Authors - Uttam Patole, Manish Shrivastava, Archana Ugale, Abhishek Deshmukh Abstract - Poor water use and late illness detection force farmers to struggle, which hurts their crops and squanders resources. This study presents a computerized precision farming system combining machine learning, humidity sensors, and robot automation to improve irrigation and disease control. The technology operates in two phases: first, humidity sensors calculate the ideal watering amount by assessing soil moisture and applying a Random Forest model. Second, disease detection sensors identify agricultural diseases and forecast disease outbreaks using a Gradient Boosting Regressor (GBR), thereby guiding robot pesticide spraying. By integrating machine learning with real-time environmental monitoring, the technology minimizes human interaction while assuring exact irrigation and tailored pesticide administration. This strategy avoids the usage of pesticides and excessive water consumption while enhancing resource efficiency and crop health. The proposed system is scalable and adaptive to varied farming situations, making it a good alternative for modern precision agriculture. By combining predictive analytics and automation, the model enables data-driven decision-making in farming, supporting sustainable agricultural practices and boosting overall output.
Authors - Rahul Dhaigude, Ruby Chanda, Shrikant Ghadge Abstract - The healthcare industry is a complex and vital sector that plays a fundamental role in society, focusing on the well-being and health of individuals. In recent years, the integration of Artificial Intelligence (AI) into hospital logistics has emerged as a transformative force in the healthcare industry. This study investigates the application of AI in hospital logistics to enhance tracking and overall operational efficiency. A questionnaire was designed to gather quantitative and qualitative data on various aspects of hospital logistics, AI integration, challenges, and potential improvements. Qualitative data from open-ended questions and interviews were analyzed thematically to extract key themes and insights. The results show that AI has become an essential tool for optimizing logistics processes, improving patient outcomes, and enhancing the overall patient experience. However, careful planning, implementation, and consideration of data privacy and staff readiness are critical for successful AI adoption in hospital logistic.
Authors - T. Sruthi, Sheshikala Martha Abstract - Extracting meaningful knowledge & actionable comprehensions from complex, multi-dimensional, and heterogeneous biomedical data residues a significant challenge in healthcare. Modern health care systems generate various types of medical data that are often intricate, diverse, and typically unstructured. These large datasets are often difficult to interpret and process. Traditionally, data mining techniques have been employed to extract features from such data, with prediction or clustering models built on top of those features. However, this approach faces numerous challenges, particularly when dealing with complex data and limited domain expertise. Recent advancements in deep learning, however, have introduced new and effective methods for building learning models from these complex datasets. In this paper, we discuss various clinical data types and their relevant features that can serve as inputs to deep learning net-works, contributing to the creation of a more reliable and sustainable healthcare system.
Authors - T. Aruna, Dhanya Kulkarni, Riya Javali, Rakshan Kulkarni, Suneeta V. Budihal, Shamshuddin K Abstract - In 5G networks, dynamic slicing is a major improvement, which makes it possible to allocate specific resources to satisfy the various quality-of-service (QoS) requirements of applications including ultra-reliable low-latency communications, enormous IoT, and enhanced mobile broadband. However, managing these slices in response to dynamic and varied traffic patterns requires real-time flexibility, which poses considerable hurdles. The methods for effective dynamic network slicing are examined in this research, with an emphasis on resource allocation optimization, QoS adherence, resource waste reduction, and network stability. To accommodate upcoming developments, the suggested solutions seek to improve 5G network performance, scalability, and adaptability.
Authors - Ajay Talele, Madhav Jagtap, Radhika Gadewar, Akanksha Katore, Sufiyan Sajan, Deepika Sidral, Yash Shinde, Gaurav Desale, Saburi Nikam, Shubham Landge, Chetan Channa Abstract - Farm trade is a key component of economic viability, but farmers usually struggle with issues of market access restriction, price volatility, and dependency on middlemen. This article introduces a Java-based mobile market that is capable of empowering farmers by creating direct links to customers, thus ensuring transparency and profitability. The new platform incorporates real-time price feeds, demand forecasts, and electronic secure transactions to form a highly efficient and consumer-friendly trading system. Further, the system incorporates IoT-based weather updates and agricultural advisory services to enable farmers to make appropriate decisions. The system utilizes a recommendation engine employing machine learning to maximize prices and predict demand trends. The system is highly secure because transactions are encrypted and involves a strong mechanism for user verification. Fair trade is promoted while minimizing post-harvest losses; the marketplace helps farmers make electronic payments, which enhances financial inclusion and sustainable agriculture. System analyses and case studies attest to its capability to revolutionize agricultural commerce, close the urban-rural digital divide, and enhance farmers’ economic performance.
