION:Authors - Aoudumber Londhe\, Ravindra Apare\, Parikshit Mahalle\, Ravindra Borhade Abstract - Aqua status quality prediction is a vital part of environmental monitoring\, with significant implications for public health\, ecosystem sustainability\, and Aqua resource management. Traditional methods for evaluating aqua quality\, is like taking the manual sample and to perform the laboratory analysis\, are often labour-intensive and limited in scope. Recent developments in deep learning have transformed this domain by empowering the expansion of predictive models accomplished with analysing non-linear relationships in Aqua quality. Hybrid deep learning models\, merging Recurrent Neural Networks (RNNs)\, Long Short-Term Memory (LSTM) networks\, Convolutional Neural Networks (CNNs)\, and Gated Recurrent Units (GRUs)\, have verified superior performance in apprehending spatial and temporal dependencies in Aqua quality data. Optimization algorithms such as Particle Swarm Optimization\, Grey Wolf Optimization\, Sparrow Search Optimization (SSO)\, and Beluga Whale Optimization (BWO) have been integrated to enhance model accuracy and efficiency. Attention mechanisms and feature selection techniques have further improved model performance\, while the integration of IoT has enabled real-time monitoring\, addressing the limitations of traditional methods. Despite these advancements\, challenges related to model interpretability\, computational complexity and most important part data availability remain as it is. This review explores the pragmatic augmentation in hybrid deep learning models for Aqua quality prediction\, focusing on their architecture\, optimization techniques\, and real-world applications.
CATEGORIES:VIRTUAL ROOM_12C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:d3f529edd8ba5edb9e62953e33edb249
URL:http://11tict4sd.sched.com/event/d3f529edd8ba5edb9e62953e33edb249
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:SpheraTech: Leveraging AI and 3D Gaussian Splatting for Immersive Historical Simulations
DESCRIPTION:Authors - Rakhi Bharadwaj\, Mohit Deo\, Pratham Jain\, Ashishkumar Jha\, Harsh Bachhav Abstract - This study introduces a novel open-source educational website that makes use of AI-powered 3D environments and interaction with historical individuals to deliver immersive historical learning experiences. The site offers both contemporary views of these environments using 3D Gaussian splatting technology and offers precise historical recreations using Pixel Streaming. Interactive conversation with AI-powered historical individuals\, dynamic quizzes to validate the knowledge of users\, and AI-powered historical narratives are all among the offerings. To support knowledge about historical events and cultures from the past\, the system merges interactive learning and storytelling for a fun and educational experience. Advanced natural language processing (NLP)\, speech-to-text\, and AI-powered tour guides are all included as part of the platform architecture to provide personalized historical tours without necessitating complicated personal details.
CATEGORIES:VIRTUAL ROOM_12C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:8905999af4703c3d9d33e6caf160a423
URL:http://11tict4sd.sched.com/event/8905999af4703c3d9d33e6caf160a423
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Unveiling hidden messages in an image using cryptography and steganography
DESCRIPTION:Authors - A. Harshavardhan\, Konkathi Nihal\, Ramini Srinidhi\, Konda Poojithasai\, Gochika Bhanu prasad\, Dhanraj Sai Ganesh Abstract - This paper presents a novel\, dual-layer secure steganographic system that combines hybrid cryptography and steganography to ensure the confidentiality\, integrity\, and security of secret communications. Initially\, the sender inputs their message and selects a cover image. The message is encrypted using a hybrid substitution (playfair cipher and columnar transposition cipher) and transposition cipher\, and then embedded in randomly selected pixel positions of the image using Least Significant Bit (LSB) steganography. A position file that records these embedding locations is generated and encrypted. To obfuscate the presence of the stego-image\, multiple duplicate images are created alongside the steganographic image. On the receiver's side\, a ResNet50-based feature extractor followed by K-means clustering is used to identify the stego-image from the duplicates. The encrypted position file enables accurate message extraction and subsequent decryption. Experimental results show excellent performance with high imperceptibility (MSE: 0.0175\, PSNR: 65.69 dB\, SSIM: 0.9994) and strong resilience to brute-force and statistical steganalysis.
CATEGORIES:VIRTUAL ROOM_12C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:05fc602548df3fb9812e4035f1329da3
URL:http://11tict4sd.sched.com/event/05fc602548df3fb9812e4035f1329da3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Agati - A Personalized Women's Safety and Empowerment App
DESCRIPTION:Authors - Dhruv Aswani\, Aman Sande\, Praful Pradhan\, Rajveer Tolani\, Pallavi Saindane Abstract - Security concerns and the empowerment of women remain highly pressing challenges in India\, with significant issues evident across urban\, semi-urban\, and rural regions alike. Women’s mobility is often constrained by the fear of harassment\, crime\, and social barriers which slows down their movement toward true empowerment. To address these problems\, this study focuses on the major drivers of women’s safety and empowerment which include social norms\, presence of crime\, supportive structures\, and women participation in technology. To address these challenges\, this paper proposes Agati\, an Android application that offers safety and empowerment features specifically tailored for women. Agati combines real-time safety alerts\, location tracking\, community support networks\, and financial literacy modules to foster both security and economic independence. By leveraging data analytics and user feedback\, the app aims to build a personalized\, data-driven solution that bridges the gap between security and empowerment. Through this integrated approach\, Agati seeks to create a safe\, supportive environment that promotes social power and holistic growth for women.
CATEGORIES:VIRTUAL ROOM_12D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:7ac270b4ef4601cb152ae4efe404684d
URL:http://11tict4sd.sched.com/event/7ac270b4ef4601cb152ae4efe404684d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Augmented Reality Based Human Anatomy Learning Platform
DESCRIPTION:Authors - Nikhil Vaishya\, Amey Sawant\, Mayank Shukla\, Suhani Pandey\, Vaishali Kosamkar Abstract - Augmented Reality (AR) is transforming the learning experience in anatomy and biology [1\, 2]. by providing an engaging and interactive alternative to traditional teaching methods. Understanding complex anatomical structures has historically been challenging due to the limitations of textbooks\, static models. AR overcomes these challenges by enabling students to explore high-fidelity 3D representations of the human body in real-time\, fostering deeper spatial understanding and retention. The technology allows learners to interact with anatomical structures\, receive immediate feedback\, and learn at their own pace\, beyond the constraints of the classroom. In addition to AR visualization\, this project integrates an AI-assisted quiz and learning platform to further enhance anatomy education. By leveraging machine learning algorithms such as ensemble methods like Random Forests and Support Vector Machines (SVMs)\, coupled with SMOTE for class imbalance handling and cross-validation for robust generalization\, the system offers adaptive quizzes\, personalized learning recommendations\, and real-time feedback. The platform dynamically adjusts to user interactions\, ensuring a tailored and effective learning experience. Developed using Unity for AR functionalities\, JSON for data management\, and machine learning for prediction models\, the system bridges the gap between theory and practice while promoting active and self-paced learning. This paper details the design\, development\, and evaluation of the AR-based Anatomy Learning Platform\, highlighting its potential to revolutionize anatomy education by offering an accessible\, immersive\, and personalized approach. The platform is designed primarily for medical students\, but it also supports general learners seeking to enhance their anatomical knowledge through immersive technologies.
