Volume 14 | Issue 4

 Abstract

The rapid expansion of Internet of Things (IoT) systems has led to increased demand for intelligent, energy-efficient, and scalable solutions for real-time monitoring and anomaly detection. Traditional cloud-based architectures suffer from high latency, excessive energy consumption, and network dependency. This paper proposes an energy-aware anomaly detection system based on Tiny Machine Learning (TinyML) deployed on an ESP32 microcontroller. The system utilizes MPU6050 sensor data to detect vibration anomalies and employs a lightweight TinyML model for local inference. To reduce communication overhead, LoRa (RA-02 SX1278) is used to transmit only relevant anomaly data over long distances with minimal power consumption. Additionally, a TP4056 battery management module is integrated to ensure efficient energy utilization and portability. The proposed system demonstrates the effectiveness of edge-based intelligence in reducing energy consumption and improving real-time performance. A Python-based dashboard is developed for visualization, displaying time-series data, anomaly status, and historical records. Furthermore, a conceptual implementation of federated learning is introduced, where multiple nodes operate independently and share summarized insights instead of raw data. The results show that the system significantly reduces unnecessary data transmission while maintaining reliable anomaly detection, making it suitable for energy-constrained IoT applications.

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 Keywords

TinyML, IoT, ESP32, LoRa, Anomaly Detection, Edge Computing, Energy-Aware Systems, Federated Learning

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Urban Co-operative Credit Societies (UCCS) play a crucial role in providing financial services to small businesses, individuals, and lower-income groups. However, their financial sustainability depends significantly on efficient Asset and Liability Management (ALM). This study examines the movement of assets and liabilities in UCCS within Solapur District, Maharashtra, focusing on their impact on financial risk management, liquidity, and overall stability. Using primary data collected from 320 UCCS, various statistical tools such as descriptive analysis, reliability tests, factor analysis, and chi-square tests were applied to evaluate financial strategies and risk mitigation practices. The findings indicate that many UCCS face challenges in liquidity management, policy formulation, and risk assessment, leading to financial instability. While some societies have established monitoring mechanisms and periodic audits, gaps remain in adopting data-driven financial strategies. The hypothesis testing confirms that the movement of assets and liabilities significantly impacts financial risk management. The study recommends enhanced financial planning, use of technology-driven financial tools, regulatory compliance, and regular audits to ensure long-term sustainability.

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Urban Co-operative Credit Societies, Asset and Liability Management, Financial Risk, Liquidity, Financial Sustainability, Risk Mitigation

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Domestic violence is one of the prevalent problems around the world and India is not an exception. It is conducted as a methodical approach to cause power and control in the confines of a house that may lead to fear, trauma and physical or emotional injuries to the victims. Motivation to commit domestic violence may be quite diverse as it can be associated with the preservation of power relations in relationships as well as the satisfaction of individual needs at the cost of other people. The prevalent domestic violence in the Indian scenario involves women perpetuated by their husbands or male relatives. It is necessary to admit that domestic violence can happen to any person, no matter his or her gender, age or sexual orientation. When domestic violence is mentioned, it is a procedural approach to gaining fear and inferiority on a person within a domestic environment, a family. The motive of this violence may be associated with the urge to uphold the power system of a person over the other or the intent to intimidate/force another person to self-gratify. Domestic Violence in India normally implies violence experienced by a person at the hands of his biological family but in particular refers to violence experienced by women at the hands of the male members or kinsmen of her family. Under the Indian law, Domestic Violence is best and most detailed defined in the Protection of Women from Domestic Violence Act (PWDVA), 2005 - Section 31. Domestic Violence, according to the law, is not only physical hitting, but any other action, failure to act or behavior that harms or puts at risk the health, safety, or well being of a woman

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Family law, Domestic Violence

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The increasing participation of women in the workforce and changing socio-economic conditions have contributed significantly to the rise of dual-career families in India. In Odisha, particularly in Nayagarh district, dual-income households are becoming increasingly common among teachers, healthcare workers, government employees, bankers, and private-sector professionals. The present study examines parenting practices, work-family balance, child-rearing patterns, and psychosocial challenges among dual-career families in Nayagarh district. A descriptive survey method was adopted with a sample of 120 dual-career couples selected through purposive sampling. Data were collected using structured questionnaires and interview schedules. Statistical tools such as percentage analysis, mean score analysis, chi-square test, and correlation analysis were used to interpret the data. The findings reveal that while dual-career families experience better economic stability and educational opportunities for children, they also face significant parenting challenges related to time constraints, emotional stress, academic supervision, and work-family conflict. The study further identifies gender disparities in household responsibilities and highlights adaptive strategies adopted by working parents. The research concludes that institutional support, flexible work policies, and shared parenting are essential for healthy family functioning in dual-career households.

