Volume 13 | Issue 4 |

 Abstract

Knee osteoarthritis (KOA) is a degenerative joint condition that affects millions globally, especially older adults. Timely and accurate diagnosis is essential to slow disease progression. This paper presents a deep learning-based system for automated KOA detection using X-ray images, graded according to the Kellgren and Lawrence (KL) scale. Four convolutional neural networks--ResNet-34, VGG-19, DenseNet-121, and DenseNet-161--are fine-tuned through transfer learning and combined using an ensemble strategy. To model the ordered nature of KOA severity, Conditional Ordinal Regression (CORN) is employed. The system integrates Explainable AI (XAI) using Eigen-CAM visualizations to highlight diagnostic regions in the X-ray images. Evaluation on the Osteoarthritis Initiative dataset shows state-of-the-art results, with 98% accuracy and a Quadratic Weighted Kappa (QWK) score of 0.99. The final model is deployed via a Streamlit web application, offering an accessible interface for real-time diagnosis. The approach provides a reliable and interpretable tool for assisting radiologists in KOA assessment.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Knee Osteoarthritis, Deep Learning, Kellgren-Lawrence Grading, Explainable AI.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In modern educational institutions, effective communication is a key pillar of administrative efficiency. Traditional notice boards, often paper-based and manually updated, pose significant limitations in terms of scalability, timeliness, and environmental sustainability. This paper presents the design and implementation of an AI-powered smart notice board system that addresses these challenges through automation, multimedia integration, and conversational AI. The proposed system is built around a Raspberry Pi 4 platform, functioning as a compact and affordable local server. It hosts a Django-based web application that allows authorized administrators to upload and manage notices in the form of text, images, and videos. These notices are dynamically rendered on a connected HDMI display in a continuous loop. The system is further enhanced by the integration of a Rasa-powered chatbot, embedded within the display interface, which enables real-time interaction with users. The chatbot is trained to handle frequently asked academic queries, including examination schedules, project deadlines, and placement updates, thereby reducing repetitive student-faculty interactions. Designed to operate fully offline, the system is ideal for deployment in environments with limited network infrastructure. It emphasizes paperless communication, user interactivity, and real-time responsiveness. Extensive testing confirms the system's stability, ease of use, and potential for scalability across departments and institutions. This work contributes to the ongoing digital transformation of educational infrastructure, combining IoT, web technologies, and natural language processing into a cohesive smart campus solution.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Smart Notice Board, Raspberry Pi, Django, Rasa Chatbot, Artificial Intelligence.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Adverse Drug Reactions (ADRs) resulting from drug-drug interactions are a major healthcare concern. While Graph Neural Networks (GNNs) effectively model these interactions, their one-dimensional processing limits complex feature extraction. This research introduces a novel extension by integrating a two-dimensional Convolutional Neural Network (CNN2D) to enhance ADR prediction. By converting drug interaction data into 2D matrices, CNN2D captures intricate spatial relationships, complementing the GNN's graph-based insights. This hybrid model achieves a superior prediction accuracy of 99.87%, significantly outperforming traditional methods like KNN and Decision Trees. The extension showcases the power of deep learning in advancing drug safety evaluation.

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 Keywords

Adverse Drug Reactions, Drug-Drug Interactions, Graph Neural Networks, Convolutional Neural Networks, Self-Supervised Learning, SMILES Representation, Deep Learning, Side Effect Prediction, Drug Safety, TF-IDF Vectorization.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The growing burden of Non-Performing Assets (NPAs) in Indian Public Sector Banks (PSBs) poses a significant threat to financial stability and economic growth. This article evaluates the effectiveness of current legal and institutional mechanisms for NPA resolution, such as SARFAESI, DRTs, and the Insolvency and Bankruptcy Code (IBC). Despite their roles, persistent issues like judicial delays, enforcement gaps, and inadequate institutional coordination hinder their success. Through critical evaluation and comparison with global practices, this article proposes a strategic shift from reactive recovery to proactive reforms aimed at sustainable resolution.

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 Keywords

NPAs, Public Sector Banks, SARFAESI, IBC, DRT, Legal Reform, Sustainable Finance, Banking Law

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Creative Commons Attribution 4.0 and The Open Definition

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Background: Suicide among adolescents is a rising global concern, particularly in low- and middle-income countries. Adolescents often face stressors that predispose them to suicidal ideation and behaviour. Objective: To evaluate the effectiveness of a structured teaching program in improving knowledge about risk factors and prevention of suicidal behaviour among adolescents. Methods: A pre-experimental one-group pre-test post-test design was used. A total of 100 adolescents aged 10-16 years from selected schools in Bangalore Urban were selected through non-probability convenience sampling. A structured self-administered questionnaire was used to assess knowledge before and after the intervention. Results: In the pre-test, 67% had inadequate knowledge, 33% had moderate knowledge, and none had adequate knowledge. In the post-test, 76% had adequate knowledge, 24% had moderate knowledge, and none remained in the inadequate category. A significant increase in mean knowledge score was observed (pre-test: 8.35, post-test: 16.31), with a mean difference of 7.96 (t=31.09, p<0.05). Conclusion: The structured teaching program was effective in enhancing adolescents' knowledge regarding suicidal risk factors and preventive measures. Keywords: adolescent mental health, suicide prevention, structured teaching program, risk factors, nursing education.

