Browsing by Author "Abeysinghe, D.V.D.S."

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Adaptive Academic Guidance System via Student Activity Performance Analysis and Profiling
(Faculty of Computing and Technology, University of Kelaniya Sri Lanka., 2023) Balasooriya, Saumya K.; Abeysinghe, D.V.D.S.; Sathsara, D.M.S.
In the ever-evolving landscape of education driven by rapid technological advancements, e-learning has emerged as a transformative force. However, it faces challenges, notably the difficulty of personalizing education in a digital environment. This paper introduces an innovative adaptive academic guidance system called "StudyMate." The system analyzes student activity, performance, and profiles to enhance the e-learning experience. The objectives encompass extensive research, surveys for primary and secondary data collection, algorithm development for student analysis, foundational database creation, source code development, and online system hosting. StudyMate leverages learning management system (LMS) functionalities to offer tailored learning experiences and demonstrates its efficacy in the digital realm. The literature review explores adaptive e-learning systems, highlighting the need for personalized education and referencing related research projects. The methodology outlines planning, design, implementation, testing, and critical evaluation phases, including sample code snippets and test cases. The findings confirm the successful implementation of adaptive concepts in StudyMate, addressing the academic question effectively. The limitations lie in the system's scope, primarily focusing on student views, leaving room for future enhancements such as lecturer and admin functionalities. The paper concludes with the potential for expanding StudyMate's features and improving its usability, making it a valuable solution for personalized e-learning.
Novel computational approaches for border irregularity prediction to detect melanoma in skin lesions
(Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Abeysinghe, D.V.D.S.; Sotheeswaran, S.
Medical image detection has been a rapidly growing field of study during the last few years. There are different challenges associated with it. Many works have been done in order to provide solutions for key challenges. This study of work is focused on melanoma detection by using Asymmetry, Border irregularity, Colour textures, and Diameter (ABCD) feature along with proposing two new approaches for border irregularity detection. The proposed two new approaches are distance difference method and gradient method, which follows the main concept as traversing along the continuous borderline of the lesion. Further, this study varies from the existing studies, since it has been taken counts of distances from the centroid to the borderline without considering the distance from the image border to the borderline of the lesion. It was able to achieve a classification rate of 79% and 78.5% using distance difference method and gradient method, respectively whereas the classification without the border irregularity feature achieved 78% of accuracy performing on PH2 dataset. Further, this study can be stated as most appropriate to classify non-melanoma rather than melanoma. It is contributed by generating simple computer science-based approaches rather than complex mathematical methods to detect border irregularity and makes the medical image detection easy.