Volume 14 | Issue 4 |

 Abstract

This research study is to find correlations between gaming disorder, self esteem and academic performances among the high school students of Kannur District. Most of the research existing today is in the context of internet addiction, smart phone addiction, decline in self - esteem and academic performance. But none of the studies were conducted in Indian Territory. The behaviour and addiction status of children are crucial as it brings in lot of challenges both for them as well as to the society as it triggers even the suicidal tendency. The proposal tries to establish correlation by considering a sample of 300 high school students (age 13 - 15), with a positive trend towards gaming disorder. These students will be administered with the shortet version of Gaming Addiction Scale and Coopersmith Self - esteem inventory. Also the academic performance of the subjects will be collected. The data will be analysed using Pearson's correlation coefficient(r), t tests and ANOVA.

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 Keywords

Gaming disorder, Self-esteem, Academic performance, High school students, Kannur District, Gaming Addiction Scale, Coopersmith Self-esteem Inventory, Internet addiction, Adolescent behaviour, Pearson correlation, t-test, ANOVA.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Vitamin and mineral deficiencies remain a widespread public health concern, particularly in developing regions where access to diagnostic healthcare is limited. Visible symptoms of such deficiencies frequently appear on external body parts including the skin, nails, eyes, lips, tongue, and hair, making image-based detection a practical and non-invasive screening approach. This paper presents a deep learning-based web application designed to detect vitamin and mineral de-ficiencies from images of human body parts. The proposed system employs InceptionV3, a convolutional neural network pretrained on the ImageNet dataset, fine-tuned through transfer learning to classify six categories of deficiencies: Vitamin A, Vitamin B complex, Vitamin C, Vitamin D, Vitamin KE, and Mineral deficiencies including zinc, iron, biotin, and protein. The dataset used for training and evaluation is publicly avail-able at https://www.kaggle.com/datasets/udaykarthik21bce9252/ vitamin-defficiency-dataset. The model achieves a classification accuracy of 85%. The system is integrated into a Django-based web application supporting user authentication, real-time image-based prediction, confidence score display, and downloadable health reports. This work establishes the feasibility of combining computer vision with accessible web technologies to support early health awareness in a user-friendly manner.

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 Keywords

Vitamin Deficiency Detection, Deep Learning, In-ceptionV3, Transfer Learning, Django, Medical Image Classifi-cation, Convolutional Neural Network.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Abstract Unintentional medicine overdose and prescriptionrelated toxicity have become critical public health challenges, particularly in settings with high polypharmacy and limited real-time clinical decision support. Traditional risk assessment techniques rely on manual chart review and static rules, which struggle to capture complex, evolving prescription patterns. In this paper, a machine-learningdriven framework is presented for predicting patient-specific overdose risk using routinely collected clinical and prescription data. The proposed approach utilizes supervised learning models, including Logistic Regression, Random Forest, and Gradient Boosting, to learn patterns associated with high-risk dosage combinations, comorbidities, and prior adverse events. The system is organized as a modular architecture consisting of a data preprocessing pipeline, a model training and evaluation core, and a risk scoring service that can be integrated into clinical applications. Experimental design and evaluation metrics are described to provide a reusable blueprint for academic and project implementations. The results from a prototype implementation indicate that the proposed system can achieve competitive accuracy and recall, demonstrating its potential to support early intervention and safer prescribing practices.

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 Keywords

Medicine Overdose Prediction, Machine Learning, Clinical Decision Support, Risk Scoring, Electronic Health Records.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Pesticides are widely used in agriculture and domestic environments, but their improper use poses significant health hazards. Housewives are particularly vulnerable due to their involvement in food handling and household pest control. This study aims to assess the knowledge regarding the effects of pesticides and the protective measures adopted by housewives in selected areas of Nashik, Maharashtra. A quantitative, non-experimental descriptive research design was used. A total of 100 housewives were selected using non-probability convenience sampling. Data were collected using a structured questionnaire. The findings revealed that the majority of participants had moderate knowledge regarding pesticide effects, while a smaller proportion demonstrated good knowledge. Although many participants practiced basic protective measures such as washing vegetables, the use of advanced protective practices was limited. A significant association was found between knowledge levels and selected demographic variables. The study concludes that there is a need for targeted educational interventions to improve awareness and promote safe practices related to pesticide use.