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Keywords: adolescent mental health, suicide prevention, structured teaching program, risk factors, nursing education.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Effective detection has become a critical challenge due to the rise of IoT devices, which has led to an increase in botnet attacks. This research extends traditional botnet detection models by incorporating advanced ensemble deep learning techniques to improve prediction accuracy. We integrate CNN, LSTM, and GRU in hybrid architectures such as CNN + LSTM + GRU and CNN + BiLSTM + GRU, which effectively capture both spatial and temporal patterns in IoT network traffic. Feature selection using Mutual Information optimises model performance, reducing computational complexity while improving detection efficiency. Additionally, a Flask is used to create a user-friendly front-end application, which allows for smooth testing and evaluation of the model. Secure user authentication protects sensitive information and ensures data integrity. The experiment's findings demonstrate that the suggested ensemble models achieve superior accuracy, surpassing 97%, in detecting botnet activity, highlighting their effectiveness in securing IoT environments.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Botnet Detection, IoT Security, Deep Learning, CNN, LSTM, GRU, Hybrid Models, Ensemble Learning, Feature Selection, Mutual Information, Flask Framework, User Authentication, Cybersecurity.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

A common dental ailment called periodontitis is brought on by bacterial infection of the tooth's surrounding bone. To avoid serious consequences like tooth loss, early identification and accurate treatment are essential. Dental experts have historically diagnosed periodontal disease by manually identifying and labelling the condition, a procedure that takes a great deal of skill and involves tedious, time-consuming activities. The goal of this work is to use dental imaging datasets to automatically detect and classify periodontitis by utilising sophisticated neural network architectures. By effectively analysing photos for early-stage illness detection using deep learning techniques, the suggested method lessens the need for manual inspection. Multiple optimisation tactics inside the neural networks are compared to show how they affect detection performance. Results reveal that the suggested technique provides greater accuracy, with a 2D Convolutional Neural Network model having a detection accuracy of 96.93%. This high-performance solution highlights the promise of automated systems in strengthening diagnostic precision, efficiency, and scalability for periodontitis, thereby improving patient outcomes and streamlining clinical procedures.

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 Keywords

YOLOv8; Tooth Position Detection; Periodontitis; Bitewing Radiograph; Convolutional Neural Networks (CNN); Deep Learning; Dental Imaging; Automated Diagnosis; Medical Image Processing; Early Disease Detection; Diagnostic Accuracy; Neural Network Optimization.

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Creative Commons Attribution 4.0 and The Open Definition

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In today's era of information overload, accessing relevant and meaningful news has become increasingly challenging. This research presents a Personalized News Aggregator with Sentiment Analysis--a system designed to deliver user-centric news content tailored to individual interests and preferences. The platform aggregates news from diverse sources and leverages Natural Language Processing (NLP) techniques to analyze the sentiment of each article, helping users better understand the emotional tone and context of the information they consume. The system integrates machine learning models for sentiment analysis with recommendation algorithms to enable personalized content delivery. By offering features such as filtering, keyword search, and sentiment-based categorization, the solution addresses limitations found in traditional news platforms. This paper explores the technical implementation of the system, including data collection, preprocessing, model selection, and web-based deployment. It also highlights the system's potential in enhancing information accessibility and improving user satisfaction through a more refined, relevant, and engaging news experience.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Personalized News Aggregator, Sentiment Analysis, Natural Language Processing (NLP), Machine Learning, Recommendation Algorithms, User Preferences, News Personalization.

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Creative Commons Attribution 4.0 and The Open Definition

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This research paper critically examines gender-based violence (GBV) in India, focusing on the legal frameworks, implementation gaps, and societal challenges affecting women's safety. Despite comprehensive legal measures, the persistence of GBV highlights systemic flaws and social impediments. This paper aims to evaluate existing laws, identify enforcement issues, and propose practical solutions to enhance women's safety.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Gender-Based Violence, Women's Safety, Legal Framework, Implementation Gaps, Societal Challenges, India

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Creative Commons Attribution 4.0 and The Open Definition

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This paper explores the intersection of digital cultural heritage and internet governance, highlighting the opportunities and challenges of preserving cultural memory in the digital era. It examines the role of technologies like the metaverse in safeguarding heritage, while addressing critical concerns related to cybersecurity, intellectual property, algorithmic bias, and data sovereignty. Emphasizing the need for inclusive, ethical, and sustainable practices, the paper advocates for a comprehensive international framework that ensures the long-term protection and equitable representation of cultural heritage in an increasingly digitized world.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Cultural heritage, Data Privacy, Security, internet governance

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Creative Commons Attribution 4.0 and The Open Definition