Autism spectrum disorder classification in videos: A hybrid of temporal coherency deep networks and self-organizing dual memory approach
Autism is at the moment, a common disorder. Prevalence of Autism Spectrum Disorder (ASD) is reported to be 1 in every 88 individuals. Early diagnosis of ASD has a significant impact to the livelihood of autistic children and their parents, or their caregivers. In this paper, we have developed an uns...
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my.um.eprints.370862023-06-01T02:12:13Z http://eprints.um.edu.my/37086/ Autism spectrum disorder classification in videos: A hybrid of temporal coherency deep networks and self-organizing dual memory approach Liang, Shuaibing Loo, Chu Kiong Sabri, Aznul Qalid Md QA75 Electronic computers. Computer science Autism is at the moment, a common disorder. Prevalence of Autism Spectrum Disorder (ASD) is reported to be 1 in every 88 individuals. Early diagnosis of ASD has a significant impact to the livelihood of autistic children and their parents, or their caregivers. In this paper, we have developed an unsupervised online learning model for ASD classification. The proposed approach is a hybrid approach, consisting, the temporal coherency deep networks approach, and, the self-organizing dual memory approach. The primary objective of the research is, to have a scalable system that can achieve online learning, and, is able to avoid the catastrophic forgetting phenomena in neural networks. We have evaluated our approach using an ASD specific dataset, and obtained promising results that are well inclined in supporting the overall objective of the research. SPRINGER-VERLAG SINGAPORE PTE LTD 2020 Conference or Workshop Item PeerReviewed Liang, Shuaibing and Loo, Chu Kiong and Sabri, Aznul Qalid Md (2020) Autism spectrum disorder classification in videos: A hybrid of temporal coherency deep networks and self-organizing dual memory approach. In: iCatse International Conference on Information Science and Applications (ICISA), 16-18 December 2019, Seoul, South Korea. https://link.springer.com/chapter/10.1007/978-981-15-1465-4_42 |
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QA75 Electronic computers. Computer science Liang, Shuaibing Loo, Chu Kiong Sabri, Aznul Qalid Md Autism spectrum disorder classification in videos: A hybrid of temporal coherency deep networks and self-organizing dual memory approach |
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Autism is at the moment, a common disorder. Prevalence of Autism Spectrum Disorder (ASD) is reported to be 1 in every 88 individuals. Early diagnosis of ASD has a significant impact to the livelihood of autistic children and their parents, or their caregivers. In this paper, we have developed an unsupervised online learning model for ASD classification. The proposed approach is a hybrid approach, consisting, the temporal coherency deep networks approach, and, the self-organizing dual memory approach. The primary objective of the research is, to have a scalable system that can achieve online learning, and, is able to avoid the catastrophic forgetting phenomena in neural networks. We have evaluated our approach using an ASD specific dataset, and obtained promising results that are well inclined in supporting the overall objective of the research. |
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Conference or Workshop Item |
author |
Liang, Shuaibing Loo, Chu Kiong Sabri, Aznul Qalid Md |
author_facet |
Liang, Shuaibing Loo, Chu Kiong Sabri, Aznul Qalid Md |
author_sort |
Liang, Shuaibing |
title |
Autism spectrum disorder classification in videos: A hybrid of temporal coherency deep networks and self-organizing dual memory approach |
title_short |
Autism spectrum disorder classification in videos: A hybrid of temporal coherency deep networks and self-organizing dual memory approach |
title_full |
Autism spectrum disorder classification in videos: A hybrid of temporal coherency deep networks and self-organizing dual memory approach |
title_fullStr |
Autism spectrum disorder classification in videos: A hybrid of temporal coherency deep networks and self-organizing dual memory approach |
title_full_unstemmed |
Autism spectrum disorder classification in videos: A hybrid of temporal coherency deep networks and self-organizing dual memory approach |
title_sort |
autism spectrum disorder classification in videos: a hybrid of temporal coherency deep networks and self-organizing dual memory approach |
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SPRINGER-VERLAG SINGAPORE PTE LTD |
publishDate |
2020 |
url |
http://eprints.um.edu.my/37086/ https://link.springer.com/chapter/10.1007/978-981-15-1465-4_42 |
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13.160551 |