Tuesday August 25, 2026 9:30am - 11:30am IST Virtual Room BGOA, India
Authors - Hemender Sai, Bipin Sai Bhaskar, P.Saranya Abstract - Tuberculosis continues to be a major global health concern, particularly in remote regions with limited access to healthcare. Early and precise diagnosis is essential to prevent its spread. This project utilizes high-performance computing (HPC) to tackle two major challenges: handling inconsistent medical data and enhancing TB detection. Due to the disproportion between healthy and TB-positive samples, Generative Adversarial Networks (GANs) are employed to create synthetic images, expanding the dataset and improving its diversity. This enriched dataset is then used to train Convolutional Neural Networks, which are highly effective in medical image processing. By leveraging HPC, we accelerate the CNN training process on large-scale, augmented datasets, significantly cutting down computation time while preserving accuracy. This approach enhances TB detection by integrating GAN-based data augmentation with CNN models, ensuring a quicker and more reliable diagnosis.
Authors - Shankar Lingam. M, Raghavendra GS, Sakthi Kamal Nathan Sambasivam Abstract - In the digital era, the integration of ICT policies and e-Governance has emerged as a critical driver for public sector modernization. ICT policy sets the groundwork for effective e-Governance systems by enabling the digital transformation of government functions and services. However, the realization of e-Governance goals faces significant challenges, particularly in fostering inter-governmental cooperation. These challenges arise from varying policy frameworks, technological disparities, and governance structures across different levels of government. In this context, navigating the intricacies of inter-governmental relationships is essential to ensure seamless information exchange and collaborative governance. The evolving digital landscape introduces both opportunities and complexities, particularly in terms of data privacy, cybersecurity, and digital inclusion. This paper presents a comprehensive review of ICT policies and e-Governance frameworks, with a focus on overcoming inter-governmental challenges in the digital era. Our methodology includes a scoping review of key studies and case analyses, such as the work of Obi (2007) on global perspectives of e-Governance [Obi, T. (2007). E-Governance: A Global Perspective on a New Paradigm], Prasad (2012) on India’s ICT policy for digital democracy [Prasad, K. (2012). E-Governance Policy for Modernizing Government through Digital Democracy in India], and Manda (2017) on South Africa’s smart governance approach [Manda, M. I. (2017). Towards "Smart Governance" through a Multidisciplinary Approach to E-Government Integration]. The findings highlight the importance of fostering interoperable and inclusive e-Governance systems to navigate the inter-governmental challenges posed by ICT adoption. Key implications for policy include the need for harmonized digital policies, the development of interoperable infrastructure, and the emphasis on inclusivity to bridge the digital divide. The results underscore the need for collaborative frameworks that enable effective governance across multiple levels of government, thus ensuring that the digital transformation of public services benefits all stakeholders.
