Faculty of Computing
Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/4776
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Item Embargo A BI Approach for Student Engagement and Retention along with Cognitive Load Analysis for Educator(979-833153098-3, 2025) Algewatta, M. N; Manathunga, KThis research presents a systematic approach to monitoring student engagement, retention, and cognitive load within higher education by integrating Business Intelligence (BI) tools with cognitive load analysis. The proposed framework utilizes a diverse range of data sources -including attendance, academic performance, mental health indicators, demographic variables, and student feedback to generate real-time insights into student behavior patterns. The BI system identified critical trends, such as irregular attendance, declining academic performance, and the influence of demographic factors, enabling educators to identify at-risk students and intervene proactively. Additionally, cognitive load analysis was employed to evaluate the mental demands of course content, categorizing learning objectives in alignment with Bloom's Taxonomy. This allowed for the identification of content that could potentially overwhelm students, facilitating adjustments in instructional complexity. The integration of BI insights with cognitive load data provided a holistic approach that not only enhanced the monitoring of student engagement but also supported the tailoring of instructional content to optimize learning without inducing cognitive overload. The findings suggest that combining BI tools with cognitive load metrics offers a robust framework for both improving student retention and assisting educators in creating a balanced, engaging, and supportive learning environment. This study contributes a practical model for institutions seeking to leverage data-driven insights to promote student success and address the dynamic challenges of modern higher education.Item Embargo A Comprehensive Approach to Secure, Accessible, and Engaging Voting Systems(Springer Science and Business Media Deutschland GmbH, 2026) Jayasinghe J.A.M.P; Bandara S.Y.T.D; Shabry S.M; Wickramasinghe W.A.R.M.; Rajapakse, K; Silva, NThis research presents a secure and accessible e-voting system for polling booths in Sri Lankan context, to overcome issues with the traditional voting system. It incorporates block-chain for fair vote storage, and homomorphic encryption for privacy preserving computation of results. The identity of voters is confirmed by face recognition, which includes measures to deterring any voting by impostors. Special identification model with multiple digits is beneficial for disabled voters. Public opinion is effectively gauged through sentiment analysis from social media and it puts concerns to rest, thus a whole lot of enhancement in the whole of customer engagement. Ease of use is also assured thanks to a very user-friendly interface which eliminates mistakes a lot with only a little effort generally. Experimental results demonstrate that security is greatly strengthened, transparency and usability are significantly increased traditional procedural integrity is still maintained efficiently.Item Embargo A Data-Driven Approach to Nutrient Deficiency Detection and Yield Optimization in Hydroponic Farming Using IoT Technology(Institute of Electrical and Electronics Engineers Inc., 2025-07-03) Wimansha R.A.I; Senarathne D.M.N.P.S; Rathnaweera W.L.B; Karunarathna H.B.T.N.; Chandrasiri, S; De Silva, AThis study explores the use of IoT sensors, such as the ESP32, for leaf pigmentation analysis to improve the accuracy of identifying nutritional deficiencies and optimizing resource allocation in hydroponic farming. The results demonstrate that a data-driven approach can significantly enhance decision-making for hydroponic farmers, contributing to improved food security and the promotion of sustainable agricultural practices. Cross-validation across multiple crop varieties and growing cycles confirmed model generalizability. Scenario simulation capabilities were implemented to allow growers to evaluate hypothetical environmental and nutritional interventions prior to deployment, with sensitivity analysis revealing key leverage points for yield optimization. Implementation of the system in three commercial hydroponic facilities demonstrated average yield improvements of 17.3 percent while reducing resource inputs by 9.6 percent. These findings suggest that data-driven predictive modeling can significantly enhance precision agriculture practices in controlled environment growing systems, thereby improving agricultural sustainability, enhancing food security, and optimizing economic outcomes for hydroponic operations.Item Embargo A Dual-Branch CNN and Metadata Analysis Approach for Robust Image Tampering Detection(Institute of Electrical and Electronics Engineers Inc., 2025) Zakey, A; Bawantha, D; Shehara, D; Hasara, N; Abeywardena, K.Y; Fernando, HImage tampering has become a widespread issue due to the availability of advanced tools such as Photoshop, GIMP, and AI-powered technologies like Generative Adversarial Networks (GANs). These advancements have made it easier to create deceptive images, undermining their reliability and fueling misinformation. To address this growing problem, we propose a hybrid approach for image forgery detection, combining deep learning with traditional forensic techniques. Our study integrates a dual-branch Convolutional Neural Network (CNN) with handcrafted features derived from Error Level Analysis (ELA), noise residuals from the Spatial Rich Model, and metadata analysis to enhance detection capabilities. Metadata analysis plays a crucial role in identifying inconsistencies in image properties such as timestamps, geotags, and camera details, which often accompany tampered images. The CASIA dataset, a publicly available benchmark for tampered images, was used to train and evaluate the proposed model. After 30 epochs of training, the hybrid method achieved an accuracy of 95%, demonstrating its effectiveness in distinguishing between authentic and tampered images. This research highlights the advantages of combining deep learning models with traditional feature extraction methods and metadata analysis, offering a robust solution for detecting manipulated images. Our findings contribute to advancing image forensics by improving detection accuracy, even in cases involving sophisticated tampering methods driven by AI.