Volume 14 | Issue 6 |

 Abstract

Primary healthcare delivery in rural Uttar Pradesh continues to face persistent challenges such as inadequate medical infrastructure, shortage of trained healthcare professionals, delayed diagnosis, limited access to specialist consultation, and poor health data management. These constraints often result in unequal health outcomes and reduced quality of care for rural populations. Artificial Intelligence (AI) has emerged as a transformative technology with the potential to address these systemic gaps through data-driven decision-making, automation, and scalable digital solutions. This paper proposes a conceptual framework to explore the potential of AI in strengthening primary healthcare services in rural settings. The framework identifies key domains where AI can contribute significantly, including disease prediction and early diagnosis through machine learning algorithms, telemedicine-enabled virtual consultations, AI-assisted diagnostic tools, automated health records management, supply chain optimization for medicines, and predictive analytics for epidemic surveillance. Additionally, AI-powered mobile health applications can empower community health workers (ASHAs and ANMs) with real-time clinical guidance, thereby improving service delivery at the grassroots level. The study also examines enabling factors such as digital infrastructure, capacity building, ethical considerations, data privacy, and government policy support necessary for successful AI integration. By synthesizing existing literature and rural healthcare needs, this paper presents a structured model demonstrating how AI technologies can enhance accessibility, affordability, efficiency, and quality of primary healthcare. The proposed framework aims to guide policymakers, healthcare administrators, and technology developers in designing context-specific AI interventions that promote equitable and sustainable healthcare outcomes in rural Uttar Pradesh. Ultimately, the integration of AI holds significant promise in bridging healthcare disparities and advancing universal health coverage in underserved regions.

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 Keywords

Artificial Intelligence (AI), Primary Healthcare, Rural Health Systems, Healthcare Delivery, Digital Health, Telemedicine, Machine Learning, Health Informatics, Community Health Workers, Predictive Analytics, Public Health Innovation, Rural Uttar Pradesh, Health Equity, Smart Healthcare Framework, Sustainable Healthcare.

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This In Industry 4.0, engineering students are required to have strong communicative, collaborative and digital skills in addition to technical competency. Yet, language education in engineering is often isolated and not integrated with employability requirements. This paper seeks to bridge this gap by proposing a framework that combines Activity-Based Learning (ABL) with 21st-century language development and digital employability skills. Drawing on Constructivism and Experiential Learning Theory and informed by the 21st Century Skills Framework, this research synthesizes the literature to develop theoretical connections between language teaching and learning outcomes. The framework defines ABL as a practical and experiential learning process that involves cognitive processing, social interaction and reflective inquiry, all of which contribute to improved communication skills and digital employability among engineering students. The paper proposes six theoretical claims on how ABL impacts language skills and digital employability potential. This research provides an integrated model for engineering education, addressing the gap in unified approaches that link language learning to digital employability.

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 Keywords

Activity-Based Learning (ABL), 21st-Century Skills, Engineering Education, Language Competence, Digital Employability, Conceptual Framework, Experiential Learning, Constructivism

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The present study examined gender differences in attitudes toward alcohol and drug use among adolescents aged 16-19 years in Ramnagar, Uttarakhand. A quantitative, descriptive, and comparative research design was adopted. The sample consisted of 200 adolescents, including 100 male and 100 female respondents. Data were collected using a structured 31-item attitude scale measuring adolescents' attitudes toward alcohol and drug use. Descriptive statistics and an independent-samples t-test were used for data analysis. Since Levene's test indicated unequal variances, the Welch independent-samples t-test was applied. The results showed that male adolescents reported higher attitude scores (M = 81.47, SD = 21.45) than female adolescents (M = 68.72, SD = 10.17). The difference was statistically significant, t (141.36) = 5.37, p < .001. The effect size was moderate to large, Hedges' g = 0.76. The findings suggest that gender plays a significant role in shaping adolescents' attitudes toward alcohol and drug use. Male adolescents appeared to hold more favourable or permissive attitudes than female adolescents, subject to the scoring direction of the scale. The study highlights the need for gender-sensitive awareness programmes, school-based counselling, and community-level prevention strategies for adolescents in semi-urban regions such as Ramnagar.

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Keywords: adolescent attitudes, alcohol use, drug use, gender differences, Ramnagar, Uttarakhand, substance use, t-test.

