International Conference on Advancements in Computing [ICAC]
Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/312
The International Conference on Advancements in Computing (ICAC) is organized by the Faculty of Computing of the Sri Lanka Institute of Information Technology (SLIIT) as an open forum for academics along with industry professionals to present the latest findings and research output and practical deployments in computing.
The primary objective of ICAC is to promote innovative research that addresses real-world challenges and contributes to the social well-being of communities. The conference provides a dynamic platform for researchers from around the world to present groundbreaking findings, exchange ideas, and establish meaningful collaborations.
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Item Embargo Data-Driven Insights for Improved Diabetes Management(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Gunawardhana D.H.M.G; Kajeevan J; De Silva L.K.N; Wimansa P.P.H.S.D.; Anjana, J; Dassanayake, G.TEffective diabetes management requires understanding the complex interplay between multiple physiological and behavioral factors affecting glycemic control. This study presents a comprehensive analysis of diabetes management data from six distinct datasets containing continuous glucose monitoring (CGM) data, insulin administration records, carbohydrate intake, physical activity, sleep quality, and stress measurements. We employed time series analysis techniques such as STL decomposition, Dynamic Time Warping, LSTM neural networks, and XGBoost modeling to identify patterns in glucose variability and their relationships with modifiable factors. Our findings revealed significant time-of-day variations in glucose levels, with highest variability in late afternoon and evening (CV = 0.41 vs. 0.34, p < 0.01). Integrated prediction models achieved 83% sensitivity for hypoglycemia and 81% for hyperglycemia detection. The insulin-to-carbohydrate ratio varied by time of day, with morning ratios (1:8) differing from evening (1:12). Physical activity demonstrated intensity-dependent effects, with moderate-intensity exercise reducing glucose levels by 42 mg/dL for approximately 12 hours post-activity. Sleep quality below rating 3 (on a 5-point scale) was associated with a 37% increase in next-day glucose variability. These findings provide evidence-based insights for developing personalized diabetes management strategies that account for chronological variations in insulin sensitivity, meal timing, physical activity scheduling, and lifestyle interventions targeting sleep and stress management.Item Embargo Data-Driven Insights for Improved Diabetes Management(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Gunawardhana D.H.M.G; Kajeevan J.; De Silva L.K.N; Wimansa P.P.H.S.D; Anjana, J; Dassanayake, G.TEffective diabetes management requires understanding the complex interplay between multiple physiological and behavioral factors affecting glycemic control. This study presents a comprehensive analysis of diabetes management data from six distinct datasets containing continuous glucose monitoring (CGM) data, insulin administration records, carbohydrate intake, physical activity, sleep quality, and stress measurements. We employed time series analysis techniques such as STL decomposition, Dynamic Time Warping, LSTM neural networks, and XGBoost modeling to identify patterns in glucose variability and their relationships with modifiable factors. Our findings revealed significant time-of-day variations in glucose levels, with highest variability in late afternoon and evening (CV = 0.41 vs. 0.34, p < 0.01). Integrated prediction models achieved 83% sensitivity for hypoglycemia and 81% for hyperglycemia detection. The insulin-to-carbohydrate ratio varied by time of day, with morning ratios (1:8) differing from evening (1:12). Physical activity demonstrated intensity-dependent effects, with moderate-intensity exercise reducing glucose levels by 42 mg/dL for approximately 12 hours post-activity. Sleep quality below rating 3 (on a 5-point scale) was associated with a 37% increase in next-day glucose variability. These findings provide evidence-based insights for developing personalized diabetes management strategies that account for chronological variations in insulin sensitivity, meal timing, physical activity scheduling, and lifestyle interventions targeting sleep and stress management.Publication Embargo VirtualPT: Virtual Reality based Home Care Physiotherapy Rehabilitation for Elderly(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Heiyanthuduwa, T.A.; Amarapala, K.W.N.U.; Gunathilaka, K.D.V.B.; Ravindu, K.S.; Wickramarathne, J.; Kasthurirathna, D.This paper describes the development of Personal computer based Virtual Reality home-care Physiotherapy system aimed for rehabilitating full body function in elders. VirtualPT is a true virtual reality platform where the environment is completely replaced by a virtual reality platform based on the mental condition of the person at the time. While doing the home-based prescribed physiotherapy exercises, the key health metrics are continuously monitored and tracked by combining the immersive