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
Authors - Jishnu Prakash k, Rithish S V, Liz Maria Liyons, Vijval Srinivasan, Deepthi L R Abstract - The rapid spread of misinformation and rumors on social media platforms, particularly Twitter, poses significant risks to public perception and decision-making. This study presents a comprehensive approach to analyzing and mitigating rumor propagation by identifying key influencers and optimizing propagation time within online communities. Our dataset consists of over 800,000 nodes with interactions categorized as retweets, mentions, and replies, each assigned an influence score to quantify user impact. Using the Infomap algorithm, we initially detected 13,500 communities and filtered them to retain 67 influential clusters with a higher number of nodes and stronger influence scores. To analyze the spread of rumors, we developed an algorithm that tracks propagation within these communities, leveraging top influencers as initial spreaders and computing the average propagation time. Furthermore, we introduced a node deletion strategy to iteratively remove high-impact influencers, reducing the overall propagation time and limiting misinformation spread. Finally, we adjusted the propagation times by normalizing them with the earliest influencer timestamps to ensure precise measurement. Our findings highlight that targeted removal of key spreaders significantly disrupts rumor diffusion, providing insights into optimizing influence-based network interventions for misinformation control.
Authors - Yukta, Nishant Yadav, Sukrati Chaturvedi Abstract - The rapid expansion of social media platforms and online news consumption has led to an increased spread of fake news, posing significant challenges to society by influencing public decision- making and causing severe consequences in domains such as politics and healthcare. Traditional methods for identifying fake news are often too slow to combat its swift dissemination. Therefore, identifying and addressing misinformation is crucial for maintaining the accuracy and trustworthiness of information disseminated on social media. Natural language processing (NLP) and artificial intelligence (AI) play a vital role in this endeavor by facilitating the effective examination of extensive datasets to uncover patterns that are suggestive of misinformation. Machine learning models, particularly transformer-based architectures, enhance fake news detection by improving accuracy and interpretability. The integration of Explainable AI (XAI) methods further enhances trasparency and trust in these models
Authors - Praful Sambhare, Nitin Choudhary, Abhay Rahangdale, Atharva Rane, Sahil Raina Abstract - The integration of artificial intelligence (AI) within the fashion industry is becoming increasingly prevalent, with the aim of delivering a shopping experience that is personalized, seamless, and engaging. As an example of this trend, GlamBot is an AI- driven fashion assistant, a sophisticated fashion assistant that employs a range of advanced AI methodologies. These methodologies include Natural Language Processing (NLP), image- based similarity searches, and voice recognition technologies, all of which together transform the interactions that users have with fashion platforms. The functionality of GlamBot significantly improves the user experience by providing tailored fashion recommendations. This is achieved through the analysis of text inputs, the execution of visual similarity searches, and the processing of voice commands. Consequently, fashion discovery is rendered more intuitive and accessible for users, thereby facilitating a more engaging interaction with the fashion domain. . By analyzing and understanding user preferences, GlamBot builds personalized profiles that evolve over time to deliver increasingly accurate recommendations . GlamBot’s image-based search feature allows users to upload pictures of fashion items they like. Using advanced ResNet50 image recognition models, GlamBot analyzes these images and provides visually similar product recommendations, bridging the gap between users’ visual preferences and available fashion products .
Authors - Merlin Priya Jacob, Sukhada Aloni, Hetal Rawat, Shrishti Sakore, Shreya Naik, Shubham Pardhi Abstract - Using Ethereum, IPFS, and the MERN stack, this project offers a transparent and safe blockchain-based examination system. The solution guarantees tamper-proof question paper management by utilising IPFS (via Pinata) for decentralised storage and Ethereum smart contracts. Instructors submit tests to IPFS, ensuring data integrity by storing their cryptographic hashes on the Ethereum blockchain. While MetaMask allows for safe user interaction and authentication, Hardhat makes it easier to design and deploy smart contracts on a testnet. A strong online application is powered by the MERN stack, with Node.js managing database functions and instructor authentication. Exam papers are safely retrieved by authorised superintendents, guaranteeing regulated access. Academic assessment integrity is improved by this decentralised method, which reduces the possibility of paper leaks and unauthorised changes while offering a scalable and effective substitute for conventional test systems.
