Faculty of Computing-Scopus
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Item Embargo SmartSphere: Bridging AI-Powered Automation and Tesla Coil Wireless Energy for Smarter Living(Institute of Electrical and Electronics Engineers Inc., 2025) Jurison Jenaraj, J.K; Umasuthan, A; Vigneswaran, V; Raveendran, K; Thiruthanigesan, K; Kasthuriarachchi, SThis paper presents SmartSphere, an intelligent, secure, and wire-free home automation system that synergizes artificial intelligence, multi-factor access control, and resonant wireless power transmission. Built on an edge-computing architecture using ESP32 and Arduino Mega 2560 microcontrollers, SmartSphere integrates facial recognition, fingerprint authentication (96.7% accuracy), and IR-based presence detection to reduce false activations by 75%. The system employs OpenCV and TensorFlow Lite for real-time anomaly detection and environmental personalization, including weather-responsive adjustments and emotion-aware lighting via facial expression analysis. A key innovation is the incorporation of a Tesla coil-based wireless power transmission module, which eliminates conventional wiring constraints and reduces installation cabling by 86%. Experimental validation demonstrates a 14% improvement in energy efficiency, 75% faster response time (0.3 s), and seamless compatibility with 92% of tested IoT devices. Through comprehensive testing and validation, this research establishes SmartSphere as a secure, intelligent, and sustainable solution for next-generation smart homes, addressing the limitations of traditional wired systems while enhancing the user experience through AI-driven personalization.Item Embargo Adaptive AI-Based Enhancement of Critical External Sounds in Insulated Vehicle Cabins for Improved Safety(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Rathnayaka D.B; Wickramasuriya L.H.N.Y; Walpalage J.V; Rathnayake, SThe increasing acoustic insulation in modern and electric vehicles improves passenger comfort but unintentionally suppresses critical external sounds such as ambulance sirens, car horns, and train alarms, creating potential safety risks. While existing research has explored sound detection or localization in isolation, few systems integrate both capabilities in a unified framework for real-time vehicular deployment. This research proposes an adaptive AI-based system that detects, classifies, and selectively enhances these critical sounds in real time while providing directional awareness. Using a convolutional recurrent neural network (CRNN) trained on the UrbanSound8K dataset, the system processes incoming audio from external microphones, extracts Mel-frequency cepstral coefficients (MFCCs), and distinguishes safety-relevant cues from non-essential background noise. A dual-microphone setup enables the estimation of sound direction (left or right), providing additional spatial awareness to the driver. Detected signals are isolated through spectral filtering and relayed into the cabin with sub-30 ms latency, ensuring timely driver and passenger awareness without compromising comfort. Experimental results achieved 91.2% classification accuracy and 87.4% directional accuracy,confirming the system's feasibility for enhancing safety in insulated vehicle cabins and supporting future autonomous driving environments.Item Embargo Fidelity-Driven Physically Constrained Adaptive Code Distance for Surface Codes under Biased Circuit-Level Noise(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Hettiarachchi, A; Dissanayaka, KSurface codes are leading candidates for fault-tolerant quantum computing, but conventional implementations use fixed code distances that result in conservative resource allocation during varying noise conditions. We propose a physically-constrained, fidelity-driven adaptive surface code framework that dynamically adjusts code distance at scheduled intervals to maintain target logical fidelity while minimizing resource overhead. Our approach introduces a syndrome-based logical error rate estimation algorithm using rolling-window statistics, a scheduled adaptation policy operating within pre-allocated qubit pools, and detailed modeling of adaptation latency and reconfiguration-induced errors for superconducting qubit devices. Through large-scale Monte Carlo simulations under realistic biased circuit-level noise, we demonstrate that hardware-constrained adaptive codes achieve 43-62% success rate improvements over fixed-distance strategies, reaching 72-82% success rates compared to 5̃0% for fixed approaches. Oracle adaptive strategies with perfect error knowledge achieve 91-94% success rates, indicating substantial theoretical potential. Our results show that adaptive surface codes provide significant performance improvements even under realistic hardware constraints, with oracle strategies consuming only 37% of maximum resources while achieving 86% higher success rates for shorter circuits.Item 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 Generative AI Based Chatbot for Customer IT Support Automation(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Lakshan