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Dual-career family, parenting, work-life balance, child-rearing, Nayagarh district, Odisha, family stress.

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Self-Realization plays a critical role in successful learning. It is important to study self-realization activity and development to determine how students can be taught to better apply their realization resources better through Self-Realization control. Learning is influenced by several factors including inadequate prior knowledge, poor study skills and problems with maintaining sustained attention, cultural or language difference or the presence of learning difficulty. On the process of instructing learners to improve the learning process, distinctions can be made between realization and Self-Realization strategies. High and low level of students in respect to self-realization capacity, this test is helpful in identifying students with poor self-realization level who may need counselling or training to help them to move towards high achievement. THIS PAPER DEALS A STUDY ON SELF-REALIZATION CAPACITY AMONG COLLEGE STUDENTS IN KARUR DISTRICT-

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SELF -REALIZATION, CAPACITY, STRATAGIES

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The increasing visibility of protests and agitations by visually impaired students in Indian universities has highlighted both the progress and persistent gaps in accessibility in higher education. Despite the legislative promise of the Rights of Persons with Disabilities (RPwD) Act, 2016, which mandates inclusive infrastructure, reasonable accommodation, and equitable participation, systemic non-compliance remains widespread. Instances from institutions such as Panjab University, Jawaharlal Nehru University, Banaras Hindu University, Jadavpur University, and Cotton University reveal recurring grievances regarding inaccessible study materials, lack of digital and physical accommodations, inadequate hostel facilities, and unsafe campus environments. At the same time, certain episodes reveal behavioural complexities--such as prolonged occupation of hostels, confrontational protests, and contested uses of the RPwD Act--raising questions about dependency, entitlement, and maladaptive coping responses among students. This paper examines the causes, manifestations, and remedies of such agitations through a mixed-methods framework that integrates case study analysis, surveys, interviews, and psychosocial assessments. It explores the interplay between structural exclusion (infrastructural, digital, and institutional) and psychological responses (stress, frustration, dependency behaviours), which together fuel cycles of conflict between students and university authorities. The study further examines the dual role of the RPwD Act as both an instrument of empowerment and a source of contention. The paper concludes with recommendations for balanced reforms--ranging from accessibility audits and institutional policy clarity to counselling interventions and community-based support--that can transform adversarial dynamics into constructive inclusion.

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Visually impaired students, RPwD Act 2016, higher education, accessibility, agitation, malpsychology

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With Asian multinational corporations (MNCs) capturing a growing portion of the global market, they confront a significant internal issue referred to as the "Lingua Franca Paradox." Although English acts as the formal corporate language, the profound diversity of more than 2,300 regional languages--accounting for about 32% of the global linguistic count--and high-context cultural standards fosters a Strategic Echo Chamber. This paper explores how language obstacles go beyond mere vocabulary problems to create systemic dangers that hinder organizational alignment and innovation, frequently leading to a gap where the explicit meaning of terms is grasped, but the deeper cultural significance is misunderstood. Our qualitative examination of corporate communication evaluations and linguistic theory reveals three key friction points that hinder global operations. The initial barrier is the High-Context "Quiet" Barrier, where direct translations frequently miss implicit situational cues, resulting in a breakdown of psychological safety and local involvement. Additionally, linguistic gaps serve as structural and psychological catalysts, forming "Information Silos" that confine essential data within language-specific communities. This leads to "Strategic Drift" as intricate executive directives are either oversimplified or misrepresented to align with the language skills of local teams. Ultimately, the Hyper-Localization Movement, spurred by the emergence of regional scripts and super-apps, complicates global coordination and renders it more challenging to uphold a unified vision across various subsidiaries. To address these risks, this document suggests a three-pillar framework for a stronger communication architecture. We suggest adopting a Layered Glocal Protocol, progressing from "English-only" requirements to embrace context-sensitive templates and approved local-language creative spaces that honor cultural subtleties. Furthermore, digital integration through AI Bridges, like Enterprise Neural Machine Translation (NMT) and real-time AI captioning, can greatly lessen the cognitive load for non-native speakers and maintain strategic alignment. Ultimately, embedding Cultural Intelligence (CQ) through "Linguistic Reverse-Mentorship" initiatives and incorporating CQ metrics into leadership KPIs will guarantee that strategic directives resonate across all tiers. By converting linguistic diversity from a point of conflict into a valuable advantage, Asian MNCs can bridge the divide between the headquarters' vision and local implementation.

