Research Publications Authored by SLIIT Staff
Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/4195
This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.
Browse
48 results
Search Results
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 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 Embargo DS-HPE: Deep Set for Head Pose Estimation(IEEE, 2023-04-18) Menan, V; Gawesha, A; Samarasinghe, p; Kasthurirathna, DHead pose estimation is a critical task that is fundamental to a variety of real-world applications, such as virtual and augmented reality, as well as human behavior analysis. In the past, facial landmark-based methods were the dominant approach to head pose estimation. However, recent research has demonstrated the effectiveness of landmark-free methods, which have achieved state-of-the-art (SOTA) results. In this study, we utilize the Deep Set architecture for the first time in the domain of head pose estimation. Deep Set is a specialized architecture that works on a “set” of data as a result of the “permutation-invariance” operator being utilized in the model. As a result, the model is a simple yet powerful and edge-computation-friendly method for estimating head pose. We evaluate our proposed method on two benchmark data sets, and we compare our method against SOTA methods on a challenging video-based data set. Our results indicate that our proposed method not only achieves comparable accuracy to these SOTA methods but also requires less computational time. Furthermore, the simplicity of our proposed method allows for its deployment in resource-constrained environments without the need for expensive hardware such as Graphics Processing Units (GPUs). This work underscores the importance of accurate and resource-efficient head pose estimation in the fields of computer vision and human-computer interaction, and the Deep Set architecture presents a promising approach to achieving this goal.Publication Embargo Smart Device and Tracer to Overcome COVID-19 Using Digital Technology for Better Protection(IEEE, 2022-12-09) Avinash, K; Dithmal, C; Wijerathne, P; Kaushan, N; De Silva, H; Kasthurirathna, DA number of nations have experienced challenging circumstances as a result of the coronavirus disease (COVID-19), which has turned into a global pandemic. As a result of the social changes it has caused, this crisis will also have an impact on future generations. With the help of this technology, health organizations can quickly locate individuals who are infected with COVID-19 and provide them with medical care. The objective of this work is to develop a COVID-19 Tracer that is capable of COVID-19 detection and mitigation. The goal of this research is to reduce the number of COVID-19-related fatalities in Sri Lanka while also enabling users who are infected with the disease to access appropriate care and hospitalization. This software uses digital technologies to acquire accurate data and provide precise interpretations based on that data. Through the proposed method, patients can be treated using the application to get a precise diagnosis of their disease, maintaining social distance, stabilizing the mental level of the patient through AI, predicting the epidemic, providing COVID-19 vaccinations, as well as ambulance services through this application. Using every preventative measure available, this mobile application has now been developed to safeguard against COVID-19.Publication Embargo Expert System for Kubernetes Cluster Autoscaling and Resource Management(IEEE, 2022-12-09) Hettiarachchi, L.S; Jayadeva, S.V; Bandara, R. A. V; Palliyaguruge, D; Samaratunge Arachchillage, U. S. S; Kasthurirathna, DThe importance of orchestration tools such as Kubernetes has become paramount with the popularity of software architectural styles such as microservices. Furthermore, advancements in containerization technologies such as Docker has also played a vital role when it comes to advancements in the field of DevOps, enabling developers and system engineers to deploy are manage applications much more effectively. However, infrastructure configuration and management of resources are still challenging due to the disjointed nature of the infrastructure and resource management tools’ failure to comprehend the deployed applications and create a holistic view of the services. This is partly due to the extensive knowledge required to operate these tools or due to the inability to perform specific tasks. As a result, multiple tools and platforms need to conFigure together to automate the deployment, monitoring and management processes to provide the optimal deployment strategy for the applications. In response to this issue, this research proposes an expert system that creates a centralized approach to cluster autoscaling and resource management, which also provides an automated low-latency container management system and resiliency evaluation for dynamic systems. Furthermore, the time series load prediction is done using a BiLSTM and periodically creates an optimized autoscaling policy for cluster performance, thus creating a seamless pipeline from deployment, monitoring scaling, and troubleshooting of distributed applications based on Kubernetes.Publication Embargo SPAVIS: Mobile Application for Visually Impaired Based on Assistive Software and Volunteerism(IEEE, 2022-12-09) Mahir, M.A.M.; Hussain, M.N.M.; Perera, R.D.D; Upendra, Y.A.M; Wickramarathne, C. J.; Kasthurirathna, DSri Lankan population accounts up to almost one million visually impaired individuals out of which are mostly students and young individuals. As the educational structure for the visually impaired improves with funds, blind schools, and free education, assistance with minute needs for most visually impaired individuals comes at a cost. There are many assistive technologies, such