Volume 14 | Issue 5 |

 Abstract

Abstract: This research introduces an integrated machine learning framework for predicting multiple diseases based on patient data. By combining various healthcare data sources, including electronic health records, laboratory results, genomic information, and lifestyle factors, our approach develops classification models for diseases such as diabetes, heart disease, breast cancer, liver disease, and Parkinson's disease [1]. These models are deployed in an intuitive web application built with Stream lit, enabling users to input relevant health parameters and receive immediate risk predictions. Model performance is evaluated using metrics such as accuracy, precision, recall, F1-score, and AUC. Our findings indicate that a unified multi-disease prediction system is feasible and can support early detection, targeted interventions, and context-aware decision-making in healthcare [2][3]. Our results demonstrate the feasibility of a multi-disease prediction system in facilitating early detection and targeted interventions. This highlights the growing importance of data-driven, context-aware ML models in healthcare [4][5].

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Keywords--Machine learning, disease prediction, healthcare, multi-disease, Streamlit.

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Women in India are recognized as one of the nation's most valuable human resources. While working women in India symbolize progress and empowerment, they often face multiple challenges that impact their health and safety. Among these challenges, the health concerns of women working night shifts have emerged as a critical issue requiring immediate attention from public health experts and policymakers. This group is particularly vulnerable to a range of health problems, including overweight and obesity, cardiovascular diseases, diabetes, sleep disturbances, stress, depression, anxiety, and workplace-related injuries. Beyond the physical and mental toll, these women also encounter significant social difficulties. Collectively, these issues contribute to a broader burden on Indian society, leading to increased rates of morbidity, disability, mortality, and socioeconomic costs. There is an urgent need for health promotion policies and targeted programs that specifically address the needs of night-shift working women. This paper aims to explore the genesis and implications of night shift work in the Indian context and examines its effects on women's health. The primary aim of the research to analysis working conditions of the study is to analyze the working conditions of women in night shifts and assess the impact on their overall well-being.

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A STUDY ON CHALLENGES FACED BY WOMEN IN NIGHT SHIFT EMPLOYMENT IN DELTA ELECTRONICS WITH SPECIAL REFERENCE TO VELLORE DISTRICT

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The rapid expansion of interdisciplinary undergraduate programs has increased the complexity of academic decision-making among higher education aspirants. Although centralized admission platforms simplify institutional admission procedures, they often lack intelligent academic guidance systems capable of assisting students in selecting suitable academic programs aligned with their abilities, interests, and career goals. The Degree Online Services Telangana (DOST) platform provides a unified admission environment for undergraduate programs in Telangana State. However, the present admission process mainly focuses on administrative automation and does not support personalized course recommendation. This study presents a hybrid artificial intelligence-based recommendation model for undergraduate course selection within the DOST ecosystem. The proposed model combines collaborative filtering, latent factor learning, and sentiment-aware analysis to generate personalized course recommendations. Academic performance indicators, contextual attributes, peer influence patterns, and qualitative feedback representations are integrated to estimate recommendation suitability scores. Mathematical formulations for similarity computation, latent interaction learning, recommendation fusion, and optimization are incorporated to establish a theoretically grounded recommendation architecture. A preliminary validation study based on survey observations highlights the necessity of intelligent recommendation support mechanisms within centralized admission environments. The proposed model provides a theoretically grounded approach for integrating academic performance, contextual attributes, and sentiment-aware learning into undergraduate course recommendation. The study contributes a domain-specific recommendation architecture for the DOST ecosystem and offers a foundation for subsequent empirical evaluation and large-scale deployment.

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Recommender Systems, DOST, Sentiment Analysis, Collaborative Filtering, Educational Data Mining, Machine Learning

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Work-life balance is increasingly critical for nurses as rising workloads, emotional strain, extended shifts, and family obligations converge to threaten their well-being and job performance. Nurses are essential to delivering high-quality healthcare, yet many face difficulty reconciling professional responsibilities with personal life, particularly in hospital and clinical settings. This study examines the principal challenges nurses encounter in maintaining work-life balance, including staffing shortages, unpredictable schedules, emotional labor associated with patient care, and the dual demands of care-giving at home. It further investigates how sustained occupational stress affects nurses' physical health--manifesting as fatigue, sleep disturbances, and somatic complaints--and mental health, presenting as burnout, anxiety, and depressive symptoms. The research emphasizes the protective roles of family support, organizational measures (such as flexible scheduling, adequate staffing, and mental health resources), and evidence-based workplace policies in mitigating stress and enhancing resilience. By synthesizing empirical findings and practical interventions, the study argues that fostering a supportive work environment and promoting policies that acknowledge nurses' personal responsibilities are essential to improving their quality of life. Achieving a healthy work-life balance is associated with higher job satisfaction, reduced turnover intent, lower absenteeism, and improved patient care outcomes. The article concludes that targeted organizational strategies and social support mechanisms are vital for sustaining a resilient nursing workforce capable of delivering safe, compassionate, and effective care.