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 A Lightweight YOLOv8n-Based Binary Waste Classification Model for Autonomous Garbage-Collecting Robots(Institute of Electrical and Electronics Engineers Inc., 2026-07-02) Perera W.B.N.; Sendanayaka H.K; Nuwanthi B.D.T.; Jayasekara R.G.S; Lokuliyana, S; Silva, SEfficient waste classification is critical for automation in modern waste management systems, especially in places where it is hard to use manual classification. In this work, a model for waste and non-waste classification based on the YOLOv8n framework, known for high efficiency and applicability for embedded systems, is designed. For this purpose, a customized dataset was collected using real photos under different lighting conditions, different backgrounds, and with varying objects to reflect a more realistic environment. The training process included augmentation, normalization, and hyperparameters tuning to improve the efficiency of the model. Evaluation metrics such as accuracy, precision, recall, F1 score, and confusion matrix were used. According to the experimental results, the developed model provides an accuracy of 92.5% and F1 score of 0.94 with maintaining fast inference performance for real-time applications. The designed framework aims to become a module in an autonomous waste collecting robot to validate objects before manipulating them by robots, ensuring reliable and efficient real-world system performance.Item Embargo 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, MNonverbal 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.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 A Secure Protocol for Computer-Based Assessments in Disrupted Environments(Institute of Electrical and Electronics Engineers Inc., 2025) Navin, D; De Zoysa, K; Karunaratna D.D; Harshanath, BExaminations are fundamental to education, yet conducting secure computer-based exams in disrupted environments presents significant challenges. This research introduces a Secure by Design protocol leveraging Delay Tolerant Networks (DTN) to overcome connectivity gaps in remote and resource-constrained areas. The proposed solution integrates physical, administrative, and technical controls to ensure the confidentiality, integrity, and availability of examination data. Through an iterative action research approach, the system evolved from a centralized Moodle server to standalone local servers, enabling offline functionality and enhanced resilience. Tested across over 180,000 candidates in Sri Lanka's largest computer-based examination, the framework effectively addressed power outages, internet disruptions, and logistical constraints. The findings demonstrate the protocol's effectiveness in promoting equitable and reliable access to education, ensuring examination continuity despite adverse conditions.Item Embargo 'AAYU', Paralyze Ease Home Suite and Mobility Companion(IEEE Computer Society, 2025) Tharushi N.K.; Ranaweera D.G.K.T.T.; Munasinghe A.S.; Wijesekara P.N; Gamage N.D.U; Pandithage DEnsuring the safety and well-being of paralyzed individuals remains a critical challenge, particularly in resource-limited settings. Limited access to assistive technology and real-time monitoring increases health risks and dependency. This paper presents AAYU (Assistive Automation for Your Upliftment) Paralyze Ease Home Suite and Mobility Companion, an intelligent system integrating home automation and wearable technology to enhance patient safety, communication, and autonomy. AAYU addresses four key challenges: (1) optimizing home environments through automated adjustments based on vital signs, (2) enabling nonverbal communication via a voice-to-text smart device, (3) detecting falls with a real-time positioning belt, and (4) preventing deep vein thrombosis (DVT) using a sensor-equipped monitoring belt. An initial evaluation demonstrates AAYU's potential to improve the quality of life for paralyzed individuals through proactive and adaptive support.Item Embargo Adaptive Robotic Voice Modulation for ASD Kids: Tailored Voice Pitch, Tone, and Speed(Institute of Electrical and Electronics Engineers Inc., 2025) Panduwawala, P; Pulasinghe, K; Rajapaksha, SChildren with Autism Spectrum Disorder (ASD) often experience sensory sensitivities, particularly auditory hypersensitivity, which can make interactions and communication challenging. This study explores the customization of the NAO robot's voice pitch, tone, and speech speed using the Kaldi Speech Recognition Toolkit to align with the preferences of children with ASD. Eight distinct voice profiles were created, offering a range of variations in pitch, tone, and speech speed. Parents or caretakers were asked to select the voice profile they felt would be most suitable for their child. Based on this feedback, we created a spectrum of voices tailored to each child's needs. Results indicate that medium-pitch and moderate-speed combinations are most effective in enhancing engagement, with Voice 2 emerging as the preferred profile. The findings underscore the potential of adaptive voice modulation in improving robotic interactions for ASD therapy and highlight opportunities for further research in real-time adaptability and long-term impact assessment.