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Linguistic Barriers, Cross-Cultural Communication, Lingua Franca Paradox, Strategic Drift, Cultural Intelligence (CQ)

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The high rate of urbanization has enhanced congestion, safety levels, and inefficiencies at intricate intersections of traffic, so that intelligent and autonomous traffic management systems are required. This report presents a proposed multi-modal urban junction analytics, which consists of the YOLOv8-based object-detection tool and the DeepSORT-tracker to identify fine-grained motions of heterogeneous road users. Graph Neural Networks are used to model interaction between agents and changing conflict patterns, whereas Temporal Convolutional Networks make it possible to predict short-term traffic flows. Bayesian Change Point Detection detects abnormal change and incident initiation, and self-supervised contrastive learning is more robust in little literature. Drone and CCTV feeds' multi-view fusion improves the spatial coverage and eliminates occlusion. Extracted trajectory and safety indicators are organized into a downstream signal, optimizing scalable datasets. Real-world intersection validation that can be conducted experimentally indicates that there is an enhanced accuracy when estimating the traffic flow, as well as the identification of risks and adaptive signal planning. The suggested framework provides an intelligent urban traffic management framework that will have a predictive and safety-conscious base.

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Urban Traffic Analytics and YOLOv8 Deep SORT Graph Neural Networks Multi-modal Fusion Traffic Safety Predictive Signal Control.

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Coral reefs are great significance in maintenance of biodiversity and the conservation of the ecosystem; however, they are facing threats due to effects of climate change, pollution and human activities. It is imperative to keep the track of the health of the coral reef. However, the existing techniques involves the manual analysis of underwater images, which takes long time to complete and not suitable for the entire ecosystem. This project aims at developing an AI-based system to automate the analyzing the coral reef using deep learning techniques. This system employs enhanced Semantic Segmentation network to locate the coral reef in the underwater images and compute the Live Coral Cover (LCC). This system also classifies the states of the reef into healthy, moderate, degrading and poor conditions. This project has been designed to include a user-friendly web-based system where the user can upload the underwater images and get the results instantly. This system is expected to be helpful in the conservation of the ecosystem

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Coral reef monitoring, Live coral cover (LCC), Deep learning, Semantic Segmentation, Reef health classification, Underwater image analysis, Environmental conservation.

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India has been an early adopter of sustainability reporting for listed entities. In 2012, the Business Responsibility Report (BRR) as released by the Securities and Exchange Board of India (SEBI) mandated listed entities to disclose their ESG performance. Sustainability is not just a feel-good theme. It is about the imperative of surviving in a world facing climate change, running out of resources, and social problems. This study objective is to study the evolution of sustainability reporting in India and analyse the sustainability reporting trends in India. The launch of the SDG India Index in 2018 provided the impetus for the localisation push, reaffirming States and UTs as key stakeholders in this transformative journey. The SDG India Index has been consistently improved over the years to provide a comprehensive and comparative analysis of progress on the goals. In 2025, sustainability reporting in India is transitioning from a compliance exercise to a strategic imperative, driven by stricter regulations, increased demand for transparency, and technological integration. The key trends are centred around mandatory and standardized reporting, enhanced data quality and assurance, and a focus on the entire value chain.

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sustainability reporting, India, Securities and Exchange Board of India, Sustainable Development, Business Responsibility and Sustainability Report.

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Clayey soils used as subgrades in highway construction often suffer from high plasticity, excessive swelling, low bearing capacity, and inadequate strength, leading to early pavement failure. To overcome these limitations in a sustainable manner, this study investigates the effectiveness of stabilizing clay with a hybrid combination of rice husk biochar, wood biochar, and lime. Biochar, a carbon-rich and porous by-product of biomass pyrolysis, has gained attention for its ability to improve soil structure and moisture behaviour, while lime remains a proven chemical stabilizer capable of inducing pozzolanic reactions and long-term strength gain. In this research, natural clay was stabilized using rice husk biochar, wood biochar, and lime, selected based on their optimum performance ranges reported in literature and confirmed through preliminary assessment. A comprehensive experimental program was conducted, including Atterberg limits, California Bearing Ratio, Moisture test, and specific gravity tests

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Rice Husk Biochar; Wood Biochar; Lime Stabilization; Clay Subgrade; Soil Stabilization; California Bearing Ratio (CBR); Atterberg Limits; Sustainable Construction Materials; Eco-friendly Stabilizer; Soil Improvement Techniques