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Agricultural credit is very important for improving the farm performance and for agricultural growth in India, a country which depends on agriculture for the livelihood of a large population. The present study deals with the role of agricultural loans in raising farm productivity, adoption of technology, farm income and rural development. Small and marginal farmers are generally financially constrained to invest in quality inputs, irrigation facilities and sophisticated farming technologies.

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 Keywords

Agricultural Credit, Farm Performance, Agricultural Productivity, Rural Development, Farm Income.

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 Abstract

Artificial Intelligence is the buzzword of the 21st Century. The world is on the threshold of the fourth Industrial Revolution. AI is fast appearing to be the most significant tool of making life easier for human beings. It is helping in increasing productivity . There is hardly any walk of life which remains untouched by the use of AI. A lot of technological innovations are taking place within the folds of AI itself. Generative AI, ChatGPT and DeepSeek are a few examples. In all areas of life like healthcare, education, agriculture, smart cities, infrastructure, smart mobility, transportation etc. AI and machine learning are presenting ample opportunities to improve our efficiency and working capacity on a large scale. However, simultaneously, its misuse is also causing concerns among the people. AI is being misused through deep fakes, data theft, cyber-crimes, and by terrorists, in particular. Moreover, there are ethical dilemmas also arising from the use of AI-whether it be the problem of unemployment due to automation, increasing economic inequality, or human biases in AI outcomes . Thus, in order to achieve a human-centric growth of AI, both the ethical and legal issues have to be tackled first. Otherwise, it would become a pliant tool in the hands of anti-social and unscrupulous elements roaming around us with impunity. Keeping in view of these aberrations, the U.S. and the European Union countries have passed Executive Orders and a full-fledged legislation respectively to regulate the use of AI in the interest of people at large. India first spurned the idea of making laws or formulating guidelines in this regard, but of late has changed its stance. Now, deliberations are on to regulate AI through passing legislation or issuing guidelines or something else in near future. Recent mishaps have put pressure on the government to regulate AI so that the common man may not become a target of evil-minded persons. This paper explores the various dimensions of this issue and suggests probable solutions.

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THE SCOPE AND NECESSITY OF REGULATING ARTIFICIAL INTELLIGENCE IN INDIA

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Keywords: Pesticides, Farmers, Planned Teaching Programme, Knowledge, Occupational Health, Community Health Nursing.

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Background: Skin cancer remains one of the most widespread malignancies globally, with over five million new diagnoses each year. Timely and precise identification plays a decisive role in improving patient survival. While conventional clinical assessment relies heavily on dermatologist expertise and is prone to inter-observer disagreement, artificial intelligence offers a promising avenue for standardized, automated detection. This study presents a dual-framework investigation into deep learning methods for binary dermoscopic image classification. The first framework evaluates a custom convolutional neural network alongside four edge-detection-enhanced variants that incorporate the Laplacian of Gaussian, Prewitt, Scharr, and Sobel operators. The second framework benchmarks five contemporary pretrained transformer-family architectures--Swin Transformer, ConvNeXt-Base, CAFormer-S18, MaxViT-Tiny, and CoaT-Lite-Medium--fine-tuned via transfer learning. Experiments were conducted on two publicly available dermoscopic datasets of differing scale: a curated HAM10000 subset (3,297 images) and the Kaggle Skin Cancer: Malignant vs. Benign collection (10,605 images). Among CNN variants, the Scharr operator consistently yielded the strongest results, achieving an AUC of 0.9564 on the larger dataset and the highest CNN accuracy of 79.55% on the smaller dataset. Among transformer-family models, ConvNeXt-Base attained the best overall accuracy of 90.61% with weighted precision, recall, and F1-score each reaching 0.90-0.91 on the HAM10000 subset.

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 Keywords

Skin Cancer Detection; Vision Transformer; ConvNeXt; Edge Detection; Convolutional Neural Network; Transfer Learning; Dermoscopy; Medical Image Classification; ROC-AUC

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Creative Commons Attribution 4.0 and The Open Definition

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Prameha, Vamana Dhauti, Virechana

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The revival of single-party dominance and the crisis of regional politics: The changing role of regional parties in the national perspective

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The revival of single-party dominance and the crisis of regional politics: The changing role of regional parties in the national perspective

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

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Keywords: Infant and Young Child Feeding; Maternal Knowledge; Structured Teaching Programme; Malnutrition; Pre-Experimental Study; Assam

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