S.N; Gamage U.R; Fernando W.S.N; Kuruppu K.A.V.U.; Kodagoda, N; Abeywardhana, LEnterprises are facing increasing challenges in delivering efficient, adaptive, and secure IT support, as traditional ticketing systems suffer from latency, rigidity, and limited scalability. Existing chatbot solutions built on commercial large language models (LLMs) often introduce high operational costs, token inefficiency, and data privacy concerns, while current retrieval and multi-agent systems lack personalization, multimodal understanding, and dynamic adaptability. This research presents a generative AI-based IT support assistant that integrates four core innovations: a multi-agent architecture enabling distributed and scalable task execution; a retrieval-augmented knowledge base enhanced with knowledge graphs and episodic memory for more accurate and personalized responses; conversational form automation using Rasa and transformer-based models to extract structured ticket information from natural queries; and an interactive 3D avatar interface with real-time speech-to-text, text-to-speech, and emotion-aware multimodal interaction to support more human-like communication. The system was evaluated using RAGAS metrics to measure accuracy, contextual recall, and response faithfulness. Results show improved domain-specific performance, reduced reliance on manual ticket handling, and lower computational overhead through caching and adaptive slot filling. This study proposes a privacy-preserving, scalable enterprise IT support framework, with future work targeting multilingual support and deeper ITSM integration.Item Embargo Mobile Application for Enhance Sustainable Tea Farming in Sri Lanka(Institute of Electrical and Electronics Engineers Inc., 2025-10-27) Lokuliyana, S; Wijesiri, P; Kulathunga H.A.S.C; Perera K.N.T.; Koongahage M.GEnsuring sustainable tea farming requires intensive monitoring of plant conditions, nutritional status, and disease infections. To that end, this research presents a smartphone application that is powered by machine learning to assist Sri Lankan tea farmers in identifying fertilizer and chemical deficiencies, predicting tea yield quality, and detecting diseases at early stages. The system makes use of a trained machine-learning model to scan images of leaves for relevant characteristics to provide instant feedback through a user-friendly smartphone interface. The app offers advice to farmers to improve yield and reduce crop loss. This approach enhances accuracy in farming, minimizes reliance on over-fertilization, and assists in efficient farming methods. The given system is designed to target small scale and far-away farmers to make it more popular in diversified agricultural lands. The research involves mass-scale agricultural image dataset collection and processing, deep learning model training, and deployment of a robust mobile application for field implementation. Outputs strive to contribute to Sri Lankan smart agriculture by allowing farmers to make data-driven decisions to ultimately improve productivity and sustainability.Item Embargo Real-Time Optimization and Maintenance of Wind Turbine Performance using Digital Twin Technology(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Jenojan P; Dhanushikan V; Herath H.M.T.S.; Dilmini N.A.C; Jayasinghearachchi V.; Perera JWind power plays a vital role in Sri Lanka's renewable energy transition, yet coastal wind farms face challenges such as lightning strikes, wind misalignment losses, turbine cut-in/out events, acoustic impacts, and costly maintenance. This study proposes a digital twin-based framework to enhance real-time optimization and predictive maintenance of wind turbine performance. The framework integrates four modules: weather risk forecasting, operational efficiency, noise impact analysis, and predictive maintenance. Using SCADA data from the Mannar Thambapavani Wind Farm, long-term meteorological and lightning records, NASA satellite observations, and the WEA-Acceptance dataset, advanced machine learning models were developed to forecast lightning (F1 = 0.81, AUC = 0.87), estimate power losses (R2 = 0.80, MAE = 3.4 kWh/h), optimize blade pitch, and produce short-term and medium-term energy forecasts. The digital twin simulation visualizes turbine dynamics, noise propagation, and maintenance scenarios. Results show improved prediction accuracy, 20-30% reduction in downtime, and a clear trade-off between energy efficiency (5-10° pitch) and noise (~56 dB > 25°). The framework strengthens situational awareness, operational reliability, and sustainability in monsoon-prone tropical environments.Item Embargo Hybrid Deep Learning Approach Using YOLO and Spatial Transformers for Kinesthetic Math Education in Low-Resource Settings(Institute of Electrical and Electronics Engineers Inc., 2025-07-03) Sellapperuma M.S; Wasana, K.H. I; Dhananjana, B.K. T; Anuruddhika, D. L. N; Krishara, J; Wijendra, DThis research presents a hybrid deep-learning system to enhance kinesthetic math education for Grade 4 and 5 students in low-resource Sri Lankan classrooms, addressing addition, subtraction, and time-telling