CATEGORIES:VIRTUAL ROOM_12D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:1ace56702b718cbaa40ff135419085f2
URL:http://11tict4sd.sched.com/event/1ace56702b718cbaa40ff135419085f2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Automated ESG Scoring and Prediction
DESCRIPTION:Authors - Shital Pawar\, Parag Dolhare\, Saish Fatangare\, Harshdeep Gawhale\, Aditya Gadgil Abstract - Environmental\, social and governance (ESG) criteria have become essential to assess the sustainability and social impact of companies. This article presents the development of an automated ESG ranking system that uses natural language processing (NLP)\, sentiment analysis\, and machine learning techniques to rank and rate companies based on ESG metrics. Using a pre-existing database of news articles\, we used the VADER sentiment analysis tool to assess the polarity of the text data\, categorizing it as positive\, negative or neutral. Sentiment scores were converted to numerical scores for each ESG component. In addition\, Node2Vec is integrated to create network graphs that represent the relationships and interconnections between companies\, allowing a comprehensive analysis of potential impacts. The results were visualized with Altair to provide a clear view of ESG trends and relationships that impact the company's performance over time. This study demonstrates the utility of combining NLP and advanced graph analytics for scalable data-driven ESG assessment.
CATEGORIES:VIRTUAL ROOM_12D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:44f76a5994a078522f141c6744bc1fb8
URL:http://11tict4sd.sched.com/event/44f76a5994a078522f141c6744bc1fb8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Blockchain-Powered Secure Federated Learning for Healthcare: Privacy-Guaranteed AI Training with ZKP-Enhanced SMPC and Tamper Proof Model Aggregation
DESCRIPTION:Authors - Chalamalasetty Nishitha\, Yelavarti Kalyan Chakravarti\, V. Esther Jyothi Abstract - Sensitive domains such as healthcare institutions are increasingly relying on Federated learning for data security. Irrespective of this approach\, they are gullible to adversarial attacks such as poisoning attacks and confidentiality breaches. To overcome these hindrances\, Blockchain driven Federated learning is put forward\, which integrates Secure Multi-Party Computation (SMPC) with Zero-Knowledge Proofs (ZKPs). This framework strives to ensure confidentiality in a distributed training environment. The individual entities train their local AI models with their exclusive datasets and generate Zero Knowledge Proofs to assert the accuracy of the model updates. The SMPC protocol encrypts the model updates\, which are later aggregated to enable computing that guarantees privacy. Later\, Smart Contracts are used to immutably store these adjustments on the Blockchain ledger\, ensuring impenetrable model ensemble. To improve the trade-off between model dependability and precision\, privacy noise is dynamically adjusted by employing adaptive differential privacy\, based on individual client’s reputation. Extensive experiments prove the fact that the proposed system prominently reduces computing overhead in comparison to the established system while strengthening the attack detection rates. This architecture establishes a benchmark for information security in delicate areas like healthcare systems while designing its data-sensitive Al models.
CATEGORIES:VIRTUAL ROOM_12D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:61cfac0f5f40fc67944a224add26a7d3
URL:http://11tict4sd.sched.com/event/61cfac0f5f40fc67944a224add26a7d3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:DETERMINANTS OF RISK-TAKING BEHAVIOR IN FINTECH APPS: THE ROLE OF GAMIFICATION\, FINANCIAL FACTORS\, AND PSYCHOLOGICAL INFLUENCES
DESCRIPTION:Authors - Nandana R\, Rithika Kannan\, Ramgeeth N Nair Abstract - The rise of fintech applications has revolutionized financial decision-making\, yet the determinants of risk-taking behavior in these digital platforms remain a critical research area. This study investigates the role of gamification\, financial knowledge\, and psychological influences in shaping users’ risk-taking behavior. Using a quantitative approach\, an Ordinary Least Squares (OLS) regression analysis was conducted on a dataset of 200 fintech users. The results indicate that gamification has a significant positive effect on risk-taking behavior (β = 0.1414\, p = 0.001)\, suggesting that game-like elements in fintech apps encourage users to take greater financial risks. However\, certain gamification effects exhibit a negative influence (β = -0.1272\, p = 0.005)\, highlighting that not all gamification strategies lead to in- creased risk-taking. Financial knowledge also emerged as a significant determinant (β = 0.1965\, p = 0.001)\, implying that financially literate users tend to take more calculated risks. Among psychological factors\, risk tolerance (β = 0.2754\, p < 0.001) was the strongest predictor\, demonstrating that individuals predisposed to risk-taking in general extend this behavior to fintech platforms. Additionally\, social efficacy (β = 0.2461\, p < 0.001) and social influence (β = 0.1598\, p = 0.004) significantly contribute to risk-taking\, emphasizing the role of self-perceived competence and peer influence in financial decision-making. The model explains approximately 48.1% of the variance in risk-taking behavior (R² = 0.481)\, confirming the robustness of these deter- minants. The findings underscore the importance of designing fintech applications that balance engagement with responsible financial behavior. Future research should explore the ethical implications of gamification and assess long-term user behavior to ensure sustainable financial decision-making in digital finance ecosystems.
CATEGORIES:VIRTUAL ROOM_12D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:edc45bb51218410457ab54f449dac53d
URL:http://11tict4sd.sched.com/event/edc45bb51218410457ab54f449dac53d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Dynamic Performance Evaluation of Utility-Linked Rural Microgrids
DESCRIPTION:Authors - Ramesh Babu Mutluri\, Vinit Kumar Singh\, D Saxena Abstract - Rural areas in developing countries are still refrained from continuous and uninterrupted power supply to power their household and run small industries. Thus\, we can say that these rural areas are weakly connected to the utility grid. The main reasons for poor power supply are weak infrastructure\, lack of adequate generation to fulfill the demand-supply gap\, dependency on long-distance transmission\, frequent load shedding\, and distributed generation. This demand-supply gap can be minimized by installing renewable energy sources with the local load forming rural microgrid and connecting to the utility grid. The grid connection would help to maintain the power supply due to the variable output characteristics of renewable energy sources thus also acting as a buffer to the local power system. This paper presents a novel approach towards modeling of utility connected rural microgrid comprising renewable energy sources considering control architecture for marinating frequency-voltage interdependency. Accordingly\, a frequency-based voltage controller is introduced. Further\, the model has been verified in view of various scenarios with a fluctuation in load demand and power input to renewables. The controllers are tuned such that in case of increase in load or decrease in power generation\, power demand is met from the utility grid\, and in case of surplus generation\, the power is fed to the grid\, therefore\, developing microgrid as business unit applicable for power trading. The model has been developed in Simulink/MATLAB. An integral square error criterion has been used for tuning the controllers to mitigate the oscillations.