Authors - Sarbjit Kaur, Jasmeen Gill Abstract - Wireless sensor networks (WSNs) play a vital role in sensing environmental conditions in far-flung areas. However, their energy consumption remains a critical issue, affecting the network's lifetime and coverage area. Clustering has emerged as an efficient strategy to prolong sensor network lifespan, and the Fruit Fly Algorithm (FFA) and Ant Colony Optimization (ACO) are promising techniques for cluster formation and efficient path establishment, respectively. In this study, we propose an innovative approach that combines FFA for cluster formation and ACO for path establishment. This novel algorithm is implemented in MATLAB and evaluated in both homogeneous and heterogeneous environments. We compare our proposed algorithm with the Biogeography-Based Optimization Algorithm (BOA) and the Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm. Our results indicate that the proposed algorithm significantly outperforms both BOA and LEACH in terms of network lifetime and coverage area, particularly in heterogeneous environments.
Authors - M Jayaram, Kodari Madhavi, Amboth Anil Kumar, Pachipala Naveen, Gajula Rithvik Abstract - Human-Computer Interaction prioritizes universally accessible systems, crucial for individuals with physical disabilities. This study introduces an Eyes as Interfaces: A Novel Eye-Tracking Mouse Cursor System, a hands-free solution enabling seamless digital environment interaction. For people those with paralysis, muscular dystrophy, or spinal injuries, this technology provides independent computing access, eliminating dependency on external assistance. A Convolutional Neural Network (CNN) is the system's backbone that provides real-time pupil detection, mapping eye gaze to exact cursor movement. Advanced image processing like this guarantees smooth operation regardless of changing lighting and user conditions. By accurately mapping eye movements to cursor actions, users can navigate and communicate with computer interfaces, opening avenues for information access, communication, and work participation. This encourages independence and enables users to access the web on their own, performing tasks like document creation and web navigation. Beyond personal benefits, this technology promotes inclusivity by bridging the digital divide, allowing for real-time, unrestricted participation in learning, employment, and social activities. Its smooth integration in widespread digital platforms ensures that it carries the highest level of potential in changing lives of people with mobility disabilities Worldwide.
Authors - Kailash Agarwal, Parikshit N. Mahalle, Bhagwan D. Thorat Abstract - Heart failure is a serious medical condition that affects millions worldwide, and early prediction is essential for timely intervention and better patient outcomes. While machine learning models have demonstrated strong predictive capabilities in healthcare, many high-performing models, such as Support Vector Machines (SVM), function as black boxes, making them difficult to interpret in clinical settings. This study examines how Explainable AI (XAI) techniques can enhance transparency in heart failure prediction.Using a publicly available dataset from Kaggle, we preprocess the data with label encoding, feature scaling and Hyperparameter tuning before training various machine learning models for binary classification. Our results indicate that the Support Vector Classifier (SVC) with a Linear kernel achieves the highest predictive accuracy. However, to improve interpretability, we compare its performance with explainable models like Decision Trees and apply post-hoc explanation techniques such as SHAP (SHapley Additive Explanations) and Permutation Importance.Through this comparative analysis, we highlight the trade-off between model accuracy and interpretability, offering insights into the feasibility of XAI-driven models in real-world clinical decision-making. Our findings reinforce the importance of developing AI systems that not only perform well but also provide understandable and trustworthy insights for medical professionals.
