Recent Submissions
Advanced Technologies for Skill Enhancement and Talent Recognition
(Institute of Electrical and Electronics Engineers Inc., 2025-07-03) Kumarasinghe D.P; Jayasuriya S.H; Semini B.V.S; Methsahani K.K.S.P; Thelijjagoda, S; Vidanaralage, A.J
Children with Down Syndrome encounter significant challenges in speech articulation, cognitive development and learning, which conventional educational approaches often struggle to address effectively. Difficulties in speech clarity, working memory and problem-solving highlight the need for adaptive learning solutions tailored to their unique needs. To address these challenges, EnlightenDS developed an artificial Intelligence system combined with educational technology that enhances learning through detection, pronunciation training, mathematics exercises and talent identification. The early detection of developmental concerns, facilitating timely intervention and module leverages support vector machine to assess the detection of Down Syndrome. The pronunciation training component integrates natural language processing and a flask-based API to process speech input, utilising google speech-to-text API and DeepSpeech AI for pronunciation evaluation. Speech-to-text conversion and phoneme-level articulation analysis further refine speech feedback. The mathematics training module applies structured learning methodologies like Kumon, Stern, and Numicon, ensuring an adaptive learning experience. Additionally, the talent identification system employs AI-driven implementation and machine learning based analysis to monitor engagement in learning, drawing, and motor activities, recognizing each child's talents and interests. Evaluation of EnlightenDS demonstrates that AI-powered assessment and personalised learning techniques significantly improve cognitive abilities and speech development while creating a more engaging educational experience. These findings emphasize the potential of AI, image processing and adaptive learning methodologies in special education, fostering enhanced communication, cognitive growth and talent recognition for children with Down Syndrome.
Enhancing DDoS Detection in SDN-Based Autonomous Vehicles using Transformer-based Feature Transformation
(Institute of Electrical and Electronics Engineers Inc., 2025-07-03) Perera, R; Senarathne, T.S; Ransika, T; Wijewardhana, P; Rathnayake, S.C; Amarasena, N.C
Autonomous vehicles (AVs) require fast, reliable, and secure communication to function safely in dynamic environments. To meet these demands, Software-Defined Networking (SDN) is increasingly used in AV systems due to its centralized control and ability to manage network traffic efficiently. However, this centralization also makes SDN vulnerable to Distributed Denial-of-Service (DDoS) attacks, which can disrupt critical vehicle operations. While earlier studies have used Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) for DDoS detection, this work introduces a Transformer-based feature selection method that leverages self-attention to focus on the most relevant network traffic features. This improves both detection accuracy and response time. To further enhance performance, we apply Bayesian Optimization with HyperNet (BOHN) for tuning model parameters. For interpretability, SHapley Additive exPlanations (SHAP) is used to highlight the impact of each feature on the model's predictions. The model is trained on the InSDN dataset, which simulates real-world SDN attack scenarios, and tested on the UNSW-NB15 dataset, a comprehensive benchmark containing a mix of normal and malicious traffic. Experimental results show significant improvements in accuracy, false positive reduction, and adaptability to new types of threats, demonstrating the value of self-attention-based approaches for securing SDN-enabled AV networks.
Project Management Predictive Framework
(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Dasanya, S; Samarakoon, K; Perera, S; Tennakoon, K; Attanayaka, B; Kahandawaarachchi, C
Project Planning is a critical phase in the software project management cycle. Traditional project management tools rely heavily on manual processes and do not provide accurate insights for the planning phase. This research focuses on developing an advanced software project management solution that utilises machine learning and AI technologies to enhance and provide accurate results. The system will automatically generate tasks from user stories, accurately estimate effort, budget, and identify risks. It also assesses team members' skills for an accurate task assignment and assignments and predicts infrastructure resource needs, suggesting cost-effective options to optimise resource use.
Data-Driven Injury-Risk Prediction in Competitive Swimming using Convolutional Neural Networks
(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Wickramagedara S.D.N; Subasinghe S.A.S.S; Ramanayake I.U; Wijesinghe S.D.R.N; Sumathipala, P; Kumarasinghe, S
Recent advancements in computer vision and machine learning have improved real-time human motion analysis surpassing conventional biomechanical studies by focusing on sports accessibility and movement improvement. This paper presents SwimSight, a mobile application designed to identify injury-prone movements in swimming techniques and provide personalized prevention recommendations. The system consists of three primary stages, which are video processing and pose estimation, joint-angle analysis, and injury-risk prediction. The preprocessing stage enhances video quality through frame stabilization utilizing OpenCV based filters, conventional computer vision technique. Pose estimation is executed through a marker less pipeline built on modern 2D pose estimators, that extract stable 2D keypoints to convert them into joint angles. An ensemble of 1D-CNN and 2D-CNN deep learning models is applied to predict identify risky shoulder and knee (the most common injury areas) movements with improved generalization. The system further integrates movement clustering to tailor recommendations. Through this pipeline, SwimSight delivers a robust and user-friendly solution that advances performance by injury prevention in aquatic sports.
Analysis of ML Models in Detecting and Mitigating Anomalies in Healthcare Networks
(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Naisar, F; Yapa, K
The increasing integration of digital technologies in healthcare has introduced significant cybersecurity challenges. The sensitivity of patient data, combined with the complexity and interconnectedness of healthcare networks, makes them prime targets for sophisticated cyberattacks. Traditional security mechanisms, including rule-based intrusion detection systems, exhibit notable limitations in detecting evolving and advanced threats. Machine learning and deep learning techniques have emerged as powerful solutions to enhance IDS capabilities, enabling real-time anomaly detection and automated threat mitigation. This study utilizes the CICIoMT2024 and IoMT Traffic data dataset to evaluate the effectiveness of ML-based IDS methodologies specifically within healthcare network environments. Initially, various models such as Random Forest (RF), Extreme Gradient Boosting (XGB), Logistic Regression (LR), and Deep Neural Networks (DNN) were assessed for their efficacy in detecting cyber threats. However, a hybrid CNN-LSTM architecture was ultimately finalized due to its superior performance in handling imbalanced datasets, modeling complex temporal feature interactions, and delivering high detection accuracy with minimal false positives. The research analyzes challenges faced by healthcare-oriented IDSs, including high false positiveness, data imbalance, computational resource constraints, and limitations inherent in available datasets. Future research directions are proposed, emphasizing the integration of federated learnings, optimized feature selection techniques, real-time IDS deployment strategies, and development of privacy-preserving cybersecurity frameworks tailored for healthcare environments.
The SLIIT Research Document Archive (RDA) is the institutional repository of SLIIT, managed by the SLIIT Library. The primary purpose of SLIIT RDA is to manage, store, and disseminate SLIIT research output with its community and beyond, reaching the wider public. This plays a pivotal role in preserving the academic legacy of the institute.
The collection comprises the research output of SLIIT staff and postgraduate research students, including research publications, conference and symposium papers, books, book chapters, theses, and other scholarly materials. Access to full texts may be restricted depending on the access and licensing terms.