CATEGORIES:VIRTUAL ROOM_12D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:e7a0915a397652f10f3c72ed86177316
URL:http://11tict4sd.sched.com/event/e7a0915a397652f10f3c72ed86177316
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Machine Learning Algorithm for Poultry Chickens Coccidiosis Disease Detection
DESCRIPTION:Authors - Ashwitha A Shetty\, Naganna Chetty\, Antony P.J Abstract - The poultry industry is a significant and prominent business sector. As the daily intake of chicken meat and eggs is rising globally\, poultry farming is gaining significance for providing protein. Additionally\, this industry raises the nation's revenue despite being a less expensive protein source. Numerous diseases that harm the chickens are the main issue affecting the poultry business. Due to the high cost of vaccinations\, poultry owners are unable to adopt these expensive methods. Consequently\, this strategy cannot be used because it requires continuous investment. This paper aims to present one of the prevalent chicken diseases\, coccidiosis and the different detection techniques used. In this regard\, the study introduces multiple strategies that can be used in tandem to identify coccidiosis-affected fowl hens automatically. The idea behind studying chicken activity monitoring is that it directly connects to the health condition of the chicken. The enhanced future research could result in a system to monitor chicken activity and detect coccidiosis among them
CATEGORIES:VIRTUAL ROOM_12D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:80991c253929931aec9b73c1df5217c1
URL:http://11tict4sd.sched.com/event/80991c253929931aec9b73c1df5217c1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Multiclass Classification of Mammographic Density and Mass Regions for Breast Cancer Diagnosis Using a Res-Net-Based Framework
DESCRIPTION:Authors - Piyush Sharma\, Harish Patidar\, Anuj Kumar Abstract - This research introduces a ResNet-based framework for multiclass classification of mammographic density and mass regions. The framework was rigorously tested using two prominent mammographic datasets\, INbreast and DDSM\, and benchmarked against other models\, including CNNs\, Random Forest (RF)\, Support Vector Machines (SVMs)\, Logistic Regression (LR)\, and K-Nearest Neighbors (KNN). ResNet demonstrated superior performance across all critical evaluation metrics—accuracy\, precision\, recall\, F1-score\, and AUC—outclassing the comparative models on both datasets. Its proficiency in extracting complex hierarchical features and addressing multiclass classification tasks positions it as a robust choice for breast cancer diagnosis. This framework offers a reliable and efficient tool for automating diagnostic processes\, with the potential to significantly improve clinical decision-making and patient care.
CATEGORIES:VIRTUAL ROOM_12D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:8e8db8bad585fbef700ab3bee58e60f5
URL:http://11tict4sd.sched.com/event/8e8db8bad585fbef700ab3bee58e60f5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Pediatric Dental Caries Classification Using Deep\, Learning: An Empirical Comparison of CNN Architectures
DESCRIPTION:Authors - Pranav Bagal\, Bhavesh Patil\, Shounak Muglikar\, Yash Sonavane\, Prajakta S. Shinde Abstract - The study addresses dental caries detection and classification using state-of-the-art deep learning architectures. We implemented and compared three pre-trained convolutional neural network models: VGG19\, DenseNet169\, and ResNet101\, to automatically identify and classify dental caries from intraoral clinical image. Our research focused specifically on pediatric populations aged 1 to 14 years\, where caries remain a significant health concern despite global prevention efforts. The models were trained and validated on a comprehensive dataset of dental images. Performance metrics demonstrated that DenseNet169 model achieved superior results with an Validation accuracy of 72.22%. These deep learning approaches show promising potential to augment traditional diagnostic methods\, particularly in resource-limited settings where expert dental practitioners may be scarce. By enabling earlier and more accurate detection of carious lesions\, our proposed system could help address disparities in oral healthcare accessibility and contribute to more effective intervention strategies\, especially for underprivileged populations where caries prevalence continues to rise. This research establishes a technological framework that could be integrated into portable diagnostic tools for use in diverse clinical environments.
CATEGORIES:VIRTUAL ROOM_12D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:5ae304555f54edff730c1af003d326a2
URL:http://11tict4sd.sched.com/event/5ae304555f54edff730c1af003d326a2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Stock Recommendations Leveraging AI/ML for Informed Long-Term Investment Decisions
DESCRIPTION:Authors - Govinda Sambare\, Lalit Deore\, Harsh Itkar\, Onkar Jadhav\, Sarthak Joshi Abstract - This research presents the development of an intelligent stock recommendation system that utilizes advanced machine learning models for informed long-term investment decisions. The system addresses the complexities of the stock market\, where traditional methods often fall short in accessibility\, accuracy\, and efficiency. By automating fundamental analysis with models like Long Short-Term Memory (LSTM) networks and the CNN-GRU-XGBoost hybrid model\, the system integrates key financial ratios\, macroeconomic indicators\, and sector performance\, providing data-driven insights. The proposed framework optimizes stock selection using XGBoost and forecasts future stock prices with LSTM\, offering precise and scalable solutions for diverse investment portfolios. The literature review highlights modern methodologies like TRAN\, Bi-LSTM\, and hybrid models\, which improve stock forecasting and trading strategies by incorporating temporal dependencies and inter-stock relationships. The algorithmic analysis explains LSTM's ability to handle sequential data and the hybrid model's powerful feature extraction and prediction capabilities. This hybrid approach enhances decision-making\, saves time\, and democratizes financial insights\, making advanced analysis accessible to individual investors\, robo-advisors\, and educational institutions. While offering benefits like scalability and reduced biases\, the system also faces challenges\, such as computational costs and market volatility. Backtesting results confirm the system's adaptability to dynamic market conditions\, ensuring sustainable investment strategies. This project showcases the transformative potential of AI/ML in financial analytics\, laying a strong foundation for long-term\, informed investment decisions.
