Authors - Dhruv Aswani, Aman Sande, Praful Pradhan, Rajveer Tolani, Pallavi Saindane Abstract - Security concerns and the empowerment of women remain highly pressing challenges in India, with significant issues evident across urban, semi-urban, and rural regions alike. Women’s mobility is often constrained by the fear of harassment, crime, and social barriers which slows down their movement toward true empowerment. To address these problems, this study focuses on the major drivers of women’s safety and empowerment which include social norms, presence of crime, supportive structures, and women participation in technology. To address these challenges, this paper proposes Agati, an Android application that offers safety and empowerment features specifically tailored for women. Agati combines real-time safety alerts, location tracking, community support networks, and financial literacy modules to foster both security and economic independence. By leveraging data analytics and user feedback, the app aims to build a personalized, data-driven solution that bridges the gap between security and empowerment. Through this integrated approach, Agati seeks to create a safe, supportive environment that promotes social power and holistic growth for women.
Authors - Nikhil Vaishya, Amey Sawant, Mayank Shukla, Suhani Pandey, Vaishali Kosamkar Abstract - Augmented Reality (AR) is transforming the learning experience in anatomy and biology [1, 2]. by providing an engaging and interactive alternative to traditional teaching methods. Understanding complex anatomical structures has historically been challenging due to the limitations of textbooks, static models. AR overcomes these challenges by enabling students to explore high-fidelity 3D representations of the human body in real-time, fostering deeper spatial understanding and retention. The technology allows learners to interact with anatomical structures, receive immediate feedback, and learn at their own pace, beyond the constraints of the classroom. In addition to AR visualization, this project integrates an AI-assisted quiz and learning platform to further enhance anatomy education. By leveraging machine learning algorithms such as ensemble methods like Random Forests and Support Vector Machines (SVMs), coupled with SMOTE for class imbalance handling and cross-validation for robust generalization, the system offers adaptive quizzes, personalized learning recommendations, and real-time feedback. The platform dynamically adjusts to user interactions, ensuring a tailored and effective learning experience. Developed using Unity for AR functionalities, JSON for data management, and machine learning for prediction models, the system bridges the gap between theory and practice while promoting active and self-paced learning. This paper details the design, development, and evaluation of the AR-based Anatomy Learning Platform, highlighting its potential to revolutionize anatomy education by offering an accessible, immersive, and personalized approach. The platform is designed primarily for medical students, but it also supports general learners seeking to enhance their anatomical knowledge through immersive technologies.
Authors - Shital Pawar, Parag Dolhare, Saish Fatangare, Harshdeep Gawhale, Aditya Gadgil Abstract - Environmental, social and governance (ESG) criteria have become essential to assess the sustainability and social impact of companies. This article presents the development of an automated ESG ranking system that uses natural language processing (NLP), sentiment analysis, and machine learning techniques to rank and rate companies based on ESG metrics. Using a pre-existing database of news articles, we used the VADER sentiment analysis tool to assess the polarity of the text data, categorizing it as positive, negative or neutral. Sentiment scores were converted to numerical scores for each ESG component. In addition, Node2Vec is integrated to create network graphs that represent the relationships and interconnections between companies, allowing a comprehensive analysis of potential impacts. The results were visualized with Altair to provide a clear view of ESG trends and relationships that impact the company's performance over time. This study demonstrates the utility of combining NLP and advanced graph analytics for scalable data-driven ESG assessment.