Authors - Manisha Mane, Gargee Nitin Rangnekar, Gayatri Kishore Kshirsagar, Adarsh Suresh Nikam Abstract - Monitoring and prediction of space weather have gained tremendous significance with the increasing reliance of the telecommunication and aviation sectors on satellite communication and navigation systems. The two sectors are very vulnerable to space weather occurrences because they can always interfere with the functioning of satellites, high-frequency radio communication, and GPS accuracy. To mitigate these exposures, we recommend that telecommunication and aviation companies utilize a machine learning Space Weather Dashboard to facilitate real-time data visualization and predictive analytics assistance. The proposed architecture employs Azure Workspace for data storage and management, Unreal Engine 5 for the production of high-fidelity graphics, and machine learning models developed in Python. Our approach is based on the utilization of Long Short-Term Memory networks (LSTMs) for historical space data, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). Various types of weather data points like solar X-ray flux, solar wind speed, coronal mass ejections, interplanetary magnetic field measurements, solar energetic particles, ionospheric data, and auroral data are utilized to enhance prediction precision. The dashboard allows for actionable insights to be built for industry professionals and real-time monitoring of critical space weather parameters. Additionally, in consideration of how it can improve their contribution to operation safety, the research here addresses the forecasting power of some of the machine learning architectures used for space weather. Our comparative research affirms that improved forecasting results in effective warning and risk assessment. This is a wonderful benchmark for the aviation and telecommunications industries, improving situation awareness and round-the-clock operating continuity.
Authors - Surya K, Rohit Kumar Abstract - The work deals with conceptual representation of sensitive issues in society as images in media using generative AI. Conceptual representation of sensitive information creates an overall societal impact. Conceptual processing involves understanding the represented information and the way how people respond and interact to the displayed information. Some of the issues like menstrual cycle representation, sex education, domestic violence, sex abuse and mental health awareness are difficult to represent conceptually and respectfully in the media. The goal of this paper is to use generative AI and represent these issues clearly to the society using media. Our work aims at understanding the challenges in representation of these images conceptually as well as providing an overview of the generative AI tools that can be used for implementing the solutions. A case study of a generative AI tool is used to understand the underlying problem of conceptual and respectful image generation for representing it in media. There are many generative AI tools for image generation, and we have chosen the Google Gemini AI as it gives more creative images compared to other tools [13]. In this paper, some of the sensitive issues are taken into consideration for representation in media in a conceptual and respectful manner.
Authors - T. A. Alka, M. Suresh, Aswathy Sreenivasan Abstract - This study aims to explore recent trends in rural entrepreneurship (RE). 439 documents available in the Scopus database are used for the trend analysis by using the R programming Biblioshiny package for bibliometric analysis. The result shows that rural entrepreneurship has been trending since the 1970s, and there is scope for further research. Rural entrepreneurs contribute to the society's upliftment, and development and ultimately result in the growth of the nation. The study is focused on the Scopus database; other databases are not considered. The study identified major themes, such as the rural agriculture entrepreneurs' contribution to developing the rural economy and the need for rural entrepreneurship education among college students in developing countries. Trend topics in this field highlight the rural entrepreneurship contribution to the development of the rural economy, agriculture, rural development, fostering innovation, promoting sustainable development, developing a sound entrepreneurial ecosystem, the role of rural entrepreneurship in the growth of developing countries, etc. The study identified North America and Asia connection, intra-European and transatlantic collaboration, Europe-Middle East, and Asia-North America regional collaboration. The study is relevant even when comparing recently published papers to map the trends; the themes used in research-related articles are always changing. This study gives insights to policymakers to help them with planning, policy formulation, program support, etc., to the development of rural entrepreneurs and also offers future research direction through thematic analysis.
Associate Professor and Head, Department of Computer Science & Engineering (Artificial Intelligence), Vishwakarma Institute of Information Technology, Pune, India
Tuesday August 25, 2026 12:28pm - 12:30pm IST Virtual Room BGOA, India
Authors - V. V. mandhare, Poonam Bhokare, P.S. Vikhe, Chandrakant Kadu Abstract - These days, billions of people use the internet worldwide. Technology for intrusion detection may be novel. a security technology generation that keeps an eye on the system to stave against malicious activity. The IDPS tracks malicious user behavior over time using a neighborhood procedure grid. Because of the rhetorical alternatives, the system suggests a security system during this project called the Intrusion Detection and Protection System at call level, which builds user pro- files to monitor usage activities. The proposed work is evaluated using intrusion detection systems and forensic techniques. The bottom paper includes a review of the literature on the Intrusion Detection System (IDS) and Internal Intrusion Detection System (IIDS). Internal Intrusion Detection System (IIDS), which employs predetermined algorithms or approaches to distinguish unauthorized user activity or attacks over a network, was designed during this research.
