Volume 14 | Issue 4 |

 Abstract

India has made remarkable progress in its journey towards becoming a digitally empowered society and knowledge economy. From expanding internet access in remote villages to revolutionising public service delivery through digital platforms, the country has bridged the urban-rural divide like never before. The digital economy, which contributed 11.74% to the national income in 2022-23, is projected to grow to 13.42% by 2024-25, driven by advancements in artificial intelligence, cloud computing and digital infrastructure.

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 Keywords

Digitalization, Internet, Telecom, Digital tools, 5g Connectivity, Rural India, India AI machine

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Stress is a pervasive factor in modern life, strongly linked to mental health disorders such as depression and anxiety, as well as physical illnesses, including cardiovascular disease. Traditional stress assessment methods, such as self-reports, cortisol testing, and EEG monitoring, are limited by subjectivity, invasiveness, or impracticality for continuous use. Advances in artificial intelligence (AI) and machine learning (ML) have created new opportunities for noninvasive, real-time stress detection through facial expression recognition, multimodal monitoring, and hybrid ensemble systems. This review synthesizes literature published between 2021 and 2025 and compares methodologies, datasets, evaluation metrics, and applications. We highlight the strengths, limitations, and future research priorities, with an emphasis on explainable AI, dataset diversity, and ethical deployment. The findings suggest that AI-based stress detection systems hold transformative potential in clinical, workplace, and personal wellness contexts, although further validation and regulation are essential for large-scale adoption.

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 Keywords

Stress detection, Facial expression recognition, Machine learning, Artificial intelligence, Mental health monitoring

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Creative Commons Attribution 4.0 and The Open Definition

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An extensive variety of biological processes and pharmaceutical treatments make use of heterocyclic compounds, which are a huge category of organic molecules that comprise nitrogen and sulfur. These compounds are utilized in a wide number of applications. A vast number of valuable chemicals, both those that occur naturally and those that are created by people, require these heterocycles in order to have a structure that is complete. For instance, antibiotics, antifungal medicines, anticancer pharmaceuticals, anti-inflammatory pharmaceuticals, antiviral pharmaceuticals, and anticonvulsant pharmaceuticals are all examples of compounds that fall under this category. The nitrogen and sulfur atoms that are contained within the ring system have a significant influence on the manner in which the electrons of the molecules are dispersed, the manner in which they react chemically, and the degree to which they attach to biological targets such as enzymes, receptors, and nucleic acids. This influence is exerted by the ring system. The biological relevance of nitrogen and sulfur heterocycles is described in this review, with a special emphasis on the ways in which their structure influences their function, how they act, and the potential that they have as medications. Specifically, the discussion focuses on the ways in which their structure influences their function. In the realm of medicinal chemistry and medication research, the use of these heterocycles has increased even more as a consequence of recent developments in the manufacturing of these heterocycles and the alteration of their functions. It has been determined by the most current research in the field of pharmacology that heterocyclic compounds that include nitrogen and sulfur continue to be a key component in the process of developing possible new drugs.

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 Keywords

Nitrogen, Sulfur heterocycles, Antibacterial, Antifungal, Antimicrobial agents.

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Creative Commons Attribution 4.0 and The Open Definition

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The rapid growth of mobile health and fitness applications has increased user awareness of physical activity and wellness; however, most existing systems function primarily as passive trackers that record historical data rather than actively supporting adaptive fitness planning. These applications commonly rely on rigid schedules and generic recommendations, which fail to accommodate the dynamic and unpredictable nature of modern lifestyles. As a result, users--particularly students and working professionals--often experience reduced adherence and high dropout rates. This review paper examines recent research on fitness goal management systems, with a focus on personalization strategies, adaptive scheduling techniques, and intelligent planning approaches. Traditional fitness trackers, rule-based planners, and artificial intelligence driven recommendation systems are analyzed to evaluate their capabilities and limitations. The review identifies key research gaps, including limited real-time schedule adaptation, insufficient user modeling, and weak integration of dietary planning with physical activity management. Furthermore, existing systems often lack constraint-aware mechanisms to reschedule missed workouts in response to changing user availability. Based on the analysis, this paper highlights the potential of adaptive and personalized dynamic fitness goal systems that leverage constraint satisfaction and intelligent scheduling techniques. Such systems can proactively generate and update workout and meal plans in real time while respecting individual lifestyle constraints. The findings emphasize the need to shift from static fitness tracking toward flexible, user-centered planning systems to improve long-term engagement and sustainability.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Adaptive Fitness Systems, Personalized Goal Planning, Dynamic Scheduling, Constraint Satisfaction, Health Informatics

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Edge AI, Precision agriculture, IoT-based agricultural monitoring, Real-time sensing, Resource-constrained devices, Event-driven communication, Model compression, On-device inference, Smart farming systems

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Creative Commons Attribution 4.0 and The Open Definition

