Research Publications
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Item Embargo Voice Agentic Smart Reader Application In Sinhala and Tamil For Visually Impaired People(Institute of Electrical and Electronics Engineers Inc., 2026-05-22) Siriwardana B.J.A; Hewagama A.D; Aluwihare M.C; Sharumathan M; Weerasinghe, M; Abeywardhana, LThis paper proposes an intelligent smart reading system to improve access to documents, news articles, and printed content for visually impaired and reading-challenged users, particularly in low-resource languages such as Sinhala and Tamil. Although recent advances in computer vision, speech processing, and large language models have enabled effective document understanding, existing systems often operate independently and provide limited support for non-visual navigation, interactive information access, and expressive audio output. The proposed system integrates four main components into a unified framework. First, a voice-guided navigation module enables users to capture documents without visual assistance by providing real-time audio guidance, ensuring proper alignment and reliable image acquisition for text recognition. Second, a Tamil document understanding and question answering module processes document content and supports accurate, context-aware information retrieval using a retrieval-based approach, while also providing personalized content recommendations. Third, a voice-assisted Sinhala reading module allows users to navigate documents and access information using natural voice commands and semantic processing. Finally, an emotional text-to-speech module generates expressive speech in Sinhala and Tamil, improving the naturalness and clarity of audio output. Experimental results demonstrate that the proposed system improves document capture accuracy, navigation efficiency, information retrieval performance, and speech quality. Overall, the system provides a practical, accessible, and user-centered solution for intelligent document interaction in underrepresented languages.Item Embargo 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, MCriminal 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.Item Embargo 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, MAssessing 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.Item Embargo Automated Bilingual Handwriting Evaluation for Early Childhood Education: A Multi-Metric Structural Analysis(Institute of Electrical and Electronics Engineers Inc, 2026-07-02) Gunawardena, T; Rathnasinghe, E; Abesinghe, C; Mihara, B; Abeywardhana, L; Weerasinghe, L; Selvaratnam, NTraditional handwriting assessment in early childhood education relies on subjective teacher evaluation and becomes particularly challenging in bilingual Sri Lankan curricula involving both English and Sinhala. This paper introduces a multi-metric handwriting quality scoring pipeline for automated evaluation of preschool handwritten letters for children aged 4-6. The proposed system integrates contour-based segmentation, language-specific convolutional neural network (CNN)-based recognition (achieving 86.73% test accuracy on EMNIST for English and 99.04% on a custom Sinhala dataset), and an eight-metric structural analysis framework. The framework combines structural similarity (SSIM), topology preservation, shape invariants, proportion analysis, stroke continuity, alignment, pixel Intersection over Union (IoU), and a 9 × 9 grid-based spatial comparison. The weighted scoring approach shows strong agreement with teacher assessments, demonstrating the reliability of the proposed system. Designed for Sri Lankan early education contexts, the approach provides a scalable, interpretable, and objective solution, addressing the gap in automated Sinhala handwriting assessment tools.Item Embargo Multimodal Knowledge Graph for Domain-Specific Intelligence(Institute of Electrical and Electronics Engineers Inc., 2025) Mohan, K; Munasinghe, M; Bandara, L; Wijesinghe, H; Rathnayake, S; Abeywardhana, LIn the era of information abundance, transforming vast amounts of data into meaningful knowledge remains a critical challenge, especially in domains like medicine, engineering, and education, where visual and multimodal elements play a vital role. Traditional Knowledge Graphs (KGs) excel in organizing structured and textual data but struggle to incorporate multimodal information and implicit relationships, limiting their effectiveness. This paper explores the potential of Multimodal Knowledge Graphs (MMKGs) to address these limitations by integrating text, images, videos, and audio into a unified framework. We investigate how MMKGs enhance knowledge retrieval, comprehension, and interactive learning through advanced techniques, including Natural Language Processing and deep learning. Our findings demonstrate that MMKGs significantly improve knowledge retention and application in specialized fields, offering a foundation for more intuitive and effective domain-specific knowledge ecosystems.Publication Embargo Automated vehicle insurance claims processing using computer vision, natural language processing(IEEE, 2022-11-30) Fernando, N; Kumarage, A; Thiyaganathan, V; Hillary, R; Abeywardhana, LTraditional insurance claims processing systems are no match for the modern world due to the increasing population of vehicles and the resulting number of accidents. In this paper, the authors present a novel idea to automate the tedious processes in the insurance industry. The presented system consists of three main components namely, re-identify the make and model of the vehicle, identify the damaged automobile component, type, and severity, and compute an accurate repair estimate using damage component identification. Also, automate the documentation process by identifying the relevant