Authors - Dev Gandhi, Adarsh Srivastava, Rachit Soni, Daksh Parekh, Lokesh Heda Abstract - Recognizing people in images and videos is the main objective of computer vision-based person identification. The past ten years have seen a great deal of research in human detection. As single-stage algorithms, YOLO is a desirable choice for object detection because it offers faster results than two-stage algorithms. The advantage of this approach is that it provides both a manual for choosing the most effective human detection methods for real-world applications and a thorough analysis of current methods. This research paper's objective is specifically to evaluate and compare the performance of YOLOv3, YOLOv4, and YOLOv5 models on various images to detect human in visual scenes. Additionally, this paper discusses various parameters according to which the model’s efficiency is determined. Our experimental results demonstrate that YOLOv5 achieves higher accuracy, precision, and recall as compared to other YOLO models; the highest accuracy attained by YOLOv5 is 0.94 with F1 score of 0.96.
Authors - Shubhamm Kumaar, Akshat Sharma, Sukrati Chaturvedi Abstract - By connecting Agentic AI with Model Context Protocol Servers (MCPS) it is made possible to work towards autonomy of decision making. work-flow automation. Agent AI which is powered by large language models can provide a live context of information using MCPS. This work mostly focuses on the making of smarter workflows which are regular and pleasing. traditional automation. This integration improves using a decentralized multi agent framework. Makes decisions better and works smoother. Healthcare, manufacturing, smart, and other fields. cities. Tesla's efficient production and Singapore's Smart city are real-life examples. Nonetheless, various obstacles regarding scalability, ethics, computation, and more can hinder its application. However, challenges like scalability, ethics, and computational demands remain. This review synthesizes current research, applications, and future directions, underscoring the promise of this technology for innovative automation solutions.
Authors - V. V. mandhare, Hemlata Mali, P.S. Vikhe, M. R. Bendre, M. R. Parkhe Abstract - This paper introduces a smart and efficient Hall Ticket Generation System that integrates QR code technology to improve the process of generating and managing hall tickets for academic examinations and events. Built using Java and MySQL, the system is designed to simplify administrative work, enhance security, and promote eco-friendly practices by eliminating the need for paper-based hall tickets. The core functionality of the system is its ability to generate dynamic QR codes for each hall ticket, which can be quickly scanned using an Android- based scanner application. This allows invigilators to instantly verify the identity and credentials of candidates, reducing the chances of fraudulent entries or unauthorized access to the examination hall. By transitioning tothisdigitalmodel,institutionsbenefitfromreducedpaperusage,lower administrative costs, and improved operational efficiency. The system is not only secure but also user-friendly, making it easier for both staff and students to manage examination logistics. In Automatic Question Paper Generator Module, which addresses the common challenges associated with manually preparing exam questions. To overcome these limitations, the system uses a key word-based randomization algorithm to create question papers swiftly and securely. This method ensures that each set of questions is unique, well-distributed across topics, and free from duplication. The system is capable of storing and managing multiple question paper sets, making it easier to conduct exams across different classes while ensuring comprehensive curriculum coverage. Overall, this project offers a complete solution that not only simplifies exam management but also enhances the quality and integrity of academic assessments through automation and intelligent design.
Authors - Madhu Shukla, Vipul Ladva, Simrin Fathima Syed, Neel H. Dholakia Abstract - Cardiovascular disease continues to be one of the leading causes of death globally, demonstrating the critical role of efficient and reliable prediction models. Here in this study a dataset that is integrated from five heart disease datasets originated from publicly available sources such as UCI for Heart Attack risk prediction and analysis with 1,888 instances are used. Fourteen primary factors that include age,abnormality of cholesterol level, type of chest pain, as well as exercise related parameters from demographic and clinical dimensions were investigated in order to find the association with heart disease. Significant trends were found using data visualization to show high heart risks with certain chest pain types and high maximum heart rate. A correlation matrix illustrates important inter-feature relationships and sheds new light on the predictabilitheckability of the features. This study highlights the possibility to exploit demographic and clinical information for early detection of high risk individuals and in the future, to allow medical interventions and improve health state.