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 Keywords

Pesticides, Knowledge, Housewives, Protective Measures, Health Effects, Awareness

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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

bone fracture detection, deep learning, ResNet50, transfer learning, Grad-CAM, musculoskeletal radiograph, DI- COM, explainable AI, web-based clinical tool, two-stage classifi- cation.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Digital technology is slowly changing the way of teaching and learning in the classroom. In many educational institutions, Information and Communication Technology (ICT) is already used to support the learning process. Artificial Intelligence (AI) combined with ICT tools makes the learning process more flexible and effective. This paper examines the role of AI-assisted technologies in supporting skill development within ICT integrated classrooms. Specifically, The discussion focuses on the Indian educational context. A variety of learning activities can be observed using AI-based tools. Based on this information, these systems should provide holistic advice on education and provide feedback in time. Hence, students receive support according to their individual learning needs. The paper also discusses major Indian digital learning initiatives such as DIKSHA, SWAYAM, and PM e-VIDYA. These programmes show the potential of digital platforms to enhance access to educational resources for teachers and students nationwide. At the same time, certain challenges still remain. Issues i.e. limited digital infrastructure, lack of teacher training, and the need for responsible use of AI must be handled carefully. The study indicates that artificial intelligence has the potential to enhance skill-based learning. This occurs when it is integrated with ICT by proper planning and support systems.

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 Keywords

Artificial Intelligence (AI), ICT Integrated Classroom, Skill Development, Digital Learning, DIKSHA, SWAYAM.

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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

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Anti-dandruff, Carpaine, papaya leaves

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Creative Commons Attribution 4.0 and The Open Definition

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The rapid advancement of Artificial Intelligence (AI) has revolutionized modern healthcare, with particular impact on medical imaging and radiological diagnostics. This paper presents the AI Radiology Co-Pilot, a comprehensive intelligent system developed to assist radiologists in the detection of chest X-ray abnormalities and the automated generation of structured diagnostic reports. The system employs a ResNet-50-based Convolutional Neural Network for accurate image classification, achieving robust detection of pathological conditions including pneumonia, effusion, and cardiomegaly. To facilitate structured report generation, the system integrates Mistral-7B-Instruct, a state-of-the-art Generative AI language model, which converts model predictions into coherent clinical reports and patient-friendly summaries. Additional features include a Grad-CAM-based explainability module for visual interpretation, a multilingual interactive chatbot for patient assistance, and a Flask-based web interface enabling real-time deployment. Experimental evaluation demonstrates significant improvements in diagnostic efficiency, report quality, and clinical communication. The proposed system bridges the gap between image-based classification and language-based report synthesis, offering a unified, interpretable, and accessible AI-powered radiology workflow.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Deep Learning, Medical Imaging, ResNet-50, Generative AI, Mistral-7B-Instruct, Report Generation, Grad-CAM, Chatbot, Healthcare AI, Flask

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Urban civic management systems often suffer from inefficiencies such as lack of transparency, delayed responses, and absence of verification mechanisms in complaint resolution. This paper presents CivicSync, a full-stack web-based e-governance platform designed to bridge the communication gap between citizens and municipal authorities. The system introduces a novel geotagged image verification mechanism that ensures authenticity in both complaint reporting and resolution phases. By integrating browser-based camera access, GPS location tracking, and reverse geocoding using OpenStreetMap services, CivicSync generates tamper-evident visual proof embedded with spatial and temporal metadata. The platform follows a three-tier architecture consisting of citizens, department workers, and administrative authorities, enabling structured workflow management. Additionally, automated email notifications with visual proof enhance user trust and engagement. Experimental implementation demonstrates improved accountability, reduced fraudulent reporting, and enhanced operational efficiency in civic issue management systems

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 Keywords

( CivicSync, Urban Civic Management, E-Governance Platform, Geotagged Image Verification, GPS Location Tracking, Browser-Based Camera Access, Tamper-Evident Proof, Three-Tier Architecture, Workflow Management, Administrative Authorities, Automated Email Notifications, Operational Efficiency.)

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 Abstract

The incidence of brain strokes has increased, mainly because of lifestyle, health, and delays in early detection. Severe brain strokes result in disabilities or fatalities, thus the need for early diagnosis and prediction. This project presents a creative approach to brain stroke detection and prediction, employing a more accurate and reliable approach through the application of machine learning. The detection model employs deep learning to perform image analysis on CT scans, whereas the prediction model evaluates factors such as age, hypertension, glucose level, BMI, smoking, job type, and living environment to determine stroke risk. This approach takes advantage of existing datasets, applying advanced techniques such as image preprocessing, data augmentation, and transfer learning to boost its performance and reliability The project has also focused on improving the accuracy of the prediction model, ensuring efficient performance with varying datasets. The dual model approach has been adopted to ensure efficient brain stroke detection and prediction, allowing healthcare experts to take preventive measures at an early stage. This will not only reduce brain stroke complications but also contribute to the development of more efficient and intelligent systems

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 Keywords

Machine Learning, Deep Learning, Stroke Prediction, Medical Image Analysis

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Creative Commons Attribution 4.0 and The Open Definition