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.
Tuesday August 25, 2026 9:30am - 11:30am IST Virtual Room AGOA, India
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.
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.
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.
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.
Tuesday August 25, 2026 9:30am - 11:30am IST Virtual Room AGOA, India
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.
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.
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%.
Tuesday August 25, 2026 9:30am - 11:30am IST Virtual Room AGOA, India
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.
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.
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.
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.
Tuesday August 25, 2026 12:30pm - 2:30pm IST Virtual Room AGOA, India
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
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.
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.
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.
Wednesday August 26, 2026 12:30pm - 2:30pm IST Virtual Room AGOA, India
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.
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.
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.
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.
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.
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%.
Wednesday August 26, 2026 12:30pm - 2:30pm IST Virtual Room AGOA, India
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.
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
Wednesday August 26, 2026 3:30pm - 5:30pm IST Virtual Room AGOA, India
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Thursday August 27, 2026 12:30pm - 2:30pm IST Virtual Room AGOA, India
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.
Thursday August 27, 2026 12:30pm - 2:30pm IST Virtual Room AGOA, India
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.
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.
Thursday August 27, 2026 12:30pm - 2:30pm IST Virtual Room AGOA, India
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
