Authors - Saraswati Patil, Mustafa Limdiyawala, M.S. Dawngliana Fanai, Meghaj Kharwadkar, Shivshankar Mahajan Abstract - Natural disasters such as earthquakes, floods, and tsunamis pose severe threats to human lives, infrastructure, and economies. Effective prediction and response strategies are vital for minimizing their impact. This paper introduces an AI-driven Disaster Prediction and Relief Dashboard, an integrated platform leveraging machine learning and geospatial mapping to forecast natural disasters and optimize relief operations. Using Random Forest and Gradient Boosting algorithms trained on historical data, the system predicts the likelihood, magnitude, and severity of disasters. Geospatial visualization highlights high-risk zones and delivers real-time situational awareness for authorities. Additionally, the platform streamlines relief management by dynamically allocating resources based on predicted disaster severity and location. By integrating predictive analytics with operational planning, the system enhances preparedness and responsiveness, contributing to more resilient disaster management.
Authors - N.N.S.S.S. Adithya, P. Vanishree Sah, B. Jyothirmai, Nidhi Mishra, D. Indira Abstract - Early prediction of lung cancer is crucial for reducing the death rate. Artificial intelligence, particularly deep learning, is employed to analyze CT scan images for more accurate automated prediction of types of lung cancer. This process of prediction is called classification. Lung cancer classification can be done with pre-networks such as VGG16 and ResNet50.But the main drawback of these techniques is that cancer cannot be detected on Histopathology images (i.e. image of tissues). As VGG16 and ResNet50 are designed for more general usage, they are not suitable for analyzing Histopathology image. This study involves the development of a customized neural network model which can solve the problem of analyzing Histopathology images. This CNN model can help us detect lung cancer at a very early state in lung tissues. Detecting lung cancer at a very early stage can help doctors to cure the patient and save the life of a patient.
Authors - Sneha S. Biradar, Suvarna Kanakaraddi, Neha Tarannum Pendari Abstract - This personalized music therapy framework for children with Autism Spectrum Disorder (ASD) involves data collection (AQ scores, age, gender, and demographics), severity identification, and customized music creation. Using a publicly available dataset, children were classified by severity, allowing the design of therapeutic music with varying duration, tempo, and complexity. Compositions were set to 15 minutes for low severity, 30 minutes for moderate, and 50-60 minutes for high severity. Preliminary results suggest this approach boosts engagement and may improve cognitive, emotional, and social outcomes, demonstrating the potential of combining advanced analytics with personalized music therapy for ASD.
Authors - Ruby S Chanda, Vanishree Pabalkar, Priya Pradipkumar Tiwary Abstract - Companies are increasingly using data analytics and AI to personalize interactions and provide tailored recommendations. Additionally, there is a growing focus on emotional intelligence and understanding customers' needs beyond their transactional behavior. Some real-world examples are – Netflix uses AI to analyze viewing history and preferences, recommending personalized content, Spotify leverages data on listening habits to create tailored playlists and discover new music. The need for AI models in NPS and CX enhancement arises from the increasing complexity of customer interactions and the vast amount of data generated. AI can help in predicting consumer’s future behavior by analysing their demographic and purchase data and identifying patterns. This empowers businesses to create more tailored and meaningful customer experiences, resulting in greater satisfaction and loyalty. This project aims to develop a Generative AI-based Net Promoter Score (NPS) predictor to enhance Customer Experience (CX) in the retail industry. By leveraging advanced AI techniques like VAEs and GANs, the model will be able to analyze vast datasets of consumer behavior and demographics, providing more accurate and personalized NPS predictions.
Authors - Shriraj A. Patil, Chudaman D. Sukte, Jayesh R. Patil, Chinmay R. Mhaske, Mandar Dakhorkar, Manohar K. Kodmelwar Abstract - This paper presents a novel IoT-based fruit-picking system that integrates Reinforcement Learning (RL), Transfer Learning (TL), and Neuroevolution to address the inefficiencies of current robotic harvesting methods. As demand for efficient agricultural practices rises, traditional fruit-picking systems face significant challenges, including operational inefficiencies, fruit damage, and limited adaptability to diverse environments. Our proposed solution leverages RL to optimize picking strategies through adaptive learning, enhancing the robotic arm's efficiency over time. TL is employed to improve fruit recognition capabilities, utilizing pre-trained models for accurate ripeness detection, even with limited training data for specific fruit varieties. Additionally, Neuroevolution evolves control strategies for the robotic arm, enabling it to adapt to dynamic harvesting conditions. Comprehensive simulations demonstrate significant improvements in picking accuracy, efficiency, and adaptability compared to existing methods. The findings highlight the potential of integrating these AI models within IoT frameworks to revolutionize fruit harvesting, ultimately contributing to smarter farming practices and enhanced agricultural productivity. This research underscores the interdisciplinary nature of modern agriculture, combining advancements in AI, robotics, and IoT technologies to provide innovative solutions for the challenges facing the agricultural sector today.