fields in the voice input provided by the user. This ensures both the parties involved in this process will be benefited from the proposed system. Presented solutions Were designed using the aid of Artificial Intelligence techniques, mainly CNN models and Natural language processing techniques.Publication Embargo Hastha: Online Learning Platform for Hearing Impaired(IEEE, 2022-11-30) Wanasinghe, D; Maddugoda, C; Ramawickrama, H; Munasinghe, T; Abeywardhana, L; Mallawarachchi, YSign language is the primary means of communication for the hearing-impaired community. Introducing a learning platform can result in many ways to make learning more accessible for the hearing-impaired community of Sri Lanka. Although many approaches are being made to build such systems, the learning platform “Hastha” aims to provide a more interactive outcome with a component that converts Youlhbe videos to sign language and a Chatbot component that acts as an intermediary between a hearing-impaired user and a Google Search Engine. Furthermore, it includes a game-based learning platform and a gesture translation component from Sri Lankan to American Sign Language while the results are displayed to the users in the form of an animation. The proposed methodology is achieved by using Natural Language Processing, speech recognition, and machine learning techniques. This web-based application enables increased interaction between the student and the system making it an effective learning environment for the hearing impaired.Publication Embargo Enhancing Conversational AI Model Performance and Explainability for Sinhala-English Bilingual Speakers(IEEE, 2022-12-09) Dissanayake, I; Hameed, S; Sakalasooriya, A; Jayasinghe, D; Abeywardhana, L; Wijendra, DNatural language processing has become essential to modern conversational tools and dialogue engines, including Chatbots. However, applying natural language processing to low-resource languages is challenging due to their lack of digital presence. Sinhala is the native language of approximately nineteen million people in Sri Lanka and is one of many low-resource languages. Moreover, the increase in using code-switching: alternating two or more languages within the same conversation, and code-mixing: the practice of representing words of a language using characters of another language, has become another major issue when processing natural languages. Apart from natural language processing, the explainability of opaque machine learning models utilized in chatbots has become another prominent concern. None of the existing modern chatbot development platforms supports explainability and relies on a performance score such as accuracy or f1-score. This paper proposes a no-code chatbot development platform with a series of built-in novel natural language processing, model evaluation, and explainability tools to tackle the problems of processing Sinhala-English code-switching and code-mixing natural language data and model evaluation in modern chatbot development platforms.Publication Embargo An ultra-specific image dataset for automated insect identification(Springer Nature, 2022-01) Abeywardhana, L; Dangalle, C; Nugaliyadde, A; Mallawarachchi, YAutomated identifcation of insects is a tough task where many challenges like data limitation, imbalanced data count, and background noise needs to be overcome for better performance. This paper describes such an image dataset which consists of a limited, imbalanced number of images regarding six genera of subfamily Cicindelinae (tiger beetles) of order Coleoptera. The diversity of image collection is at a high level as the images were taken from diferent sources, angles and on diferent scales. Thus, the salient regions of the images have a large variation. Therefore, one of the main intentions in this process was to get an idea about the image dataset while comparing diferent unique patterns and features in images. The dataset was evaluated on diferent classifcation algorithms including deep learning models based on diferent approaches to provide a benchmark. The dynamic nature of the dataset poses a challenge to the image classifcation algorithms. However transfer learning models using softmax classifer performed well on the current dataset. The tiger beetle classifcation can be challenging even to a trained human eye, therefore, this dataset opens a new avenue for the classifcation algorithms to develop, to identify features which human eyes have not identifed.Publication Open Access New record of Tricondyla gounellii Horn 1900 (Coleoptera, Cicindelinae), an arboreal tiger beetle from Sri Lanka(: https://www.researchgate.net/publication/336107511, 2019-09) Abeywardhana, L; Dangalle, C; Mallawarachchi, YArboreal tiger beetles belong to tribe Collyridini of order Coleoptera, family Carabidae, subfamily Cicindelinae and can be found predominantly in the tropical and subtropical regions of Asian countries mainly in forest habitat types (Toki et al., 2017). Tribe Collyridini is divided in to five genera - Collyris, Neocollyris, Protocollyris, Derocrania and Tricondyla. According to records provided by Fowler (1912) from his studies in the Fauna of British India’ five species of genus Tricondyla reside in Sri Lanka - Tricondyla femorata , Tricondyla tumidula , Tricondyla coriacea , Tricondyla nigripalpis , Tricondyla granulifera ). Three of these species, T. coriacea, T. nigripalpis, T. granulifera are endemic to the country, while the other two species also reside in India. However, the sources of this information is far outdated and unreliable and requires current investigations and revision. Thus, the present study was conducted to investigate the current species of arboreal tiger beetles of Sri Lanka, their morphology, locations, habitats and habitat preferences.