Volume 14 | Issue 4

 Abstract

childhood is an unforgettable period of one's life so happy childhood is right of every child and the facility the same is concern of every welfare state where happiness is not available to maturity of children they are exploride in work field being a labour which is essential for their strengths child labour in the employment under a specified legal age avoid confusion its best to explain child labour working under the age of 18 in same way harm and exploits them physically mentally and morally and block them from education child labour is very modicle a ruler carbon for Noman ruler area account for 85% of child worker and the incidence of child labour is a higher in a rural area.

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Child Labour, Stress, Legal protection, Rules and Regulation Child Labour

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Synovitis is a key pathological feature in multiple joint disorders, including rheumatoid arthritis, osteoarthritis, and traumatic joint injuries. This review aims to synthesize the histopathological alterations in synovial tissue reported in previously published literature. A comprehensive review of PubMed-indexed articles was conducted focusing on synovial lining changes, inflammatory infiltrates, vascular proliferation, and stromal responses. Findings indicate that synovitis demonstrates a spectrum of pathological changes depending on aetiology, with rheumatoid arthritis showing aggressive inflammatory features, while osteoarthritis exhibits milder, fibrotic alterations. Histopathological evaluation remains essential for diagnosis, disease grading, and therapeutic planning.

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Synovitis, Histopathology, Rheumatoid arthritis, Osteoarthritis, Synovial membrane

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The increasing sophistication of cyber threats such as ransomware, phishing, and zero-day attacks has ex posed critical limitations in traditional signature-based and reactive security systems. This paper presents an Autonomous AI-Based CyberDefenceAgent, implementedasadesktopap plication, designed to provide real-time threat detection and automated response at the endpoint level.In order to elimi nate inter-process communication delays and facilitate quick decision-making, the suggested system makes use of a Unified Architecture that combines the monitoring agent and back end server into a single process. File system activity is cap tured with low latency by an event-driven monitoring sys tem. To successfully identify both known and undiscovered threats, the detection engine uses a hybrid approach that com bines signature-based techniques, Shannon entropy analysis, and heuristic pattern matching.Malicious entities are neutral ized and quarantined without the need for human interac tion thanks to a risk-based autonomous response system that is activated upon discovery. The system also offers tamper resistant logging methods and a real-time dashboard for mon itoring and forensic analysis. With an average reaction time of less than 200 ms and low resource consumption, experimental assessment under controlled settings shows near-perfect de tection and quarantine performance for simulated assault sit uations. These findings show that the suggested system pro vides a lightweight, scalable, and effective solution for con temporary endpoint security.

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The increasing sophistication of cyber threats such as ransomware, phishing, and zero-day attacks has ex posed critical limitations in traditional signature-based and reactive security systems. This paper presents an Autonomous AI-Based CyberDefenceAgent, implementedasadesktopap plication, designed to provide real-time threat detection and automated response at the endpoint level.In order to elimi nate inter-process communication delays and facilitate quick decision-making, the suggested system

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Large language models can write fluently but they cannot tell you what is actually in your PDF. They hallucinate, confabulate, and confidently miss the point. This paper describes a Retrieval-Augmented Generation (RAG) system built to close that gap.

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Retrieval-Augmented Generation, RAG, Large Language Models, Vector Database, ChromaDB, Mistral, LangChain, Document Question Answering, Semantic Search, Nomic Embeddings, Flask, Next.js

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Abstract--The rapid digitization in education has accelerated the demand for secure and efficient examination systems. Traditional paper-based assessments suffer from a number of issues, including high administrative overhead, time-consuming evaluation, and vulnerability to human error. This study describes the design and implementation of an Online Examination Platform System that is scalable, reliable, and academically integrous. The design features secure authentication, automated grading, and real-time analytics to provide immediate scores while easing the burden of examiners. Multiple question types, such as multiplechoice, short, and descriptive, are enabled to increase assessment flexibility. Experimental results show increased efficiency, reduced expenses, and overall positive feedback from students and institutions. The study concludes that online examination platforms can serve as a sustainable alternative to traditional modes of examination, with further possibilities for future enhancement by AI-driven proctoring and integration with adaptive learning.

