Faculty of Computing

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Start date: 2025Subject: search.filters.subject.Machine learning

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Now showing 1 - 5 of 5
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    PublicationOpen 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, C
    The 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.
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    PublicationOpen 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, S
    Satellite 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 metrics
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    PublicationEmbargo
    Assessing the Efficacy of Machine Learning Algorithms in Predicting Critical Properties of Gold Nanoparticles for Pharmaceutical Applications
    (Springer, 2025-07-08) Fernando, H; Mohottala, S; Jayanetti, M; Thambiliyagodage, C
    Au nanoparticles are increasingly used in pharmaceuticals, but their synthesis is costly and time-intensive. Machine Learning can help optimize this process. In this research, eight distinct Machine Learning models were implemented and optimized on a dataset comprising 3000 records of gold nanoparticles. The performance of these models was assessed using four accuracy metrics and the time required for training and inference. The results are promising, with all seven models demonstrating high accuracy and low time requirements. Notably, the XGBoost and Artificial Neural Network models exhibited exceptional performance, with Mean Squared Error values of 0.0235 and 0.0098, Mean Absolute Error values of 0.1021 and 0.0674, Mean Absolute Percentage Deviation values of 0.4945 and 0.3590, R2 scores of 0.9995 and 0.9998, and inference times of 0.0029 and 0.4299 s, respectively. The Explainable Artificial Intelligence analysis of the resulting models revealed some interesting insights into how the models make the predictions and what factors heavily contribute to the nanoparticle AVG_R, allowing chemists to optimize the synthesis for gold nanoparticles better. The key contributions of the research include the design and development of eight Machine Learning models using industry-standard frameworks, the training, tuning, and evaluation of these eight models using five different metrics, and further assessment of these trained models using Explainable Artificial Intelligence. The findings indicate a substantial potential for applying neural networks in the design phase of nanoparticle synthesis, which could lead to significant reductions in both the time and cost required for synthesizing Au nanoparticles for pharmaceutical applications.
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    PublicationOpen 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. W; Sewvandi, G
    Perovskite 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
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    PublicationOpen 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. W.
    erovskite 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.