Faculty of Computing-Q1
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Publication Open Access Comparison of cardiovascular risk prediction models developed using machine learning based on data from a Sri Lankan cohort with World Health Organization risk charts for predicting cardiovascular risk among Sri Lankans: A cohort study(BMJ Publishing Group, 2025-01-15) Mettananda, C; Solangaarachchige, M; Haddela, P; Dassanayake, A.S; Kasturiratne, A; Wickremasinghe, R; Kato, N; De Silva, H.JIntroduction Models derived from non-Sri Lankan cohorts are used for cardiovascular (CV) risk stratification of Sri Lankans. Objective To develop a CV risk prediction model using machine learning (ML) based on data from a Sri Lankan cohort followed up for 10 years, and to compare the predictions with WHO risk charts. Design Cohort study. Setting The Ragama Health Study (RHS), an ongoing, prospective, population-based cohort study of patients randomly selected from the Ragama Medical Office of Heath area, Sri Lanka, focusing on the epidemiology of non-communicable diseases, was used to develop the model. The external validation cohort included patients admitted to Colombo North Teaching Hospital (CNTH), a tertiary care hospital in Sri Lanka, from January 2019 through August 2020. Participants All RHS participants, aged 40-64 years in 2007, without cardiovascular disease (CVD) at baseline, who had complete data of 10-year outcome by 2017, were used for model development. Patients aged 40-74 years admitted to CNTH during the study period with incident CV events or a disease other than an acute CV event (CVE) with complete data for CVD risk calculation were used for external validation of the model. Methods Using the follow-up data of the cohort, we developed two ML models for predicting 10-year CV risk using six conventional CV risk variables (age, gender, smoking status, systolic blood pressure, history of diabetes, and total cholesterol level) and all available variables (n=75). The ML models were derived using classification algorithms of the supervised learning technique. We compared the predictive performance of our ML models with WHO risk charts (2019, Southeast Asia) using area under the receiver operating characteristic curves (AUC-ROC) and calibration plots. We validated the 6-variable model in an external hospital-based cohort. Results Of the 2596 participants in the baseline cohort, 179 incident CVEs were observed over 10 years. WHO risk charts predicted only 10 CVEs (AUC-ROC: 0.51, 95% CI 0.42 to 0.60), while the new 6-variable ML model predicted 125 CVEs (AUC-ROC: 0.72, 95% CI 0.66 to 0.78) and the 75-variable ML model predicted 124 CVEs (AUC-ROC: 0.74, 95% CI 0.68 to 0.80). Calibration results (Hosmer-Lemeshow test) for the 6-variable ML model and the WHO risk charts were χ 2 =12.85 (p=0.12) and χ 2 =15.58 (p=0.05), respectively. In the external validation cohort, the sensitivity, specificity, positive predictive value, negative predictive value, and calibration of the 6-variable ML model and the WHO risk charts, respectively, were: 70.3%, 94.9%, 87.3%, 86.6%, χ 2 =8.22, p=0.41 and 23.7%, 79.0%, 35.8%, 67.7%, χ 2 =81.94, p<0.0001. Conclusions ML-based models derived from a cohort of Sri Lankans improved the overall accuracy of CV-risk prediction compared with the WHO risk charts for this cohort of Southeast Asians.Publication Embargo Eco-friendly bismuth halide chalcogenide perovskites for solar energy harvesting(Royal Society of Chemistry, 2025-03-04) Don Muditha Akmal, U. K; Hu, D; Wijesekara Abeygunawardhana, P.K; Sewvandi, G. AThe quest to eliminate lead (Pb) content in perovskite photovoltaic materials has significantly shifted focus towards identifying viable Pb-free alternatives. This study provides a comprehensive theoretical investigation of CH3NH3BiI2Se and CH3NH3BiI2S as Pb alternative candidates. Density Functional Theory (DFT) calculations and the solar cell capacitance simulator (SCAPS) were used. The DFT analysis reveals that both CH3NH3BiI2Se and CH3NH3BiI2S possess indirect band gaps of 1.35 eV and 1.39 eV, respectively. CH3NH3BiI2Se demonstrates a higher absorption coefficient, stronger absorption in the UV-visible regions, a broader absorption spectrum and better charge carrier mobilities compared to CH3NH3BiI2S. CH3NH3BiI2Se and CH3NH3BiI2S based solar cells which show 24.06% and 21.85% power conversion efficiencies (PCEs), respectively. This study emphasizes the potential of CH3NH3BiI2Se as a promising bismuth mixed halide chalcogenide compound for the development of sustainable perovskite solar cells. The findings provide