CATEGORIES:VIRTUAL ROOM_12D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:6a89505f8557ef829fddcc9ea2a0cf09
URL:http://11tict4sd.sched.com/event/6a89505f8557ef829fddcc9ea2a0cf09
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Advanced Road Condition Monitoring using Machine Learning and Computer Vision
DESCRIPTION:Authors - Vedant Chandore\, Niranjan Pardeshi\, Sai Sinare\, Samruddhi Akude\, Sahil Dhawane\, Kartik Gawande\, Rahul Sadgir\, Shravani Nigade\, Ajay Talele Abstract - For transportation infrastructure to be safe\, effective\, and long-lasting\, road condition monitoring is essential. Conventional techniques\, which depend on human inspections\, are frequently ineffective and prone to mistakes. To overcome these constraints\, this study suggests a machine learning-based smart road condition monitoring system. Utilizing cameras installed on vehicles\, the system gathers pictures and videos of the state of the roads\, which are subsequently processed by sophisticated machine learning algorithms. These algorithms categorize surface conditions\, identify irregularities in the road\, and offer information on repair requirements. Through comprehensive field testing and data analysis\, the study shows how effective the system is\, showing notable gains in both the efficiency of maintenance procedures and the accuracy of identifying road issues. By concentrating on image and video analysis\, this smart monitoring system offers a revolutionary solution for urban infrastructure management\, opening the door for safer\, more intelligent\, and sustainable road maintenance procedures. This strategy not only lowers operating costs but also improves road safety and infrastructure sustainability.
CATEGORIES:VIRTUAL ROOM_12E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:21f10ddb6bc6f29ca1a719ea00aa174e
URL:http://11tict4sd.sched.com/event/21f10ddb6bc6f29ca1a719ea00aa174e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:An Enhanced SVM Model Optimized with Minimum Bayes Error Rate for Mental Disorder Detection
DESCRIPTION:Authors - Sai Himagnya Parisaneni\, Vemula Surya Teja\, Revanth Guthula\, Sushama Rani Dutta Abstract - This study presents an optimized approach for detecting mental disorders by integrating support vector machines (SVM) enhanced through Minimum Bayes Error Rate (MBER) optimization. The proposed framework uses MBER Optimization and refines classification boundaries through SVMs improve decision-making. Unlike conventional deep learning approaches that rely solely on CNN based end-to-end learning\, our method uses SVM for classification that minimizes errors\, enhancing model robustness and generalization. The experimental evaluation on EEG-based datasets assesses the effectiveness of the hybrid approach in terms of accuracy\, computational efficiency\, and scalability. The results provide insights into the potential of MBER-optimized SVM models for real-world applications in mental health diagnostics.
CATEGORIES:VIRTUAL ROOM_12E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:a85e4c7526f9e0ce76e0331c9d0d29f9
URL:http://11tict4sd.sched.com/event/a85e4c7526f9e0ce76e0331c9d0d29f9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Emoji Prediction for Sentiment Analysis: A Comparative Study of LSTM and BERT Models
DESCRIPTION:Authors - Satish Chikkamath\, Shreya Pattanashetti\, Pooja V Gadad\, Vidya Revanakar\, Bhoomika Hosamani Abstract - Emojis serve as an established means for people to express emotions and sentiments while interacting on social media. This paper examines the task of emoji prediction from text by developing accurate classification methods. The model uses a pre-trained and fine-tuned BERT framework on a dataset consisting of text sentences along with their corresponding emojis. This structured data allows the model to capture contextual meaning and emotional nuances\, which are crucial for practical applications. Challenges associated with emoji usage are addressed through tokenization techniques in text preprocessing\, while performance advances are achieved using stemming and feature extraction. Research conclusions indicate that the BERT-based model outperforms traditional deep learning approaches like LSTM. This study spotlights how NLP and sentiment analysis contribute to emoji prediction and shows its practical applications in social media monitoring\, sentiment analysis\, and enhancing user experiences.
CATEGORIES:VIRTUAL ROOM_12E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:74787c72f252b181082a060f103ea4e0
URL:http://11tict4sd.sched.com/event/74787c72f252b181082a060f103ea4e0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Entity Recognition for Defense Intelligence
DESCRIPTION:Authors - Sagar Janokar\, Krish Deshpande\, Krishna Masane\, Shriyash Kothe\, Varad Kulat\, Krish Chabria\, Rushikesh Kuchekar Abstract - This project demonstrates how a machine learning based approach can revolutionize the analysis of unstructured text data in defense intelligence. By automating key processes\, the system will enable faster and more accurate identification of threats and patterns\, improving decision-making and operational efficiency. This innovative application highlights the transformative role of technology in addressing real-world challenges in intelligence gathering.
CATEGORIES:VIRTUAL ROOM_12E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:7f229a9759f7ec1543bbeb4b2f365640
URL:http://11tict4sd.sched.com/event/7f229a9759f7ec1543bbeb4b2f365640
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:IoT-Based Agribot for Sustainable and Smart Pest Control using CNN & ResNet
DESCRIPTION:Authors -&nbsp\;Dhanaselvam J\, Dhanalakshmi R\, Prashaanth S\, Hariprasath S\, Harish R\nAbstract -&nbsp\; India\, with three-fourths of its population dependent on agriculture\, is plagued by severe crop loss due to pest infestation\, particularly in staple crops like rice\, wheat\, maize\, and soybeans. This paper proposes an embedded system of real-time pest detection and precise pesticide spraying to enhance productivity. The system employs deep learning with a Residual Neural Network (ResNet) and Quadra-attention\, residual\, and dense fusion techniques for enhanced pest image classification. High-resolution images of crop leaves are captured\, pre-processed\, and analyzed for pest detection. Upon detection\, the system selects the appropriate pesticide and activates an autonomous robotic sprayer. Driven by an Arduino NANO-based module with an L293D motor driver\, the robotic system automatically navigates through fields\, ensuring precise pesticide application without waste and infrastructure costs. With IoT integration and 99.80% validating accuracy\, this system optimizes pesticide use\, enhances crop health\, and enhances yield\, offering a cost-effective automated pest management system for sustainable agriculture.
CATEGORIES:VIRTUAL ROOM_12E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:ce42576708b3bd34054fa414289c38e1
URL:http://11tict4sd.sched.com/event/ce42576708b3bd34054fa414289c38e1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Joint Feature Learning and Hashing for Multi-Modal Data via Cosine Normalization
DESCRIPTION:Authors - Nikita Bhatt\, Nirav Bhatt\, Purvi Prajapati Abstract - In today’s data-rich world\, we often deal with multiple types of information such as images\, text\, and audio. Traditional deep learning models usually focus on a single type of data\, but real-world applications need systems that can understand and connect across these different formats — a concept known as multi-modal learning. This paper explores cross-modal retrieval\, where a user can input one type of data (like an image) and retrieve another (like related text). To make this possible\, we map different data types into a common space using deep learning methods like CNN for images and LSTM for text. One of the key challenges in this area is comparing vectors of different lengths\, which affects similarity estimation. Most traditional methods use inner product similarity\, which is not ideal for vectors with varying magnitudes. To overcome this\, we normalize the vectors using cosine similarity\, which focuses only on the angle between vectors\, not their length. This improves retrieval accuracy by reducing noise caused by vector size differences. We also discuss the benefits of using deep learning to jointly learn features and generate hash codes for faster and more accurate retrieval. Experiments on datasets like Google News show that cosine similarity outperforms Euclidean distance in terms of retrieval performance\, especially when combined with models like CBOW.