Authors - Bhagwan Thorat, Omkar More, Prathamesh Medage, Aditi Mali, Janhavi Maske, Sanika Maind Abstract - Traditional healthcare systems have primarily focused on physical health, often overlooking mental well-being. With the rapid advancement of technology, integrating AI-driven emotion detection into mental health management can offer valuable insights. This paper presents a comprehensive mental health management system that utilizes Haar Cascade classifiers and a Keras deep learning model for real-time emotion recognition via OpenCV. A Flask-based web interface, built using HTML, CSS, and Python, enables users to monitor their emotional states and facilitates therapist booking and automated receipt generation. By leveraging facial expression analysis, the system provides a data-driven approach to mental health assessment, enabling early intervention. The platform also ensures accessibility and efficiency, reducing the burden on healthcare providers. Experimental evaluations demonstrate the system’s effectiveness in accurately detecting emotions and its potential in AI-assisted psychological support. Future enhancements will focus on multi-modal emotion detection, incorporating natural language processing (NLP) and IoT-based physiological monitoring for a more holistic approach to mental health assessment. This research contributes to the growing field of AI-powered mental health solutions, bridging the gap between technology and psychological well-being while promoting early detection and accessible care.
Authors - Sonali Patil, Siddhesh Arun Patil, Ayush Patil, Piyush Pawar, Siddhesh Sandeep Patil Abstract - Around 970 million people face mental health disorders across the world and depression affects 75% of these people because of their sleep disturbances. The connection between persistent sleep problems and depression emerges when affected individuals become twice as likely to develop depression thus establishing sleep as a major sign for mental health forecasting. Current approaches to this problem deal with three key issues which are dataset biases, small available sample sizes along with the reliance on self-reported symptoms instead of actual physiological signals. Our deep learning solution relies on multi-channel Convolutional Neural Networks (CNNs) to analyze wearable sensor data because it tackles existing analysis limitations. DreamT-150 contains heart rate (HR), blood volume pulse (BVP) and electrothermal activity (EDA) measurements from 150 sleep patients. Three models including MultiChannelCNN and MultiChannelEfficientNet and MultiChannelResNet analyzed the signals which appeared as time-series graphs. The best model proved to be EfficientNet-B0 because it demonstrated superior generalization. The pre-trained layers from EfficientNet adjusted the vulnerability of training loss which led to stable model performance. The research demonstrates sleep-derived physiological signals' usefulness for non-invasive mental health predictions which can lead to real-time monitoring systems. The upcoming research aims to boost both dataset range and better models for clinical adoption requirements.
Wednesday August 26, 2026 12:30pm - 2:30pm IST Virtual Room BGOA, India
Authors - Bhoomi C. Parikh, Zankhana Shah Abstract - Stress is any type of mental imbalance that can lead to mental disorders ranging from low to high severities which can be classified as acute and chronic stress conditions. Chronic stress leads to hyperactivation of the sympathetic nervous system, resulting in physical, psychological, and behavioural problems. Currently, there is no recognised standard for stress assessment. Thus depressive disorders leading to stress are a flight or fight response to the stimulus generated by human nervous system caused due to any unacceptable behaviour or circumstance. Throughout this response adrenaline hormones are secreted that leads to increased respiration and heart rates, along with increased muscle activity. Such type of biological alterations prime the organism for a physical response that affects human body mechanisms in terms of sleep abnormalities, digestive disorders or work imbalances in routine lives. Thus WESAD is a multimodal wearable dataset which combines both affective states(baseline, depression and happy) and other sensor modalities such as blood pressure, ECG, skin conductivity , EMG, breathing, and three-axis acceleration. There are also other classification parameters based on physiological changes which are also found in WESAD dataset and by using different types of Machine Learning Classifiers analysis is done . The algorithms with the highest accuracy can be used for developing a novel and a hybrid model which can categorise stress based on Heartrate Variability and stating HRV as a biomarker for stress detection. Both characteristics related to time and frequency of heart rate are categorized in the research study. In the context of the three-class classification based on three affect states , baseline, stress, and amusement result up to 99% was obtained. Use of two affective states like stress and amusement gave an accuracy up to 84% using DT classifier.
