An Intelligent Matchmaking System for Peer-Assisted Learning in Educational Communities
This study presents an AI-driven intelligent matchmaking system that facilitates peer-assisted learning in educational communities by dynamically connecting students based on academic profiles, shared interests, and availability. The system addresses critical challenges in modern digital learning environments, including inefficient peer matching, a lack of personalization, and low student engagement. By leveraging machine learning algorithms, particularly TF-IDF vectorization and cosine similarity, the platform intelligently forms collaborative study groups that promote peer-to-peer knowledge exchange. The system integrates gamified learning features, including badges, points, and leaderboards, to sustain motivation, and incorporates real-time progress tracking through comprehensive analytics dashboards. Built using a microservice architecture with React.js frontend and FastAPI backend, the platform demonstrates superior scalability and modularity compared with traditional learning management systems. The testing results indicate the successful implementation of all core functionalities with 100% test case pass rates across the user management, matchmaking, resource sharing, and gamification modules. The system represents a significant advancement in creating student-centered, adaptive digital learning ecosystems that foster meaningful academic collaboration beyond conventional classroom boundaries.
Published by: Vinay Kumar Siddha, Poojitha Karuturi, Palli Kalyan Babu, Kona Karthik, Sneha Pradhan
Author: Vinay Kumar Siddha
Paper ID: V11I6-1169
Paper Status: published
Published: November 16, 2025
The Role of Mathematics in Sports Statistics: Analyzing Player Performance
Mathematics forms the backbone of modern sports analytics, enabling the transformation of raw performance data into structured, quantifiable insights that enhance both individual and team evaluation. This study examines how mathematical and statistical tools—including weighted averages, regression analysis, principal component analysis (PCA), z-score standardization, clustering methods, Monte Carlo simulations, and Expected Goals (xG/xA) models—collectively contribute to a sophisticated understanding of athlete performance. By applying these techniques, the research demonstrates how multidimensional player data can be normalized, compared, and interpreted to produce objective ratings and identify key performance indicators. Furthermore, the study highlights the predictive power of mathematical modelling in sports. Regression-based forecasting and probabilistic simulations allow analysts to estimate goal-scoring likelihood, evaluate tactical strategies, and anticipate match outcomes with greater accuracy. Dimensionality-reduction methods, such as PCA and factor analysis, streamline complex datasets into meaningful components, revealing hidden patterns and correlations within player behavior. Beyond individual assessments, mathematics also enhances broader strategic decision-making in areas such as player recruitment, opponent analysis, load management, and game planning. The findings emphasize that the integration of quantitative models not only improves analytical precision but also reduces subjective bias, resulting in a more transparent and reliable framework for performance evaluation. Overall, this research illustrates the essential role mathematics plays in shaping the contemporary sports analytics landscape, demonstrating how rigorous quantitative analysis contributes to improved tactical understanding, more informed coaching decisions, and data-driven competitive advantage.
Published by: Kashyap Jalali
Author: Kashyap Jalali
Paper ID: V11I6-1147
Paper Status: published
Published: November 15, 2025
Harnessing the Wind: Evaluating Technological Advancements, Policy Frameworks, and Environmental Strategies for Large-Scale Wind Energy Adoption in India
India’s transition to clean energy is crucial for achieving its goal of 500 GW of non-fossil fuel capacity by 2030 and mitigating its dependence on coal. This research examines how advancements in wind energy technology, supportive policy frameworks, and sustainable environmental practices can collectively drive large-scale wind energy adoption in India. It explores the evolution of turbine design and efficiency, the promise of offshore wind energy, and the integration of digitalization and energy storage systems that enhance reliability and cost-effectiveness. The study further analyzes India’s policy landscape—including Power Purchase Agreements (PPAs), Renewable Purchase Obligations (RPOs), and incentives under the National Action Plan on Climate Change—highlighting their role in promoting investor confidence and infrastructure development. Moreover, it discusses environmental sustainability through Environmental Impact Assessments (EIAs), community engagement, and wildlife protection strategies. By comparing global best practices from Denmark, Germany, and China, the research identifies pathways for India to overcome challenges such as grid integration, regulatory inconsistencies, and land acquisition barriers. Ultimately, this paper concludes that a synergistic approach—combining technological innovation, policy support, and environmental stewardship—can position India as a global leader in sustainable wind energy development.