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 Advancing Speech Therapy for Sinhala-Speaking Children with Autism Spectrum Disorder Through an Intelligent Dialog System(Institute of Electrical and Electronics Engineers Inc., 2025) Jayawardena, A; Pulasinghe, K; Rajapakshe, SThis paper presents a dialog system integrated with a NAO socially assistive robot, designed to support Sinhala-speaking children with Autism Spectrum Disorder (ASD). The system leverages a pipeline-based architecture implemented using the RASA framework, consisting of Natural Language Understanding (NLU), Dialog Management (DMU), and Natural Language Generation (NLG) units. The NLU unit processes user input by identifying intents, entities, and dialogue acts, incorporating custom tools like the SpokenSinhalaVerbTokenizer for handling spoken Sinhala. The DMU includes a Dialog State Tracker (DST) to maintain conversation context and a Dialog Policy Generator, which employs rule-based, TED, and UnexpecTED policies to adapt conversation flows dynamically. The NLG unit generates natural responses to foster interactive and goal-oriented conversations. Integrated with the NAO robot, the system engages children through meaningful dialogues, such as discussing toy preferences, aiming to enhance social interaction and communication skills. This work highlights the potential of conversational AI and robotics in therapeutic interventions for ASD in low-resource languages.Item Embargo Agri-Sense: Autonomous Tomato Farming Robotic Vehicle(Institute of Electrical and Electronics Engineers Inc., 2026-05-21) Bandara K.M.D.I.; Yomal M.S.; Weerasekara W.M.G.V; Nilessh P; Jayakody J.A.B.C.; Gamage N.D.U.The precision greenhouse cultivation of tomatoes is often hindered by the expensive labor costs, the easy spread of diseases, and the unavailability of robust digital technologies. This paper proposes Agri-Sense, a comprehensive autonomous robotic system that covers the entire scope of greenhouse management. The system includes a robotic platform that allows the crops to be traversed autonomously without the need for human input. The ripeness and leaf disease detection are done using advanced YOLO-based computer vision techniques. The robotic arm has 4 degrees of freedom, which includes a modular gripper and a precise sprayer system for disease treatment. This reduces the usage of chemicals and protects the healthy crops from disease. To solve the problem of connectivity in rural greenhouse environments, the system includes a sensor mesh using LoRa sensors and a Software-Defined Networking controller. This controller uses Quality of Service and VPN tunnels to provide robust communication services even in the presence of unreliable connectivity. Experimental results showed that the proposed system bridges the gap between passive monitoring and autonomous greenhouse management. The system is robust and efficient in greenhouse management.Item Embargo AgriSense LK: Weekly Automated Machine Learning for Sri Lankan Produce Prices with Business Continuity Plan, Market Opportunity Ranking, Cultivation Targeting, and Yield Quality Valuation(Institute of Electrical and Electronics Engineers Inc., 2026-05-22) Matharaarachchi, Charaka J.; Samarasinghe, Ravindu T; Vidyasarani G.G.T.; Fasnas, M; Siriwardana, D; Wijesooriya, AIn Sri Lanka, agricultural decision-making remains largely traditional: decisions are often based on historical practices, informal consultation, and heuristic judgment. The primary barrier is that market price data is difficult to interpret without analytical expertise, resulting in unpredictable price volatility and suboptimal farmer income. AgriSense LK is a machine learning platform that converts historical price records into actionable recommendations for farmers and traders. The system comprises four components: business strategy classification, market opportunity ranking, cultivation targeting, and smartphone-based produce quality grading. The platform was trained on 123,985 real price records sourced from the Central Bank of Sri Lanka (CBSL), spanning 2017 to 2025. Key results include a MAPE of 0.7% and MAE of Rs. 1.86 on weekly price forecasting (a 98.1% improvement over the naive baseline), a ROC-AUC of 0.9056 on cultivation targeting, and 91.49% crop classification accuracy with 89.84% quality grade accuracy in the computer vision component. Direct price regression over a seven-day horizon proved unreliable; a binary profitability classifier was adopted instead and substantially outperformed the regression approach. While results are promising, further validation under real-world deployment conditions is required.Item Embargo AI Driven Smart Tourism Platform for Personalized Safe and Sustainable Travel Planning(Institute of Electrical and Electronics Engineers Inc., 2025-12-08) Srikanthan, S; Senevirathne, C; Rasarathnam, T; Jayalath, T; Rajendran, KTourism planning remains challenging due to the need for group preference alignment, personalized itinerary generation, and real-time budget control, challenges that are not adequately supported by existing platforms. This paper presents an AI-driven modular framework that integrates three components: a semantic-aware group recommender that uses Sentence-BERT embeddings with a learning-to-rank model to match travelers; a hybrid itinerary planner that fuses content-based