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The personal laws of the citizens have played a major role in helping and supporting the secular feature of our nation. This secular aspect of the Preamble allows its citizens to practice, profess, and propagate the religion of their choice as enumerated in the fundamental rights thereby creating a constant rise in need for a homogenous law for all so that no person shall feel deprived of a right. The Uniform Civil Code (UCC) finds its place under Article 44 in Part IV of the Constitution of India, 1950 as a Directive Principle of State Policy which provides that it is the duty of the state to secure a UCC throughout the country. To bring a UCC the aim is to change the personal laws of various religious communities. The personal laws include marriage, divorce, inheritance, adoption, maintenance, and succession which form the main domain to bring about requisite change in the societal norms to enact UCC. The said code differentiates between public laws and private laws. The theme of this paper revolves around the UCC being an ancient concept that has evolved into the notion of codification of a UCC. The main objective of the lawmakers has always been to foster harmony and equality amongst all but the question lies in whether the administration will be able to do so while retaining the title of being secular; whether the need to ratify an enactment which decrees the equal personal rights to all, is recurring and if so, what measures have been taken by the respective leaderships to satiate the said need. The prime objective of this paper is to crystallize the real essence of UCC by discussing variegated strands of the UCC including but not restricted to the existence of the UCC in the radicle of the Indian realm, the compelling need and requisite for the said code to be structured and tabulated by virtue of profound judgments postulated by the learned judges of the Hon'ble Supreme Court.

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Uniform Civil Code, Uttarakhand, Directive Principles of State Policy, Fundamental Rights, Article 44, Article 25

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 Abstract

improper crop selection, inefficient fertilizer usage, and delayed detection of plant diseases, which can reduce crop yield and quality. This project presents a software-based system for crop and fertilizer recommendation along with plant disease detection using machine learning techniques. The system analyzes soil parameters including nitrogen, phosphorus, potassium, pH, and moisture to recommend the most suitable crop and appropriate fertilizers based on nutrient requirements. In addition, the system detects plant diseases at an early stage using image processing and deep learning models. A convolutional neural network (CNN) is used to analyze plant leaf images and accurately identify diseases, along with suggesting suitable treatment measures. The proposed system provides a user-friendly interface and supports data-driven decision-making, helping farmers improve productivity, reduce losses, and adopt sustainable agricultural practices.

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Precision Agriculture, Machine Learning, Crop Recommendation, Fertilizer Recommendation, Plant Disease Detection, Image Processing, Convolutional Neural Network (CNN), Soil Nutrient Analysis.

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The Indian steel sector, as part of the country's infrastructural dreams and economic sovereignty, is undergoing a paradigm shift driven by Industry 4.0 principles. This change goes beyond a technological upgrade and will lead to a paradigm shift in human capital strategies. This paper examines a symbiotic and yet discordant association between digital transformation (DT) and human resource management (HRM) practice in this vital industry. The study uses a one- longitudinal case study methodology, which will utilize an in-depth approach to study, focusing on the example of Jindal Steel and Power Limited (JSPL), a well-known leader in technological adoption. Four research objectives (to map the DT initiatives at JSPL; to analyze the effect on strategic HRM functions; to define the mediating role of organizational culture and leadership in this interaction; and to synthesize a contextual framework of human-technology integration in capital-intensive sectors in the emerging economies) guide the study. Triangulation of data was based on 42 semi-structured interviews, non-participant observation, and internal document and performance metrics analysis between 2019-2024. It has been found that DT is a disruptor and enabler of HRM. Although it has resulted in the establishment of data-centric roles, AI-driven recruitment, and VR-based training, it has also contributed to the creation of a so-called digital skills dichotomy, employee anxiety, and the creation of conflicts between algorithmic management and tacit and experiential knowledge. The results of the study confirm that the success of DT does not depend on the technological sophistication but the active, human-oriented approach of HRM based on transparent change management, ongoing dialogue, and investment in the skills of the soft adaptation alongside the hard technical adaptation. The study has contributed to the emerging body of literature on HRM 4.0 in emerging economies and provides evidence-based suggestions to practitioners and policymakers, who are aiming at future-proofing the conventional manufacturing industries.

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Digital Transformation; Strategic Human Resource Management; Industry 4.0; Indian Manufacturing; Technological Change.

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Artificial Intelligence (AI) is reshaping training and development in organizations by enabling personalized, adaptive, and data-driven learning. AI-powered tools analyze employee behavior, identify skill gaps, and recommend tailored learning paths, enhancing engagement and performance. Applications include intelligent tutoring systems, Chabot's, predictive analytics, and content curation. Real-world examples from IBM, Amazon, Bank of America, DHL, Microsoft, and healthcare organizations demonstrate AI's effectiveness in improving skill development, training efficiency, and employee retention. Challenges such as implementation costs, data privacy, and algorithmic bias must be managed carefully. Future trends point to AI integration with virtual reality, adaptive learning platforms, and continuous reskilling initiatives. Overall, AI offers organizations a strategic advantage in developing a competent and agile workforce.