Authors - Bhukya Rakesh, Dasari Yaswanth, Kumari Nidhi Lal Abstract - Mobile Ad Hoc Networks (MANETs) are dynamic, decentralized networks that require efficient routing mechanisms to ensure reliable communication. Traditional routing protocols struggle with issues such as high node mobility, energy constraints, and unpredictable topology changes. This research explores the integration of artificial intelligence, specifically neural networks, to enhance routing efficiency in MANETs. Our approach leverages deep learning models to predict optimal routes by analyzing network parameters such as node density, mobility patterns, and link stability. The proposed AI-driven routing mechanism dynamically adapts to network variations, improving packet delivery ratio, reducing latency, and optimizing energy consumption. Comparative evaluations against conventional routing protocols, such as AODV and DSR, demonstrate significant improvements in network performance. The results highlight the potential of neural networks in revolutionizing adaptive routing for MANETs, paving the way for more intelligent and resilient communication systems.
Authors - Ayush Tiwari, Ayushi Tomar, Prabhjot Kaur Abstract - Online banking fraud constitutes illegal access to accounts for the payment transfer purposes. There are several reasons for difficulty in the detection of such crimes-from the imbalance of data to the fraudsters' techniques that are ever-changing. The set of tools used for this purpose are machine learning, economic optimization, and risk assessment. By combining these techniques, a maximum reduction in the losses due to fraud and false positives will be achieved. The machine learning models, when validated against real datasets, were able to reduce losses by 52%, allowing for just 0.4% false positives. The improvement in behavior analysis for fraud detection is conducted through transaction clustering, sliding window aggregation, and adaptive classifiers. The algorithms such as KNN, SVM, Logistic Regression, Local Outlier Factor, and Isolation Forest are used to predict fraud in credit card transactions so that it is accurately detected, false alarms being at a minimum, and customers are availed against unauthorized charging.
Tuesday August 25, 2026 12:30pm - 2:30pm IST Virtual Room BGOA, India
Authors - Nisarg Chaudhari, Urva Dave, Zeel Patel, Manasvi Vachhani, Dweepna Garg, Bhavika Patel, Kashyap Patel, Parth Goel Abstract - Fertilizer recommendation plays a crucial role in optimizing crop yield while minimizing resource wastage. The integration of machine learning techniques enables precise fertilizer prediction based on soil and environmental conditions, leading to improved agricultural productivity. However, traditional methods often result in overuse or underuse of fertilizers, negatively impacting soil health and crop growth.This study employs various machine learning algorithms, including RandomForest, XGBoost, LightGBM, HistGradientBoosting, CatBoost, and Neural Networks, to classify and recommend fertilizers based on soil parameters. The models were trained on a synthetic fertilizer dataset containing diverse soil compositions and fertilizer requirements.Experimental results indicate that Neural Networks outperform tree-based models, achieving the highest testing accuracy of 84.10%, demonstrating strong generalization capabilities. Accuracy and loss trends over epochs confirm stable learning, while a confusion matrix reveals minimal misclassifications. This study highlights the effectiveness of deep learning in optimizing fertilizer recommendations, contributing to more sustainable and efficient agricultural practices.
Authors - Nirav Narayan, Martin Parmar, Parth Shah, Mrugendra Rahevar Abstract - Water quality is a critical concern for human health, ecosystems, and sustainable resource management. Traditional water quality monitoring methods are expensive, time-consuming, and often lack real-time data availability. This research proposes a Secure Water Quality Monitoring framework integrating IoT, LoRa WAN, and secure data transmission to enable real-time, cost-effective monitoring in remote and urban areas. The system employs low-power LoRa technology for long-range communication, ensuring reliable data transmission even in connectivity-challenged regions. ESP32 microcontrollers process sensor data, measuring key parameters such as pH, turbidity, TDS, EC and DO. Security is ensured using AES-128-bit encryption and SHA-256 hashing, safeguarding environmental data against tampering. The proposed framework addresses challenges in conventional monitoring, such as high costs and limited scalability, by offering a low-cost, energy-efficient, and scalable approach. This study demonstrates the potential of IoT-driven smart monitoring framework to enhance water resource management and environmental sustainability, paving the way for future advancements in sensor technology, energy efficiency, and data security.