CATEGORIES:VIRTUAL ROOM_12E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:53aeebc2dd46a66914f67796f827ba08
URL:http://11tict4sd.sched.com/event/53aeebc2dd46a66914f67796f827ba08
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:MediaGPT for Image Generation with Enhanced Text
DESCRIPTION:Authors - Ria Ashish Gawali\, Christopher Sachin Chopde\, Aryan Gupta\, Tashmeet Kaur Jasbeersingh Hora\, Rachna Karnavat Abstract - MediaGPT is a Generative AI system that combines natural language and image synthesis to create unified\, visually appealing media content. By combining strong language models such as ChatGPT and Phi-3 with image synthesis models such as Stable Diffusion and ControlNet\, MediaGPT facilitates intelligent text-image alignment on an interactive canvas. The layout can be easily customized along with semantic coherence and aesthetic balance. Developed for designers\, educators\, marketers\, and content creators\, MediaGPT improves the creative process by facilitating effortless multi-modal integration and providing easy-to-use tools for creating high-quality\, contextually appropriate content.
CATEGORIES:VIRTUAL ROOM_12E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:7500afaf54a0edc73a15b953bf092a38
URL:http://11tict4sd.sched.com/event/7500afaf54a0edc73a15b953bf092a38
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Natural Disaster Response System
DESCRIPTION:Authors - Lokesh Khedekar\, Atharva Kassa\, Kartavya Sharma\,Tejas Kedar\, Sarthak Kasar\, Kaustubh Kelgandre\, Sharad Kasralikar Abstract - Natural Disasters have been a major threat to the living beings\, environment and the infrastructure\, in mainly areas where they have poor access to early warnings systems. This paper provides AI-based Natural Disaster Response System which helps to evaluate the impact of natural disasters and gives better of the existing systems. The system has historical Geographic Information System (GIS) datasets with real-time data from Internet of Things (IoT) sensors and predictive modeling to check out the natural disaster’s magnitude\, area of impact\, and resources. The methodology includes data preprocessing\, feature extraction\, and machine learning model training to achieve effective predictive accuracy. A Convolutional Neural Model (CNN) model was created and tested which further achieved 93% accuracy of predicting the impact of the disaster incident. The system was then compared with other machine learning models\, then was proved to be more effective. The suggested method gives efficient\, cost-effective and scalable way of utilizing the emergency resources at the maximum.
CATEGORIES:VIRTUAL ROOM_12E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:70b3a763b20df3a0c28119059c2261d7
URL:http://11tict4sd.sched.com/event/70b3a763b20df3a0c28119059c2261d7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Navigating the Future: Emerging Technologies and the Evolution of ICT Policy in India
DESCRIPTION:Authors - Prasanna Lakshmi T\, Shankar Lingam. M Abstract - This paper explores the intersection of emerging technologies and ICT policy evolution in India\, with a focus on Artificial Intelligence (AI)\, blockchain\, the Internet of Things (IoT)\, and 5G technologies. As India navigates its digital transformation through initiatives like Digital India\, the paper examines how the nation's ICT policy framework has adapted to accommodate these disruptive technologies. Using a theoretical approach based on Technological Innovation Systems (TIS)\, the study traces the historical development of India's ICT policies\, from early telecom regulations to the modern-day focus on digital infrastructure and smart technologies. Challenges such as the digital divide\, cybersecurity\, and data privacy are also analyzed. By identifying key policy milestones and evaluating India's current efforts in integrating emerging technologies\, this paper provides insights into the future direction of ICT policy in India. The findings highlight both opportunities and barriers to sustainable technological advancement and offer policy recommendations to better align ICT governance with global trends.
CATEGORIES:VIRTUAL ROOM_12E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:a89103984a57d18de8ac810fbcea3ed8
URL:http://11tict4sd.sched.com/event/a89103984a57d18de8ac810fbcea3ed8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Optimizing LLMs Using Quantization For Mobile Execution
DESCRIPTION:Authors - Agatsya Yadav\, Renta Chintala Bhargavi Abstract - Large Language Models (LLMs) offer powerful capabilities but their significant size and computational requirements hinder deployment on resource-constrained mobile devices.This paper investigates Post-Training Quantization (PTQ) for compressing LLMs for mobile execution. We specifically apply 4-bit PTQ using the BitsAndBytes library via the Hugging Face Transformers framework to Meta’s Llama 3.2 3B model. The quantized model is further converted to the GGUF format using llama.cpp tools for optimized mobile inference. The proposed PTQ workflow achieved a 68.66% reduction in model size through 4-bit posttraining quantization\, enabling the Llama 3.2 3B model to run efficiently on a standard Android device. Qualitative validation confirmed the 4- bit quantized model’s ability to perform inference tasks successfully. We demonstrate the feasibility of running the final quantized GGUF model on an Android device using the Termux environment and the Ollama framework. PTQ\, particularly down to 4-bit precision combined with mobile-optimized formats like GGUF\, presents a viable pathway for deploying capable LLMs directly on mobile devices\, balancing model size and functional performance.
CATEGORIES:VIRTUAL ROOM_12E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:0713abecfd56fdf2c1a59f42fff9a247
URL:http://11tict4sd.sched.com/event/0713abecfd56fdf2c1a59f42fff9a247
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Unified NL interface for automating system commands and advanced dev ops tasks
DESCRIPTION:Authors - Dwayne Nixon\, Shaun Menezes\, Ramya Kulkarni\, Phiroj Shaikh Abstract - In today’s fast-paced development environment\, where efficiency and speed are paramount\, manual tasks such as taking screenshots\, converting files\, rebooting systems\, and managing repositories have become increasingly tedious and time-consuming. These routine activities disrupt developer workflow and hinder productivity\, consuming valuable time. To address these inefficiencies\, this work proposes a comprehensive automation tool that extends beyond handling basic tasks to streamline workflows and optimize productivity. Firstly\, this tool centralizes a wide range of operations\, including automating code generation\, creating detailed reports\, and developing websites. By integrating these functionalities\, developers can eliminate redundant tasks and focus on high-level problem-solving. Secondly\, the automation tool enhances accuracy and consistency across development projects\, ensuring higher standards of work and reducing errors associated with manual processes. Furthermore\, the tool aligns with evolving technological demands\, enabling teams to adapt to increasing project complexities while maintaining efficient workflows. This solution represents a transformative approach to software development\, combining automation and centralization to reduce manual workloads and optimize developer productivity. The implementation of such an all-in-one automation platform promises to significantly improve efficiency and foster innovation in the industry.