a foundation for the guided design of novel bismuth chalcogenide compounds for optoelectronic applications and experimental studies.Publication Open Access A novel application with explainable machine learning (SHAP and LIME) to predict soil N, P, and K nutrient content in cabbage cultivation(Elsevier B.V., 2025-03-06) Abekoon, T; Sajindra, H; Rathnayake, N; Ekanayake, I, U; Jayakody, A; Rathnayake, UCabbage (Brassica oleracea var. capitata) is commonly cultivated in high altitudes and features dense, tightly packed leaves. The Green Coronet variety is well-known for its robust growth and culinary versatility. Maximizing yield is crucial for food sustainability. It is essential to predict the soil’s major nutrients (nitrogen, phosphorus, and potassium) to maximize the yield. Artificial intelligence is widely used for non-linear predictions with explainability. This research assessed the predictive capabilities of soil nitrogen, phosphorus, and potassium levels with explainable machine learning methods over an 85-day cabbage growth period. Experiments were conducted on cabbage plants grown in central hills of Sri Lanka. SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) were used to clarify the model’s predictions. SHAP analysis showed that high feature values of the number of days and plant average leaf area negatively impacted for nutrient predictions, while high feature values of leaf count and plant height had a positive effect on the nutrient predictions. To validate the results, 15 greenhouse-grown cabbage plants at various growth stages were selected. The nitrogen, phosphorus, and potassium levels were measured and compared with the predicted values. These insights help refine predictive models and optimize agricultural practices. A user-friendly application was developed to improve the accessibility and interpretation of predictions. This tool is a user-friendly platform for end-users, enabling effective use of the model’s predictive capabilities.Item Embargo Wet-Neuromorphic Computing: A New Paradigm for Biological Artificial Intelligence(Institute of Electrical and Electronics Engineers, 2025-03-31) Perera, J; Balasubramaniam, S; Somathilaka, S; Wen, Q; Li, X; Kasthurirathna, D; Roohi, A; Nelson, M. TAs we delve into a life governed by artificial intelligence (AI), ongoing research continues to discover new forms of intelligence that are efficient and closely mimic an organism’s brain in terms of performance. This article presents a new concept termed wet-neuromorphic computing, in which biological cells or organisms are leveraged to perform computational tasks using their natural molecular functions. We map key neuromorphic properties to natural biological computing observed in bacteria, 3-D organoids, and Caenorhabditis elegans. To expand beyond the inspiration of the brain to create conventional neuromorphic computing, the study presents a case study that demonstrates bacterial AI computing using the gene regulatory neural network derived from Escherichia coli’s gene regulatory network for pattern recognition, validated through wet lab experiments. Finally, challenges and future directions are discussed.Publication Open Access Real-Time Coordinate Estimation for SCARA Robots in PCB Repair Using Vision and Laser Triangulation(Multidisciplinary Digital Publishing Institute (MDPI), 2025-04-07) Sanjeewa, N; Wathudura, V. M; Kahatapitiya, N. S; Silva, B. N; Subasinghage, K.; Wijesinghe, R.EThe Printed Circuit Board (PCB) manufacturing industry is a rapidly expanding sector, fueled by advanced technologies and precision-oriented production processes. The placement of Surface-Mount Device (SMD) components in PCB assembly is efficiently automated using robots and design software-generated coordinate files; however, the PCB repair process remains significantly more complex and challenging. Repairing faulty PCBs, particularly replacing defective SMD components, requires high precision and significant manual expertise, making automated solutions both rare and difficult to implement. This study introduces a novel real-time machine vision-based coordinate estimation system designed for estimating the coordinates of SMD components during soldering or desoldering tasks. The system was specifically designed for Selective Compliance Articulated Robot Arm (SCARA) robots to overcome the challenges of repairing miniature PCB components. The proposed system integrates Image-Based Visual Servoing (IBVS) for precise X and Y coordinate estimation and a simplified laser triangulation method for Z-axis depth estimation. The system demonstrated