Faculty of Computing-Scopus
Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/4892
Browse
69 results
Search Results
Item Embargo The Multi-Tenant Customization Paradox: Formalising the Intrinsic Conflict Between Scalable Shared Codebases and Tenant-Specific Operational Customization in SaaS Architecture(Institute of Electrical and Electronics Engineers Inc., 2026-07-07) Jayasuriya, R; Piyarisi, T; Awandya, S; Wickramasooriya, S; Thelijjagoda, S; Kasthurirathna, DMulti-tenant Software-as-a-Service (SaaS) architectures promise economies of scale through a single shared codebase serving multiple tenants. However, enterprise tenants increasingly demand deep operational customization (bespoke workflows, domain-specific business rules, and industry-specific computational logic) that fundamentally conflicts with the sharedcodebase constraint. Despite the centrality of this tension to SaaS architecture, no formal definition exists in the literature. This paper formally defines the Multi-Tenant Customization Paradox (MTCP): the structural impossibility of simultaneously maximizing codebase unity and tenant customization depth without incurring costs that grow super-linearly with the number of tenants. We introduce a formal model quantifying this tension through the Paradox Coefficient $P(S)$, establish a five-dimensional customization taxonomy with per-dimension formal measures, derive the Feasibility Region governed by an architectural sophistication parameter $α(B)$, propose an operational rubric for estimating $α(B)$ in practice, and derive an upper bound on the achievable unity-depth trade-off for four canonical resolution strategies. Our formalization demonstrates that the paradox is inherent to the mathematical structure of multi-tenancy rather than incidental to implementation choices, providing architects and researchers with a theoretical foundation for reasoning about customization trade-offs.Item Embargo Multimodal AI Framework for Personalized and Context-Aware Skin Disease Diagnosis, Monitoring, and Treatment Support(Institute of Electrical and Electronics Engineers, 2026-05-22) Wijesinghe H.W.M.O.P.; Laksopan R; Mihisandali W.K.M.; Devindi K.P.T.; Weerasinghe, L; De Silva, ADermatoscopic assessment of skin diseases based on visual morphology may not provide sufficient discrimination due to differences in cutaneous appearance, the severity of disease symptoms and individual biological or environmental factors. Current artificial intelligence (AI)-based dermatological systems mainly integrate unimodal image-based data which is constrained by comparative diagnostic performance in visually ambiguous conditions and across different skin complexions. Image-only approaches also do not utilize patient-reported symptoms needed to tailor treatment plans. In this study, we explore the potential of a unified multimodal AI framework towards robust, context-aware and patient- centric skin disease diagnosis, monitoring and treatment support. The framework combines deep learning-based image analysis with symptom-aware inputs extracted from voice recordings and structured text, which allows for improved diagnostic reliability. The proposed framework also introduces an explainable severity assessment module which evaluates disease progression via interpretable features and rule-based score. Domain adaptation methods further employed lead to better generalization for out-of-distribution data originating from different populations and reduce model bias. A knowledge-driven recommendation module generates context-aware personalized treatment recommendations based on predicted disease categories and patient-related information. Experimental results demonstrate that the proposed multimodal framework improves contextual understanding and robustness in visually ambiguous cases while enhancing interpretability, improved generalization, and practical applicability in teledermatology environments, while adding interpretability, fairness and real-world applicability of teledermatology systems.Item Embargo Ai-Based Urine Microscopy Image Analysis for Predicting Urinary Tract and Renal Diseases(Institute of Electrical and Electronics Engineers, 2026-05-29) Senanayake, K; Panagoda, P; Dharmapriya, S; Chathurya, R; Wijendra, D; De Silva, H; Jayawardana, DThe manual microscopic examination of urine is a crucial step in diagnosis of urinary tract and renal diseases. However, the process is time-consuming and operator dependent. Most of the existing automated urinalysis techniques only consider the microscopic examination of individual components or the black-box-based prediction models. There is a lack of a comprehensive framework that incorporates the microscopic examination of the microscopic components with the clinical diagnostic logic. In this regard, the present work proposes an artificial intelligence-based urinalysis system for the microscopic examination of the components in the urine sample to generate diagnostic outcomes. In the proposed system, the microscopic components like white blood cells, red blood cells, bacteria, yeast, crystals, and casts are detected and analyzed to generate the diagnostic outcomes for the diagnosis of urinary tract infection, kidney stone risk, hematuria causes, and casts-related renal diseases. In the proposed system, efficient lightweight models ensure precision and effectiveness in identifying various biological entities. White blood cells are detected with a mAP@0.5 score of 0.96, yeast with over 0.94, and crystals with more than 0.91, yielding a classification accuracy of 99.41% for crystals. The system detects microscopic elements like casts with a mAP@0.5 score of 0.80. The system also incorporates auxiliary clinical data to enhance diagnostic results for various diseases.Item Embargo AI-Driven Integrated Caregiving and Health Monitoring Framework for Elderly Well-Being(Institute of Electrical and Electronics Engineers, 2026-07-22) Nugaliyadde, S; Nikeshi, N; Marasinghe, M; Rajapaksha, C; Rajapaksha, S; Thelijjagoda, SThe rapid growth of the aging population has brought about some serious challenges, particularly in managing illnesses, feelings of loneliness, cognitive decline, and mental health issues. Traditional caregiving methods often depend on occasional assessments and hands-on supervision, which can fall short in providing the ongoing and adaptable support that’s really needed. This paper introduces an innovative caregiving and monitoring framework powered by AI, aimed at offering integrated, real-time, and comprehensive assistance for older adults. The system harnesses the power of Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and health data analytics to combine physical health monitoring, nutrition planning, smart routine coaching, and therapist-led mental health support all in one platform. With features like voice-based conversations and journaling, it makes emotional expression and behavioral analysis more accessible, helping to gain a deeper insight into users’ mental well-being. Predictive analytics and anomaly detection are used to spot early signs of health risks and shifts in behavior, allowing for timely interventions. Plus, remote access means caregivers and healthcare professionals can keep an eye on users and offer informed advice. By shifting caregiving from a reactive approach to a proactive and preventive one, this system not only improves quality of life but also encourages independent living and eases the burden on caregivers.