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E-Learning, Secure Examination, Web-Based Application, Real-Time Monitoring and Exam Management System

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The advent of Artificial Intelligence (AI) in Human Resource Management has profoundly changed the conventional performance management systems by establishing data-based, objective and ongoing evaluation processes. This paper will discuss how the AI-driven performance management systems are perceived by employees and how this affects employee motivation and job satisfaction, with the main variables under consideration being trust, fairness, transparency, understanding, and comfort. A structured questionnaire was used to gather primary data on 60 respondents working in different industries. Statistical techniques such as descriptive analysis, correlation, and regression were used to examine relationships among variables and test hypotheses. The results show that employees have a somewhat positive but skeptical attitude towards AI-based systems. Perceived fairness was found to have the strongest impact on both motivation and trust, and transparency and trust had weaker and inconsistent impacts. The strongest predictor of job satisfaction was found to be understanding of AI systems, followed by motivation and comfort. Moreover, a hybrid (AI + human) solution was strongly preferred by employees, which suggests the value of human involvement should not be overlooked. Although AI has proven useful in terms of efficiency and bias reduction, issues associated with a lack of human interaction, data privacy, and algorithmic bias remain. The research concludes that the success of AI-based performance management systems is determined not only by the level of technological advancement but also by the perception and acceptance of employees. Successful implementation and long-term organizational results require a balanced, transparent and human-focused approach.

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Artificial Intelligence (AI), Performance Management Systems, Employee Perception, Fairness and Transparency, Employee Motivation, Job Satisfaction, Human-AI Interaction

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The rapid advancement of Artificial Intelligence (AI) and Internet of Things (IoT) technologies has enabled the development of intelligent systems for modern agriculture. This project presents an integrated solution for automated mango grading and environmental monitoring using deep learning and IoT-based control mechanisms.The system employs a YOLOv5-based object detection model to identify and classify mangoes into three categories: ripe, medium ripe, and unripe. A live video stream from a webcam is processed in real time using computer vision techniques, allowing accurate detection and counting of mangoes. The trained model, deployed in ONNX format, ensures efficient and fast inference suitable for real-time applications.The detection results, including the count of each mango category, are transmitted to an ESP32 microcontroller through serial communication. The ESP32 acts as a central IoT node, integrating multiple environmental sensors such as DHT11 (temperature and humidity), soil moisture sensor, and LDR (light intensity sensor). These sensors continuously monitor the surrounding conditions to support smart agricultural decision-making.The system is further integrated with the Blynk platform, enabling remote monitoring and control through a smartphone interface. Real-time data, including mango grading results and environmental parameters, are displayed on the mobile application. Additionally, automation features are implemented using relay modules to control devices such as a water pump and lighting system based on sensor thresholds and user input.An LCD display is also incorporated for local visualization of sensor data, ensuring accessibility even without internet connectivity. The system operates efficiently with minimal human intervention, reducing manual labor and improving grading accuracy. Furthermore, it provides scalability for integration into larger agricultural or industrial environments. Overall, the proposed system demonstrates a cost-effective, reliable, and intelligent approach to fruit grading and smart farming, combining AI-driven decision-making with IoT-enabled monitoring and automation. This solution has the potential to significantly enhance productivity, reduce post-harvest losses, and support precision agriculture practices.

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YOLO, ESP32, IOT, Embedded systems _____________________________________________________

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Organizational Citizenship Behaviour (OCB) refers to the voluntary and discretionary actions of employees that contribute positively to the overall functioning of the organization beyond their formal job requirements. Employee performance reflects how effectively an employee carries out job responsibilities and achieves work-related goals. This study examines the impact of OCB on employee performance at Bhargave Rubber Pt. Ltd., Madurai, a manufacturing company engaged in the production of rubber seals and moulded rubber components. Data was collected from 100 employees using a structured questionnaire and analysed using correlation analysis, regression analysis, ANOVA, and coefficient analysis through SPSS. The results reveal a strong positive relationship between OCB and employee performance (r = 0.693, p = 0.000), with OCB explaining approximately 48% of the variation in employee performance. The findings confirm that promoting organizational citizenship behaviours such as helping colleagues, following organizational rules, and maintaining a positive attitude can significantly improve employee performance and organizational effectiveness.

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Organizational Citizenship Behaviour, Employee Performance, Correlation, Regression, ANOVA, Bhargave Rubber Pt. Ltd.