accuracy rates of 98% for X and Y axes and 99% for the Z axis, coupled with high operational speed. The developed solution highlights the potential for automating PCB repair processes by enabling SCARA robots to execute precise picking and placement tasks. When equipped with a hot-air gun as the end-effector, the system could enable automated soldering and desoldering, effectively replacing faulty SMD components without human intervention. This advancement has the potential to bridge a critical gap in the PCB repair industry, improving efficiency and reducing dependence on manual expertise.Publication Embargo Spatio-temporal graph neural network based child action recognition using data-efficient methods: A systematic analysis(Elsevier Inc, 2025-06-03) Mohottala, S; Gawesha, A; Kasthurirathna, D; Samarasinghe, P; Abhayaratne, CThis paper presents implementations on child activity recognition (CAR) using spatial–temporal graph neural network (ST-GNN)-based deep learning models with the skeleton modality. Prior implementations in this domain have predominantly utilized CNN, LSTM, and other methods, despite the superior performance potential of graph neural networks. To the best of our knowledge, this study is the first to use an ST-GNN model for child activity recognition employing both in-the-lab, in-the-wild, and in-the-deployment skeleton data. To overcome the challenges posed by small publicly available child action datasets, transfer learning methods such as feature extraction and fine-tuning were applied to enhance model performance. As a principal contribution, we developed an ST-GNN-based skeleton modality model that, despite using a relatively small child action dataset, achieved superior performance (94.81%) compared to implementations trained on a significantly larger (x10) adult action dataset (90.6%) for a similar subset of actions. With ST-GCN-based feature extraction and fine-tuning methods, accuracy improved by 10%–40% compared to vanilla implementations, achieving a maximum accuracy of 94.81%. Additionally, implementations with other ST-GNN models demonstrated further accuracy improvements of 15%–45% over the ST-GCN baseline. The results on activity datasets empirically demonstrate that class diversity, dataset size, and careful selection of pre-training datasets significantly enhance accuracy. In-the-wild and in-the-deployment implementations confirm the real-world applicability of above approaches, with the ST-GNN model achieving 11 FPS on streaming data. Finally, preliminary evidence on the impact of graph expressivity and graph rewiring on accuracy of small dataset-based models is provided, outlining potential directions for future research. The codes are available at https://github.com/sankamohotttala/ST_GNN_HAR_DEML.Item Open Access Optimizing Inotropic Infusion With Cluster Specific AI Decision Models and Digital TwinsNair, V(Institute of Electrical and Electronics Engineers, 2025-06-20) Nair, V. S; Niranga, G. D. H; Aryalakshmi C.S; Sathyapalan, D. T; Madathil, T; Pathinarupothi, R.KInotropes are critical care medications essential for maintaining normal blood pressure (BP) in hospitalized patients. Titrating infusion rates of inotropes such as noradrenaline, vasopressin, and adrenaline based on fluctuating BP presents significant challenges in critical care settings. Typically, clinicians set a constant infusion rate for one hour, which may not accommodate the dynamic variability of BP inherent in critically ill patients, potentially leading to inadvertent hypotension or hypertension. Conventional feedback controllers, including fuzzy logic controllers (FLC), struggle to adapt to complex BP variations due to fixed algorithms and intracohort variability in drug responses. We propose an AI-enhanced closed-loop noradrenaline infusion control mechanism utilizing long short-term memory (LSTM) networks. This approach captures variability in drug responses through clustering of patients using LSTM autoencoders and K-means algorithms, subsequently developing LSTM-based decision models for infusion rates tailored to clusters. Additionally, a digital twin cardiac model serves as a simulation tool for validating the impact of inotropic infusion as indicated by the decision model. Comparative performance analyses demonstrate that our AI-enhanced closed-loop feedback method outperforms conventional systems like FLC regulators and pharmacokinetic-pharmacodynamic (PK-PD) models while ensuring patient safety as well as reducing the workload of clinicians.Publication Open Access Hybrid neural network methods to model the external