Research Publications
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Publication Embargo Smart Waste Segregation for Home Environment(IEEE, 2023-06-12) Abeygunawardhana, P.K. W; Muhammed Rijah, U.LThe segregation of waste and recycling is essential for effective waste management. Due to the busy schedule most of the people do not have a time to separate their waste. However, there is a significant issue with the segregation of the collected garbage. The implementation of an intelligent trash-management architecture is essential for the removal or reduction of waste and the maintenance of a clean, corporate environment. An IoT-stationed smart waste management device is proposed in this study that uses sensor devices to identify rubbish in the dustbins. With the aid of sensors, the waste substances in it will be separated through IoT as soon as it is discovered. Sensors and the IoT module are connected through a microcontroller. To detect the presence of garbage, an ultrasonic sensor is used. All garbage entered will be caught by the web camera and processed by the machine learning module once it has been processed. Using the model, we can identify the many forms of waste, such as paper, plastic, and glass, which account for most of the garbage materials found in a home area. This aids in removing the trash from the trash can in the most efficient and effective manner possible. This research presents an IoT, and Machine Learning based completely intelligent trash segregation and management System that recognizes the dustbins' wastes using sensor systems. This project aims to develop an automated waste segregation system using a CNN algorithm that will capture waste images from a camera with object detection and classify waste materials such as paper, plastic, and glass so that the waste can be recycled appropriately. The proposed architecture with CNN gives an accuracy of 84.67%. This system will help in garbage disposal by categorizing it, contributing to a cleaner environment.Publication Embargo Stress Analysis and Care Prediction System for Online Workers(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Amarasinghe, A.A.S.M.; Malassri, I.M.S.; Weerasinghe, K.C.N.; Jayasingha, I.B.; Abeygunawardhana, P.K.W.; Silva, S.Working from home (WFH) online during the covid-19 pandemic has caused increased stress level. Online workers/students have been affecting by the crisis according to new researches. Natural response of body, to external and internal stimuli is stress. Even though stress is a natural occurrence, prolonged exposure while working Online to stressors can lead to serious health problems if any action will not be applied to control it. Our research has been conducted deeply to identify the best parameters, which have connection with stress level of online workers. As a result of our research, a desktop application has been created to identify the users stress level in real time. According to the results, our overall system was able to provide outputs with more than 70% accuracy. It will give best predictions to avoid the health problems. Our main goal is to provide best solution for the online workers to have healthy lifestyles. Updates for the users will be provided according to the feedback we will have in the future from the users. Our System will be a most valuable application in the future among online workers.Publication Embargo Stress Analysis and Care Prediction System for Online Workers(IEEE, 2021-12-09) Amarasinghe, A. A. S. M; Malassri, I. M. S; Weerasinghe, K. C. N; Jayasingha, I. B; Abeygunawardhana, P. K. W; Silva, SWorking from home (WFH) online during the covid-19 pandemic has caused increased stress level. Online workers/students have been affecting by the crisis according to new researches. Natural response of body, to external and internal stimuli is stress. Even though stress is a natural occurrence, prolonged exposure while working Online to stressors can lead to serious health problems if any action will not be applied to control it. Our research has been conducted deeply to identify the best parameters, which have connection with stress level of online workers. As a result of our research, a desktop application has been created to identify the users stress level in real time. According to the results, our overall system was able to provide outputs with more than 70% accuracy. It will give best predictions to avoid the health problems. Our main goal is to provide best solution for the online workers to have healthy lifestyles. Updates for the users will be provided according to the feedback we will have in the future from the users. Our System will be a most valuable application in the future among online workers.