2015 |
Isa, N R M; Yusoff, M; Khalid, N E; Tahir, N; Nikmat, Binti A W Autism severity level detection using fuzzy expert system Conference Institute of Electrical and Electronics Engineers Inc., 2015, ISBN: 9781479957651, (cited By 2). Abstract | Links | BibTeX | Tags: Autism, Autism Severity Level, Autistic Children, Children with Autism, Data Acquisition, Developmental Disorders, Diseases, Education, Expert Systems, Fuzzy Expert Systems, Level Detections, Manufacture, Robotics, Social Communications, Surveys, System Architectures, Teaching @conference{Isa2015218, title = {Autism severity level detection using fuzzy expert system}, author = {N R M Isa and M Yusoff and N E Khalid and N Tahir and A W Binti Nikmat}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959503922&doi=10.1109%2fROMA.2014.7295891&partnerID=40&md5=63e742d59b785d14f87d98dac7dd71ee}, doi = {10.1109/ROMA.2014.7295891}, isbn = {9781479957651}, year = {2015}, date = {2015-01-01}, journal = {2014 IEEE International Symposium on Robotics and Manufacturing Automation, IEEE-ROMA2014}, pages = {218-223}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, abstract = {Autism is a neuro developmental disorder that is recently well known among Malaysian. Many researches on autism detection have been conducted worldwide. However, there is lack of research conducted in detecting autism severity level. Therefore, this paper focuses on autism severity level detection using fuzzy expert system. Two main autistic behavioral criteria are selected which are social communication impairment and restricted repetitive behavior. Data acquisition was based on interview sessions with clinical psychologist and distribution of 36 questionnaires to teachers and parents that have autistic children. It was then analyzed and the cut off points for each severity level; level 1 (mild), level 2 (moderate), and level 3 (severe) is determined. The fuzzy expert system processes are employed to detect the severity levels. The processes involve Fuzzy system architecture, fuzzification, rules evaluation, rules evaluation and defuzzification. The finding demonstrates that the system is able to detect autism severity level with a good accuracy. This system also accommodates with suitable recommendation based on the generated result whether the suggestion is to go for speech therapy or behavior therapy. © 2014 IEEE.}, note = {cited By 2}, keywords = {Autism, Autism Severity Level, Autistic Children, Children with Autism, Data Acquisition, Developmental Disorders, Diseases, Education, Expert Systems, Fuzzy Expert Systems, Level Detections, Manufacture, Robotics, Social Communications, Surveys, System Architectures, Teaching}, pubstate = {published}, tppubtype = {conference} } Autism is a neuro developmental disorder that is recently well known among Malaysian. Many researches on autism detection have been conducted worldwide. However, there is lack of research conducted in detecting autism severity level. Therefore, this paper focuses on autism severity level detection using fuzzy expert system. Two main autistic behavioral criteria are selected which are social communication impairment and restricted repetitive behavior. Data acquisition was based on interview sessions with clinical psychologist and distribution of 36 questionnaires to teachers and parents that have autistic children. It was then analyzed and the cut off points for each severity level; level 1 (mild), level 2 (moderate), and level 3 (severe) is determined. The fuzzy expert system processes are employed to detect the severity levels. The processes involve Fuzzy system architecture, fuzzification, rules evaluation, rules evaluation and defuzzification. The finding demonstrates that the system is able to detect autism severity level with a good accuracy. This system also accommodates with suitable recommendation based on the generated result whether the suggestion is to go for speech therapy or behavior therapy. © 2014 IEEE. |
2009 |
Yusoff, Mohd N; Wahab, Abdul M H; Aziz, M A; AshaÁri, Jalil F ESSE: Learning disability classification system for autism and dyslexia Journal Article Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5614 LNCS (PART 1), pp. 395-402, 2009, ISSN: 03029743, (cited By 2). Abstract | Links | BibTeX | Tags: 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 @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} } 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. |
Testingadminnaacuitm2020-05-28T06:49:14+00:00
2015 |
Autism severity level detection using fuzzy expert system Conference Institute of Electrical and Electronics Engineers Inc., 2015, ISBN: 9781479957651, (cited By 2). |
2009 |
ESSE: Learning disability classification system for autism and dyslexia Journal Article Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5614 LNCS (PART 1), pp. 395-402, 2009, ISSN: 03029743, (cited By 2). |