Faculty of Computing-Scopus

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Start date: 2025Subject: search.filters.subject.Explainable AI

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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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    PregAssist: Pregnancy Support Mobile Application for Pregnant Mothers and Doctors
    (Institute of Electrical and Electronics Engineers Inc., 2026-05-22) Buddika, B. P; Jayasinghe N.; Perera A.N.M; Wijethunga D.N; Weerasinghe, M; Dunuwila, O
    Pregnant care involves constant follow-ups, timely risk detection, and successful interactions between mothers and medics. This paper introduces PregAssist, a mobile integrated support system that is a synthesis of four products, including fetal health decision support, physical health risk prediction, AI-based mental health monitoring, and AR-driven emergency training. The fetal health module provides explainable and offline-capable cardiotocography (CTG) classification to assist clinical decision-making. XGBoost classifier with SHAP-based interpretability is used to analyze physical risks to generate individual recommendations and alerts. Mental health assessment integrates questionnaire-based indicators with CNN-driven facial emotion recognition, while federated learning preserves data privacy through on-device training. AR-based deterministic expert system offers protocol-adherent emergency simulations to improve preparedness activity when facing high-level of risk. Through the convergence of predictive analytics, explainable AI, privacy preserving learning, and training design, PregAssist assists in proactive maternal attention in urban and resource constrained environments via combining a hybrid mobile architecture.
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    Predictive Policing with Neural Networks: A Big Data Approach to Crime Forecasting in Sri Lanka
    (Institute of Electrical and Electronics Engineers Inc., 2025) Nauzad, H; Dayawansa, D; Dias, N.Y; Haddela, P.S; Ratnayake, S
    The surge in crime rates, particularly in urban regions, has underscored the importance of predictive policing within law enforcement strategies. This research introduces a neural network-based crime prediction model, specifically tailored to address the complexities of Sri Lanka's crime landscape. By combining big data analytics with advanced machine learning methods - including ensemble models such as Random Forest and Gradient Boosting, alongside Artificial Neural Networks (ANNs) - our study presents a robust framework to forecast crime incidents, locations, and time spans. While neural networks excel in predictive accuracy, their "black-box"nature can hinder practical applications in critical fields like law enforcement. To address this, our model integrates Explainable AI (XAI), making the decision-making process of the system transparent and interpretable for end-users. XAI helps break down complex neural network predictions, ensuring trust and clarity in the model's insights. With a prediction accuracy rate of 85%, this approach demonstrates substantial potential to improve crime prevention efforts and optimize resource allocation. Our research not only highlights the predictive strengths of neural networks but also showcases the essential role of interpretability for deploying these models effectively in real-world policing.