Authors - Payel Das, Siri Kethineedi Abstract - This study explores the elements of consumer resistance to sustainable marketing with an integrated theoretical approach adopting cognitive dissonance theory, institutional theory, and theory of planned behaviour. Although awareness of sustainability is increasing, consumers frequently do not accept sustainable products because of psychological discomfort, institutional barriers, and perceived behavioural limits. Using interpretive structural modelling (ISM), this study elucidates the hierarchy relationships among the main barriers, such as greenwashing, lack of transparency, price sensitivity, norm conformity, and instantaneous gratification. The most impactful of these drivers were identified as greenwashing and transparency deficits, both of which contribute to distrust and ultimately erode consumer confidence. Weak regulations and social norms that perpetuate these problems are demonstrated by Institutional Theory, while price premiums and limited access reduce perceived behavioural control and are described in the theory of planned behaviour. This study proposes a multi-tiered effort for policymakers and businesses to address resistance. Transparency will be enforced through independent certifications and stringent sustainability standards regulated by the regulatory frameworks. To regain consumer trust, companies must embrace true sustainability and communicate honestly. Price premiums can also be lowered through innovation, subsidies, and supply chain efficiencies to help play a role in them become more affordable. Understanding these barriers allows businesses to understand how they can build consumer trust, policymakers to enact effective regulations, and society as a whole to begin moving toward more sustainable consumption habits.
Authors - Payel Das, Sonali Bolisetty, Digumarthi Iswarya Abstract - This study seeks to identify the success enablers of omnichannel retailing by using Interpretive Structural Modeling (ISM) for building a hierarchical framework. From findings in a consumer survey with 108 consumers and expert evaluations, the research uncovers user enablers such as technological infrastructure, data analytics capability, personalization, mobile optimization, and seamless integration. The study is based on Service-Dominant Logic (SDL) and the Technology Acceptance Model (TAM) to investigate theory around foundational, operational, and experiential issues that result in customer engagement and brand loyalty. The findings underscore the critical importance of strong technological infrastructure and the use of real-time data in helping with friction reduction between digital and physical touchpoints. Using AI-powered analytics, it can improve personalization, which affects perceived system usefulness and thus, customer satisfaction. In addition, the study emphasizes the need for brand consistency and proper employee training to provide trouble-free service experiences. We also explore privacy and security concerns and their impact on consumer trust and omnichannel adoption. To policymakers, this research calls for prescriptive regulations that will protect data privacy and grow the space of technological innovation. For practitioners, it provides actionable insights to maximize omnichannel universality, improve customer pursuits, and develop sustainable total brand loyalty. In doing so, with the introduction of SDL and TAM, the study contributes to theoretical knowledge and proposes a comprehensive framework for the businesses who are dealing with the complexities of omnichannel retailing.
Authors - Ketki Kshrisagar, Chinmay Kalbhor, Sudarshan Chitte, Atharva Chivate, Pragati Chopade, Sanika Chougule Abstract - This paper describes the design and implementation of an automated control system for grain storage temperature and humidity. Operations begin using a microcontroller, Arduino Uno, and DHT11 or thermocouple sensors, for real-time environmental conditions, whereas the temperature and humidity are controlled through a Peltier module and a USB spray humidifier, to give the ideal storage conditions. Another complementing feature is an I2C LCD, which visualizes real-time parameters for the users locally to monitor environmental conditions. Besides, the system also includes a Blynk app, which allows the users to monitor and control it through a phone interface from a remote location. The main function of this is to act as a standalone and inexpensive system, which is primarily aimed at reducing grain spoilage and ensuring quality. Test results have confirmed that it was able to provide applicable environmental control for various storage scenarios
Wednesday August 26, 2026 3:30pm - 5:30pm IST Virtual Room BGOA, India
Authors - Fathima Mariya A K, Sarath S, Jyothisha J Nair, Sunitha E V Abstract - Thermal images often hide mixed signals, making accurate analysis challenging. However, segmentation and analysis are significantly compromised with the task of mixed pixels (a pixel containing the signals from several endmember sources). This study proposes a hybrid approach combining gradient-based thresholding (80 percentile and 85 percentile) and different clustering techniques (K-Means, Variational Bayesian GMM, Dirichlet Process GMM and Constrained GMM) to boost precision in mixed pixel identification. Results show that the gradient threshold has a positive effect on detection error (20.77 percentile), closely matching the values of K-Means (20.82 percentile) and Constrained GMM (20.69 percentile). The deviation from those methods to VBGMM and DP GMM is more moderate by 13.60 percentile. This study confirms the usefulness of an integrated approach for a more accurate interpretation of thermal images. Deep learning and multi-spectral will be researched to boost segmentation accuracy in the future.