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Rapid urbanization has significantly increased road traffic, creating a strong demand for intelligent traffic monitoring systems. Accurate estimation of vehicle density and recognition of multiple vehicle classes remain challenging due to occlusion, illumination variation, and complex road environments. This study proposes a hybrid deep learning framework for real-time vehicle density estimation and vehicle classification in intelligent transportation systems. Initially, adaptive bilateral filtering is applied to improve image quality while preserving edge information. EfficientNet-B3 is then utilized for discriminative feature extraction from traffic scenes. Subsequently, YOLOv8 is employed for precise vehicle localization, and an attention-based BiLSTM classifier is used to determine traffic density levels and vehicle categories. Experimental evaluation demonstrates that the proposed framework achieves superior accuracy, precision, and computational efficiency compared with conventional CNN and Faster R-CNN-based approaches. The proposed system can support intelligent traffic management and smart city applications through reliable real-time performance.

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Enhanced CNN, Automatic feature extraction, Faster R-CNN optimization, Vehicle detection, Traffic density analysis

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 Abstract

Bharat mein jaivik krishi ki samasyaen evam sambhavnayen - ek bhaugolik adhyayan

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Bharat mein jaivik krishi ki samasyaen evam sambhavnayen - ek bhaugolik adhyayan

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The rapid adoption of Artificial Intelligence (AI) in recruitment has created new opportunities for improving the efficiency, consistency, and scalability of interview evaluation. Traditional interview processes often rely on subjective human judgment, delayed feedback, and inconsistent assessment criteria, which can negatively affect hiring quality and candidate experience [1], [2]. Recent studies have shown that AI-assisted interview systems can improve evaluation accuracy by analyzing candidate responses, communication patterns, and behavioral cues in virtual interview settings [1], [4], [9]. In parallel, advancements in Large Language Models (LLMs) have demonstrated strong capability in contextual understanding, semantic reasoning, and automated feedback generation, making them highly suitable for intelligent interview assessment applications [1], [8], [14]. This paper presents an AI-powered automated interview feedback generation system that integrates Speech-to-Text (STT), Natural Language Processing (NLP), and Large Language Models (LLMs) to provide real-time and structured candidate evaluation. The proposed system begins with role-specific interview initialization through a lightweight web-based interface, where candidate details and job preferences are collected. Based on the selected role, the system dynamically generates technical and behavioral interview questions tailored to the candidate profile [3], [8]. During the interview session, candidate responses are captured through audio input and converted into text using speech recognition. The transcribed responses are analyzed using NLP techniques to evaluate communication clarity, response relevance, logical flow, and answer quality [9], [11], [12]. An LLM-based semantic evaluation module further assesses contextual understanding, identifies strengths and weaknesses, and generates personalized feedback. The system then produces a detailed audit report comprising competency-wise scores, overall performance summaries, skill gap analysis, and actionable recommendations. The proposed framework offers a lightweight, scalable, and practical end-to-end solution for intelligent interview assessment, reducing evaluator bias, improving feedback consistency, and enabling efficient decision-making in modern digital recruitment workflows [5], [6], [15].

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Artificial Intelligence, Speech-to-Text, Natural Language Processing, Large Language Models, Automated Interview Assessment, Candidate Evaluation, Real-Time Feedback, Semantic Analysis, Recruitment Analytics, Web-Based Interview System.

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 Abstract

The rapid advancement of computer vision technolo-gies and the growing demand for contactless human-computer interaction have opened new possibilities for gesture-based con-trol systems. Traditional input devices such as the mouse and keyboard impose physical constraints on users and are unsuitable for hygienic, futuristic, or accessibility-oriented environments. This paper presents a Touchless Augmented Reality (AR) Virtual Workspace that enables users to interact with a computer system using only hand gestures captured through a standard webcam. The proposed system integrates MediaPipe for real-time hand landmark detection, OpenCV for frame processing, and PyAutoGUI for executing system-level actions. Gesture logic is applied to interpret specific hand positions and movements, enabling operations such as cursor movement, left and right click, scrolling, application launching, and system controls. Additionally, the system provides a floating AR workspace where virtual application icons are rendered on-screen with physics-based behavior including collision detection and body avoidance. Experimental results demonstrate that the system achieves real-time performance with low latency, natural inter-action, and efficient gesture recognition using only a standard webcam. The proposed system proves that gesture-based control combined with augmented reality can significantly enhance user experience and eliminate the need for physical input devices.

