Volume 14 | Issue 3 |

 Abstract

In modern sports, data and artificial intelligence are widely used to improve player performance, predict injuries, and support strategic planning. However, many AI models are difficult to interpret because they do not clearly explain how their predictions are generated, which reduces trust among coaches and analysts. This research proposes an Explainable Artificial Intelligence (XAI)-based sports analytics system that integrates player performance analysis, injury risk prediction, and match outcome prediction within a unified framework. The system analyzes sports data, including player statistics, match history, and injury records, using machine learning techniques, while XAI methods are applied to clearly explain the reasoning behind each prediction. The results demonstrate that the proposed system provides accurate predictions along with meaningful explanations, thereby enhancing decision-making and increasing confidence in AI-based systems. Overall, the study highlights the importance of XAI in making sports analytics more transparent, reliable, and suitable for real-world applications.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Explainable AI, Sports Analytics, Machine Learning, Injury Risk Prediction, Player Performance Analysis, Match Outcome, SHAP, Data Science

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

n the modern era, digitalization is transforming traditional sectors, including agriculture. Despite being the backbone of India's economy, agriculture still faces challenges such as limited access to quality resources, middlemen exploitation, and lack of real-time weather information. AgroPrime is a full-stack web application developed using the MERN (MongoDB, Express.js, React.js, Node.js) architecture to provide a smart digital solution for farmers. The platform offers an integrated e-commerce system for purchasing seeds, fertilizers, and agricultural equipment, along with a real-time weather forecasting module and an informational section covering pest control and government schemes. The system is designed with a responsive interface and multi-language support (English, Marathi, Hindi) to ensure accessibility for rural users. AgroPrime aims to bridge the gap between traditional farming practices and modern digital technology, improving efficiency, transparency, and productivity in agriculture

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

MERN Stack, Smart Agriculture, E-Commerce Platform, Real-Time Weather Forecasting, Digital Farming, MongoDB, React.js, Agricultural Technology

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The Gaze-Based Mouse Control System with Voice Assistant is designed to provide a hands-free and user-friendly way to interact with a computer without using a traditional mouse or keyboard. The system uses a standard webcam to track the user's eye movements and control the mouse cursor based on gaze direction, while eye blinks are used to perform mouse click actions. In addition to eye tracking, a voice assistant is integrated to recognize spoken commands for tasks such as opening applications, scrolling, typing, and executing system operations. Computer vision techniques are used for real-time face and eye detection, and speech recognition is employed to convert voice commands into actions. This approach is especially beneficial for physically challenged users and also supports touch-free computing for general users. The proposed system is low-cost, easy to use, and improves accessibility by offering a natural and intuitive human-computer interaction experience.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Gaze-Based Mouse Control, Eye Tracking, Voice Assistant, Computer Vision, Speech Recognition, Human-Computer Interaction, Assistive Technology, Hands-Free Computing, Accessibility, Webcam-Based Interaction.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

the observational study to assess Sthula Purusha Lakshana in overweight and obese individuals, has shown the classical features of Sthula Purusha Lakshana, as described in Ayurvedic texts, are observed with varying severity among overweight and obese individuals, as measured by the Body Adiposity Index (BAI).

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

sthoulya, body adiposity index, overweight , obese

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Artificial General Intelligence (AGI) represents the next stage in artificial intelligence, aiming to develop systems with human-like cognitive abilities such as reasoning, learning, adaptability, and autonomous decision making across multiple domains, in contrast to narrow AI designed for specific tasks. The rapid growth of the Internet of Things (IoT), with its vast network of interconnected sensors, devices, and cyber-physical systems, offers a rich environment for AGI by continuously generating large volumes of heterogeneous, real-time data that enhance contextual awareness and intelligent behavior. The integration of AGI into IoT infrastructures holds transformative potential for applications including smart cities, healthcare monitoring, industrial automation, autonomous transportation, and intelligent energy management, enabling systems to evolve from rule-based automation to self learning, predictive, and context-aware intelligence capable of operating in complex and dynamic environments. However, this convergence also introduces significant challenges such as computational scalability, energy efficiency, privacy and security risks, ethical concerns, and the difficulty of deploying robust, generalizable learning models on decentralized and resource-constrained IoT devices. Addressing these challenges through approaches like edge intelligence, federated learning, and hybrid cloud-edge architectures is essential to realizing the full potential of AGI-enabled IoT systems, which could ultimately redefine intelligent digital ecosystems by enabling autonomous, resilient, and human-like decision-making.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial General Intelligence (AGI), Internet of Things (IoT), Cognition-Driven Connectivity, Intelligent IoT Systems, AIoT, Edge Intelligence, Distributed Intelligence, Context-Aware Computing, Autonomous Decision-Making