wind pressure on a low-rise flat-roofed building in an irregularly shaped urban environment(Elsevier Ltd, 2025-06-23) Sajindra, H; Dharmawansha, S; Wijesundara, H; Herath, S; Rathnayake, U; Meddage D.P.PThe present study used hybrid artificial neural networks to model the wind pressure (mean and fluctuating) on a flat-roofed, low-rise building in an irregularly shaped urban environment. Four neural networks, each combined with an artificial bee colony (ABC), genetic algorithm (GA), particle swarm optimisation (PSO), and independent component analysis (ICA), along with an individual artificial neural network (ANN) model and a convolutional neural network (CNN), were used for the wind pressure predictions. The data was obtained from Tokyo Polytechnic University’s boundary layer wind tunnel and was used to train the neural network models. The results revealed that all models accurately captured the wind pressure on the low-rise building in a dense urban environment. Specifically, the genetic algorithm-artificial neural network (GA-ANN) model outperformed the remaining models, achieving good prediction accuracy for test data (coefficient of determination (R²) = 0.96 for mean pressure R² = 0.84 for fluctuation pressure). The use of machine learning explainability methods confirmed the consistency of GA-ANN with the fundamentals of wind engineering. Notably, the GA-ANN approach accurately modeled the special flow features on the building surface, such as flow separation, vortex formation, and pressure gradients, to a greater extent compared to the wind tunnel results. Therefore, the authors propose this method as an complementary approach for predicting wind pressure on low-rise buildings in complex urban environmentsPublication Open Access Exploring nontoxic perovskite materials for perovskite solar cells using machine learning(Discover, 2025-07-06) Pabasara, W.G.A.; Wijerathne, H.A.H.M; Karunarathne, M.G.M.M.; Sandaru, D.M.C.; Abeygunawardhana, Pradeep K. Werovskite solar cells are promising renewable energy technology that faces significant challenges due to the Pb induced toxicity. The current study addresses this issue by leveraging machine learning techniques to explore Pb-free perovskite materials that ensure environmental sustainability and human safety. A highly accurate machine learning model was developed to predict Goldschmidt factor and the band gap, aiming to discover lead-free perovskites. Extreme Gradient Boost (XGBoost), Random Forest (RF), Gradient Boost Regression (GBR), and Ada Boost Regression (ABR) models were employed for this purpose. The findings exhibit that XGBoost delivers the most precise and reliable results for Goldsmith tolerance factor prediction with an accuracy of 98.5%. Furthermore, GBR model, combined with K-nearest neighbors (KNN) model delivers an impressive accuracy of 98.7% for the band gap predictions. 49 Pb-free perovskite materials were screened out considering the toxicity and the abundance. Utilizing Principal Component Analysis (PCA) and K-means clustering, six optimal materials (KBiBr3, KZnBr3, RbBiBr 3, RbZnBr3, MAGeI3, and FAGeI3null) were identified as the potential environment-friendly materials for photovoltaic applications. These results show the crucial role of machine learning and statistical analysis in discovering nontoxic and environmental-friendly perovskite materials, advancing the development of sustainable energy solutions.Publication Embargo Tuning of Optoelectronic Properties of Chalcohalides by Tailoring Pnictogen Composition for Sustainable Photovoltaics(John Wiley and Sons Inc, 2025-08) Hu, D; Abeygunawardhana, P. K. W; Asha, GThis study investigates Sb1-xBixSeI pnictogen chalcohalides as lead-free materials for photovoltaic and optoelectronic applications using density functional theory (DFT) calculations. Increasing Bi content from 0.5 to 0.6 reduces the bandgap from 1.60 to 1.43 eV, enhancing the light absorption and aligning with the optimal range for solar energy conversions. Structural analysis reveals that higher Bi substitution expands the lattice, reduces the hole effective mass, and improves the hole mobility, while the electron mobility decreases slightly. Sb0.4Bi0.6SeI demonstrates quasi-direct bandgap characteristics attributed to Bi-induced lattice distortion and strong spin–orbit coupling (SOC), which reduces the conduction band minimum and facilitate direct-like electronic transitions. Enhanced absorption near the band edge and localized states contribute to higher sub-bandgap absorption, broadening the spectral response. Reduced bandgap falls within the optimal