filtering, collaborative filtering, and machine-learning-based rating prediction to generate preference-aligned and geographically coherent travel plans and a predictive budgeting system that applies regression-based forecasting with live API data to provide dynamic cost estimation. The platform is developed specifically for the Sri Lankan tourism context, incorporating regional travel behavior patterns and destination characteristics into its models. Experiments indicate strong performance across all modules, including high-quality group matching, accurate itinerary prediction, and a substantial improvement in budget estimation accuracy compared with static baselines. Early user testing further highlights increased satisfaction with itinerary relevance and budget transparency. Overall, the framework demonstrates a scalable and adaptive approach to smart tourism planning, advancing personalization, collaboration, and sustainable travel support.Item Embargo AI Interviews with Facial Emotion Recognition for Real-Time Feedback and Career Recommendations(Institute of Electrical and Electronics Engineers Inc., 2025) Herath R.P.N.M; Arachchi D.S.U.; Gunaratne M.H.B.P.T.; Hansana K.T.; Wijayasekara, S.K; Jayasinghe, DThe hiring process is complex, requiring evaluation of candidates across multiple dimensions, including technical proficiency, behavioral traits, and credibility. Traditional interviews often suffer from biases and inefficiencies. This research presents an AI-driven Interview System integrating Machine Learning (ML), Natural Language Processing (NLP), and Computer Vision to automate and enhance recruitment. The system generates contextual interview questions, evaluates candidate responses using LLM-based scoring models, and provides real-time feedback for engagement. It includes speech-to-text transcription and offensive word detection to ensure professionalism. The behavioral analysis module leverages facial emotion recognition and computer vision to assess non-verbal cues such as confidence and attentiveness. Additionally, Curriculum Vitae (CV) parsing and LinkedIn data extraction use NLP-based entity recognition to extract educational background, work experience, and key skills, enabling personalized interviews. The technical assessment module administers real-time coding challenges, evaluating solutions for correctness, efficiency, and best practices while providing AI-generated feedback. By automating these key hiring aspects, this system enhances objectivity, efficiency, and decision-making, ensuring a data-driven, unbiased, and scalable selection process while improving the candidate's experience and employer insightsItem Embargo AI Powered Integrated Code Repository Analyzer for Efficient Developer Workflow(Institute of Electrical and Electronics Engineers Inc., 2025) Akalanka, I; Silva, S.D; Ganeshalingam, M; Abeykoon, A; Wijendra, D; Krishara, JTransitioning between new and legacy codebases in diverse project environments poses significant challenges for developers, especially with traditional Knowledge Transfer (KT) methods, which are often resource intensive and prone to obsolescence. These limitations hinder the Software Development Life Cycle (SDLC), particularly in fast-paced industrial settings. This research introduces an AI-driven automation solution that leverages large language models (LLMs) and advanced artificial intelligence technologies to address critical gaps in technical knowledge transfer, with a focus on modern software frameworks. The proposed system reduces development costs, improves team performance, and accelerates adaptation to complex codebases. Key features include a documentation generation tool that cuts manual effort by up to 90%, with an average generation time of 6.8 minutes. Additionally, a virtual knowledge transfer assistant enhances onboarding efficiency, potentially reducing senior developer involvement by 50-60%. The system also includes an automated diagram generator that achieves 97% validation accuracy and a code smell detection tool with 71% accuracy, resulting in better code quality assessments. These findings demonstrate the effectiveness of AI-driven automation in improving developer productivity, streamlining onboarding processes, and optimizing software development workflowItem Embargo AI-Assisted Criminal Investigations: Enhancing Testimony Analysis and Case Correlation(Institute of Electrical and Electronics Engineers Inc., 2025-12-08) Karunarathna K.A.D; Ediriweera E.R.L.C; Fernando N.D.R.N; Abeywardana B.A.I.S; Abeywardhana, LThis paper presents Lexa AI, a holistic AIpowered solution for improving criminal investigative practices through four phases that target key drawbacks found in traditional practices. The first model provides an automated data/information collection stage that accepts legal documents in multiple formats and utilizes a three-stage processing pipeline based on Gemini 2.0 Flash model, which performs Optical Character Recognition (OCR) with a higher level of accuracy and speed compared to alternative approaches. The collected data will be forwarded to the next phase by leveraging dynamic question generation that uses Reinforcement Learning (RL), instantaneous multilingual capabilities, and real-time scoring of relevance. The third phase conducts Multimodal Behavioral and Physiological Analysis (MBPA), which includes facial signals, speech signals, and heart rate signals to create a combined Stress Index as an objective indicator and avoids subjective judgment. Finally, semantic similarity will be measured to correlate incidents, assess risk for victims, and provide explainable predictionsItem 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.