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work-life balance, nurse well-being, occupational stress, organizational support

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Uttarakhand's socioeconomic stability depends on the Kumaon Himalaya's freshwater resources, which include its famous lakes and perennial rivers. Climate variability and human activity are placing greater strain on the Kumaon region of the Uttarakhand Himalaya, known for its lacustrine systems and complex network of spring-fed rivers. The Gola, Kosi, and Saryu rivers, as well as the central Kumaon lakes (Nainital, Bhimtal, and Naukuchiatal), are among the important aquatic bodies in the area where the spatial and temporal dynamics of freshwater quality are examined in this study. A spatial study reveals significant variation in water chemistry, and land-use patterns and lithological weathering mostly control this variation. In comparison to higher-altitude, pristine headwaters, heavily urbanized catchments, especially those near Nainital and the lower sections of the Gola River, have greater levels of Total Dissolved Solids (TDS), Electrical Conductivity (EC), and nutrient enrichment (nitrate and phosphate). The Lesser Himalayas' limestone-dominant geology is reflected in the hydrogeochemical facies, which are primarily Ca-Mg-HCO3 types. However, rising Na-Cl and SO4 signatures in some zones show the growing impact of domestic sewage and agricultural runoff. The Indian monsoon cycle provides a clear definition of temporal trends. Due to decreased dilution capacity and peak tourist influx, water quality frequently declines throughout the pre-monsoon (summer) season, resulting in lower dissolved oxygen (DO) and greater biochemical oxygen demand (BOD). Conversely, the monsoon period introduces high turbidity ( >50 NTU) and increased coliform counts due to surface runoff and sediment transport. Post-monsoon phases generally show a recovery in water clarity and DO levels (7.5-11.0mg/L). The findings suggest that while most remote spring sources remain potable, downstream river stretches and urban lakes are increasingly vulnerable to eutrophication and heavy metal contamination (specifically Pb, Fe, and Cr). The study emphasizes the need for integrated watershed management and more rigorous seasonal monitoring to preserve the ecological integrity of these vital Himalayan water resources.

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Kumaon Himalaya, Water Quality Index (WQI), Physicochemical Parameters, Spatial, Temporal, River.

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Prominent Arab scholars of the pre-medieval period, renowned scholars of the European Renaissance as well as many modern scholars, have openly acknowledged the contributions of ancient India to the pursuit of knowledge and science. According to them, among the ancient civilizations, India was the foremost in the cultivation of knowledge and scientific inquiry. Naturally, the tradition and structure of intellectual pursuits in ancient India deserve discussion. We are familiar with the universities and centers of higher learning of ancient India such as Taxila, Nalanda, Vikramashila, Mithila and few others. The fact is that higher learning centres had developed virtually in almost every part of the Indian subcontinent during that era. And we are not acquainted with most of these. The objective of this discussion is to present a concise overview of the higher education institutions of ancient India.

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Universities, Vihara, Gurukuls, Brammapuris, Ghatikas, Maths, Brammapuris

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The rapid development of artificial intelligence (AI) has fundamentally transformed the understanding of intelligence in the modern world. While human intelligence has long been associated with consciousness, moral reasoning, and emotional depth. AI represents a computational and data-driven form of intelligence capable of performing complex tasks with remarkable efficiency. This paper explores the ethical boundaries between artificial intelligence and human intelligence, focusing on differences in moral agency, decision-making, accountability, and social impact. It argues that although AI can simulate aspects of human cognition, it lacks intrinsic ethical awareness and depends heavily on human-designed frameworks. The study further examines the risks associated with delegating ethical decisions to machines, including bias, lack of accountability, and over-reliance on algorithmic authority. By analyzing contemporary research and ethical debates, this article highlights the necessity of maintaining human-centered control over AI systems. Ultimately, it concludes that ethical boundaries must be clearly defined to ensure that AI serves as a supportive tool rather than a replacement for human moral judgment.

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Human, intelligence, Artificial, ethical, decision making

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Creative Commons Attribution 4.0 and The Open Definition

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Bhutan is cornerstone of India's neighbourhood first policy. The India-Bhutan ties are characterised by mutual warmth and goodwill, strengthened by frequent high-level exchanges. This bond gained significant importance following India's independence as both nations have established deep-rooted cultural, historical, geographical, social, and economic connections.This study analysis examines the multidimensional nature of their partnership, which has evolved from the 1949 Treaty of Friendship into a comprehensive framework encompassing political, economic, security, energy, and cultural cooperation. And contemporary Geopolitical situation both countries have shared mutual interest. Bhutan holds significant importance for India under its "Neighbourhood First Policy" (NFP). This policy emphasises cooperation based on the principle of non-reciprocity and demand-driven specific needs and requirements of neighbouring countries.

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Strategic Analysis:- India Bhutan multidimensional relations and Mutual interest

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Smart backpacks are emerging as an effective solution for enhancing personal safety, security, and convenience through the integration of Internet of Things (IoT) technologies. This paper presents the design and implementation of an IoT-enabled intelligent backpack for real-time security and emergency monitoring. The proposed system incorporates an Arduino Uno as the central controller, integrated with a GPS module, NodeMCU ESP8266, ESP32-CAM, SOS switch, buzzer, LED indicator, LCD display, battery unit, and mobile charging facility. The GPS and NodeMCU modules provide real-time location tracking and wireless communication with an Android application, enabling remote monitoring of backpack status and user location. The ESP32-CAM enhances security by supporting image capture and surveillance functions, while the SOS switch facilitates emergency alert generation during critical situations. Visual and audio notifications are provided through the LED indicator and buzzer to improve user awareness. The battery-powered design also offers portable charging support for mobile devices. Experimental evaluation demonstrates reliable communication, effective emergency response, and continuous monitoring capabilities, making the proposed smart backpack suitable for students, travelers, and working professionals.

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Internet of Things (IoT), Smart Backpack, Arduino Uno, ESP32-CAM, GPS Tracking, Emergency Monitoring, NodeMCU ESP8266, Android Application, Security System, Wireless Communication.

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