range for single-junction solar cells, increasing photocurrent generation. While defect-induced recombination poses challenges, passivation and compositional tuning can optimize its performance. This study identifies the potential of Sb0.4Bi0.6SeI as a versatile absorber material in emerging solar cell architectures. The findings provide a pathway toward designing cost-effective and sustainable materials with tailored properties for next-generation photovoltaic and optoelectronic technologies.Publication Embargo Deep Q-Network-Based Path Planning in a Simulated Warehouse Environment with SLAM Map Integration and Dynamic Obstacles(Department of Agribusiness, Universitas Muhammadiyah Yogyakarta, 2025-09-19) Medagangoda, H; Jayawickrama, N; de Silva, R; Samantha K.R.U.U; Abeygunawardhana, P. K. WWith the rise of e-Commerce and the evolution of robotic technologies, the focus on autonomous navigation within warehouse environments has increased. This study presents a simulation-based framework for path planning using Deep Q-Networks (DQN) in a warehouse environment modeled with moving obstacles. The proposed solution integrates a prebuilt map of the environment generated using Simultaneous Localization and Mapping (SLAM), which provides prior spatial knowledge of static obstacles. The reinforcement learning model is formulated with a state space derived from grayscale images that combine the static map generated by SLAM and dynamic obstacles in real time. The action space consists of four discrete movements for the agent. A reward shaping strategy includes a distance-based reward and penalty for collisions to encourage goal-reaching and discourage collisions. An epsilon-greedy policy with exponential decay is used to balance exploration and exploitation. This system was implemented in the Robot Operating System (ROS) and Gazebo simulation environment. The agent was trained over 1000 episodes and metrics such as the number of actions executed to reach the goal and the cumulative reward per episode were analyzed to evaluate the convergence of the proposed solution. The results across two goal locations show that incorporating the SLAM map enhances learning stability, with the agent reaching a goal approximately 150 times, nearly double the success rate compared to the baseline without map information, which achieved only 80 successful episodes over the same number of episodes. This indicates faster convergence and reduced exploration overhead due to improved spatial awareness.Publication Open Access Optical Coherence Imaging Hybridized Deep Learning Framework for Automated Plant Bud Classification in Emasculation Processes: A Pilot Study(Multidisciplinary Digital Publishing Institute (MDPI), 2025-09-25) Tharaka, D; Withanage, A; Kahatapitiya, N. S; Abhayapala, R; Wijenayake, U; Wijethunge, A; Ravichandran, N. K; Silva, B. N; Jeon, M; Kim, JA vision-based autonomous system for emasculating okra enhances agriculture by enabling precise flower bud identification, overcoming the labor-intensive, error-prone challenges of traditional manual methods with improved accuracy and efficiency. This study presents a framework for an adaptive, automated bud identification method to assist the emasculation process, hybridized optical coherence tomography (OCT). Three YOLOv8 variants were evaluated for accuracy, detection speed, and frame rate to identify the most efficient model. To strengthen the findings, YOLO was hybridized with OCT, enabling non-invasive sub-surface verification and precise quantification of the emasculated depth of both sepal and petal layers of the flower bud. To establish a solid benchmark, gold standard color histograms and a digital imaging-based method under optimal lighting conditions with confidence scoring were also employed. The results demonstrated that the proposed method significantly outperformed these conventional frameworks, providing superior accuracy and layer differentiation during emasculation. Hence, the developed YOLOv8 hybridized OCT method for flower bud identification and emasculation offers a powerful tool to significantly improve both the precision and efficiency of crop breeding practices. This framework sets the stage for implementing scalable, artificial intelligence (AI)-driven strategies that can modernize and optimize traditional crop breeding workflows.Publication Open Access Machine learning-based criminal behavior analysis for enhanced digital forensics(Public Library of Science, 2025-10-06) Dananjana, W. P; Arambawela, J. S; Gonawala, D.G. S. N; Rathnayaka, R.K. G.H; Senarathne, A. N; Siriwardena, S.M. D.NIn