@article{Liang2020421,
title = {Autism Spectrum Disorder Classification in Videos: A Hybrid of Temporal Coherency Deep Networks and Self-organizing Dual Memory Approach},
author = {S Liang and C K Loo and A Q Md Sabri},
editor = {Kim H -Y Kim K.J.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077496650&doi=10.1007%2f978-981-15-1465-4_42&partnerID=40&md5=8d885d212faf9e5a9d686c58a2e4eecd},
doi = {10.1007/978-981-15-1465-4_42},
issn = {18761100},
year = {2020},
date = {2020-01-01},
journal = {Lecture Notes in Electrical Engineering},
volume = {621},
pages = {421-430},
publisher = {Springer},
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. © Springer Nature Singapore Pte Ltd 2020.},
note = {cited By 0},
keywords = {Artificial Intelligence, Autism Spectrum Disorders, Autistic Children, Catastrophic Forgetting, Children with Autism, Diagnosis, Diseases, E-learning, Hybrid Approach, Learning, Neural Networks, Primary Objective, Scalable Systems, Temporal Coherency, Unsupervised Online Learning},
pubstate = {published},
tppubtype = {article}
}