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Cardiovascular disease continues to be one of the primary contributors to mortality across the globe. The inability to detect its early signs in time leads to delayed treatment and, in many cases, fatal outcomes. This paper presents a machine learning based prediction system for heart disease that makes use of two well-known classification algorithms, namely Decision Tree and K-Nearest Neighbors (KNN). The Decision Tree model is chosen because it provides rule-based outputs that are easy to understand and interpret, making it particularly suitable for medical applications where reasoning behind a decision matters. The KNN model, on the other hand, works by identifying similarities between a new patient record and previously seen data, offering reliable classification results. The system is built on the UCI Heart Disease dataset, which includes clinical attributes such as age, resting blood pressure, serum cholesterol, fasting blood sugar, maximum heart rate, and chest pain type. Before training the models, the dataset is carefully prepared through steps like missing value treatment, feature normalization, and standard scaling. Both classifiers are trained and tested using performance indicators including accuracy, precision, recall, F1-score, and confusion matrix analysis. The system also features a simple web-based interface through which a user can enter patient details and receive an instant prediction. This work aims to support early diagnosis of heart disease and assist healthcare professionals in making faster and more informed decisions.

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Heart Disease Prediction, Decision Tree, K-Nearest Neighbors, Machine Learning, UCI Dataset, Classification, Healthcare, Data Preprocessing

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Creative Commons Attribution 4.0 and The Open Definition

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Parental divorce has emerged as a significant social issue influencing children's emotional stability, educational performance, and social integration. Although divorce rates in India remain comparatively lower than in Western countries, states such as Kerala have experienced a rapid increase in marital dissolution over the last decade. The present study examines the social, economic, and psychological effects of parental divorce among children in Kerala, using empirical data collected from 600 children of divorced parents. Employing a descriptive-analytical research design, the study investigates differences in children's experiences based on gender, education, and religion. The findings indicate that parental divorce significantly affects children's emotional well-being, academic concentration, social relationships, and economic security, with female and school-going children exhibiting higher vulnerability. The study highlights the urgent need for integrated sociological, educational, and psychosocial interventions to support children from divorced families.

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Creative Commons Attribution 4.0 and The Open Definition

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Socio-Economic and Psychological, Parental Divorce

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Creative Commons Attribution 4.0 and The Open Definition

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Over the past ten years, the relationship between organisations and society has been the subject of intense discussion, often accompanied by harsh comments. To meet the requirements of both present and future generations, it is imperative to protect the integrity of natural systems and adhere to international norms that safeguard fundamental, social, and environmental principles. Since employees are strategically important in assisting the company in achieving its requirements, their involvement is crucial to the success and effectiveness of Corporate Social Responsibility (CSR). With employees' active participation, an organisation's chances of accomplishing sustainable goals are greatly reduced, which is why employee environmental empowerment is crucial for CSR projects.

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Corporate Social Responsibility, Pro-Environmental Behaviour, Meta-Analysis, CSR, Subgroup Analysis on Type of Industry

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Abstract This research paper investigates the effectiveness of constructivist learning approaches in higher education by analyzing both existing literature and empirical data. Constructivist learning theory emphasizes active engagement, personal experiences, and social interactions to construct knowledge, contrasting with traditional lecture-based methods. This study examines how constructivist methodologies impact students' academic achievement, retention, engagement, and self-regulation. A mixed-methods approach was used, incorporating surveys, academic records, and interviews with undergraduate and graduate students across multiple disciplines. Quantitative results indicate that students exposed to constructivist learning environments exhibit significantly higher academic achievement, engagement, and self-regulation compared to those in traditional settings. Qualitative findings support these results, highlighting increased motivation, deeper conceptual understanding, and improved collaboration. The findings suggest that constructivist learning fosters more meaningful educational experiences, advocating for the adoption of active learning strategies in higher education. The study provides valuable insights for educators, curriculum designers, and policymakers on the benefits of constructivist pedagogy and its potential to improve student learning outcomes.

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Creative Commons Attribution 4.0 and The Open Definition

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Keywords: Constructivist learning, higher education, academic achievement, student engagement, self-regulation, educational outcomes

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Influencer marketing has emerged as a dominant force in digital commerce, particularly in shaping how Generation Z (born 1997-2012) discovers, evaluates, and purchases products. This paper investigates the extent to which social media influencers alter the buying behaviour of Gen Z consumers, drawing on a primary Google Form survey (n = 16) conducted in April 2026 alongside a comprehensive secondary review of marketing literature and established consumer behaviour theories. Survey results show that 62.5% of respondents purchased a product on an influencer's recommendation, while 81.25% rated influencer marketing as superior to conventional advertising. Instagram dominated platform preference (75%), with trust and product reviews consistently cited as the foremost purchase triggers.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Trust, Buying behaviour, social media, Digital marketing , Consumer behaviour, Generation Z, Influencer Marketing

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Creative Commons Attribution 4.0 and The Open Definition