an increasingly digital world, uncovering criminal activity often relies on analyzing vast amounts of online behavior. Traditional methods in digital forensics struggle to keep up with the complexity and volume of data, particularly when trying to detect subtle deviations in online activity that could signal illegal intent. This research introduces an innovative approach that leverages machine learning to analyze internet activity—specifically browser artifacts—shedding new light on criminal behaviors that would otherwise remain hidden.Using advanced machine learning techniques such as Long Short-Term Memory (LSTM) networks and Autoencoders, this study focuses on detecting suspicious patterns and anomalies in browsing activity. By understanding the sequence and timing of a user’s online actions, this method enhances digital forensics investigations, allowing for faster and more accurate detection of criminal intent and behavior. The research aims to improve the speed and accuracy of identifying malicious online activity but also offers law enforcement and investigators a powerful tool to make sense of complex data. These findings represent an important step towards advancing digital forensics, enabling deeper insights into criminal behavior and more effective investigations, ultimately contributing to a safer digital environment.Publication Open Access Blockchain–AI–Geolocation Integrated Architecture for Mobile Identity and OTP Verification(Multidisciplinary Digital Publishing Institute (MDPI), 2025-11-23) SULOCHANA, G. G. D.; De Silva, D.IOne-Time Passwords (OTPs) are a core component of multi-factor authentication in banking, e-commerce, and digital platforms. However, conventional delivery channels such as SMS and email are increasingly vulnerable to SIM-swap fraud, phishing, spoofing, and session hijacking. This study proposes an end-to-end mobile authentication architecture that integrates a permissioned Hyperledger Fabric blockchain for tamper-evident identity management, an AI-driven risk engine for behavioral and SIM-swap anomaly detection, Zero-Knowledge Proofs (ZKPs) for privacy-preserving verification, and geolocation-bound OTP validation for contextual assurance. Hyperledger Fabric is selected for its permissioned governance, configurable endorsement policies, and deterministic chaincode execution, which together support regulatory compliance and high throughput without the overhead of cryptocurrency. The system is implemented as a set of modular microservices that combine encrypted off-chain storage with on-chain hash references and smart-contract–enforced policies for geofencing and privacy protection. Experimental results show sub-0.5 s total verification latency (including ZKP overhead), approximately 850 transactions per second throughput under an OR-endorsement policy, and an F1-score of 0.88 for SIM-swap detection. Collectively, these findings demonstrate a scalable, privacy-centric, and interoperable solution that strengthens OTP-based authentication while preserving user confidentiality, operational transparency, and regulatory compliance across mobile network operators.Item Open Access Exploring emergent topological properties in socio-economic networks through learning heterogeneity(2025-12-10) Karavita, C; Lyu, Z; Kasthurirathna, D; Piraveenan, MUnderstanding how individual learning behavior and structural dynamics interact is essential to modeling emergent phenomena in socio-economic networks. While bounded rationality and network adaptation have been widely studied, the role of heterogeneous learning rates–both at the agent and network levels–remains underexplored. This paper introduces a dual-learning framework that integrates individualized learning rates for agents and a rewiring rate for the network, reflecting real-world cognitive diversity and structural adaptability. Using a simulation model based on the Prisoner’s Dilemma and Quantal Response Equilibrium, we analyze how variations in these learning rates affect the emergence of large-scale network structures. Results show that lower and more homogeneously distributed learning rates promote scale-free networks, while higher or more heterogeneously distributed learning rates lead to the emergence of core-periphery topologies. Key topological metrics–including scale-free exponents, Estrada heterogeneity, and assortativity–reveal that both the speed and variability of learning critically shape system rationality and network architecture. This work provides a unified framework for examining how individual learnability and structural adaptability drive the