Authors - Chalamalasetty Nishitha, Yelavarti Kalyan Chakravarti, V. Esther Jyothi Abstract - Sensitive domains such as healthcare institutions are increasingly relying on Federated learning for data security. Irrespective of this approach, they are gullible to adversarial attacks such as poisoning attacks and confidentiality breaches. To overcome these hindrances, Blockchain driven Federated learning is put forward, which integrates Secure Multi-Party Computation (SMPC) with Zero-Knowledge Proofs (ZKPs). This framework strives to ensure confidentiality in a distributed training environment. The individual entities train their local AI models with their exclusive datasets and generate Zero Knowledge Proofs to assert the accuracy of the model updates. The SMPC protocol encrypts the model updates, which are later aggregated to enable computing that guarantees privacy. Later, Smart Contracts are used to immutably store these adjustments on the Blockchain ledger, ensuring impenetrable model ensemble. To improve the trade-off between model dependability and precision, privacy noise is dynamically adjusted by employing adaptive differential privacy, based on individual client’s reputation. Extensive experiments prove the fact that the proposed system prominently reduces computing overhead in comparison to the established system while strengthening the attack detection rates. This architecture establishes a benchmark for information security in delicate areas like healthcare systems while designing its data-sensitive Al models.
Authors - Nandana R, Rithika Kannan, Ramgeeth N Nair Abstract - The rise of fintech applications has revolutionized financial decision-making, yet the determinants of risk-taking behavior in these digital platforms remain a critical research area. This study investigates the role of gamification, financial knowledge, and psychological influences in shaping users’ risk-taking behavior. Using a quantitative approach, an Ordinary Least Squares (OLS) regression analysis was conducted on a dataset of 200 fintech users. The results indicate that gamification has a significant positive effect on risk-taking behavior (β = 0.1414, p = 0.001), suggesting that game-like elements in fintech apps encourage users to take greater financial risks. However, certain gamification effects exhibit a negative influence (β = -0.1272, p = 0.005), highlighting that not all gamification strategies lead to in- creased risk-taking. Financial knowledge also emerged as a significant determinant (β = 0.1965, p = 0.001), implying that financially literate users tend to take more calculated risks. Among psychological factors, risk tolerance (β = 0.2754, p < 0.001) was the strongest predictor, demonstrating that individuals predisposed to risk-taking in general extend this behavior to fintech platforms. Additionally, social efficacy (β = 0.2461, p < 0.001) and social influence (β = 0.1598, p = 0.004) significantly contribute to risk-taking, emphasizing the role of self-perceived competence and peer influence in financial decision-making. The model explains approximately 48.1% of the variance in risk-taking behavior (R² = 0.481), confirming the robustness of these deter- minants. The findings underscore the importance of designing fintech applications that balance engagement with responsible financial behavior. Future research should explore the ethical implications of gamification and assess long-term user behavior to ensure sustainable financial decision-making in digital finance ecosystems.
Authors - Ramesh Babu Mutluri, Vinit Kumar Singh, D Saxena Abstract - Rural areas in developing countries are still refrained from continuous and uninterrupted power supply to power their household and run small industries. Thus, we can say that these rural areas are weakly connected to the utility grid. The main reasons for poor power supply are weak infrastructure, lack of adequate generation to fulfill the demand-supply gap, dependency on long-distance transmission, frequent load shedding, and distributed generation. This demand-supply gap can be minimized by installing renewable energy sources with the local load forming rural microgrid and connecting to the utility grid. The grid connection would help to maintain the power supply due to the variable output characteristics of renewable energy sources thus also acting as a buffer to the local power system. This paper presents a novel approach towards modeling of utility connected rural microgrid comprising renewable energy sources considering control architecture for marinating frequency-voltage interdependency. Accordingly, a frequency-based voltage controller is introduced. Further, the model has been verified in view of various scenarios with a fluctuation in load demand and power input to renewables. The controllers are tuned such that in case of increase in load or decrease in power generation, power demand is met from the utility grid, and in case of surplus generation, the power is fed to the grid, therefore, developing microgrid as business unit applicable for power trading. The model has been developed in Simulink/MATLAB. An integral square error criterion has been used for tuning the controllers to mitigate the oscillations.