Published by: Vivaan Dumir
Author: Vivaan Dumir
Paper ID: V11I6-1164
Paper Status: published
Published: November 15, 2025
Financial Literacy among Youth
Financial literacy has emerged as a critical life skill in the 21st century, particularly for young people navigating an increasingly digital and complex financial environment. This paper examines the growing financial literacy gap among youth, emphasizing its implications for personal well-being, national productivity, and sustainable economic growth. It analyzes barriers such as limited access to structured financial education, overreliance on social media for financial advice, and the rising incidence of digital scams. Drawing on global comparisons, it highlights how countries like Singapore, Canada, and Australia have successfully integrated financial literacy into school curricula, while India continues to lag. The study underscores the urgent need to embed financial education into formal schooling, enhance teacher training, and strengthen digital financial awareness. Government initiatives such as the National Strategy for Financial Education (NSFE 2020–2025) demonstrate progress, yet a stronger post-2025 roadmap is essential. The paper concludes that equipping youth with financial skills not only improves individual financial resilience but also drives entrepreneurship, savings, and inclusive national development.
Published by: Ridhaan Jain
Author: Ridhaan Jain
Paper ID: V11I6-1165
Paper Status: published
Published: November 15, 2025
A Study on the Relationship between Air Pollution and Housing Prices in Indian Metropolitan Cities
This paper aims to examine whether air pollution has a significant impact on residential housing prices in Indian metropolitan cities, which include NCR, Mumbai, Kolkata, Bangalore, and Chennai. The data for both variables is from 2017 to the beginning of 2025. It uses secondary data from the Housing Price Index (HPI) and the Central Pollution Control Board (CPCB). This paper is purely a quantitative analysis, and compares the trends of PM2.5, PM10, NO₂, and Ozone, with the movements of the housing prices. The findings reveal that there is no direct correlation between the air quality and housing prices across the selected cities. However, factors such as infrastructural and connectivity development, employment opportunities, migration, etc., are found to play a significant role in the housing prices to experience an upward trend. The dependence on mainly two indices limits the scope of the study; however, it also highlights an important implication. Pollution does not affect the valuation of residential complexes. Therefore, there is little to no incentive for sustainable urban development. Measures like Government intervention, inclusion of social/environmental costs in the real estate business, could encourage eco-friendly and greener housing practices.
Published by: Mrittika Sen
Author: Mrittika Sen
Paper ID: V11I5-1324
Paper Status: published
Published: November 14, 2025
Early Diabetic Retinopathy Detection using Federated Learning
Diabetic Retinopathy (DR) is a leading cause of vision loss worldwide, primarily affecting individuals with prolonged diabetes. Early detection is crucial to prevent irreversible blindness. This project proposes a secure and intelligent framework for the early detection of Diabetic Retinopathy using Federated Learning (FL), ensuring both data privacy and efficient model training across multiple healthcare institutions. The system utilizes Optical Coherence Tomography (OCT) images for accurate retinal analysis and employs Convolutional Neural Networks (CNNs) such as ResNet-50 and VGG-16 for disease classification. To enhance data security, Multi-Factor Authentication (MFA) is integrated, allowing only authorized medical professionals to access sensitive information. Unlike traditional centralized AI models, the proposed system prevents raw data sharing, thus maintaining patient confidentiality while improving diagnostic performance. Future enhancements, including Homomorphic Encryption (HE) and Explainable AI (XAI), will further strengthen data protection and interpretability of results. Overall, this system contributes to Sustainable Development Goal (SDG 3): Good Health and Well-being, by promoting accessible, secure, and intelligent healthcare solutions for early eye disease diagnosis.
Published by: Dharish Prasath D, Kalai arasi. M, Faziya. A, Jai Ganesh. S, Kavitha. I
Author: Dharish Prasath D
Paper ID: V11I6-1161
Paper Status: published
Published: November 14, 2025
