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.
Browse
6 results
Filters
Advanced Search
Filter by
Settings
Search Results
Publication Embargo Analyzing Payment Behaviors And Introducing An Optimal Credit Limit(2019 1st International Conference on Advancements in Computing (ICAC), SLIIT, 2019-12-05) Bandara, H.M.M.T.; Samarasinghe, D.P.; Manchanayake, S.M.A.M.Identifying an optimal credit limit plays a vital role in telecommunication industry as the credit limit given to customers is influence on the market, revenue stabilization and customer retention. Most of the time service providers offer a fixed credit limit for customers which may cause customer dissatisfaction and loss of potential revenue. Therefore, it is essential to determine an optimal credit limit that maintains customer satisfaction while stabilizing the company revenue. Clustering algorithms were used to group customers with similar payment and usage behaviors. Then the optimal credit limit derived for each cluster is applicable to all the customers within the cluster. In order to identify the most suitable clustering algorithm, cluster validation statistics namely, Silhouette and Dunn indexes were used in this research. Based on the scores generated from these statistics KMeans algorithm was chosen. Furthermore, the quality of the KMeans clustering was evaluated using Silhouette score and the Elbow method. The optimal number of clusters are identified by those validation statistics. The significance of this approach is that the optimal credit limits generated by these clustering models suit dynamic behaviors of the customer which in turn increases customer satisfaction while contributing to reducing customer churn and potential loss of revenue.Publication Embargo An Automated Tool for Memory Forensics(2019 1st International Conference on Advancements in Computing (ICAC), SLIIT, 2019-12-05) Murthaja, M.; Sahayanathan, B.; Munasinghe, A.N.T.S.; Uthayakumar, D.; Rupasinghe, L.; Senarathne, A.In the present, memory forensics has captured the world’s attention. Currently, the volatility framework is used to extract artifacts from the memory dump, and the extracted artifacts are then used to investigate and to identify the malicious processes in the memory dump. The investigation process must be conducted manually, since the volatility framework provides only the artifacts that exist in the memory dump. In this paper, we investigate the four predominant domains of registry, DLL, API calls and network connections in memory forensics to implement the system ‘Malfore,’ which helps automate the entire process of memory forensics. We use the cuckoo sandbox to analyze malware samples and to obtain memory dumps and volatility frameworks to extract artifacts from the memory dump. The finalized dataset was evaluated using several machine learning algorithms, including RNN. The highest accuracy achieved was 98%, and it was reached using a recurrent neural network model, fitted to the data extracted from the DLL artifacts, and 92% accuracy was reached using a recurrent neural network model,fitted to data extracted from the network connection artifacts.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-