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Author: search.filters.author.Nawarathne, M

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    A Mobile Application to Enhance Skills in Children with Nonverbal Learning Disability
    (Institute of Electrical and Electronics Engineers Inc., 2025-07-03) Harshana W.C; Hettiarachchi H.K.Y.K; Mendis T.S.P; Madanayake P.C.S.; Weerasinghe, L; Nawarathne, M
    Nonverbal Learning Disability (NVLD) is a neurodevelopmental disorder affecting children, characterized by strong verbal skills but significant challenges in visual-spatial processing, motor skills, and communication. This paper introduces a mobile application designed to address these challenges through interactive and personalized activities. The app leverages machine learning and artificial intelligence to improve visual-spatial abilities, communication skills, and cognitive development. By engaging children in pattern recognition, word recognition, and touch screen integration, the app aims to enhance critical thinking, decision-making, and relationship identification in children aged 10-13. The algorithms used in this research are the Dynamic Difficulty Adjustment Algorithm, Random Forest Classifier, Real-Time Object Detection Algorithm, and Deep Q-Network (DQN). This paper explores the development, features, and impact of the app in supporting children with NVLD.
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    An Integrated Approach to Smart Criminal Judgment Analysis in Sri Lankan Courts
    (Institute of Electrical and Electronics Engineers, 2026-05-29) Sundaresan, K; Ekanayake, D; Thavachchelvam, N; Sivaanbu, A; Abeywardhana, L; Nawarathne, M
    Criminal justice practitioners in Sri Lanka face considerable challenges when accessing and analyzing past court judgments for case preparation, as most research tasks remain manual and time intensive. This study presents an integrated system comprising four interconnected components to address these challenges. A Legal Resource Extractor uses a Hybrid Neuro-Symbolic Architecture with domain-adapted Legal-BERT, fine-tuned on 133,338 legal text chunks, to extract structured legal knowledge from multilingual inputs including voice evidence submitted by lawyers. A Case Analysis and Argument Generation module employs LegalBERT embeddings over 1,601 Court of Appeal judgments with a Nearest Neighbors retrieval model achieving 94% perfect retrieval rate for precedent-based argument generation. The Appeal Outcome Prediction component has been optimized through a Hybrid Feature Engineering pipeline that integrates 1,000 TF-IDF lexical features, 49 traditional legaldomain indicators, and 768-dimensional Legal-BERT embeddings. By utilizing SMOTE and a Calibrated Voting Ensemble, the system predicts appeal outcomes with 79.75% accuracy across three outcome classes. A Public Legal Assistant system provides offline legal information in Sinhala, Tamil, and English through hybrid FAISS based retrieval using 1,146 legal documents and locally hosted language model generation. Together, these components offer practitioners an end-to-end platform for strengthening legal research, argument preparation, and access to justice.
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    A Reinforcement Learning Approach with Human in the Loop to Explainable Insurance Risk Scoring and Intelligent Policy Portfolio Optimization
    (Institute of Electrical and Electronics Engineers, 2026-05-29) Gamage, C; Kasthuriarachchi, T; Denuwan, C; Mallawaarachchi, P; Abeywardhana, L; Nawarathne, M
    Assessing individual risk accurately and optimizing insurance portfolios in real time remain major challenges due to static actuarial tables, opaque models, and fragmented analytical pipelines. This paper proposes a two-part Explainable AI (XAI) framework addressing both issues. The first component, Artificial Intelligence-driven risk scoring with human-in-the-loop (HIL) weight adjustment, uses a Proximal Policy Optimization (PPO) agent to suggest feature-based changes to an insurer's risk-equation weights. Shapley Additive Explanations(SHAP) attributions and Generative AI reasoning make these changes interpretable, allowing human reviewers to approve modifications that are immediately applied to future customers, creating a self-improving loop. The second component, AI-driven policy optimization, leverages a PPO supported by an XGBoost expense regressor, SHAP/LIME explainability, PPO agent and a Retrieval-Augmented Generation (RAG) layer for rider assignment. Both components share a data backbone of 100,000 anonymized insurance records stored in MongoDB, enabling incremental updates without reprocessing. Experiments show the XGBoost regressor achieves Root Mean Square Error (RMSE) 0.4406 and Mean Absolute Error (MAE) 0.3600, the HIL guided agent increases average episodic reward by 10.3%, and the RAG layer reaches 91.7% rider-assignment accuracy. The framework significantly enhances predictive accuracy, interpretability, regulatory traceability, and portfolio adaptability compared to traditional actuarial and black-box approaches.
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    Machine Learning-Based Prediction of Settled Water Turbidity for Optimizing Alum Dosage in Drinking Water Treatment
    (Association for Computing Machinery, Inc, 2026-06-17) Cooray, S; Fernando, D; Jeyachandran, P; Jayakodi, D; Sumathipala, P; Fousdeen, S; Nawarathne, M
    Efficient chemical dosing is essential in drinking water treatment plants to ensure effective turbidity removal while minimizing chemical consumption and operational costs. Conventional alum dosing methods rely on manual jar tests and operator experience, which are time-consuming and often inadequate for handling rapid variations in raw water quality. Key parameters such as turbidity, pH, and electrical conductivity significantly influence the coagulation process, making accurate dosage determination a complex and dynamic challenge. This study proposes a machine learning-based approach to support optimal alum dosage selection by predicting settled water turbidity using historical treatment data. The dataset includes raw water turbidity, pH, conductivity, and applied alum dosage, which are used to train regression models capable of capturing nonlinear relationships between input parameters and treatment performance. The study used 7,975 hourly operational records collected from Ambathale Water Treatment Plant during January-December 2024, and model evaluation was performed using a time-aware 80:20 chronological train-test split. Multiple algorithms, including Gradient Boosting, Random Forest, XGBoost, and LightGBM, were explored alongside feature engineering techniques to improve model effectiveness. Experimental results demonstrate that the Random Forest model achieves superior performance, with an R² score of 0.7909, RMSE of 0.5197, and MAE of 0.3166, indicating strong predictive reliability under varying water quality conditions. The trained model enables simulation of different dosing scenarios, allowing identification of the alum dosage that produces the lowest predicted turbidity under varying raw water conditions.The proposed approach provides a practical decision- support solution for improving treatment efficiency, reducing chemical overuse, and supporting more reliable operation of drinking water treatment processes.