Browsing by Author "Wickramarathne, Jagath"

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A Comparative Study of Three User Experience Frameworks for Enhancing Health Mobile Applications
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Goonatillaka, W.A.D.B.C.; Kodithuwakku, C.K.; Sandaruwan, B.W.G.A.; Bandara, H.M.V.T.W.; Wickramarathne, Jagath
Apps for mobile health (mHealth) have proliferated and offer a variety of features to help users achieve better health outcomes. Thousands of mHealth apps are giving many great options for end users and they also introduce different options for different requirements. In this study the focus is specifically on the fitness mobile health apps. There are a variety of UX evaluation frameworks that are being used for the UX evaluation of those apps. However, not much research work is available in evaluating the UX frameworks relevant to mHealth mobile apps. The three frameworks evaluated in this study are the hook model, the mental model, and the double diamond model as those models have shown considerable success in this context. Five main user case studies are used in the user testing relevant to the UX of the selected mHealth app. At least three casual interviews together with three observation sessions are conducted per respondent to gather feedback on the usability, accessibility, and the effectiveness of the three frameworks. Thereby, the three frameworks are compared for their suitability and recommendations are tendered in suggesting a better suited framework for the UX evaluation purposes for mHealth apps. The Double Diamond Hook Mental (DDHM) hybrid model is proposed as the main outcome of this study to overcome the inherent drawbacks of each framework if used individually. After usability testing, it has proven that this proposed model enables to guide improved UX of mHealth apps.
Effectiveness of Using Deep Learning for Blister Blight Identification in Sri Lankan Tea
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Hewawitharana, G.H.A.U.; Nawarathne, U.M.L.A.; Hassan, A.S.F.; Wijerathna, L.M.; Sinniah, Ganga D; Vidhanaarachchi, Samitha P.; Wickramarathne, Jagath; Wijekoon, Janaka L.
Ceylon tea industry faces a major challenge in the form of pathogen-induced crop loss, with Blister Blight (BB) caused by Exobasidium vexans posing the greatest threat, leading to harvest losses of over 30%. This fungus attacks the tender tea shoots, resulting in a direct negative impact on the tea harvest. This paper presents a system to identify the suspicious tea leaves and BB disease at its early stages along with an assessment of severity, offering a potential solution to this critical issue. By utilizing real-time object detection, the system filters out non-tea leaves from the captured initial image of a segment of a tea plant. The identified tea leaves are then subjected to BB identification and severity assessment based on differing visual symptoms of the BB stages. This approach enables the system to accurately identify BB in the initial stage and severity stage, allowing for timely and targeted intervention to minimize crop losses. The YOLOv8 model has been able to correctly identify 98% of the objects it has detected as relevant (precision), and it has been able to correctly identify 96% of all the relevant objects present in the scene (recall). The Residual Network 50 (Resnet50) convolutional neural network (CNN) model was selected as the final model, achieving an accuracy of 89.90% during the training phase and an accuracy of 88.26% during the testing phase.