Authors - Amrithesh TV, Sajin John Shaji, Sangeeth Gopinath Abstract - Family businesses play significantly in the world economy; yet, the majority can somehow manage to survive through leadership change from impacts of family relationships. Unlike some business corporations with formalized transitions, family companies dominantly depend on family relationships and informal decision-making, hence bringing about business instability as well as conflicts. This research examines just how internal family systems indeed influence successor choice, and shape leadership transition on business resilience. Through in-depth qualitative research with strict thematic analysis of family firm owner interviews, the research discovers a number of important succession determinants, such as traditional heirarchy, mentering, and resistance to modernization of the modern kind. The research does uncover that in fact successor selection is usually highly influenced by family influence, in contrast to planned planning, and therefore jeopardizing some degree of business stability. This paper suggests the adoption of comprehensive succession planning and participative decision-making. Systematic leadership development programs, coupled with open communication, will ensure successful leadership succession and long-term business success.
Associate Professor and Head, Department of Computer Science & Engineering (Artificial Intelligence), Vishwakarma Institute of Information Technology, Pune, India
Tuesday August 25, 2026 2:30pm - 2:32pm IST Virtual Room BGOA, India
Authors - Ketki Kshirsagar, Samarth Chikane, Dev Desai, Avdhut Hande, Aniruddha Deobhankar, Arun Govind, Shubham Derkar, Arjun Gupta Abstract - The one of the most important sensory organ of our body is an eye, it is the reason why people can enjoy its beautiful surroundings. What if we would not have this important organ?, the answer is quite obvious, it would be very challenging, he would be isolated. There are millions of people across the globe living such miserable lives. So to overcome this challenge we come up with an idea to build an assistive aid for blind needy. The project is AI-Powered Smart glasses for visually impaired. This basically notifies the blind person about the obstacle in front of him/her. This tool has the ability to tell the user the distance of the obstacle and what particularly the obstacle is like the tree is 60 cm away. This paper includes the brief information about how this glasses work. The glasses are full advanced technical tools like ultrasonic sensors, IR sensors, ESP32 microcontroller, ESP32 cam module, earpiece for user’s enhanced listening. The software we used is YOLOv5 for object recognition, Tensor Flow Lite, text-to-speech software, MATLAB, etc.
Authors - Sparsh Kumar, Satyadhyan Chickerur, Prashanth Kumar Malkiwodeyar Abstract - Accurately capturing unsteady flow phenomena and complex fluid dynamics is essential for understanding and predicting arterial blood flow behavior. This study leverages computational fluid dynamics (CFD) and machine learning (ML) to detect regions of elevated shear stress in the aorta, focusing on the dynamic responses of arteries under varying hemodynamic conditions. The geometric model of the human aorta was sourced from the Vascular Database, which is supported by SimVascular and includes all necessary boundary conditions. Simulations were performed using the Navier-Stokes equations within SimVascular to generate vtk files containing velocity and pressure data. Since these files could not be directly used for ML training, additional postprocessing was conducted in ParaView. By applying specific functions, we extracted key metrics such as pressure, velocity, wall shear stress, and other parameters at multiple spatial coordinates. This resulted in a CSV dataset comprising 23,777 points with corresponding attributes for further analysis of high wall shear stress regions. A Random Forest classifier was trained on this dataset to predict regions of high wall shear stress of the aorta by analyzing attributes like pressure, velocity, and wall shear stress, providing precise coordinate-based predictions of areas with abnormal hemodynamic stress. In our analysis, regions of elevated shear stress were detected at 1,288 points, representing 5.42% of the total dataset. By integrating CFD with ML, we successfully identified regions of high wall shear stress in the aorta, enhancing clinical diagnostic accuracy and offering a data-driven alternative to traditional cardiovascular diagnostic techniques. This study underscores the value of combining CFD and ML to reduce reliance on traditional cardiovascular tests, streamlining diagnosis and therapeutic planning while reducing costs and complexity.