CATEGORIES:VIRTUAL ROOM_12E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:046a15b5507464e3fcac0fa7e98526e6
URL:http://11tict4sd.sched.com/event/046a15b5507464e3fcac0fa7e98526e6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:A Bidirectional Picture Exchange Communication System for Persons with CCN
DESCRIPTION:Authors - Piyali Karmakar\, Pabitra Mitra\, Manjira Sinha Abstract - Communication is fundamental to human connection. Individuals with complex communication needs (CCN)\, such as those with cerebral palsy\, often face significant barriers to speech and language expression. Augmentative and Alternative Communication (AAC) systems address these challenges through the use of graphical symbols. In this work\, we strengthen AAC capabilities by creating specialized datasets that support bidirectional translation between symbolic language and natural English text using NLP techniques (Sym2NL). The datasets are enriched with tense and narrative features to improve contextual accuracy.We also present PictoGen\, a text-to-picture generation module designed to visually represent unfamiliar words or concepts. Together\, these contributions support more natural\, expressive\, and accessible communication.
CATEGORIES:VIRTUAL ROOM_12F
LOCATION:Virtual Room F\, GOA\, India
SEQUENCE:0
UID:ef7cde90564d7b083356d41440e68129
URL:http://11tict4sd.sched.com/event/ef7cde90564d7b083356d41440e68129
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:A Comprehensive Analysis of Fundamental Parameters Regulating Malware Detection Performance
DESCRIPTION:Authors - Pallavi Patil\, Mansing Rathod Abstract - The rapid evolution of malware\, including polymorphic and fileless variants\, has weakened traditional detection methods. This paper looks at sophisticated malware detection frameworks that use deep learning and machine learning to assess important malware features such network anomalies\, opcode sequences\, and API requests. Signature-based techniques are effective at identifying known dangers\, but they are not very effective at thwarting zero-day assaults. While they offer improvements\, alternative strategies including behavior-based\, cloud-based\, and deep learning techniques also have drawbacks. The current detection frameworks unify real-time threat information with two IDS detection approaches to enhance security capabilities. The review extends its analysis to model interpretability and evaluates the computational burden. The assessment of experimental findings helps researchers enhance adaptable malware security through the display of improved detection precision and resilient capabilities versus evolving cyber threats
CATEGORIES:VIRTUAL ROOM_12F
LOCATION:Virtual Room F\, GOA\, India
SEQUENCE:0
UID:4bccbb25c8010ba6dd89c730da3ee340
URL:http://11tict4sd.sched.com/event/4bccbb25c8010ba6dd89c730da3ee340
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:A Comprehensive Review of Hand Sign Recognition Systems
DESCRIPTION:Authors - Pragathi Guduru\, Ramya S\, Anitha H Abstract - Hand sign recognition systems play a crucial role in bridging communication gaps for people with hearing and speech impairments. This review paper explores various methodologies and algorithms employed in previous research on hand sign recognition\, analyzing their performance\, accuracy\, computational efficiency\, and effectiveness in real-world applications. Special emphasis is given to algorithms related to the Discrete Fourier Transform (DFT)\, including the Hebbian Classifier\, Radial Basis Function (RBF) networks\, and Self-Organizing Maps (SOMs)\, which have been utilized for feature extraction\, pattern recognition\, and classification. The study also examines deep learning approaches such as Convolutional Neural Networks comparing their strengths and limitations. Additionally\, the paper highlights how these advances contribute to assistive technologies in healthcare\, aiding doctors during medical procedures\, and improving accessibility for individuals in need. By providing a comparative analysis of these techniques\, this review aims to offer insights into the most effective strategies for enhancing hand sign recognition systems\, paving the way for future research and innovation in the field.. . .
CATEGORIES:VIRTUAL ROOM_12F
LOCATION:Virtual Room F\, GOA\, India
SEQUENCE:0
UID:9c868ce4118b71922d9f35eaa5ddcb79
URL:http://11tict4sd.sched.com/event/9c868ce4118b71922d9f35eaa5ddcb79
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Book Recommendation Platform
DESCRIPTION:Authors - Ajay Talele\, Sujal Tawale\, Tushar Ghorpade\, Nikhil Wagh\, Aryan Sable\, Pallav Vaniya\, Yashashree Mehare\, Parishnav Thokal\, Samruddhi wayal\, Vedant Motale Abstract - Book discovery in the digital age is extremely difficult which is the result of various factors such as users struggling with information overload as well as trying to find the content most closely to their needs. Albeit there are many recommendation systems available in the market\, most of them are just based on general ratings and neither do they use the rich metadata that is available from external book sources nor do they provide the functionality of exploring related work in the best way. Through the use of our proposed book recommendation system\, the constraints that are in place currently can be very easily overcome effectively by the use of more advanced reinforcement machine learning and data integration techniques. What the model would do is to analyze users' reading history and preferences and combining data from bookstores so that an efficient and effective model would be built which would give accurate suggestions of books to the users according to their preference. The Python language is chosen as the basis for development and for the backend\, the Flask framework is used while for finding the most appropriate document for the reader\, TF-IDF vectorization\, and cosine similarity are employed. Moreover\, the linkage of outside APIs not only makes it more in-depth to look at but also increases the system's accuracy. Our approach enables the users to discover new and personalized books in a simple and efficient manner. Our project is a direct contribution to a highly interactive reading journey and it also contributes to increasing love for literature by giving the users the opportunity to find books that will truly engage them.