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Artificial Intelligence, Generative AI Revenue, HR Analytics, Enhanced Productivity, Operational Cost Reduction

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This paper presents an AI-enabled edge-based system for real-time grid fault detection and analysis. Electrical parameters such as voltage, current, frequency, and phase angle are continuously monitored using sensors. The data is processed locally on an edge device such as Raspberry Pi or ESP32 integrated with a lightweight AI model. The trained machine learning model identifies abnormal patterns and classifies fault types including single line-to-ground, line-to-line, and three-phase faults. Immediate alerts are generated and transmitted to the control center through IoT communication. By performing analytics at the edge, the system minimizes latency, reduces bandwidth usage, and ensures rapid fault isolation. This approach enhances grid reliability and supports smart grid automation. The increasing complexity of modern power systems demands fast and reliable fault detection mechanisms to ensure system stability and safety.

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KEYWORDS : Artificial Intelligence, Edge Computing, Fault Detection, Smart Grid, IoT, Power System Protection

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Recent advancements in generative modeling have significantly enhanced the capability of text-to-image synthesis systems. Despite these improvements, achieving precise spatial control over generated content remains a persistent challenge. This paper introduces an adaptive region-aware conditioning framework designed to improve controllability in diffusion-based generative models. The proposed approach dynamically integrates spatially localized conditioning signals derived from textual prompts, enabling fine-grained manipulation of specific regions within generated images. By incorporating region-level attention mechanisms and adaptive weighting strategies, the model effectively aligns semantic descriptions with corresponding spatial locations. Experimental evaluations demonstrate that the proposed method outperforms conventional conditioning approaches in terms of spatial accuracy, visual coherence, and semantic consistency. The findings suggest that adaptive region-aware conditioning provides a promising direction for controllable and interpretable text-to-image synthesis.

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Diffusion Models, Text-to-Image Synthesis, Region-Aware Conditioning, Generative AI, Spatial Control, Deep Learning.

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This paper proposes CyberFraudShield, an AI-based real-time fraud detection system designed to enhance the security of digital financial transactions. With the rapid adoption of online payment systems, fraudulent activities such as unauthorized transactions, identity theft, and phishing have increased significantly. Traditional fraud detection systems rely on static rules and delayed processing, making them ineffective against evolving fraud patterns. CyberFraudShield addresses these challenges by implementing a multi-factor risk analysis model that evaluates transactions based on parameters such as transaction amount, location, device, and time anomalies. The system assigns a dynamic risk score between 0 and 100 and classifies transactions as safe, suspicious, or fraudulent. High-risk transactions trigger real-time alerts and enable immediate user actions such as blocking or reviewing. Experimental evaluation using simulated transaction datasets demonstrates improved detection accuracy, faster response time, and enhanced visualization through interactive dashboards. CyberFraudShield provides a scalable and efficient solution for modern fraud detection systems.

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Fraud Detection, Risk Scoring, Real-Time Monitoring, Cybersecurity, Anomaly Detection, Financial Security.

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With the rapid digital transformation of educational and professional environments, certificate verification has become a critical security concern. Traditional certificate authentication systems rely on centralized repositories and manual validation processes, making them vulnerable to forgery, unauthorized modification, and operational inefficiencies. To address these challenges, this paper presents CertChain, a blockchain-based certificate authentication system that ensures secure, decentral- ized, and tamper-proof credential verification. The proposed sys- tem integrates Ethereum smart contracts, cryptographic hashing, and off-chain storage mechanisms to enable immutable certificate management. In addition, a decentralized governance mechanism is introduced to validate institutions through majority voting, ensuring trust and authenticity in certificate issuance. The system further incorporates AI-based modules for identity verification and automated marklist extraction, enhancing accuracy and preventing fraudulent activities such as deepfakes and proxy registrations. Certificates are generated as digital documents, hashed using SHA-256, and stored on the blockchain, allowing real-time verification by comparing cryptographic signatures. Experimental analysis demonstrates that the proposed system significantly reduces verification time, improves security, and eliminates dependency on centralized authorities. The integration of blockchain, artificial intelligence, and decentralized governance makes CertChain a scalable and robust solution for next- generation credential authentication systems.

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Blockchain, Certificate Authentication, Ethereum, Smart Contracts, AI Verification

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 Abstract

A constitutional study of violation of child marriage and Pocso act under Muslim law and violation of fundamental rights

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A constitutional study of violation of child marriage and Pocso act under Muslim law and violation of fundamental rights

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