Volume 13 | Issue 8 |

 Abstract

Religion is a unified system of sacred norms, values, beliefs, and objects. In Indian society, religion plays a significant role in shaping people's lives, behavior, and interactions. People consult their religion in every aspect of life and act according to their teachings. However, modernization, globalization, and social change have drastically transformed all spheres of life. These changes have both positive and negative impacts on society. Modernization has also brought significant changes to India's religious institutions. The country is home to multiple religions, including Hinduism, Islam, Christianity, Sikhism, Buddhism, and Jainism. While modernization has led to greater tolerance and secularism, it has also resulted in the commercialization of religion, a decline in traditional religious practices, and a rise in sectarianism in some cases. This study examines the impact of modernization on religious institutions in India. This research highlights how factors such as urbanization, digital media, education, and economic growth are reshaping religious beliefs and practices in the region.

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Keywords: Religion, Modernization, Sectarianism, Secularism, Social Change

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ABSTRACT In today's digitally driven world, smartphones have become an integral part of adolescent life, particularly in urban areas. While they offer benefits in communication, learning, and entertainment, excessive use especially during night-time has raised serious health concerns. This study aims to investigate the impact of smartphone-induced sleep disruption on the risk factors associated with early-onset hypertension among urban adolescents. Using a quantitative research approach, a sample of 100 urban adolescents aged 13 to 17 years was surveyed using the Pittsburgh Sleep Quality Index (PSQI) to assess their sleep quality. Participants were categorized based on their night-time smartphone usage: those using smartphones for more than two hours at night (high users) and those using them for two hours or less (low users). Simple statistical tools such as mean, standard deviation, and independent samples t-tests were used to analyse the data. Findings revealed a significant difference in sleep quality between the two groups, with high smartphone users reporting poorer sleep. Furthermore, male adolescents demonstrated significantly higher sleep disruption scores compared to females, indicating a gender-based disparity in how smartphone use impacts sleep. The study also identified a consistent pattern between longer screen time and increased levels of sleep disturbance, a known contributing factor to hypertension. Although direct blood pressure measurement was not included in this study, the results underline the potential health risks linked to poor sleep in adolescence. The study concludes that smartphone overuse at night significantly disrupts sleep, which may indirectly increase the risk of early-onset hypertension among adolescents. This research fills a critical gap in the literature by linking digital behaviour to emerging health risks, offering a valuable foundation for health educators, parents, and policymakers to develop awareness programs and preventive strategies focused on digital wellness and adolescent health.

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 Keywords

Sleep Disruption, Adolescents, Early-Onset Hypertension, Urban Youth, Screen Time, Sleep Quality

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Women entrepreneurship is a relatively recent phenomenon that is steadily gaining momentum, driven by growing awareness of women's roles, responsibilities, and economic contributions both within the family and in society at large. However, for women entrepreneurs in India, starting and managing a business remains a significant challenge due to deep-rooted social norms and gender-based constraints. This study aims to explore the challenges and opportunities faced by women entrepreneurs, particularly in relation to financial, marketing, and production constraints. A descriptive research design was adopted for the study, with a sample of 100 respondents selected through non-probability convenience sampling in the Coimbatore District. Both primary and secondary data sources were utilized. The findings highlight the need for critical infrastructure and support systems, including access to proper workspaces, financial subsidies, raw materials, and reliable transportation facilities. It is evident that government agencies and funding institutions need to play a more proactive role in addressing these needs. Furthermore, suppliers and marketers should prioritize the promotion and distribution of products made by women entrepreneurs. The study also reveals key issues with government assistance programs, such as bureaucratic red tape, lack of timely guidance, and inadequate advisory services. To enhance the status of women-led enterprises, both government and non-governmental organizations must offer liberal support and strengthen the infrastructure and policy framework necessary for the growth of women entrepreneurship.

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KEYWORDS: Entrepreneurship, women, society, business, government.

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Abstract--Modern organizations generate massive volumes of security telemetry--from endpoints, network appliances, identity providers, and cloud services--making manual triage of threats infeasible. This paper presents an AI-driven framework for anomaly detection in security events that couples representation learning with streaming inference to surface rare, high-risk behaviors in near real time. We unify heterogeneous logs through a compact event schema, learn temporal and relational patterns via deep sequence models and graph encoders, and compute calibrated anomaly scores that adapt to environment drift. The system blends unsupervised methods (autoencoders, isolation- based detectors), weakly supervised signals (heuristics, watch- lists), and supervised fine-tuning when ground truth is available. We address practical challenges such as extreme class imbalance, concept drift, noisy labels, and high-latency pipelines, and we incorporate privacy-preserving and explainability mechanisms suitable for regulated settings. Experiments on mixed enterprise- like datasets show consistent gains in precision at low false- positive rate and significant reductions in mean time to detect. We release a set of implementation guidelines covering feature design, thresholding under uncertainty, and robust evaluation for security operations (SecOps) workflows.

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Index Terms--Anomaly Detection, Cybersecurity Analytics, Intrusion Detection, SIEM, Streaming ML, Concept Drift, Graph Learning, Explainable AI

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Fungal Classification, Deep Learning, MobileNetV3, Image Classification, Transfer Learning, Medical Image Analysis, Fungal Infection Detection, Artificial Intelligence in Microbiology, Convolutional Neural Network (CNN).

