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.
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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.Publication Open Access Disassortative Mixing and Systemic Rational Behaviour: How System Rationality Is Influenced by Topology and Placement in Networked Systems(MDPI, 2022-09-12) Kasthurirathna, D; Ratnayake, P; Piraveenan, MInterdependent decisionmaking of individuals in social systems can be modelled by games played on complex networks. Players in such systems have bounded rationality, which influences the computation of equilibrium solutions. It has been shown that the ‘system rationality’, which indicates the overall rationality of a network of players, may play a key role in the emergence of scale-free or core-periphery topologies in real-world networks. In this work, we identify optimal topologies and mixing patterns of players which can maximise system rationality. Based on simulation results, we show that irrespective of the placement of nodes with higher rationality, it is the disassortative mixing of node rationality that helps to maximize system rationality in a population. In other words, the findings of this work indicate that the overall rationality of a population may improve when more players with non-similar individual rationality levels interact with each other. We identify particular topologies such as the core-periphery topology, which facilitates the optimisation of system rationality. The findings presented in this work may have useful interpretations and applications in socio-economic systems for maximizing the utility of interactions in a population of strategic players.Publication Embargo SMART Garbage Bin Kit Expandable and Intelligent Waste Management System using Deep Learning and IoT for Modern Organizations(IEEE, 2021-12-02) Hewagamage, P.; Perera, D; Thilakarathna, T; Kasthurirathna, D; Fernando, R; Mihiranga, AAccording to published statistics, Sri Lanka produces garbage around 7000MT per day, and every organization directly contributes this national amount depending on the waste management practices. 'Waste contamination' is a critical issue that affects waste management, and it should be addressed during the garbage collection process. This has led to environmental hazards resulting in health and other social issues. Hence, it is a responsibility of an organization to separate the garbage during the collection process using a suitable technique. In this paper, we are proposing a smart garbage bin kit that automates the separation of garbage collection, which minimizes human error using AI-based technologies. IoT-based devices connected to a smart garbage bin kit guide the user to the correct bin. At the same time, our proposed system can be easily expanded for new special waste categories as well. The other important issue of the current garbage management is improper time management of the garbage removal process in organizations. This happens due to the lack of real-time data on waste bins, and collection is based on the fixed time interval irrespective of the status and location of garbage bins. In the proposed system of SMART Garbage Bin Kit, the group of all interconnected garbage bins is monitored in real-time to identify the optimum collection path considering the location and the status of garbage bins using an optimized algorithm. Hence, the study presented in this paper integrates several intelligent approaches together with IoT based network to build a cutting-edge device, declared as SMART Garbage Bin kit. The prototype system has been built as a part of the research study to demonstrate its feasibility and sustainability.Publication Open Access A Singlish Supported Post Recommendation Approach for Social Media(SCITEPRESS – Science and Technology Publications, 2022-01) Sandamini, U; Rathnakumara, K; Pramuditha, p; Dissanayake, M; Sriyaratna, D; De Silva, H; Kasthurirathna, DSocial media is an attractive means of communication which people used to exchange information. Post recommendation eliminates the overflooding of information in social media to the users’ news feed by suggesting the best matching information based on users’ preference that in return increase the usability. Social media users use different languages and their variations where most of the Sri Lankan users are accustomed to use Sinhala and Romanized Sinhala. However, post recommendation approaches used in current social media applications do not cater to code-mixed text. Therefore, this paper proposes a novel post recommendation approach that supports Singlish. The study is separated into two major components as language identification and transliteration, and post recommendation. In this study, script identification was performed using regular expressions while a Naïve Bayes classification model that accomplished 97% of accuracy was employed for language identification of Romanized text. Transliteration of Singlish to Sinhala was conducted using a character level seq2seq BLSTM model with a BLEU score of 0.94. Furthermore, Google translation API and YAKE were used for Sinhala-English translation and keyword extraction respectively. Post recommendation model utilized a combination of rule-based and CF techniques that accomplished the RMSE of 0.2971 and MAE of 0.2304.