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 Open Access An integrated data-driven approach for Chronic Kidney Disease of Unknown Etiology (CKDu) risk profiling and prediction in Sri Lanka(SPIE, 2025) Rajapaksha, N; Rajawasan, H; Ubeysinghe, R; Perera,S; Swarnakantha, N.H.P.R.S; Gamage, M; Nanayakkara, N; Wijayakulasooriya, J; Herath, D; Lakmali, MChronic kidney disease of unknown etiology is a significant public health issue in Sri Lanka, especially in rural farming communities. The exact causes remain unclear, with potential links to environmental and socio-economic factors. This research employs Biological Data and Geographic Information Systems to analyze risk factors such as water quality, agricultural practices, climatic conditions, Demographic Factors, Socio-economic Factors. This study uses data from government health records, the Centre for Research-National Hospital Kandy, and field surveys. By identifying patterns and correlations, the study aims to inform public health interventions and reduce the impact of CKDu, ultimately improving health outcomes for affected populations. This will greatly contribute to preventing the disease, reducing the risk, and identifying patients at an early stage.Item Embargo Adaptive Voice Communication in Emotion-Aware Digital Companions(Institute of Electrical and Electronics Engineers Inc., 2025) Rathnayake, P; Rathnaweera, C; Jithma, U; Aththanayake, I; Rathnayake, S; Gunaratne, MThis paper presents an adaptive voice communication system for emotion-aware digital companions that dynamically responds to users' affective states through expressive speech and synchronized 3D avatar animation. The system integrates real-time voice input, emotion recognition, and context-aware dialogue generation using GPT-3.5, followed by emotional text-to-speech synthesis via neural TTS. Lip-sync data is generated using phoneme alignment and rendered in sync with the avatar's facial expressions and gestures. To enhance user trust and engagement, the avatar visually mirrors the emotional tone of the speech. A cultural adaptation layer is introduced to align voice output and speech style with Sri Lankan communication norms, including tone, pacing, and formality. Implemented using a Node.js backend and React + Three.js frontend, the system demonstrates strong potential for emotionally intelligent, culturally adaptive AI interactions. This work contributes a modular pipeline for building empathetic voice agents capable of enhancing realism and trust in human-AI communication.Item Embargo A Game Centric E-Learning Application For Preschoolers(Institute of Electrical and Electronics Engineers Inc., 2025) Kulasekara D.A.M.N.; Nipun P.G.I.; Dombawela H.M.D.L.B.A; Manilka G.S; Manilka G.S; De Silva D.I.This research explores the potential of advanced technologies such as pose detection (PD), augmented reality (AR), object detection (OD), and voice recognition (VR) in creating a game-centric e-learning application for preschoolers. The proposed application, Kidstac, integrates cognitive and physical development through interactive activities with real world interaction, addressing gaps in traditional e-learning methods that often neglect physical engagement. The app features real-time feedback mechanisms and structured modules like virtual zoo explorations, exercise games, treasure hunts, and pronunciation activities. Testing results indicate significant improvements in motor skills, knowledge retention, problem-solving abilities, and language proficiency. These findings demonstrate the effectiveness of blending physical and digital learning experiences to enhance early childhood education. The study establishes a foundation for scalable, activity-based learning tools, emphasizing the holistic development of young learners.Item Embargo Predictive Modelling of Egg Production Yields on Farms based on Environmental Factors(Institute of Electrical and Electronics Engineers Inc., 2025) Nawod G.A.D; Rathnayake R.M.D.A.; Dodangoda P.N; Deshitha N.A.M.P; Vidanaralage A.J; Vidanaralage A.JThis research presents an integrated smart farming system aimed at optimizing egg yield on poultry farms by leveraging artificial intelligence (AI), Internet of Things (IoT), and environmental sensing technologies. The system is structured around four core components - Animal Stress Monitoring, Temperature Control and Predator Detection, Humidity and Ventilation Management, and AI-Driven Smart Lighting Optimization each contributing to real-time environmental adaptation and accurate egg production prediction. Animal stress is assessed using physiological and environmental metrics (e.g., heart rate, body temperature, feed/water intake), with predictions generated via an XGBoost model trained on 3000+ real farm entries. Temperature and security are managed through a hybrid system combining DHT11/DHT22-based climate control with YOLO-based computer vision for predator detection. The humidity and ventilation module incorporates Bi-LSTM and XGBoost models to predict and regulate airflow and moisture levels based on real-time sensor inputs. The lighting optimization component dynamically adjusts LED spectrum and intensity using LSTM-based forecasting models, operating via ESP32 and MQTT-enabled architecture to simulate ideal lighting conditions. These components are unified through a.NET-based backend and a mobile-friendly dashboard, enabling low-latency decision support and seamless farm management. The system's modularity, edge deployment capabilities, and adaptability to local conditions make it an innovative and scalable approach for enhancing egg yield, poultry welfare, and farm automation.