Faculty of Computing-Scopus

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/4892

Browse

Filter results by typing the first few letters
Now showing 1 - 3 of 3
  • Thumbnail Image
    ItemEmbargo
    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.
  • Thumbnail Image
    ItemEmbargo
    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.
  • Thumbnail Image
    ItemEmbargo
    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, L
    In 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.