CATEGORIES:VIRTUAL ROOM_12F
LOCATION:Virtual Room F\, GOA\, India
SEQUENCE:0
UID:2bf342c9d2833ccdf6447c13bb0d32aa
URL:http://11tict4sd.sched.com/event/2bf342c9d2833ccdf6447c13bb0d32aa
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Consumer Reactions to Greenwashing: Awareness\, Attitude and Actions
DESCRIPTION:Authors - Mariya Joseph\, Vinod Kumar K Abstract - Greenwashing\, the practice of brands making false or exaggerated environmental claims\, has become a major concern across industries\, especially in the food\, fashion\, and beauty sectors\, as consumer demand for sustainable and ethically made goods rises. This study examines how consumers react to greenwashing\, with a particular emphasis on their awareness\, attitudes\, and behaviors in the face of false sustainability promises. This study investigates how consumers recognize and interpret greenwashing\, the emotional and cognitive elements affecting their reactions\, and the actions they take in response—such as boycotting brands or looking for more transparent alternatives—by analyzing consumer surveys and existing literature. The paper also delves into the role of brand trust\, social media\, and regulations in shaping consumer reactions to green-washing. Results indicate that although consumers are become more conscious of greenwashing\, there is still a sizable gap in their capacity to recognize false claims. The study emphasizes how crucial third-party certification\, brand openness\, and consumer education are to reducing the damaging effects of greenwashing. In the end\, the study urges consumers and brands to take a more proactive and knowledgeable stance inorder to guarantee that sustainability initiatives are sincere and significant.
CATEGORIES:VIRTUAL ROOM_12F
LOCATION:Virtual Room F\, GOA\, India
SEQUENCE:0
UID:cb575708f9e27a17c166f34ed30a938e
URL:http://11tict4sd.sched.com/event/cb575708f9e27a17c166f34ed30a938e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Multi-Thread File Sharing System Tech Stack: Java Sockets\, Multi threading
DESCRIPTION:Authors - Ajay Talele\, Omkar Shinde\, Shrey Rai\, Shreyash Mutha\, Rohan Shelke\, Sumedh Malode\, Soyam Maykar\, Siddhesh Manjare Abstract - With the increased demand for secure and efficient ways to transfer files\, peer-to-peer systems have developed as good solutions. Seen with client-server systems\, they can be very successful\, however\, typically suffer from bottlenecks and single points of failure\, along with being less efficient for large quantities of data exchange - P2P networks tend to instead successfully allocate workloads over different peer nodes and distributed information along with concurrent and fault-tolerant characteristics. This paper details the design and implementation of a multi-threaded file-sharing system using Java Sockets\, multi-threading concepts\, and other relevant networking ideas. This system allowed multiple users to exchange files in a secure way over a network efficiently\, allowing for concurrency in this system through efficient thread synchronization. Each peer operated independently as both a client and a server\, allowing for collaboration and facilitated file transfers across nodes without a central authority.
CATEGORIES:VIRTUAL ROOM_12F
LOCATION:Virtual Room F\, GOA\, India
SEQUENCE:0
UID:1dc24ee903cc7b7ec7bda06867796040
URL:http://11tict4sd.sched.com/event/1dc24ee903cc7b7ec7bda06867796040
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:REWARDING FATHERS\, PENALIZING MOTHERS- A QUANTITATIVE EVIDENCE ON THE UNEQUAL GAINS OF PARENTS IN INDIAN LABOR MARKET
DESCRIPTION:Authors - Ayushi Sensharma\, Ashish Sharma\, Swati Agrawal Abstract - The gender discrimination is a significant issue in the labor market. “Motherhood Penalty” is one of the important contributors to this issue. This study aims to find the evidence of impact of parenthood on employment to population ratio and mean nominal monthly earnings concerning factors – household structure and number of children under age six. Using interactive multiple linear regression models\, we have derived meaningful conclusions from data collected from the International Labor Organization (ILO). Our findings reveal that there is a significant motherhood penalty in India. Women’s employment probability decreases by 12.4% with one child and up to 19.09% with three or more children. Meanwhile\, men experience a fatherhood bonus\, with employment rates rising by up to 24.79% as they have more children. Wage disparities are also evident—mothers with two or more children earn substantially less than childless women\, whereas the fatherhood wage premium is weaker than in developed economies. Wage disparities are also evident. Mothers with two or more children earn substantially less than childless women\, whereas the fatherhood wage premium is weaker than in developed economies. Through this study\, we also see the probable reasons behind the results observed from the models. Lack of institutional support for working moms\, workplace prejudice\, and deeply rooted gender stereotypes are some of the main reasons attributing to the “Motherhood Penalty”. This disparity is further exacerbated by strict work rules\, poor childcare facilities\, and lax paternity leave regulations. Overall\, the motherhood penalty is a serious phenomenon affecting the lives of many mothers and degrading their standards of living.
CATEGORIES:VIRTUAL ROOM_12F
LOCATION:Virtual Room F\, GOA\, India
SEQUENCE:0
UID:79e80049be2e3a526515b372ef07c6c7
URL:http://11tict4sd.sched.com/event/79e80049be2e3a526515b372ef07c6c7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Seamless Data Orchestration and Analytics pipeline for E-commerce using Azure
DESCRIPTION:Authors - Rupali Parte\, Vaishali Kapure\, Pranav Bankar\, Shrutika Mandharne\, Avadhoot Khandagale Abstract - This research presents a cloud-integrated machine learning (ML) system designed to enhance e-commerce operational efficiency. Leveraging Microsoft Azure’s data services (Azure Data Factory\, Databricks\, ADLS Gen1 and Gen2\, and Power BI)\, along with a Streamlit user interface\, the system processes large-scale transactional data for real-time analytics and decision-making. Four specialized ML models address key challenges: logistics clustering optimizes shipping routes\; sales forecasting improves inventory management\; fraud detection strengthens security\; and order cancellation prediction enhances customer retention. The automated data pipeline ensures efficient ingestion\, transformation\, and storage\, minimizing latency and maximizing data accessibility. The interactive Streamlit interface allows users to select and deploy models\, while Power BI dashboards provide dynamic visualizations. This integrated approach demonstrates the potential of cloud computing and ML to improve logistics\, enhance fraud prevention\, and optimize revenue forecasting.While offering scalability\, the system necessitates robust security measures to address data privacy concerns. The reliance on historical data also necessitates continuous model monitoring and retraining to mitigate potential biases. This research contributes a practical framework for e-commerce businesses seeking to leverage data-driven insights for improved performance.
CATEGORIES:VIRTUAL ROOM_12F
LOCATION:Virtual Room F\, GOA\, India
SEQUENCE:0
UID:1b54b9782519f71ba26cf53183fa722c
URL:http://11tict4sd.sched.com/event/1b54b9782519f71ba26cf53183fa722c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Spatio-Temporal Crime Rate Prediction Using Hybrid Machine Learning Models with Socio-Economic Feature Integration
DESCRIPTION:Authors - Vangala Thanusree\, Rekapalli Bhagya Srilakshmi\, Kanikireddy Harshitha\, Sushama Rani Dutta\, A. Pranathi\, Boga Sudharshini Sree Abstract - Crime is a persistent social issue that impacts public safety and urban development. With the rise in data availability and machine learning techniques\, predictive modeling of crime rates has become a valuable tool for law enforcement and policy planning. We suggest a combination machine learning strategy in this paper that integrates both spatial and temporal data\, alongside social and economic indicators such as decographic and Economic Indicators rate\, literacy\, and income levels\, to enhance crime rate prediction accuracy. We evaluate the effectivness of multiple models\, including RF\, XGBoost\, and a hybrid ensemble of both\, on a real-world dataset comprising crime statistics from multiple Indian states. Our results demonstrate that integrating socio-economic factors significantly improves model performance\, offering deeper insight into crime patterns and enabling data-driven intervention strategies. The proposed model outperforms traditional single-model baselines\, achieving higher accuracy and F1 scores across various crime categories. This approach serves as a robust framework for smart policing and proactive crime prevention in high-risk zones.
