Volume 14 | Issue 5 |

 Abstract

Language is not merely a system of words but a living expression of culture, identity, and human connection. In a country like India, where linguistic diversity reflects centuries of civilization and tradition, the significance of Indian languages becomes profound. They serve as vessels of emotion, philosophy, and collective memory. However, globalization and the growing dominance of English have created a gap between cultural inheritance and contemporary communication. This paper explores the importance of Indian languages in shaping thought and preserving cultural integrity, while also analyzing how modern Indian author Preeti Shenoy employs English as a tool to express Indian values, emotions, and relationships. Although her works are written in English, they echo the rhythm, simplicity, and warmth of Indian linguistic traditions. Through her novels, Shenoy successfully bridges the local and the global--demonstrating that language, regardless of its medium, can retain the essence of Indian identity when rooted in authentic emotion and cultural consciousness.

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 Keywords

Language, Indian Literature, Preeti Shenoy, Cultural Identity, Indian English, Communication, Indian Languages, Globalization

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Ovarian cysts are among the most common gynecological disorders affecting women across various age groups and may lead to serious health complications if not detected and classified at an early stage. Accurate identification of cyst types such as simple, complex, and polycystic ovarian conditions plays a critical role in determining appropriate treatment strategies and improving patient outcomes. However, conventional diagnostic approaches primarily depend on manual interpretation of ultrasound images by radiologists, which can be time-consuming, subjective, and prone to inter-observer variability. To address these challenges, this research proposes OvaSense, an intelligent deep learning-based diagnostic framework designed for automated detection and classification of ovarian cysts using ultrasound imaging. Furthermore, the OvaSense system is designed with scalability and usability in mind, allowing integration into clinical workflows and assisting healthcare professionals in early diagnosis and treatment planning. The results highlight the effectiveness of combining object detection and deep learning-based classification techniques for medical image analysis, making the proposed framework a promising decision-support tool for modern intelligent healthcare systems.

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 Keywords

Ovarian Cyst Detection, Deep Learning, YOLOv8, CNN, VGG16, Ultrasound Imaging, Medical Image Analysis, Artificial Intelligence in Healthcare.

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This paper focuses on the importance of the inclusion of the Indian Knowledge System into the concept of the New Education Policy 2020 aimed at changing the education system in India by introducing inclusiveness, interdisciplinary, and contextual learning. Although the importance of indigenous knowledge traditions in policy has increased, academic studies of how these are being incorporated in the modern educational reforms are scarce. In this gap, this paper intends on critically evaluating the applicability, extents, and consequences of integrating the Indian Knowledge System into mainstream curricula and pedagogy. The study presents, based on a qualitative policy and document analysis of the New Education Policy 2020 and supporting academic literature, that the incorporation of indigenous knowledge improves epistemic diversity and cultural sustainability and leads to holistic and learner-centered education. The results also indicate that integrating the Indian Knowledge System has a potential to close the gap between the traditional wisdom and the contemporary science, which will enhance curriculum design, pedagogical methods, and teacher education. The research enhances the literature on the topic by providing a theoretically informed and policy-based approach to understanding the process of indigenous knowledge integration in higher education. It finds that proper implementation of Indian Knowledge System with New Education Policy 2020 has far-reaching consequences to the governance of education, innovative knowledge curricula, and the sustainability of the knowledge ecosystem in India in the long run.

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 Keywords

Indian Knowledge System, New Education Policy 2020, Indigenous Knowledge, Higher Education Reform, Curriculum Integration, Educational Policy

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This research presents the design and implementation of a Real-Time Face Recognition System for CCTV Surveillance using deep learning techniques to enhance public safety and surveillance efficiency. The system automatically detects and identifies individuals from live video streams, enabling reliable recognition of suspects or missing persons in real time. It integrates face detection, feature extraction, and similarity matching to achieve high recognition accuracy while handling variations in pose, lighting, and orientation. Additionally, the system supports dual-mode surveillance by incorporating vehicle detection and license plate recognition, allowing comprehensive monitoring of both individuals and vehicles. The proposed approach is capable of processing multiple camera feeds simultaneously while maintaining low latency and real-time performance. It generates instant alerts upon successful identification, ensuring quick response and decision-making. Overall, the system reduces dependence on manual monitoring, minimizes human error, and provides an efficient, scalable solution for modern intelligent surveillance applications.

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Keywords: Face Recognition, CCTV Surveillance, FaceNet, MTCNN, YOLOv8, License Plate Recognition, Real-Time Alert, Deep Learning.

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The abrogation of Article 370 in 2019 marked a significant constitutional and administrative shift in Jammu and Kashmir,resulting in greater alignment with national policy frameworks.In this context,the implementation of the National Education Policy 2020 assumes particular importance, as it represents a comprehensive reform agenda aimed at transforming India's education system in terms of Access,Equity,Quality and Governance.This paper presents a conceptual assessment of the National Education Policy in Jammu and Kashmir in the Post Article 370 period,focusing on the changing structures of educational governance and policy integration.Drawing on constitutional provisions,policy documents and existing scholarly literature,the study examines how the removal of special status has reshaped the institutional environment for education in the region.The paper conceptually analyses key components of NEP related to school education,higher education and discusses emerging opportunities and challenges with in the regional context.The study emphasizes the importance of context-sensitive implementation of national education reforms and identifies the need for future empirical research on educational outcomes in Jammu and Kashmir

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 Keywords

National Education policy, Article 370, Educational Governance, Jammu and Kashmir