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Despite the financial security offered by home insurance, a significant proportion of homeowners in India choose not to purchase it. This study investigates the underlying factors influencing such decisions. Using a survey-based approach and statistical factor analysis, six primary determinants were identified: limited financial capacity, perceived risk, lack of trust, reliance on alternative risk management methods, psychological biases, and inadequate awareness. The findings highlight the necessity for targeted policy interventions, enhanced consumer education, and improved transparency in the insurance sector to increase adoption rates

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Home insurance, financial limitations, perceived risk, awareness, trust, behavioural factors.

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This research paper explores the intricate relationship between individual and cultural identity in post-colonial Indian English literature, with a specific focus on poetry and fiction written after India's independence. The postcolonial condition in India initiated a literary movement characterised by existential questioning, spiritual realignment, and resistance to both colonial hangovers and modern homogenisation. Indian English writers, navigating a language fraught with colonial residues, undertook the complex task of reasserting their Indian identity while participating in a global literary discourse. This paper analyses how prominent Indian English poets and novelists have responded to this cultural and linguistic predicament by blending indigenous elements with global aesthetics, creating a literary voice that is both unique and resonant. Drawing upon the works of poets such as Nissim Ezekiel, Kamala Das, Arun Kolatkar, Jayanta Mahapatra, and A. K. Ramanujan, and novelists like Arundhati Roy and Kiran Desai, this study examines how literature becomes a potent medium for reconstructing fragmented identities and cultural selfhood in a post-colonial society.

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Identity, Culture, Duality, Complexity

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This study explores the multifaceted concept of teacher professional identity through thematic analysis of reflective narratives provided by teachers. It analysed open-ended responses to identify how teachers perceive their roles, values, and the challenges inherent in maintaining a coherent professional identity. The findings suggest that identity is fluid and contextually embedded, shaped by personal values, institutional environments, professional relationships, and evolving pedagogical commitments. Implications are discussed in light of teacher education, policy reform, and sustainable professional development.

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teacher identity, thematic analysis, reflective practice, qualitative research, professional development

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This review paper examines the performance of outrigger systems in supertall structures subjected to combined seismic and wind hazards. Through comprehensive analysis of 45 contemporary studies (2020-2025). The authors, evaluated how outriggers transform structural behavior by engaging perimeter columns to enhance lateral stability beyond conventional core-only systems. Experimental wind tunnel tests and nonlinear dynamic simulations demonstrate that strategically designed outriggers achieve 18 to 35% reductions in top displacement, inter story drift, and floor acceleration-critical metrics for occupant safety and serviceability. Multi-tiered configurations optimized through machine learning algorithms show efficacy, with height ratios of 0.58 to 0.65 yielding balanced wind-seismic performance, while aerodynamic modifications at outrigger levels suppress vortex-induced vibrations by up to 24%. Hybrid innovations integrating buckling-restrained braces and tuned mass dampers further enhance resilience during seismic sequences and typhoon events, limiting residual drifts to under 0.45% and reducing collapse probability by 35 to 40%. However, these systems introduce nuanced trade-offs: stiffer outriggers increase foundation demands in soft soils, while conventional welded connections risk fatigue failure after 50,000 wind load cycles. Life-cycle assessments confirm that initial cost premiums of 7 to 14% deliver net economic benefits through 17 to 22% long-term savings in non-structural repairs, though viability depends on regional hazard profiles. Persistent challenges include soil-structure interaction effects in clay soils, torsional vulnerabilities in asymmetric plans, and fire-induced connection degradation. Crucially, research has evolved from single-hazard analysis to address synergistic typhoon-earthquake scenarios in Pacific Rim regions. This synthesis establishes evidence-based design principles for optimizing outrigger placement, stiffness, and detailing-providing engineers with actionable strategies to balance structural efficiency, economic feasibility, and occupant safety in next-generation supertall construction.

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Outrigger systems,Supertall buildings, Core-perimeter engagement, Wind-seismic interaction

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Brain cancer is a critical health condition that poses a great threat to human life due to its rapid progression and high mortality rate. Accurate and timely detection plays a pivotal role in enabling effective treatments and improving patient's survival outcomes. Recently deep learning has raised as a great tool for medical imaging analysis, particularly in the domain of automated cancer diagnosis. This study presents an efficient brain cancer classification framework based on the LeNet-5 CNN architecture, optimized for analysis of magnetic resonance imaging (MRI) scans. The proposed model leverages the inherent capability of CNNs to automatically extract hierarchical features from medical images, thereby reducing reliance on manual feature engineering. The research employs a publicly available brain MRI dataset encompassing multiple tumor types as well as non-tumorous cases. Data preprocessing steps like normalizing, resize, and augmentation were applied to enhance model generalization and mitigate over fitting. The modified LeNet-5 architecture was trained and validated using stratified value splits to ensure class representation. Model's performance evaluated using standard classification metrics, such as Accuracy, Precision, Recall, and F1-Score. Results demonstrated that the proposed algorithm achieves competitive accuracy compared to contemporary deep learning models, while maintaining lightweight architecture suitable real-time clinical applications. The findings suggest that an optimized LeNet-5 model can work as a cost effective and computationally efficient solution for brain cancer-detection, potentially assisting radiologists in early diagnosis and treatment planning. This work contributes to the growing field of AI-driven healthcare by illustrating how classical CNN architectures, when appropriately adapted, can deliver reliable diagnostic performance in medical imaging tasks.

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 Keywords

Brain Cancer Classification, LeNet-5, CNN, MRI, Deep Learning, Image Processing, Medical Image Analysis, Data Augmentation, Precision, Recall, F1-Score.

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