as audio books, screen magnifiers, braille books, and screen readers, prevalent around the island. However, there are several limitations to these technologies, mainly their availability and affordability. In Sri Lanka, many individuals, societies, clubs, and many more are willing to volunteer to help those in need, even those that require physical attention. As much as it is anticipated to aid those in need, there is very little attention to the ways it can be done. Hence, this research provides a way to develop a user-friendly mobile application with assistive software and volunteerism to aid visually impaired students with their daily needs.Publication Embargo An evolutionary prototype of a self-care application for type 2 diabetes(IEEE, 2022-12-26) Widanarachchi, K; Mayadunne, S; Disanayake, K; Gunathilake, V; Kahandawaarachchi, C; Kasthurirathna, D; Jayasekera, PDiabetes Mellitus or Diabetes is a chronic health condition. As there is no cure for both type 1 and 2 diabetes yet, the only solution is to manage the condition by improving lifestyle activities like eating and exercising and seeking medical advice. There are applications for diet planning, to analyze meals for nutrients, to suggest diabetic-friendly recipes and devices like blood glucose trackers to support type 2 diabetic patients. But there is no application or a device that can support a patient by addressing the diabetes condition. So, the plan is to conduct applied research on developing a mobile app for type 2 diabetes, capable of not only monitoring the patient’s physical activities but also for diet planning, monitoring diabetic peripheral neuropathy and diabetic foot ulcer (DFU) complications. This application provides point-of-care monitoring features that can help diabetic patients to understand their condition and to identify complication in advance and get necessary treatments. There are 3 main components focusing on patients’ diet, physical conditional, the possibility of diabetic peripheral neuropathy and DFUs. In order to implement these components, the intention is to use classification, clustering techniques in machine learning and CNN techniques for image processing. While the accuracies of the selected models built upon each feature (component) is more than 90%, the models have then been tested and concluded that each feature works accurately on patients.Publication Embargo Graph Neural Network based Child Activity Recognition(IEEE, 2022-08-25) Mohottala, S; Samarasinghe, P; Kasthurirathna, D; Abhayaratne, CThis paper presents an implementation on child activity recognition (CAR) with a graph convolution network (GCN) based deep learning model since prior implementations in this domain have been dominated by CNN, LSTM and other methods despite the superior performance of GCN. To the best of our knowledge, we are the first to use a GCN model in child activity recognition domain. In overcoming the challenges of having small size publicly available child action datasets, several learning methods such as feature extraction, fine-tuning and curriculum learning were implemented to improve the model performance. Inspired by the contradicting claims made on the use of transfer learning in CAR, we conducted a detailed implementation and analysis on transfer learning together with a study on negative transfer learning effect on CAR as it hasn’t been addressed previously. As the principal contribution, we were able to develop a ST-GCN based CAR model which, despite the small size of the dataset, obtained around 50% accuracy on vanilla implementations. With feature extraction and fine tuning methods, accuracy was improved by 20%-30% with the highest accuracy being 82.24%. Furthermore, the results provided on activity datasets empirically demonstrate that with careful selection of pre-train model datasets through methods such as curriculum learning could enhance the accuracy levels. Finally, we provide preliminary evidence on possible frame rate effect on the accuracy of CAR models, a direction future research can explore.Publication Embargo 'xīnl' The Social Media App to Replenish Mental Health with the Aid of an Egocentric Network(Institute of Electrical and Electronics Engineers, 2022-11-03) Kasthurirathna, D; Kalansooriya, S; Kaluarachchi, A; Weerawickrama, C; Nanayakkara, D; Adeepa, DThe impact of social groups on one's emotional health is a crucial issue that must be addressed correctly. Emotions and social groups play significant roles in human mental and physical activities. It is difficult to detect and maintain track of changing emotional states. The main goal of this study is to build a social media app called Xinli, that proposes an aggregated method to predict emotions using a multimodal approach and to predict personalized activities based on the user's mental state, and to further track the improvement of emotional state with the impact of recommended activities and social support groups. The results suggest that the aggregated modalities method is more accurate in recognizing emotions, and activity prediction using reinforcement learning is a clean way to personalize activities based on the emotional state from user to user, which is the novelty of the proposed study.Publication Embargo Better you: Automated tool that evaluates mental health and provides guidance for university students(Institute of Electrical and Electronics Engineers Inc., 2022-11-04) Eeswar, S. S; Samaratunga, J. S; Nivethika, G; Anjana, W.W.M.; Jayasingha, T.B.; Pandithakoralage, S; Kasthurirathna, DThis research paper proposes a system that evaluates mental health through text-based, voice-based and facial emotion recognition. After predicting the user's overall emotional state, activity suggestions and close contact interactions will be suggested to improve their mental health.