@article{Yusoff2011433,
title = {Le-ADS: Early learning disability detection system for autism and dyslexia},
author = {N M Yusoff and N S Rusli and R Ishak},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79960415721&doi=10.1007%2f978-3-642-22095-1_87&partnerID=40&md5=81c7ed311b28be5a6b9017df102e4d58},
doi = {10.1007/978-3-642-22095-1_87},
issn = {18650929},
year = {2011},
date = {2011-01-01},
journal = {Communications in Computer and Information Science},
volume = {174 CCIS},
number = {PART 2},
pages = {433-437},
abstract = {Screening test is one of common approaches to detect learning disabilities among children. The Early Learning Disability Detection System for Autism and Dyslexia (Le-AdS) is developed to help primary school teachers to recognize signs and students' behaviour. Studies and researches for the system have been done to understand these types of disorder. Research on the system architecture has also been carried out to know how the system should work based on the requirements and needs of the user. Interviews, reading and overview have been applied throughout the development process of this standalone software. This paper presents the work of Early Learning Disability Detection for Autism and Dyslexia (Le-ADS). © 2011 Springer-Verlag.},
note = {cited By 1},
keywords = {Detection System, Development Process, Diseases, Dyslexia, Early Learning, Engineering Research, Handicapped Persons, Human Computer Interaction, Know-how, Knowledge Management, Learning Disorder, Mild Autism, Primary Schools, Screening System, Screening Tests, Standalone Software, System Architectures},
pubstate = {published},
tppubtype = {article}
}

@article{MohdYusoff2009395,
title = {ESSE: Learning disability classification system for autism and dyslexia},
author = {N Mohd Yusoff and M H Abdul Wahab and M A Aziz and F Jalil AshaÁri},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-76249116153&doi=10.1007%2f978-3-642-02707-9_45&partnerID=40&md5=f51c6dd35a86b7eef7ee117d1daa41dd},
doi = {10.1007/978-3-642-02707-9_45},
issn = {03029743},
year = {2009},
date = {2009-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {5614 LNCS},
number = {PART 1},
pages = {395-402},
abstract = {This paper presents an Expert System for Special Education (ESSE) based on scenario in Malaysia. This system is developed through the process of knowledge-gaining which is gathered from various expertise in chosen domain. Realizing the limitation of traditional classification system that teachers adopted, we developed ESSE to automate a centralized decision making system. ESSE is also able to provide consistent answers for repetitive decisions, processes and tasks. Besides, teachers using this system hold and maintain significant level of information pertaining both learning disabilities, thus reduce amount of human errors. ESSE knowledge-based resulted from the knowledge engineering called Qualifiers and Choice. Both are gathered from the analysis of symptoms that are experienced by Autism and Dyslexia patients. Every type of disability is divided to several categories and sub-category to facilitate question's arrangement. This paper presents a review of Expert System for Special Education (ESSE), problems arises and the knowledge-based classification systems. © 2009 Springer Berlin Heidelberg.},
note = {cited By 2},
keywords = {Autism, Centralized Decision Making, Classification System, Decision Making, Errors, Expert Systems, Human Computer Interaction, Human Errors, Knowledge Engineering, Knowledge Management, Knowledge-Based Classification, Learning Disorder, Malaysia, Special Education, Teaching},
pubstate = {published},
tppubtype = {article}
}