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.