Authors - Vanishree Pabalkar, Ruby Chanda, Yash Yadav, Megha Patil Abstract - Sentiment analysis, is termed as opinion mining, is a significant tool to assess customer’s opinions and expressions by analyzing textual data from various digital platforms. In marketing, sentiment analysis provides invaluable insights into customer feedback, helping companies to customize the products and services, and marketing strategies to meet consumer needs. This paper explores the application of sentiment analysis specifically through a case study of the Samsung Galaxy S24 Ultra. The study involves collecting data from various sources, like the news forums, and news articles, and employing natural language processing (NLP) techniques to classify and analyze sentiments into positive, negative, or neutral categories. The outcome conveys the essence of sentiment analysis in identifying consumer preferences and issues, such as high prices or software problems, which directly impact marketing strategies and product development. By using sentiment analysis, companies like Samsung can make data-driven decisions to retain satisfied customers and ensure brand loyalty. This study also highlights the issues and constraints of current sentiment analysis methods, that include the need for improved accuracy in sentiment classification and the handling of complex linguistic nuances. Future research directions include enhancing ML tools to classify the sentiment detection and exploring the use of sentiment analysis in real-time applications to provide instant feedback for marketers. The implications of sentiment analysis extend beyond marketing into areas like public relations, customer service, and product innovation, making it an indispensable tool in today's digital age. As digital communication continues to grow, the role of sentiment analysis is expected to expand, offering inputs into consumer behavior and enabling more personalized, effective strategies.
Authors - Kumar Rahul, Ramjee Prasad Gupta, Neeraj Arora, Surender Kumar Kulshrestha Abstract - Artificial Neural Network (ANN) plays an important role in shaping modern social media platforms. The networks assist in presenting content recommendations, analyzing user engagement, detecting sentiment, and automating moderation processes. Through the processing of user data, ANNs enhance personalization, improve advertising strategies, and detect harmful content, ensuring a seamless and engaging user experience. This paper explores the diverse applications of ANNs in social media, highlighting their impact on user interaction, content curation, and platform security. This review highlights key advancements, challenges, and open issues related to data variations, and evaluation criteria in social media analysis. Additionally, a structured framework is proposed for future studies focused on leveraging ANNs to gain social media insights.
Authors - Tejas Nadagadalli, Vishwanath Baligar Abstract - The assistance of deep learning for medical image diagnosis is often crucial in the timely treatment of patients suffering from diseases like brain tumors and lung cancer. This paper evaluates the performance of VGG 16 and Efficient-Net deep learning models for the classification of MRI and CT scans of the brain and lungs. A new low complex algorithm referred to as PDBS was promoted to increase the efficiency of model optimizations. The lesion detection models were assessed for accuracy, training time, and level of generalization attained. Experimental results highlight that the PDBS model consistently outperformed traditional CNN architectures, achieving higher classification accuracy with 97% testing accuracy for brain MRI scans and 96.5% for lung CT scans while maintaining efficiency. These results above illustrate the depth of the contribution offered by deep learning methods to the enhancement of medical image analysis to support clinical workflow.
Authors - Sweety Singhal, Uma Sharma Abstract - Healthcare domains deal with massive amounts of sensitive data, such as patient health records, diagnostic results, and clinical notes, which must be secured under privacy regulations (like HIPAA). Traditional security technologies cover many problems but struggle against more advanced iterative threats. Implementing deep learning algorithms has resulted in the creation of texts of advanced encryption techniques, which can discover obvious patterns and improve the security of health systems. When combined with Natural Language Processing (NLP), these algorithms can also anonymize and de-identify patient information, allowing healthcare providers to share and collaborate on data without violating patient confidentiality. This will prove valuable for further medical research and improving the quality of telemedicine, where better information transfer is key for better treatment results. Key Approaches: Data encryption, differential privacy, tokenization, and access control are all essential methods of protecting healthcare data, and NLP plays a vital role in ensuring that sensitive health information is handled securely.