Authors - Ashwitha A Shetty, Naganna Chetty, Antony P.J Abstract - The poultry industry is a significant and prominent business sector. As the daily intake of chicken meat and eggs is rising globally, poultry farming is gaining significance for providing protein. Additionally, this industry raises the nation's revenue despite being a less expensive protein source. Numerous diseases that harm the chickens are the main issue affecting the poultry business. Due to the high cost of vaccinations, poultry owners are unable to adopt these expensive methods. Consequently, this strategy cannot be used because it requires continuous investment. This paper aims to present one of the prevalent chicken diseases, coccidiosis and the different detection techniques used. In this regard, the study introduces multiple strategies that can be used in tandem to identify coccidiosis-affected fowl hens automatically. The idea behind studying chicken activity monitoring is that it directly connects to the health condition of the chicken. The enhanced future research could result in a system to monitor chicken activity and detect coccidiosis among them
Thursday August 27, 2026 3:30pm - 5:30pm IST Virtual Room DGOA, India
Authors - Piyush Sharma, Harish Patidar, Anuj Kumar Abstract - This research introduces a ResNet-based framework for multiclass classification of mammographic density and mass regions. The framework was rigorously tested using two prominent mammographic datasets, INbreast and DDSM, and benchmarked against other models, including CNNs, Random Forest (RF), Support Vector Machines (SVMs), Logistic Regression (LR), and K-Nearest Neighbors (KNN). ResNet demonstrated superior performance across all critical evaluation metrics—accuracy, precision, recall, F1-score, and AUC—outclassing the comparative models on both datasets. Its proficiency in extracting complex hierarchical features and addressing multiclass classification tasks positions it as a robust choice for breast cancer diagnosis. This framework offers a reliable and efficient tool for automating diagnostic processes, with the potential to significantly improve clinical decision-making and patient care.
Authors - Pranav Bagal, Bhavesh Patil, Shounak Muglikar, Yash Sonavane, Prajakta S. Shinde Abstract - The study addresses dental caries detection and classification using state-of-the-art deep learning architectures. We implemented and compared three pre-trained convolutional neural network models: VGG19, DenseNet169, and ResNet101, to automatically identify and classify dental caries from intraoral clinical image. Our research focused specifically on pediatric populations aged 1 to 14 years, where caries remain a significant health concern despite global prevention efforts. The models were trained and validated on a comprehensive dataset of dental images. Performance metrics demonstrated that DenseNet169 model achieved superior results with an Validation accuracy of 72.22%. These deep learning approaches show promising potential to augment traditional diagnostic methods, particularly in resource-limited settings where expert dental practitioners may be scarce. By enabling earlier and more accurate detection of carious lesions, our proposed system could help address disparities in oral healthcare accessibility and contribute to more effective intervention strategies, especially for underprivileged populations where caries prevalence continues to rise. This research establishes a technological framework that could be integrated into portable diagnostic tools for use in diverse clinical environments.
Authors - Govinda Sambare, Lalit Deore, Harsh Itkar, Onkar Jadhav, Sarthak Joshi Abstract - This research presents the development of an intelligent stock recommendation system that utilizes advanced machine learning models for informed long-term investment decisions. The system addresses the complexities of the stock market, where traditional methods often fall short in accessibility, accuracy, and efficiency. By automating fundamental analysis with models like Long Short-Term Memory (LSTM) networks and the CNN-GRU-XGBoost hybrid model, the system integrates key financial ratios, macroeconomic indicators, and sector performance, providing data-driven insights. The proposed framework optimizes stock selection using XGBoost and forecasts future stock prices with LSTM, offering precise and scalable solutions for diverse investment portfolios. The literature review highlights modern methodologies like TRAN, Bi-LSTM, and hybrid models, which improve stock forecasting and trading strategies by incorporating temporal dependencies and inter-stock relationships. The algorithmic analysis explains LSTM's ability to handle sequential data and the hybrid model's powerful feature extraction and prediction capabilities. This hybrid approach enhances decision-making, saves time, and democratizes financial insights, making advanced analysis accessible to individual investors, robo-advisors, and educational institutions. While offering benefits like scalability and reduced biases, the system also faces challenges, such as computational costs and market volatility. Backtesting results confirm the system's adaptability to dynamic market conditions, ensuring sustainable investment strategies. This project showcases the transformative potential of AI/ML in financial analytics, laying a strong foundation for long-term, informed investment decisions.
