Volume 14 | Issue 3 |

 Abstract

Career guidance and job recommendations have become increasingly complex due to the rapid evolution of industry skill requirements and the diversity of individual user profiles. Accurate identification of skill gaps and alignment between candidate capabilities and job market expectations is essential for improving employability outcomes. This paper presents PathFinder AI, an intelligent career guidance system that leverages Retrieval-Augmented Generation (RAG) to analyze resumes, recommend suitable job roles, and generate personalized learning paths. The system processes unstructured resume data to extract relevant skills and experience, which are then semantically compared with industry job descriptions using vector embeddings. A knowledge base containing curated job requirements and learning resources is employed to retrieve contextually relevant information through similarity-based search. The retrieved knowledge is integrated with a lightweight language model to generate explainable job recommendations and structured learning guidance. By combining semantic retrieval with controlled language generation, PathFinder AI improves the accuracy, relevance, and interpretability of career recommendations compared to traditional keyword-based systems.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Retrieval-Augmented Generation (RAG), Career Guidance System, Resume Analysis, Skill Gap Analysis, Job Recommendation, Personalized Learning Path, Semantic Search, Vector Embeddings, Artificial Intelligence, Full-Stack Web Application.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Regional histories play a crucial role in decentralizing dominant historiographical narratives and highlighting the dynamic interplay between ecology, society, and political transformation. Purulia District, situated at the western frontier of West Bengal and historically associated with the Manbhum region, represents a distinctive cultural landscape shaped by environmental constraints, tribal resilience, religious plurality, colonial restructuring, and post-independence integration. Despite its historical significance, Purulia has often remained peripheral within mainstream Bengal historiography, which tends to privilege deltaic and urban-centered developments. This study critically examines the cultural heritage and historical evolution of Purulia from early historical periods to the contemporary era. Employing a qualitative historical-analytical methodology based on archival records, archaeological findings, anthropological accounts, and policy documents, the research situates Purulia within broader debates on regional historiography, subaltern agency, and intangible cultural heritage. The findings suggest that Purulia's socio-cultural identity evolved through processes of negotiation rather than passive assimilation. The district's tangible heritage--including temple architecture, sculptural remains, and settlement patterns--reflects trans-regional connectivity, while intangible traditions such as Chhau dance, Tusu, Bhadu, and Karam festivals demonstrate continuity within transformation. Colonial administrative interventions restructured agrarian relations and forest governance, yet indigenous frameworks of identity persisted. Post-independence linguistic reorganization and development policies further reshaped the district's political and cultural orientation. The study argues that Purulia should be understood as an active historical agent whose ecological setting and tribal foundations shaped a distinctive regional modernity. Recognizing its layered heritage contributes to broader discourses on cultural sustainability and decentralized historiography in India.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Purulia; Regional Historiography; Tribal Society; Cultural Heritage; Chhau Dance; Colonial Transformation; Indigenous Identity; Eastern India.

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Creative Commons Attribution 4.0 and The Open Definition

 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

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 Keywords

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Creative Commons Attribution 4.0 and The Open Definition