SLIIT Conference and Symposium Proceedings

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All SLIIT faculties annually conduct international conferences and symposiums. Publications from these events are included in this collection.

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Now showing 1 - 9 of 9
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    PublicationOpen Access
    POSTUREEASE: A Web Based Application for Monitoring the Sitting Posture in Computer Based Working Environment
    (SLIIT City UNI, 2025-07-08) Thennakoon, T.M.C.L; Worthington, A.E
    In today’s digital era, prolonged computer usage is commonplace, particularly in professional environments. However, extended periods of improper sitting posture can result in musculoskeletal disorders, fatigue, and chronic health complications. Addressing this concern, this research presents PostureEase, a web-based posture analysis application designed to promote ergonomic awareness and encourage healthy sitting habits. The system leverages computer vision and machine learning technologies to monitor posture in real time using webcam input. Developed with a React-based frontend and a Python-Flask backend, PostureEase processes live video streams through OpenCV and MediaPipe to detect poor posture based on facial and shoulder landmarks. Upon detecting improper alignment, the system provides immediate alerts to the user. Key features include posture history tracking, automated report generation, and exercise and ergonomic recommendations. Evaluation of the system demonstrated reliable performance under typical working conditions, with responsive detection and user-friendly interaction. This research contributes to the domain of health technology by offering a practical and preventive tool for posture correction. Future enhancements may include mobile integration and personalized analytics to further improve user experience and effectiveness. With a modular architecture and high usability, PostureEase achieved an accuracy of 92% in posture classification under normal lighting and device conditions. The system was evaluated through both user testing and technical validation, highlighting its potential for scalable deployment in ergonomic health monitoring.
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    PublicationOpen Access
    Nutria: An AI-Driven Personalized Meal and Exercise Recommender System for Diabetes Management
    (SLIIT City UNI, 2025-07-08) Kumari, V.W.I.D; Seneviratne, O
    The prevalence of diabetes has led to a growing demand for personalized dietary management tools, leading to the development of Nutria, a web-based food recommendation system tailored for individuals with diabetes. Nutria application is leveraging artificial intelligence, machine learning, and image processing. Nutria analyzes individual health data to provide realtime meal suggestions. The system also features predicting blood glucose level, feature of a chatbot that supports user engagement by offering dietary advice, tracking user progress and exercise recommendation for control their disease condition. The inclusion of a chatbot serves as a vital component of Nutria, facilitating ongoing user engagement and support. Users can interact with the chatbot to receive personalized dietary advice, track their progress over time. This interactive feature not only helps users stay motivated but also fosters a sense of accountability in their dietary choices. Findings from the system evaluation revealed a high level of user satisfaction, with over 85% of participants reporting improved dietary awareness and adherence.
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    PublicationOpen Access
    Explainable AI Powered Mental Health State Capturing Application to Support Students’ Mental Wellness and Academic Stress Mitigation
    (SLIIT City UNI, 2025-07-08) Welarathna, J.H; Nallaperunma, P.
    Mental health is a state of well-being that enables individuals to manage stress, work effectively, and contribute to society. However, reports show that serious mental health problems among students worldwide are increasing rapidly. A critical problem is that students often fail to recognize mental health issues or the sources of their academic stress, leading to silent suffering that escalates over time. A significant research gap exists as current assessments methods lack the ability to identify root causes of academic stress and provide explainable decisions for clinical use. This significant rise in many students’ mental health issues have indeed opened important discussions about its underlying causes, consequences, and the need for a comprehensive support system. Voices are an important part for identifying emotional expressions, as speech is the most vital channel of communication, enriched with emotions. The system analyzes emotional patterns in students' voices using Natural Language Processing (NLP) techniques to identify eight emotions and reveal the root causes of their mental health challenges and academic or non-academic stress. Additionally, Explainable AI (XAI) techniques are employed to provide a comprehensive analysis of these patterns, enhancing understanding and supporting managerial decision-making. The system achieves 93.46% accuracy using Random Forest algorithm with reliable confidence levels for clinical applications. It operates effectively in uncontrolled environments with language-independent features, ensuring adaptability across diverse student populations. While students typically seek support from counselors and healthcare professionals who base their decisions on clinical experience, this system offers an additional diagnostic tool to complement and validate professional evaluations. This research aims to better understand student mental health issues and contribute to improved students’ wellness and academic success.
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    PublicationEmbargo
    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.
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    PublicationEmbargo
    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.
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    PublicationEmbargo
    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.
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    PublicationEmbargo
    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.
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    PublicationEmbargo
    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.
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    PublicationEmbargo
    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-