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Augmented Reality, Hand Gesture Recognition, MediaPipe, OpenCV, PyAutoGUI, Human-Computer Interac-tion, Touchless Interface, Real-Time Processing, Cursor Control, Virtual Workspace

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Landslides are the most dangerous natural hazards, often occurring without warning and causing significant loss of life, property damage, and environmental destruction. Accurate and timely prediction of landslides is essential for effective disaster management and risk mitigation. This project presents a machine learning-based landslide prediction system using a Random Forest classifier to analyse multiple environmental and geological parameters influencing landslide occurrence.The proposed system utilizes features such as rainfall intensity, slope angle, soil saturation, vegetation cover, earthquake activity, proximity to water bodies, and soil type to predict the likelihood of a landslide. Standard normalization methods are used to preprocess and scale the input data in order to boost model performance. The trained machine learning model determines whether the situation is safe or high-risk and provides an estimate of the likelihood of a landslide occurring. To enable real-time integration with embedded and IoT systems, the prediction results are transmitted through serial communication at 9600 baud rates in a simple comma-separated

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Random Forest algorithm, IOT, Embedded systems

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Abstract: The ageing population has emerged as a significant concern in both developed and developing countries, including India. In the contemporary world, the proportion of elderly individuals is steadily increasing, leading to complex social and healthcare challenges. In India, the issue is more pronounced due to the gradual decline of the traditional joint family system, while adequate institutional care facilities are still insufficient. This review aims to explore the symptoms of depression, quality of life, and the major challenges faced by elderly individuals, particularly in India and West Bengal. Existing literature indicates that many older adults experience feelings of loneliness, isolation, helplessness, and reduced self-worth, even when living in well-equipped care homes. Studies have shown that chronic diseases play a crucial role in determining quality of life, whereas factors like age, education, and marital status have limited influence. Overall, the review underscores the increasing burden of depression among the elderly and highlights the urgent need for comprehensive primary healthcare services and community-based interventions to improve their quality of life and ensure early detection and management of mental health issues.

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Keywords: Depression, Quality of life, Elderly People, West Bengal, India

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 Abstract

Indian Classical Dance is a rich cultural heritage that employs intricate hand gestures (Mudras), facial expressions (Bhavas), and body postures to convey profound emotional and narrative content. However, the computational analysis of these elements has remained a challenging and largely unexplored domain. In this paper, we present NrityaAI, a comprehensive AI- driven framework that automates the recognition and analysis of Bharatanatyam dance performances from video input. The pro- posed system integrates multiple deep learning models including MediaPipe for skeletal pose estimation, a custom Convolutional Neural Network (CNN) based on EfficientNetB0 for Mudra classification, and DeepFace for facial expression analysis with Rasa mapping. The system processes dance videos through a multi-stage pipeline comprising video validation, frame extrac- tion, Region of Interest (ROI) detection, Mudra identification, expression analysis, context fusion, and automated storyline generation. Our framework achieves high accuracy in Mudra detection while providing rich contextual output including anno- tated videos with text overlays, SRT subtitle files, and human- readable narrative descriptions that map detected Mudras to their cultural Rasa/Bhava significance. The system is deployed as a full-stack web application with a React TypeScript frontend and Python FastAPI backend, supporting user authentication, analysis history, and real-time video playback with synchro- nized captions. Experimental evaluations demonstrate that the proposed system provides accurate, efficient, and culturally meaningful analysis of classical dance performances, bridging the gap between traditional art and modern artificial intelligence.

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Indian Classical Dance; Mudra Recognition; Convolutional Neural Network; EfficientNet; MediaPipe; Deep- Face; Multimodal Analysis; Narrative Generation; Dance Video Analysis; Cultural Computing

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This paper deals with the concept of Dharma and explains its importance in its entirety. It showcases the importance of the term Dharma, studies its originality, and explores how it came into being. It portrays that Dharma is described through different meanings. Moreover, it expresses Dharma through one of the epic Indian narratives, the Mahabharata. Through the text of the Mahabharata, this research focuses on different characters such as Yudhishthira and Arjuna, presenting them through their lens and depicting their version of righteousness. But even though these characters are important, this study foregrounds its emphasis on the character of Krishna, who is depicted as an advisor to Arjuna during the Kurukshetra war, explaining to him the importance of duty and righteousness without thinking of the repercussions. The study also incorporates the philosophical significance of the Bhagavad Gita, embedded within the Mahabharat, which provides a metaphysical resolution to the epic's moral turbulence. In conclusion, it also examines the modern view of the tales of the Mahabharat in contemporary society and the current interpretations of terms such as duty or righteousness, along with the diverse range of adaptations of the Mahabharat.