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The increasing threat of climate change poses significant challenges to global agricultural productivity, food security, and environmental sustainability. Climate Smart Agriculture (CSA) is the most suitable strategic framework to address the triple challenge of sustainably increasing productivity, reducing greenhouse gas emissions and enhancing resilience. This review provides current evidence on CSA as a roadmap for sustainable farming and long-term food security. The conceptual framework of CSA is explained with its three core pillars, namely productivity, adaptation, and mitigation and its link to sustainable agriculture and food system resilience. Key CSA practices, including conservation agriculture, agroforestry, crop diversification, integrated nutrient and pest management and climate-resilient crop varieties, were studied. The role of digital technologies such as remote sensing, crop modelling, and artificial intelligence in enhancing farm-level decision-making is discussed, along with persistent barriers to adoption among smallholder farmers. Empirical studies from Africa and other developing regions highlight the potential of CSA to improve yields, household nutrition, and climate resilience, while also highlighting the influence of socioeconomic conditions, governance, and policy design. The paper concludes with research gaps and policy recommendations for mainstreaming CSA within national agricultural strategies and global sustainability agendas, particularly the Sustainable Development Goals.

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 Keywords

Climate Smart Agriculture, sustainable agriculture, food security, climate resilience, smallholder farmers, green house gas mitigation.

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Creative Commons Attribution 4.0 and The Open Definition

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Keywords--Machine Learning, Sleep Quality Prediction, Smartphone Usage Patterns, Random Forest Regression, Predictive Modeling, Digital Wellness, Behavioural Analytics

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Creative Commons Attribution 4.0 and The Open Definition

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In recent lots of people are experiencing depression, anxiety, or stress and these conditions really damage people's health. The problem is well recognized, but most individuals do not receive a proper diagnosis or treatment due to the social stigma associated with mental disorders, lack of mental health services, and our traditional method of diagnosing diseases, which is dependent upon self-reporting. Luckily, artificial intelligence is improving rapidly and because we have so much digital information, we can now create new algorithms based on data to identify mental health problems. This project is about using artificial intelligence to find mental disorders and it does this by looking at both organised and less organised information using machine learning. In order to make things clear, it uses traditional approaches such as Logistic Regression, SVM, and Random Forest; as well as sophisticated deep learning models such as LSTM and BERT. The older approaches are good because they are easy to understand and work brilliantly with straightforward information, while Deep Learning models are for finding hidden patterns and using those to predict what's going on. The system uses a chatbot to get information as it's needed. Accuracy-wise, the proposed hybrid approach shows excellent performance accuracy, having an accuracy prediction rate of 92.50% with the AUC-ROC score of 0.9759. This means it's more accurate than any of the models used on their own, but it is just as good at being precise, and at finding all the relevant information.

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 Keywords

Artificial Intelligence, Mental Health Prediction, Machine Learning, NLP, Chatbot, Lifestyle Data, Digital Psychiatry

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Nursing practice is changing internationally as a result of the healthcare industry's fast digital transformation, putting nurses at the forefront of technology-driven care delivery. Since they make up about 59% of the healthcare workforce worldwide, nurses are essential to the effective implementation of digital health advances. Digital technologies like artificial intelligence (AI), telemedicine, and electronic health records (EHRs) are being used more and more to fill staffing gaps and improve treatment efficiency as the global healthcare sector is expected to have a deficit of 5.7 million nurses by 2030. Digital solutions have proven to have the capacity to enhance productivity, lower mistakes, and improve patient outcomes on a global scale. Artificial intelligence (AI) and robots are revolutionizing clinical decision-making and patient monitoring, allowing nurses to provide accurate, data-driven care. Additionally, real-time data gathering is made easier by digital health systems, which enhance population health management and promote evidence-based nursing practice. Government initiatives like national digital health programs and more private sector investment are driving a major digital transformation in healthcare in India. The COVID-19 pandemic further accelerated the use of digital platforms and telemedicine, increasing accessibility, especially in underserved and rural areas. According to recent estimates, hospitals in India are progressively using AI and digital solutions to lessen burden; these technologies may save medical workers two to three hours of their daily workday.

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 Keywords

:- Digital transformation, Cutting edge technology, AI, Telemedicine and global health workforce.

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Abstract: Scheduled tribes of Jangle Mahal, eastern fringe of Chotanagpur plateau region are socio-economically backward and marginalised ethnic groups. Tribal people accounts 8.6 percent of India's total population. Since, after seven decades of independence, Scheduled Tribes (ST) are still at the margin of society. This study seeks a deeper insight into the demographic situation of the tribal communities. Primary data were collected from 117 and 114 sample houses of the Sabar and Santal communities, drawn from 9 selected villages following a stratified random sampling technique. The result indicates that Santals perform better in education than Sabar community. Both communities have high sex ratio compared to the national sex ratio. The average dependency ratio of the Sabar community is more (47.8 %) than Santal community (42.02 %). Also, the Sabar community show higher Child Women Ratio (284.72), indicating a higher level of fertility than the Santals (235.29). Sabar has a nuclear family system, with average family size of 3.65 and Santals shows joint family system with average family size of 4.59. The unemployment problem is very high among the Sabar community. The study reveals that illiteracy, a greater degree of dependency, and limited access to the mainstream socio-economic activities make the tribal people vulnerable. Collective and coordinated efforts of Govt. policy makers, planners, economists, and social scientists may formulate community specific plan for economic as well as social upliftment of these marginalised groups.

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 Keywords

Tribal Society, Literacy, Dependency Ratio, Child Women Ratio

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Creative Commons Attribution 4.0 and The Open Definition