skills in Sinhala. Integrating fine-tuned You Only Look Once version 11 (YOLOv11) for real-time abacus recognition and pre-trained Spatial Transformer Network (STN) with Residual Network 50 (ResNet50) for analog clock analysis, the system bridges physical manipulatives with digital feedback. Optimized for low-cost hardware via post-training quantization, it reduces model sizes by up to 56.6% and inference times by 45.9%, enabling deployment on standard PCs. The methodology employs YOLOv11 to detect abacus beads (mean Average Precision, mAP50-95: 0.898) and STN + ResNet50 to correct clock perspectives (95.2% accuracy on SynClock), delivering immediate Sinhala feedback through a culturally adapted interface. The system was evaluated on an Intel Core i5 PC with 8GB RAM and achieved sub-second inference (YOLOv11: 170.1 ms, STN + ResNet50: 32 ms post-quantization) while retaining accuracy. A pilot study with 20 students showed a 25% improvement in arithmetic scores and 30% in time-telling accuracy, with 85% reporting higher engagement. These findings demonstrate the system's efficacy in boosting numeracy and interaction in resource-scarce settings, aligning with the Visual, Aural, Read/Write, Kinesthetic (VARK) framework. Despite challenges like hardware constraints and real-world variability, this scalable, offline-capable solution offers a novel approach to educational technology, with potential for broader deployment in Sri Lanka and similar contexts, addressing gaps in artificial intelligence-driven kinesthetic learning tools. ©Item Embargo A Scalable Microkernel Inspired Architecture for Transparent and Adaptive Supply Chain Management(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Vithanage H.D; Dehipola H.M.S.N; Manditha K.D.R.; Weedagamaarachchi K.S.; Jayasinghearachchi, V; Perera, JSupply Chain Management (SCM) in industries such as coconut peat production increasingly demands systems that are not only efficient but also transparent and adaptable to rapid changes. Traditional monolithic architectures often fail to scale effectively and struggle to integrate emerging technologies such as IoT and blockchain, limiting their usefulness in dynamic environments. To address these limitations, this research proposes a microkernel-inspired architecture tailored for SCM. The architecture separates core system functions from domain-specific processes through the use of dynamically loadable plugins, allowing for modularity, fault isolation, and real-time adaptability. The impact of this study lies in its integration of multiple technologies to enhance transparency and operational flexibility. The system incorporates IoT sensors for real-time data acquisition, blockchain for immutable traceability, gRPC for high-performance communication, and K3S for lightweight container orchestration. A workflow customization tool allows non-technical users to define or modify supply chain processes without altering core logic. We employed the Architectural Trade-off Analysis Method (ATAM) and Cost-Benefit Analysis Method (CBAM) to evaluate architectural decisions and conducted runtime performance testing using simulated workloads. Results showed improved scalability, flexibility, and fault tolerance, with moderate latency introduced by inter-process communication and CGo overhead. Despite some limitations in performance variability, the architecture maintained high availability and was able to recover from plugin failures seamlessly. These findings suggest that the proposed model provides a viable foundation for next-generation SCM systems. The contributions include a modular, resilient framework that effectively integrates advanced technologies to support adaptable and transparent supply chain operations across various industries.Item Embargo Framework for Ethical Governance of Autonomous AI Systems: Balancing Innovation, Accountability, and Risk Management(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Dabare, C; Wijegunasighe, Y; Herath, H; Nethsara, R; Jayasinghe, PThe advent of rapid deployment of autonomous artificial intelligent systems is a twofold challenge: how to ensure this is happening in an ethical way, and at the same time, how to keep the innovative power. These are already outdated models of governance that render many of these models inadequate in accommodating the risk landscape as well as the applied complexity of autonomous artificial intelligent systems. This paper suggests a new conceptual model of governance that combines ethical standards, risk stratified regulatory feedback and allowing innovation via regulatory sandboxes. Based on a qualitative synthesis of worldwide AI policies, scholarly sources, and background information, the framework provides a hierarchical, modular paradigm that can be applied in different domains and jurisdictions. It focuses on transparency, accountability, fairness and incorporates flexibility of changing technology. The proposed model would play the role of a policymaker, industry practitioners, and researchers strategic road map in finding balanced and operationally feasible solutions in deploying ethical AI.