Browsing by Author "Rathnayake S."

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    An Ethical and Emotionally Intelligent Social Media Plugin Using Responsible & Explainable AI
    (Institute of Electrical and Electronics Engineers Inc., 2026-08-04) Wickremasinghe N.S.; Ullandupitiya U.P.L.I.; Jayawardena D.S; Jayanetti J.K.D.S.D; Rathnayake S.; Nawarathne M.
    Social media platforms encounter ongoing difficulties with fragmented moderation and interaction operations that do not collaborate effectively to deal with destructive content, inauthentic behavior, emotion-insensitive interaction, and opaque recommendations. This paper features an ethical and emotionally intelligent social media plugin using responsible and explainable AI implemented as a unified real-time service for social media applications, proven through implementation on the Open-Source Social Network (OSSN). The plugin incorporates four elements, namely multimodal cyberbullying detection, behavior-based social bot detection, explainable friend recommendation, and an emotion-aware reaction system. The cyberbullying module is a combination of transformer-based text analysis, image processing, OCR, and keyword fusion to moderate content in an explainable manner, and the fake account module uses temporal behavioral features to detect bot accounts regardless of content. The recommendation system offers interpretable recommendations with context, and the emotion-aware module provides empathetic interaction via emotion recognition and filtering. Experimental results indicate high performance across task-appropriate metrics such as accuracy for classification tasks and MCC/ROC-AUC for bot detection due to class imbalance, yielding 88.60% on text-based cyberbullying, 68.81% on image moderation, MCC 0.9704 and ROC-AUC 0.9981 on bot detection, and 73.10% F1 on sarcasm detection. The outcomes of deployments demonstrate the potential of a single, transparent, and responsible AI system to have safer and more meaningful social media interactions
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    CodeHarbor: A Code Analysis Tool
    (Springer Science and Business Media Deutschland GmbH, 2026) Dewmin T.Y; Kodithuwakku Y.S.; Dayananda I.H.M.B.L; Fernando K.R.A.W; De Silva D.I; Rathnayake S.
    As software systems grow more complex, developers face increasing challenges in maintaining and evolving codebases, often resulting in higher costs and longer development cycles. To address these issues, this study presents CodeHarbor, an intelligent tool that integrates machine learning with code analysis to simplify complex code segments. CodeHarbor calculates complexity metrics and offers personalized, context-aware suggestions for improving code quality. By automating code reviews, detecting anomalies, and recommending optimized refactoring strategies, it enables early issue resolution and enhances maintainability. The backend leverages artificial intelligence to identify patterns, enforce coding standards, and generate actionable insights, while the intuitive frontend provides real-time feedback, visualizations, and detailed improvement summaries. CodeHarbor also highlights repetitive patterns and compliance issues, helping developers track progress and reduce manual review effort. With its seamless integration of analysis and interface, CodeHarbor streamlines development workflows and promotes sustainable, high-quality software engineering.