Authors - Rashmi S. Bhumbare, Pallavi S. Gaikwad, Anjali M. Gutte, Araju M. Shaikh, Gayatri K. Chaudhari Abstract - In this paper, ensuring secure, transparent, and tamper-proof elections is a critical challenge in modern democratic processes. Traditional voting systems, including paper ballots and electronic voting machines (EVMs), suffer from issues such as fraud, lack of transparency, and centralized control. This project presents a Blockchain-Based Voting System, implemented as an Android application using Java/XML, with SHA- 256 encryption ensuring vote security and Firebase Realtime Database handling user authentication and data management. The system leverages blockchain technology to record votes in an immutable and decentralized ledger, preventing manipulation and unauthorized access. The implementation includes secure voter authentication, encrypted vote submission, blockchain-based integrity verification, and real-time result compilation. This approach eliminates traditional vulnerabilities such as vote tampering, duplicate voting, and unauthorized system access. Furthermore, the decentralized nature of blockchain ensures transparency, allowing voters to independently verify their votes while maintaining anonymity.
Authors - Beena B.M, Devika Madhusoodanan, Nithin Sagar, Vismaya R, Hridyalakshmi Santhosh Abstract - Energy conservation in cloud data centers remains one of the biggest research challenges today. Energy efficiency has become an important concern in the management of contemporary data centers due to the rapidly growing computational needs and the environmental impact of power consumption. This study examines various power management techniques, including Dynamic Voltage and Frequency Scaling (DVFS), Dynamic Power Management (DPM), and Adaptive Voltage Scaling (AVS), to optimize CPU power consumption. Using frequency data from historical and current CPU usage, these algorithms control CPU frequency settings and assess their impact on energy consumption and performance. The results indicate that DVFS reduces power consumption by 25-30%, DPM achieves energy savings of 28-35%, and AVS provides savings of 35-40% by dynamically adjusting both voltage and frequency. A performance matrix evaluates the power savings and utilization efficiency of these strategies to determine the most suitable approach. AVS was found to be 5-10% more energy efficient than DVFS and DPM, demonstrating its advantage in real-world applications. Furthermore, AVS exhibited the highest precision (96%) to adapt to workload fluctuations, compared to 95% for DVFS and 92% for DPM. This study focuses on adaptive power management and provides key findings on algorithmic solutions for energy efficiency in software-defined cloud infrastructures. The findings contribute to reducing data center energy consumption while maintaining performance, aligning with the UN Sustainable Development Goals by promoting sustainable and eco-friendly cloud operations.
Authors - Ajay Menon, Anjali Sivan, Navya S, Sandhya G, Astha Santhosh T Abstract - The micro, small and medium enterprise sector plays a crucial role in Kerala’s rural economy and makes a substantial contribution to socio-economic development and job creation. This study explores business continuity intentions among women-led micro enterprises in rural Kerala, using thematic analysis of in-depth interviews with six units from agro-processing, dairy and fisheries sectors. Drawing insights from qualitative data, this study uses the Theory of Planned Behaviour (TPB) to show that continuity intentions are strongly influenced by perceived behavioural control, strong family support, and positive attitudes. However, institutional inefficiencies and financial limitations create significant obstacles. This study also introduces ‘Team-led resilience’ and ‘Gendered leadership dynamics’ as critical factors, highlighting collaborative support and autonomous female leadership. These findings highlight the importance of financial literacy, access to credit, and supportive government policies, which will also help to expand the traditional TPB framework, emphasizing the importance of social and financial resilience.