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In Tamil Nadu, 55.4% of urban women workers are employed in the formal sector in regular wage or salaried jobs; around 32% of women employed in Chennai are often considered a proxy for formal sector employment; however, their participation in stock market investments continues to be significantly low. This study examines the level of financial autonomy, risk appetite, and investment portfolio of urban working women in Chennai. Using primary data of 100 respondents collected through a structured questionnaire, the study analyses the influence of demographic and socio-economic factors such as education, income, risk appetite and investment experience on women's investment decisions. Descriptive statistics, chi-square analysis, and graphical representations are employed to assess decision-making autonomy and risk behaviour. The findings indicate that higher levels of education and income enhance women's financial autonomy and confidence in investment decision-making. Nevertheless, most respondents prefer low-risk and secure investment instruments such as mutual funds, gold bonds, and government securities, while direct participation in equities and other high-risk assets remains limited. Persistent risk aversion, gaps in financial literacy, and social influences continue to constrain women's active involvement in the stock market. The research highlights the need for targeted financial literacy initiatives, women-centric investment advisory services, and supportive institutional frameworks to promote informed risk-taking and greater stock market participation among women. Key Words: In Tamil Nadu, 55.4% of urban women workers are employed in the formal sector in regular wage or salaried jobs; around 32% of women employed in Chennai are often considered a proxy for formal sector employment; however, their participation in stock market investments continues to be significantly low. This study examines the level of financial autonomy, risk appetite, and investment portfolio of urban working women in Chennai. Using primary data of 100 respondents collected through a structured questionnaire, the study analyses the influence of demographic and socio-economic factors such as education, income, risk appetite and investment experience on women's investment decisions. Descriptive statistics, chi-square analysis, and graphical representations are employed to assess decision-making autonomy and risk behaviour. The findings indicate that higher levels of education and income enhance women's financial autonomy and confidence in investment decision-making. Nevertheless, most respondents prefer low-risk and secure investment instruments such as mutual funds, gold bonds, and government securities, while direct participation in equities and other high-risk assets remains limited. Persistent risk aversion, gaps in financial literacy, and social influences continue to constrain women's active involvement in the stock market. The research highlights the need for targeted financial literacy initiatives, women-centric investment advisory services, and supportive institutional frameworks to promote informed risk-taking and greater stock market participation among women.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Financial Literacy, Autonomy, Investment Portfolio, Risk Appetite

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This paper examines evolving trends in household finance in Paschim Bardhaman district of West Bengal, focusing on income levels, savings behaviour, and expenditure patterns. Drawing on primary survey data and broader state-level socioeconomic statistics, the study documents the financial behaviour of households within the district and situates local patterns within broader Indian household finance trends. The analysis highlights the linkages between income levels, savings choices, and consumption behaviour, offering policy implications for financial inclusion and socioeconomic welfare.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Household finance, savings, expenditure, income distribution, Paschim Bardhaman, financial behaviour.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

?????????? ??? ??????? ????? ???? ????? ?????????? ?????????? ??? ????????? ????? ????. ?????, ?????, ???????, ????? ?????????? ???????????? ?????????? ????? ????. ?????????? ????? ????? ??? ??????????? ???? ???????????? ?????? ???? ???, ???? ?????????? ????? ??? ???????? ?? ???? ?????????? ????. ????? ???????????? ???????? ??????? ????????, ?????????? ????, ??????, ??.??.?????, ???? ??????, ?????????? ?????????? ?????? ??????? ??????. ??????????? ?????????????? ????? ???? ????, ????? ???????? ?????, ????? ?????, ?????? ?????? ????? ???? ???? ????. ?? ???????? ????? ???? ???? ????? ????? ?????? ????. ??????? ?????????? ?????????? ??????? ?????? ???? ??????????, ??? ??? ??????? ?? ??????? ??? ???????? ???????? ????. ????????? '? ???????' (2009) ?? ???????? ?????????? ??????????, ??????? ????????? ??? ????????? ???????? ???????? ???????? ?????????? ?????? ???. "???? ??????", "???? ??", ????????? ???????", "?? ???? ?????" ?? ????? ?????????? ???????????? ??????, ???????? ??? ?????????? ????? ????? ????? ??? ?????. ???? ????? ??????? ?????? ??????? ???????, ??????????, ??, ???, ????????? ? ????????????? ?????? ?????? ?????? ???????? ??????. ??????? ????? ???, ??????? ??? ???????? ???? ????? ????????? ?? ???????? ??? ??????? ?????? ???.

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Water Quality, Seasonal Variation, Physico-Chemical Parameters, TDS, Hardness, Dissolved Oxygen, Water Quality Index.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The proposed project offers a web-based chess system with dual functionality: Play Mode for normal play and Learn Mode with AI support for move validation, hints, and optimal move suggestions. There is also a Puzzle Mode for tactical training with pre-set scenarios, and a Post-Game Review Module for analysis of accuracy, errors, and decision time. The complexity of the system is in combining deterministic rule validation, adaptive AI search depth based on difficulty levels, natural language processing interfaces for voice-controlled move execution, and statistical models for performance analysis. The system employs chess.js for rule validation, Stockfish.js for move calculation, Web Speech APIs for voice automation, and gamification through achievement patches. The combination of game logic, AI calculation, and speech functionality provides a seamless platform for both play and learning.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Chess Learning System, Interactive Chess Training, Voice-Controlled Chess, Piece Movement Visualization, User Interface Design, Game Automation, Skill-Based Customization, Human-Computer Interaction, Web-Based Chess Application)

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Creative Commons Attribution 4.0 and The Open Definition