List of Publication by tag - National Autism Resource Centre (NARC)

List of Publications

There are numbers of autism related research can be found in Malaysia that generally focus on the ASD, learning disorder, communication aids, therapy and many more. The list of publications is provided below:

How to search Publication using Text Search

Click this to search using keyword determined by the user.
It will lead to a new web page with a text search box.
Type your keyword in the search box

How to Search Publication using Keyword

Click on Keyword to search for any publication. Bigger words indicate the most used tags and smaller words indicate the least used.
Click on dropdown buttons to choose any years, types of publications or authors of your choice.
Click on underlined words in the publication detail to view more information.

Adolescent Adult Animals Anthropomorphic Robots Anxiety Article Assistive Technology Attention Autism Autism Spectrum Disorders Autistic Children Biomedical Engineering Brain Caregiver Case Report Child Parent Relation Children Children with Autism Classification (of information) Clinical Article Cognition Communication Communication Skills Computer Aided Instruction Controlled Study Cross-Sectional Study Depression Developmental Disorders Disease Severity Diseases E-learning Early Intervention Education Electroencephalogram Electroencephalography Emotion Epilepsy Facial Expression Family Feature Extraction Female Gait Analysis Gene Genetics Health Care Human Human Computer Interaction Human Robot Interaction Humanoid Robot Humanoid Robot NAO Infant Intellectual Impairment Intelligent Control Learning Learning Disorder Letter Major Clinical Study Malaysia Male Manufacture Mental Disorders Mental Health Middle Aged Mobile Applications Neural Networks Nonhuman Parents Pathophysiology Patient Rehabilitation Pervasive Phenotype Physiology Preschool Preschool Child Priority Journal Procedures Psychology Quality of Life Questionnaires Research Review Robotics Robots Schizophrenia School Child Serious Games Signal Processing Smart Sensors Social Behaviour Social Communications Social Interactions Social Skills Special Education Statistics Students Surveys Teaching Unclassified Drug User Interfaces Young Adult

Show all

2016

Ilias, S; Tahir, N M; Jailani, R; Hasan, C Z C

Classification of autism children gait patterns using Neural Network and Support Vector Machine Conference

Institute of Electrical and Electronics Engineers Inc., 2016, ISBN: 9781509015436, (cited By 5).

Abstract | Links | BibTeX | Tags: Accuracy Rate, Autism, Classification (of information), Diseases, Gait Analysis, Gait Parameters, Gait Pattern, Industrial Electronics, Kinematics, Neural Networks, NN Classifiers, Sensitivity and Specificity, Support Vector Machines, Three Categories

@conference{Ilias201652,
title = {Classification of autism children gait patterns using Neural Network and Support Vector Machine},
author = {S Ilias and N M Tahir and R Jailani and C Z C Hasan},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992135613&doi=10.1109%2fISCAIE.2016.7575036&partnerID=40&md5=55c6d166768ed5fa3b504a2bd3441829},
doi = {10.1109/ISCAIE.2016.7575036},
isbn = {9781509015436},
year = {2016},
date = {2016-01-01},
journal = {ISCAIE 2016 - 2016 IEEE Symposium on Computer Applications and Industrial Electronics},
pages = {52-56},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {In this study, we deemed further to evaluate the performance of Neural Network (NN) and Support Vector Machine (SVM) in classifying the gait patterns between autism and normal children. Firstly, temporal spatial, kinetic and kinematic gait parameters of forty four subjects namely thirty two normal subjects and twelve autism children are acquired. Next, these three category gait parameters acted as inputs to both classifiers. Results showed that fusion of temporal spatial and kinematic contributed the highest accuracy rate for NN classifier specifically 95% whilst SVM with polynomial as kernel, 95% accuracy rate is contributed by fusion of all gait parameters as inputs to the classifier. In addition, the classifiers performance is validated by computing both value of sensitivity and specificity. With SVM using polynomial as kernel, sensitivity attained is 100% indicated that the classifier's ability to perfectly discriminate normal subjects from autism subjects whilst 85% specificity showed that SVM is able to identify autism subjects as autism based on their gait patterns at 85% rate. © 2016 IEEE.},
note = {cited By 5},
keywords = {Accuracy Rate, Autism, Classification (of information), Diseases, Gait Analysis, Gait Parameters, Gait Pattern, Industrial Electronics, Kinematics, Neural Networks, NN Classifiers, Sensitivity and Specificity, Support Vector Machines, Three Categories},
pubstate = {published},
tppubtype = {conference}
}