Authors - Madhuri Badole, Rohit Rathod, Pavan Bachhav, Devanshu Parulekar, Harsh Kulkarni Abstract - Brain tumor diagnosis is a critical area of medical research as it directly impacts patient survival and treatment. Early diagnosis is essential for improving prognosis and facilitating therapy. Magnetic Resonance Imaging (MRI) is particularly reliable for detecting brain tumors due to its superior image quality and contrast. This study provides a comprehensive review of recent advancements in brain tumor diagnosis methods. Evolutionary algorithms based on natural selection principles offer optimal strategies for image processing, segmentation, feature extraction, and classification in tumor diagnosis. Random Forest algorithms are used in this study to classify MRI images, distinguishing between normal brain tissues and malignancies. Additionally, the study explores hybrid models that integrate evolutionary algorithms with neural networks (CNN) to enhance accuracy. This research offers insights into the benefits and limitations of these approaches, paving the way for further neuropsychology research.
Authors - Pritee Parwekar, Chinmayee Ambarish Parwekar, Kshitij Bhushan Abstract - As growing dependence on wind and solar energy brings about new challenges in both maximizing energy generation and ensuring the stability of grids due to the intermittent nature by virtue of dependence on variable atmospheric conditions, optimization of hybrid solar-wind plant output is achieved through the present study by means of a DE algorithm for maximum energy yield enhancement and grid robustness. The approach includes a simulation of a small hybrid energy system, which consists of a 10 m² solar panel and three wind turbines, each with a capacity of 2 kW, over a period of 24 hours. Using real meteorological data in the form of solar irradiance and wind speed profiles, the differential evolution (DE) algorithm minimizes two most important parameters: tilt angle of solar panels from 0 to 90 degrees and the spacing of the wind turbines, variable from 5 to 50 meters. The objective function is to maximize energy output in total and minimize hour-by-hour power oscillations, a surrogate for grid stability. The results indicate that the optimized configuration with tilt angle 15.23° and turbine spacing of 35.67 m produces a total of 135.82 kWh, up 12.7% from the baseline (120.45 kWh at tilt angle 30° and 10 m spacing). Moreover, the stability penalty, expressed as the sum of hourly output differences, reduces from 48.73 to 42.19, reflecting better grid compatibility. These results underscore the potential of DE as a successful method for the optimization of renewable energy, with a real application to harmonize energy production and stability in hybrid systems. This research supports more efficient and trustworthy renewable grids and contributes to sustainable energy infrastructures transition
Authors - Amogh M, Satyadhyan Chickerur, Prashanth Kumar Malkiwodeyar Abstract - Cardiac arrhythmias pose a significant challenge in clinical diagnostics, necessitating accurate and efficient detection methods. This study explores the classification of arrhythmias using advanced machine learning models, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Physics-Informed Neural Networks (PINNs). A dataset of 16,000 simulated ECG signals, generated using SimVascular, provided the foundation for training and evaluation. CNNs achieved high accuracy in spatial feature extraction, while LSTMs excelled in capturing temporal dependencies in sequential ECG data. PINNs emerged as the most robust model, achieving a training accuracy of 97.8% and a testing accuracy of 97.2%, leveraging domain-specific constraints from the FitzHugh-Nagumo equations. The results highlight the complementary strengths of these models, with PINNs offering superior interpretability and physiological consistency. Future work will focus on integrating multi-modal data and developing real-time systems to advance arrhythmia diagnostics and improve cardiac care outcomes.
Tuesday August 25, 2026 3:30pm - 5:30pm IST Virtual Room BGOA, India
Authors - Siddhant Sawant, Sajal Nampalliwar, Ansh Masand, Variza Negi Abstract - Financial literacy is a crucial yet often overlooked skill in formal education. Our research presents an AI-driven framework that democratises financial education through four interactive modules: budgeting tools, a newsletter, a virtual market simulator and a structured course. At the core lies an Intelligent Learning Agent. A chatbot first gauges prior knowledge, then steers each learner to the appropriate level. Machine-learning, NLP and reinforcement-learning techniques continually adapt the pathway in response to engagement signals. We detail the conceptual framework and system architecture, illustrating how AI delivers scalable, personalised financial learning.
