Smart Computing and Systems Engineering - 2021 (SCSE 2021)

Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/25343

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Author: search.filters.author.Meedeniya, Dulani

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    Architectural framework for an interactive learning toolkit
    (Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Jayasiriwardene, Shakyani; Meedeniya, Dulani
    At present, a significant demand has emerged for online educational tools that can be used as replacement for classroom education. Due to the ease of access, the preference of many users is focused on m-learning applications. This paper presents an architectural framework for an interactive mobile learning toolkit. This study explores different software design patterns and presents the implementation details of the prototype. As a case study, the application is applied for the primary education sector in Sri Lanka, as there is a lack of adaptive learning mobile toolkits that allow teachers and students to interact effectively. The study is concluded to be user-friendly, understandable, useful, and efficient through a System Usability Study.
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    Autism spectrum disorder diagnosis support model using InceptionV3
    (Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Lakmini, Herath; Marasingha, M. A. J. C.; Meedeniya, Dulani; Weerasinghe, Vajira
    Autism spectrum disorder (ASD) is one of the most common neurodevelopment disorders that severely affect patients in performing their day-to-day activities and social interactions. Early and accurate diagnosis can help decide the correct therapeutic adaptations for the patients to lead an almost normal life. The present practices of diagnosis of ASD are highly subjective and time-consuming. Today, as a popular solution, understanding abnormalities in brain functions using brain imagery such as functional magnetic resonance imaging (fMRI), is being performed using machine learning. This study presents a transfer learning-based approach using Inception v3 for ASD classification with fMRI data. The approach transforms the raw 4D fMRI dataset to 2D epi, stat map, and glass brain images. The classification results show higher accuracy values with pre-trained weights. Thus, the pre-trained ImageNet models with transfer learning provides a viable solution for diagnosing ASD from fMRI images.