Virtual Reality with the wearable VirtualPT Sensor kit. Virtual Reality combined with 3D motion capture lets real time movements to be accurately translated onto the virtual reality avatar that can be viewed in a virtual environment to assist physiotherapist to add exercises to the system easily. This ultimate virtual reality Physiotherapy assistant avatar is used to provide guidance to elders at home, to demonstrate and assist elders in adhering to the prescribed exercises. As a significant aspect of social interactions, mirroring of movements has been added to focus on whether the elder is able to accurately follow the movements of avatar. Furthermore, the insightful dashboard offers the elders and physiotherapists an interactive platform through virtual reality capabilities. VirtualPT physiotherapy system is cost effective and makes recovery and more convenient to elders at home while the participatory and immersive nature of Virtual Reality offers a unique realistic quality that is not generally existing in clinical-based physiotherapy. When looking at the broader concept of VirtualPT; continuity of care, integration of services, quality of life and access are equally important criteria which add more value.Publication Embargo Facial Emotion Prediction through Action Units and Deep Learning(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Nadeeshani, M.; Jayaweera, A.; Samarasinghe, P.With the recent advancements in deep learning techniques, attention has been given to training and testing facial emotions through highly complex deep learning systems. In this paper we apply machine learning techniques which require less resources to produce comparable results for emotion prediction. As the underlying technique for the emotion prediction in this research is based on clinically recognized Facial Action Coding System (FACS), a further analysis is given on the contribution of each of the Action Units (AUs) for the predicted emotion. This analysis would complement, strengthen and be a main resource for addressing many different health issues related to facial muscle movements.Publication Embargo An Integrated Framework for Predicting Health Based on Sensor Data Using Machine Learning(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Jayaweera, K.N.; Kallora, K.M.C.; Subasinghe, N.A.C.K.; Rupasinghe, L.; Liyanapathirana, C.According to recent studies, the majority of the world's population shows a lack of concern in their health. As a consequence, the non-communicable disease rate has increased dramatically. Amongst these diseases, heart diseases have caused the most catastrophic situations. Apart from the busy lifestyle, studies also show that stress is another factor that causes these diseases. Therefore, the focus of our research is to provide a user-friendly health monitoring system that causes minimum disturbance to its users. However, many studies have focused on predicting health; very few have focused on its usability. The objective of our research is to predict the possibility of cardiac arrests and the presence of stress in real-time using a wearable device prototype. The system uses biometric signals obtained from the photoplethysmogram sensor embedded in the wearable device to perform real-time predictions. We trained three models using random forest, k-nearest neighbor, and logistic regression classification algorithms to predict sudden cardiac arrests with accuracies 99.93%, 99.10%, and 94.47%, respectively. Further, we trained three additional models to predict stress using the same algorithms with accuracies 99.87%, 96.83%, and 65.00%, respectively. Thus, the results of this study show that an integrated framework, capable of predicting different health-related conditions, through sensor data collected from wearable sensors, is feasible.Publication Embargo Mobile Based Solution to Weight Loss Planning for Children (with Obesity) in Sri Lanka(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Rajapakse, R.M.M.P.K.; Mudalige, J.M.A.I.; Perera, L.A.D.Y.S.; Warakagoda, R.N.A.M.S.C.B.; Siriwardana, S.Obesity is a condition where there is excess fat in the body, and it is one of the world's most extreme and dangerous dietary diseases. Genetic factors, lack of physical activity, unhealthy eating patterns, or a combination of these factors are the most common causes of obesity. This is important because it influences every part of a child's life. More, in particular, this disorder leads to poor health and negative social standing with perceptions. Nowadays, children are paying keen interest in technology and related devices. Therefore, in this research, we are planning to give a mobile-based solution with a smart band that is used to monitor the child. In this solution, we are mainly focusing on Sri Lankan children with obesity who are aged between 5-10. In our solution, there are four main sections which are, monitoring child activities, recognizing the activities, and getting relevant data, then based on those data and previous activity completion levels, this solution will suggest activities for losing weight, provide specific diet plans for each child considering the health conditions and predict the probability of having main obesity-