CATEGORIES:VIRTUAL ROOM_12F
LOCATION:Virtual Room F\, GOA\, India
SEQUENCE:0
UID:802fd81373c99f0f07ef82fde4b4b005
URL:http://11tict4sd.sched.com/event/802fd81373c99f0f07ef82fde4b4b005
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:State-of-the-art Artificial Intelligence Security Taxonomies
DESCRIPTION:Authors - Varun Mittal\, Madhan Kumar Srinivasan Abstract - Artificial Intelligence (AI) is revolutionizing industries with its capabilities in automating tasks\, enhancing decision-making\, and providing predictive insights. A clear way to frame the current state of AI is to acknowledge that it’s still a technology. To fully leverage its benefits\, whether for business or personal purposes\, one must understand and learn to use it effectively and adapt workflows to align with its strengths and weaknesses. Organizations all around the world are transforming their existing systems and building new systems to leverage the power of artificial intelligence\, but these advancements to enhance their businesses come with significant security challenges. These security threats pose a challenge to both the service providers (developers) as well as the customers. This paper delves into the security issues within AI that organizations and their users can face with AI systems\, categorized under state-of-the-art AI security taxonomies.
CATEGORIES:VIRTUAL ROOM_12F
LOCATION:Virtual Room F\, GOA\, India
SEQUENCE:0
UID:e4f9fd9c9aed148d96c6e95f9523adac
URL:http://11tict4sd.sched.com/event/e4f9fd9c9aed148d96c6e95f9523adac
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T100000Z
DTEND:20260827T120000Z
SUMMARY:Target Recognition Using Synthetic Aperture Radar (SAR) Imagery
DESCRIPTION:Authors - Santhameena S\, Shaunak Agrawal\, Shobith R Prabhu\, Shaurishail M Awanti\, Siddharaj Dhegaskar Abstract - This work focuses on military vehicle detection using Synthetic Aperture Radar (SAR) images from the MSTAR dataset. Challenges such as speckle noise\, limited data size\, and classification accuracy are addressed using preprocessing techniques\, dataset augmentation via Spectral Normalization GANs (SN-GANs)\, and a custom-designed Convolutional Neural Network (CNN). The proposed methodology achieves an accuracy of 98.1%\, showcasing the potential of GAN-augmented SAR datasets in target recognition tasks.
CATEGORIES:VIRTUAL ROOM_12F
LOCATION:Virtual Room F\, GOA\, India
SEQUENCE:0
UID:055349e1af9d1ba8db03254d060df34b
URL:http://11tict4sd.sched.com/event/055349e1af9d1ba8db03254d060df34b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T120000Z
DTEND:20260827T120200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_12A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:8ede3e50e5b91fbb2a251c5ee8f0bb2f
URL:http://11tict4sd.sched.com/event/8ede3e50e5b91fbb2a251c5ee8f0bb2f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T120000Z
DTEND:20260827T120200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_12B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:8317947e3292fa170667782cff94beca
URL:http://11tict4sd.sched.com/event/8317947e3292fa170667782cff94beca
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T120000Z
DTEND:20260827T120200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_12C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:aca5e3b643987079628fec87437f3125
URL:http://11tict4sd.sched.com/event/aca5e3b643987079628fec87437f3125
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T120000Z
DTEND:20260827T120200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_12D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:3bf5f5930e4c2731d55efed608998d9b
URL:http://11tict4sd.sched.com/event/3bf5f5930e4c2731d55efed608998d9b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T120000Z
DTEND:20260827T120200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_12E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:67dbb8367b1baf0ae959859b2e875421
URL:http://11tict4sd.sched.com/event/67dbb8367b1baf0ae959859b2e875421
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T120000Z
DTEND:20260827T120200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_12F
LOCATION:Virtual Room F\, GOA\, India
SEQUENCE:0
UID:a9d9d3fd6d7051bf44fe8b5f19dcf8e5
URL:http://11tict4sd.sched.com/event/a9d9d3fd6d7051bf44fe8b5f19dcf8e5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T120200Z
DTEND:20260827T120500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_12A
LOCATION:Virtual Room A\, GOA\, India
SEQUENCE:0
UID:95a5577acfa8be69a1ace22d4609b37d
URL:http://11tict4sd.sched.com/event/95a5577acfa8be69a1ace22d4609b37d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T120200Z
DTEND:20260827T120500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_12B
LOCATION:Virtual Room B\, GOA\, India
SEQUENCE:0
UID:0f6801af211ccea26c1f816377c2b92c
URL:http://11tict4sd.sched.com/event/0f6801af211ccea26c1f816377c2b92c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T120200Z
DTEND:20260827T120500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_12C
LOCATION:Virtual Room C\, GOA\, India
SEQUENCE:0
UID:9c831d23c37995035b0758af9d0c4171
URL:http://11tict4sd.sched.com/event/9c831d23c37995035b0758af9d0c4171
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T120200Z
DTEND:20260827T120500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_12D
LOCATION:Virtual Room D\, GOA\, India
SEQUENCE:0
UID:0554025e6641979f4436cf00a3a0e4b2
URL:http://11tict4sd.sched.com/event/0554025e6641979f4436cf00a3a0e4b2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T120200Z
DTEND:20260827T120500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_12E
LOCATION:Virtual Room E\, GOA\, India
SEQUENCE:0
UID:4b9c1bd3fcd6bff2615c4da8f32e9e76
URL:http://11tict4sd.sched.com/event/4b9c1bd3fcd6bff2615c4da8f32e9e76
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260827T103510Z
DTSTART:20260827T120200Z
DTEND:20260827T120500Z
SUMMARY:Session Closing and Information To Authors
DESCRIPTION:
CATEGORIES:VIRTUAL ROOM_12F
LOCATION:Virtual Room F\, GOA\, India
SEQUENCE:0
UID:9e14f16157a2c794587c7a2478966b66
URL:http://11tict4sd.sched.com/event/9e14f16157a2c794587c7a2478966b66
END:VEVENT
END:VCALENDAR