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Dharma, Mahabharat, Krishna, literature, righteousness, modern-day adaptation, Bhagavad Gita

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India is one of the richest and diverse countries in the South Asian region. Basically India is assumed as a living museum because of its repository of ancient values and traditions in terms of civilization, progress, education, knowledge and technology. It has always given priority to the vision of Atmanirbhar (self- reliant) since its independence from colonial rule. It has one of the dominant geopolitical locations as it is landlocked in nature with its adjacent neighbours. Since its liberation India tried to develop friendly relations with south Asian nations, who had faced similar colonial dominance in their historical context. India followed the ancient policy of Saptang theory of Kautilya i.e. to maintain cordial relations with neighbours as it does not believe in existence but in coexistence. It follows the path to become self- sufficient in order to achieve the landmark pattern of Atmanirbharata in all the sectors like agriculture, health , technology etc. This paper further examines how India's soft power approach in South Asian neighbours which gives her the status of vishwaguru with self- sufficiency. The purpose of this paper is to assess that how the concept of Atmanirbhar bharat is often misunderstood by nations as the policy of isolationism. The focus is to highlight the parameter that vishwaguru cannot lead if it is dependent on others for basic pre-requisites. A true leadership comes from a position of strength and self- sufficiency. This paper will be the attempt to scrutinize that Atmanirbhar Bharat is not just an economic policy of import substitution but it evolves India's national image from a passive recipient to an active provider of solutions to global problems. The paper further focuses on India's self- reliance in various forms like Digital Public Infrastructure (DPI) , education, Pharmaceuticals (Vaccine Maitri), space technology. It also provides a soft power influence of self-reliance in south Asia that portrays India's image as a vishwaguru.

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India, Soft Power, Atmanirbhar Bharat , Vishwaguru , South Asia

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This study aimed to create a dark chocolate that not only delights the palate but also provides nutritional benefits by fortifying it with whey protein concentrate, candied dehydrated orange peels, and roasted pumpkin seeds. Dark chocolate (90 g) was enriched with 3 g whey protein, 4 g pumpkin seeds, and 3 g candied orange peel. The nutritional content, including protein, fiber, fat, ash, and carbohydrates, was analyzed, and sensory evaluation was conducted with panelists using a 9-point hedonic scale. Results showed notable increases in protein and fiber content, while taste, texture, aroma, and overall acceptability remained highly favorable. The study demonstrates that carefully combining functional ingredients can transform traditional chocolate into a nutritious, enjoyable, and health-promoting treat. This approach also supports sustainable use of food by-products, such as orange peel, adding environmental value to the product.

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Functional chocolate, Whey protein, Pumpkin seeds, Candied dehydrated orange peel, Sensory evaluation

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 Abstract

Peeli Aandhi : Marwari Stree Jeevan Ke Vibhinn Swar

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Peeli Aandhi : Marwari Stree Jeevan Ke Vibhinn Swar

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Farming plays a key role in India's economy, yet handling water wisely and tracking soil conditions live remains tricky. Most fields rely on fixed schedules or basic automated setups - these can lead to too much water used and loss. One way around these issues involves using connected devices powered by IoT tools. These setups use physical sensors across the land to track key dirt properties like heat, wetness, water content, and acidity levels. A small computer called ESP32 picks up details about the environment, such as moisture and light. These readings travel to the internet network, where complex math happens using past results for different plants. What comes next is a forecast showing how much water each field might need. Instead of relying on old schedules or guesswork, the new method adjusts timing based on actual physical rules, not rigid rules set ahead of time. Farmers far away can now tap into smarter watering decisions without needing to physically visit the land. Putting auto and hand-operated setups together makes the new system work better under pressure. It demonstrates how using IoT alongside data handling leads to sharper, long-term farming decisions. Testing with 2,880 data entries confirmed its reliability. Outcomes included smarter watering for crops depending on weather shifts. Watering slowed when rain was predicted. During hot spells, supplies tightened but without loss. Overall use rose against older methods relying on fixed schedules.

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Smart irrigation, Internet of Things (IoT), soil moisture sensing, ESP32 microcontroller, precision agriculture

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