formation of socio-economic networks with diverse topologies, offering new insights into adaptive behavior, systemic organization, and resilience.Publication Open Access A cost effective machine learning based network intrusion detection system using Raspberry Pi for real time analysis(PLOS ONE, 2025-12-29) Wijethilaka R.W.K.S; Yapa, K; Siriwardena, DIn an increasingly interconnected world, the security of sensitive data and critical operations is paramount. This study presents the development of a Network Intrusion Detection System (NIDS) that analyzes both inbound and outbound network traffic to detect and classify various cyber attacks. The research begins with an extensive review of existing intrusion detection techniques, highlighting the limitations of traditional methods when addressing the unique security challenges posed by distributed networks. To overcome these limitations, advanced machine learning algorithms, including Random Forest, Long Short Term Memory (LSTM) networks, Artificial Neural Networks (ANN), XGBoost, and Naive Bayes, are employed to create a robust and adaptive intrusion detection system. The practical implementation utilizes a Raspberry Pi as the central processing unit for real time traffic analysis, supported by hardware components such as Ethernet cables, LEDs, and buzzers for continuous monitoring and immediate threat response. A comprehensive alert system is developed, sending email notifications to administrators and activating physical indicators to signify detected threats. Our proposed NIDS achieves 96.5 detection accuracy on the NF-UQ-NIDS dataset, with a significantly reduced false positive rate after applying SMOTE. The system processes real time network traffic with an average response time of 50 milliseconds, outperforming traditional IDS solutions in accuracy and efficiency. Evaluation using the NF-UQ-NIDS dataset demonstrates a significant improvement in detection accuracy and response time, establishing the system as an effective tool for safeguarding networks against emerging cyber threats.Publication Open Access Hybrid ABC–HBA feature optimization with self-training using simulated unlabelled data for robust intrusion detection(Elsevier Ltd, 2026) Harischandra, S; Rajapaksha, U.U. S; Silva, B.N; Jayawardena, CThe increasing scale and heterogeneity of network traffic pose significant challenges for intrusion detection systems (IDS), particularly in detecting extremely rare attack classes and generalising to previously unseen threats under severe class imbalance. This study proposes a hybrid intrusion detection framework that integrates swarm intelligence–based feature optimisation with self-training using unlabelled data simulation to address these limitations. A novel ABC–HBA feature selection strategy is introduced, combining the efficient exploration capability of the Artificial Bee Colony (ABC) algorithm with the strong global exploitation and fast convergence of the Honey Badger Algorithm (HBA), resulting in a highly discriminative and compact feature subset. A Random Forest(RF) classifier augmented with a pseudo-labelling mechanism is then employed to enhance learning from unlabelled and unseen attack samples, enabling effective detection of novel attack patterns absent from the training set. To further mitigate extreme class imbalance, a hybrid resampling strategy is applied. Experimental evaluation on the KDD Cup 1999 dataset demonstrates that the proposed framework achieves an overall accuracy of 99.95% and a detection rate of 98.16%, while significantly improving the recognition of extremely rare attack classes, including a 92.86% detection rate for U2R attacks, which constitute less than 0.01% of the dataset. The proposed method consistently outperforms baseline RF, ABC-based, and several other state-of-the-art meta-heuristic and deep learning approaches, confirming its effectiveness in enhancing rare attack detection and generalisation to unseen threats in realistic intrusion detection scenarios.Publication Open Access A physics-informed machine learning for detecting suspicious satellite maneuvers (orbital manipulation)(Elsevier B.V., 2026) Karunathilake K.K.H; Abeywardena, K. Y ; Vecchini, SSatellite systems have become prime targets for cyberthreats given their critical role in global infrastructure and general lack of security. Among these, orbital manipulation, a form of satellite hijacking, is a particularly severe threat that can disrupt essential operations and impact national security. To address these concerns, this research proposes an Artificial Intelligence (AI)-based