Authors - Gatla Vijayendher, K Sai Karthikeya, E Jayanth Madhav, Tadepalli Satya Kiranmai Abstract - The increasing number of tumor cases has caused an alarming situation in the health care space. The tumor detection and diagnosis is a very computationally heavy and requires multiple medical imaging devices such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). It is very vital in order for early detection of tumor which can be done by precisely measuring their size which can improve a treatment by a huge factor in the patients. Existing diagnostic approaches often face challenges due to the diverse appearances of tumors and the constraints of current models. This study examines the different cutting-edge deep learning methods, with a focus on utilizing Generative Adversarial Networks (GANs) to enhance tumor identification across various categories, types, and imaging techniques. We also investigate the role of data augmentation strategies in enhancing the model performance. Furthermore, we examine the integration of Convolution Neural Networks (CNNs) to achieve accurate and robust results while preserving the data privacy. The goal of this study is to understand the detailed current scenario of the early detection and ways about the different techniques in various kinds of tumors which are present in the human body.
Authors - Amol More, Sanjeev Kumar, Sandeep Kore Abstract - This study carefully checks how well three different types of heat sinks move heat under controlled laboratory conditions. The main focus is on Copper Pin Fin Heat Sinks, Aluminum Phase Change Material (PCM) Pocketed Heat Sinks, and Aluminum Plate Fin Heat Sinks (PFHS). These were put through a wind tunnel test that simulated forced convection. This gave a thorough comparison of how well they kept heat in. To make the experiments work, heat was applied to the bottom of the heat sinks with 10W, 20W, and 30W of power, to represent various thermal loads. The speed of the air was changed from 1 m/s to 5 m/s to see how speed affected how well heat was removed. It was also improved by making changes like adding a copper plate to the aluminum fins and making holes in both the shield connection and the pin fins which were used in the experiment. By changing the surface area and turbulence, these changes are meant to see if they can improve the rate of heat transfer. The study's results should give us useful information about how to build heat sinks so they work best in a wide range of situations, from home electronics to industrial systems. It is expected that the results will help make thermal management solutions that work better, which will improve the performance and life of heat-sensitive parts.
Wednesday August 26, 2026 9:30am - 11:30am IST Virtual Room BGOA, India
Authors - Ayinampudi Siva Rama Raju, Suneetha Dwarapu, Gaddiboyina Sai Jahnavi, Tummalapalli Sai Sri Varshit, Kalimahanthi Sai Nikhil Kartikeya Abstract - Sentiment analysis, a key task in natural language processing (NLP), identifies emotional tone in text. This study compares two sentiment classification approaches: a lexicon-based method using VADER (Valence Aware Dictionary and sEntiment Reasoner) and a transformer-based deep learning method with RoBERTa (Robustly Optimized BERT Pretraining Approach). Using the Amazon Fine Food Reviews dataset of 568,454 customer reviews, the analysis categorizes sentiments as positive or negative. Preprocessing steps, including text normalization and handling missing values, ensure data reliability. VADER efficiently processes short, informal texts using a predefined lexicon but struggles with complex linguistic structures and contextual subtleties. RoBERTa leverages transformer-based architectures to capture intricate word relationships, enabling superior accuracy and nuanced sentiment detection in contextually rich texts. A comparative evaluation demonstrates that RoBERTa outperforms VADER by a significant margin, underscoring the strengths of deep learning for detailed sentiment analysis. These findings emphasize the trade-offs between speed and contextual depth in sentiment analysis models and provide valuable insights for customer feedback interpretation, opinion mining, and broader NLP research.
Wednesday August 26, 2026 9:30am - 11:30am IST Virtual Room BGOA, India
Authors - Ruby S Chanda, Rahul Dhaigude Abstract - In order to predict stock values, this study investigates the combination of machine learning with sentiment analysis. The quick spread of news and the growth of social media sites like Twitter have made public opinion a bigger factor in financial markets. This study extracts market sentiment from tweets and news stories using Natural Language Processing (NLP) techniques, namely the VADER sentiment analysis tool, which has been tailored with financial lexicons. To forecast stock price fluctuations for firms like Amazon and Tesla, sentiment data is included into a Generative Adversarial Network (GAN) model together with technical indicators like moving averages and Bollinger Bands. The model is evaluated using performance metrics like Root Mean Square Error (RMSE), demonstrating its ability to capture price trends and market sentiment dynamics. While results highlight the potential of GANs for real-world applications in financial trading, the study also acknowledges limitations such as data quality and model uncertainty. Future directions include improving sentiment algorithms and incorporating additional market factors..
Authors - Aanjaneya K, Anjana P, S Sameera, Ajith Sundaram Abstract - The environment-related worries of Generation Z together with sustainability-based activities established them as leaders who champion green consumerism. Digital natives of this generation opt to make buying choices on social media platforms according to their established reputation. The platforms of Instagram together with YouTube and LinkedIn function as essential spaces for spreading sustainability content which affects how people behave regarding their purchasing choices. Social media promotes consumer engagement through direct communication and enables fast information flow about green events so it stands as a key factor in developing positive green purchasing attitudes. Current research analyzes the impact of social media information sharing on Gen Z sustainable buying motivation through an investigation of green-value and subjective-norms as intervening variables. This research depends on the Stimulus-Organism-Response (SOR) model to see how social media leads consumers toward buying green products. This research study addresses the mental factors behind environmentally conscious buying to provide concrete recommendations for business organizations and government institutions. Companies can use the research results as a foundation to create better sustainability-oriented marketing plans that aim at Gen Z consumers.
Wednesday August 26, 2026 9:30am - 11:30am IST Virtual Room BGOA, India
