Autism spectrum disorder classification in videos: A hybrid of temporal coherency deep networks and self-organizing dual memory approach
Document Type
Conference Item
Publication Date
1-1-2020
Abstract
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.
Keywords
Artificial intelligence, Neural networks
Divisions
ai
Funders
Frontier Research Grant from University of Malaya (FG003-17AFR),University Malaya Research Grant (RP061C-18SBS),Office of Naval Research (ONRG-NICOP-N62909-18-1-2086)/IF017-2018,UAEU-AUA Joint Research Program Fund
Volume
621
Publisher
SPRINGER-VERLAG SINGAPORE PTE LTD
Publisher Location
152 BEACH ROAD, #21-01/04 GATEWAY EAST, SINGAPORE, 189721, SINGAPORE
Event Title
iCatse International Conference on Information Science and Applications (ICISA)
Event Location
Seoul, South Korea
Event Dates
16-18 December 2019
Event Type
conference
Additional Information
iCatse International Conference on Information Science and Applications (ICISA), Seoul, SOUTH KOREA, DEC 16-18, 2019