anomaly detection system that utilizes Machine Learning (ML) models to analyze telemetry data for possible orbital manipulations with a multi-gate physics architecture grounded in orbital mechanics, to verify that detected anomalies are kinematically inconsistent and are therefore genuine integrity failures. This research demonstrates that temporal-based models like LSTM are essential for this domain, achieving high recall rates which are then validated by the physics component. While the framework includes multiple physical constraints, this study specifically validates the energy-based Vis-Viva gate, with the Tsiolkovsky and Angular Momentum gates established as architectural designs for future verification. This study concludes that successful AI deployment in orbital cybersecurity requires a comprehensive approach that integrates domain-specific context and physics-informed validation beyond traditional performance metricsPublication Embargo Data-centric single teacher guided knowledge distillation for alleviating sub-optimal supervision in image classification(Elsevier Ltd, 2026-02-23) Sharma, K; Silva, B. NIn recent years, larger, deeper, and more complex deep learning models have emerged as a result of advancements in deep learning techniques. Nevertheless, the computational costs have also increased with the growing model size. Thus, Knowledge Distillation has evolved into a cornerstone in contemporary machine learning, facilitating the transfer of knowledge from cumbersome teacher models to more compact student models. However, student learning is persistently challenged by sub-optimal supervision caused by erroneous and ambiguous teacher predictions. Moreover, the learning process is further deteriorated by the complications introduced through frequently encountered noisy labels in real-world datasets. Existing methods often resort to the ensemble of teachers, introducing additional complexity. We propose a novel, simple, and efficient learning method, Corrective Knowledge Distillation (CKD), to alleviate these drawbacks while relying solely on a single-teacher model. The proposed work employs a two-phase learning paradigm. In the initial phase, the teacher selectively teaches extremely confident knowledge to the student, and in the subsequent phase, the student leverages its own past learning experiences, conditioning its knowledge acquisition on the guidance of the teacher. The proposed method consistently exhibits superior performance in addressing sub-optimal supervision, as evidenced by comprehensive experiments on benchmark datasets such as CIFAR-100, CIFAR-100N-Fine, and ImageNet-1K. Notably, CKD surpasses established baselines, achieving substantial accuracy gains of up to 3.53% in real-world scenarios. Furthermore, CKD exhibits exceptional robustness in highly noisy environments, outperforming ensemble techniques by a significant margin of up to 5.18%. Our code is available at https://github.com/Karthick47v2/ckd.Publication Embargo High-resolution optical imaging for sustainable fish freshness and safety assessment(Elsevier GmbH, 2026-04) Madhubhashini, M. N; Kahandawala, B.S; Sandaruwan, H.H.P. B; Silva, B.N; Wijenayake, U; Wijesinghe, R.EFish freshness evaluation is crucial to ensure consumer safety, and rapid assessment is essential for effective and accurate quality control. To overcome the limitations of the gold standards, such as lack of structural depth information, high-time consumption, and labor-intensiveness, high-resolution Optical Coherence Tomography (OCT) was employed for real-time monitoring of fish freshness non-invasively. Microstructural changes of eye and skin of Indian Anchovies ( Stolephorus indicus ) specimens were considered as the main freshness parameters during refrigeration storage. Both eye and skin tissues exhibited decreased internal scattering, loss of clarity, boundary weakening, and gradual structural degradations through the OCT observations. The quantitatively assessed variance intensity, entropy, energy, and edge density clearly revealed the internal tissue disruption over storage time due to protein denaturation, oxidative damage, and fluid imbalance. The findings of this study indicate that OCT shows an insightful correlation with microbiological and biochemical spoilage processes, enabling the advanced identification of subtle microstructural changes in fish skin and eye, even at a prior stage of deterioration. Such capability offers an objective and rapid freshness evaluation approach that could greatly benefit supply chain management and post-harvest seafood quality monitoring.