Faculty of Computing-Scopus

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

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Start date: 2025Has files: Yes

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    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, M
    Chronic 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.
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    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, M
    This 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.
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    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.
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    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.J
    This 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.
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    Gamifying Coding Education for Beginners: Empowering Learners with HTML, CSS and JavaScript
    (Institute of Electrical and Electronics Engineers Inc., 2025) Chandrasekara, S; Hewavitharana, D; Weerasinghe, M; Gayasri, B; Wijendra, D; De Silva, D
    Traditional coding education often fails to engage and motivate beginners due to its lack of interactivity and personalized learning experiences. This paper presents a gamified learning platform designed to teach Hypertext Markup Language (HTML), Cascading Style Sheets (CSS), and JavaScript (JS) to beginners. The platform incorporates interactive lessons, AI (Artificial Intelligence)-powered coding assistance, and advanced gamification mechanics to enhance learner motivation, engagement, and success. Furthermore, key features include performance-based recommendation engines, virtual coding environments with real-time feedback, and a collaborative platform for peer interactions. The integration of AI provides personalized feedback and adaptive learning paths, while gamified elements such as badges, points, and leaderboards foster competitive and enjoyable experiences. Preliminary findings demonstrate a 40% increase in student engagement metrics and a 35% improvement in coding competency compared to traditional methods. This research lays the groundwork for future expansion to additional programming languages and broader educational applications, with potential implications for transforming computer science education on a scale.
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    Kube5GC: Kubernetes-Native Orchestration for 5G Core Network
    (Institute of Electrical and Electronics Engineers Inc., 2025) Wijesekera, T; Jayarathna, L; Pathirana, G; Banu, R; Wickramarathne, J
    Telecommunications service providers face challenges such as rigid infrastructures, manual configuration, inefficient routing, and security risks, especially in 5G deployments. This paper presents Kube5GC, a Kubernetes-based framework for 5G Core network orchestration. Leveraging Kubernetes orchestration, NFV, and SDN, Kube5GC automates deployments, optimizes resource allocation, and manages network slices with efficiency. Architecture reduces operational complexity and costs through automated, secure, and scalable workflows. Kube5GC integrates CI/CD pipelines via GitOps, deploys containerized 5G functions using Open5GS, and enforces secure inter-service communication with robust secret management. During validation, the platform achieved rapid pod readiness, low latency encrypted traffic, and reliable operation under telecom workloads. Integrated observability with Prometheus, Grafana, and distributed tracing enables comprehensive monitoring of control and user plane metrics, while automated backup and policy-driven configuration management enhance operational resilience. These results confirm Kube5GC as an efficient, scalable, and secure orchestration platform for 5G Core networks in cloud-native environments.
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    Bovitrack:Animal behavior monitoring using Machine learning and IoT
    (Institute of Electrical and Electronics Engineers Inc., 2025) Viraj, H; Wijesekara, S; Tharuka, K; Fernando, S; Jayakody, A; Wijesiri, P
    Analyzing dairy cattle behavior and anomalies is a critical component of precision livestock farming, allowing farmers to remotely monitor animals for health and behavior. In order to accomplish this task better, the use of IoT technology and machine learning algorithms is more appropriate as per the time. The YOLO (you only look once) object recognition algorithm is more suitable for that, and the use of this algorithm allows these processes to be performed automatically and in real time with high accuracy. YOLO's ability to recognize multiple objects in images or videos makes Yolo ideal for cattle detection and tracking.
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    Throat AI - An Intelligent System For Detecting Foreign Objects In Lateral Neck X-Ray Images
    (Institute of Electrical and Electronics Engineers Inc., 2025) Baddewithana, P; Krishara, J; Yapa, K
    Foreign Object ingestion is a commonly encountered medical condition within the Ear, Nose, and Throat clinical domain. Timely and accurate detection of such objects is vital, as it often guides the need for surgical intervention. Among the available imaging techniques, lateral neck X-rays are the most widely used radiographs to visualize and assess the presence of FOs in the throat. However, manual interpretation of these images can be time-consuming and subject to human error, potentially leading to misdiagnosis or delayed treatment. This research presents a deep learning-based software solution, deployable via web and mobile platforms, aimed at assisting medical professionals with the automated detection of FOs in lateral neck X-rays. The system leverages state-of-the-art YOLO object detection models, specifically evaluating novel versions such as YOLO-NAS-s, YOLOv11s, and YOLOv8s-OBB to ensure high detection accuracy and deployment efficiency. The best-performing model, YOLO-NAS-s, achieved a validation accuracy of 96.3%. For deployment, the model was hosted on the Roboflow platform and accessed via a FastAPI-based middleware server. Performance evaluation showed an average inference time of approximately 2 seconds and a memory footprint of around 100 MB on standard computing hardware, demonstrating its suitability for integration into resource-constrained clinical environments. This setup highlights the system's lightweight design and real-world applicability. Training, evaluation, and testing of the deep learning models were conducted using a dataset curated from public local healthcare institutions and online medical imaging repositories.
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    Genetic Algorithm-Based Unmanned Aerial Vehicle (UAV) Path Planning in Dynamic Environments for Disaster Management
    (Institute of Electrical and Electronics Engineers Inc., 2025) Wijerathne V.R; Theekshana W.G.P; Prabhanga K.G.B.; De Silva K.P.C; Wijayasekara, S; Weerathunga, I; Hansika, M. M.D.J.T
    Unmanned Aerial Vehicles (UAVs) hold immense potential in disaster management by enabling rapid response, real-time aerial reconnaissance, and improved situational awareness without endangering human lives. This research proposes a real-time UAV path-planning system based on a Hierarchical Recursive Multiagent Genetic Algorithm (HR-MAGA). Unlike traditional methods that struggle with adaptability in dynamic 3D environments, our system employs localized waypoint updates to reduce the computational cost of full-path recalculations. A multi-objective fitness function guides the optimization process by balancing safety, energy efficiency, altitude smoothness, turbulence resistance, and travel time. Additionally, the system integrates a decoupled real-time collision avoidance module for immediate response to sudden threats. While obstacle detection is abstracted in this study, the framework is designed to be easily integrated with real-time sensing technologies such as LiDAR for dynamic obstacle awareness. Experimental evaluations show a 20-30% improvement in path efficiency and a 40% increase in convergence speed compared to conventional genetic algorithms, highlighting the system's adaptability and robustness in disaster response scenarios.
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    Low-Cost, High-Precision Vibration Analysis: Enhancing SHM and Seismic Data Acquisition Systems
    (Institute of Electrical and Electronics Engineers Inc., 2025) Sashik, D; Iddamalgoda, A; Manchanayake, N; Prasanna, R; Abeygunawardhana, P.K.W
    The development of a low-frequency vibration detection device is essential for testing vibrations in crucial applications such as structural integrity, seismic activity detection and industrial machinery maintenance. Current data acquisition systems suffer from lack of sensitivity and high cost. This paper presents the design and development of a low-cost vibration detection device that uses a 3-axis accelerometer and a geophone with a resonant frequency of 4.5 Hz, where the signals are filtered using a second order low-pass filter with a 250 Hz cutoff frequency. The device is integrated with a 24-bit analog to digital (ADC) converter for ensuring a precise data acquisition and the device ensures to monitor real-time vibrations of x, y and z axis using a accelerometer and the z axis with a geophone. The effectiveness of the device was validated via experimental simulations. This developed device is a solid and cost-effective solution for engineers and professionals, where reliable low-frequency vibration monitoring is required in critical applications.