Wednesday August 26, 2026 3:30pm - 5:30pm IST Virtual Room BGOA, India
Authors - M. Suresh, T. A. Alka, Aswathy Sreenivasan Abstract - The main purpose of this study is to theoretically explore the evolution of trends in teaching entrepreneurship through a Bibliometric analysis. The final number of documents selected is 1375, which are analysed through the Biblioshiny package under R programming. The results show that there are technology-related and non-technology-related trends that have evolved in teaching entrepreneurship. Major trends are happening in teaching methods, learning, courses, global reach, teamwork, and the emergence of technology trends such as artificial intelligence, virtual reality, etc. Bibliometric results draw that the major themes evolved in this domain are related to innovation trends in teaching entrepreneurship for shaping entrepreneurs for tomorrow, transformation, learning culture, technology trends, academic entrepreneurship in the covid-19 pandemic, learning types, concepts, skills required, sustainability and teaching entrepreneurship, entrepreneurialism and thinking in teaching entrepreneurship. The major future research avenues are; entrepreneurial intention; effectuation; entrepreneurship, business model innovation; innovation; digital transformation, and entrepreneurial university; academic entrepreneurship; innovation. The limitations of the research are; the Scopus database is only used for the search. Only the documents in the English language and final publication stage papers were selected. The inherent drawbacks of the bibliometric methodology may influence the results. The study offers theoretical implications for future research work including Sci-Val future research topics and practical implications by offering insights to entrepreneurs, investors, researchers, academicians, policymakers, etc. The novelty and the originality of the study are underlying in the in-depth theoretical exploration through a comprehensive literature review of the past thirty years.
Authors - Ganga S, Nitharshana P, Varun Madhusoodan, Rojalin Patri Abstract - Advancement of financial technology has resulted in the emergence of automated investment solutions, such as robo-advisors. While Gen-Z investors are typically receptive to digital innovations, their adoption of robo-advisory services remains an underexplored area. This study investigates the primary factors affecting Gen-Z's inclination to use robo-advisors, applying the Technology Acceptance Model (TAM). A quantitative methodology was utilized, with data collected from 161 respondents and analyzed through multiple regression techniques. Findings indicate that trust and attitude have a significant impact on the adoption intent of robo-advisory services in investment decisions made by Gen-Z investors. The results suggest that fostering trust and shaping positive perceptions of robo-advisors are more crucial for adoption than enhancing usability. This study contributes to fintech literature and offers insights for financial institutions and policymakers aiming to increase robo-advisory adoption among young investors.
Authors - Apolinar P. Datu, Annaliza C. Sinfuego, Garry C. Bayran, Dominic T. Urgelles, Julius R. Beltran, Rossana B. Liray, Janina Odette S. Vidallon, Erwin Joel B. Layug Abstract - The rapid transition to online learning, catalyzed by the global pandemic, has necessitated a critical examination of its implications within the context of general education. This study investigates the multifaceted factors influencing the implementation, delivery, and reception of online classes in general education programs across selected higher education institutions. Employing a mixed-methods research design, quantitative data were gathered through structured surveys while qualitative insights were obtained via in-depth interviews with students and faculty members. Results indicate that technological accessibility, digital competency, instructional quality, learner motivation, and institutional support are central determinants of effective online learning. The research highlights disparities in students’ digital readiness and access to conducive learning environments, which significantly affect their academic engagement and performance. Moreover, pedagogical adaptability and the integration of interactive tools were found to be critical in maintaining student interest and participation in virtual settings. The findings underscore the necessity for higher education institutions to invest in sustainable digital infrastructures, provide continuous faculty development programs, and adopt inclusive, student-centered online learning strategies. This study contributes to the growing body of literature on e-learning by offering empirical evidence on the challenges and enablers of online education in general education curricula. It also presents actionable recommendations aimed at enhancing the quality and equity of online instruction. In doing so, the research supports the advancement of resilient and adaptive educational systems capable of meeting the evolving demands of 21st-century learners.
Wednesday August 26, 2026 3:30pm - 5:30pm IST Virtual Room BGOA, India
