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:
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2020 |
Khowaja, K; Banire, B; Al-Thani, D; Sqalli, M T; Aqle, A; Shah, A; Salim, S S Augmented reality for learning of children and adolescents with autism spectrum disorder (ASD): A systematic review Journal Article IEEE Access, 8 , pp. 78779-78807, 2020, ISSN: 21693536, (cited By 0). Abstract | Links | BibTeX | Tags: Adolescent, Augmented Reality, Autism Spectrum Disorders, Bibliographic Database, Children, Classroom Environment, Data Acquisition, Data Collection, Diseases, Evaluation Parameters, Information Services, Maintenance, Parameter Estimation, Research, Social Communications @article{Khowaja202078779, title = {Augmented reality for learning of children and adolescents with autism spectrum disorder (ASD): A systematic review}, author = {K Khowaja and B Banire and D Al-Thani and M T Sqalli and A Aqle and A Shah and S S Salim}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084863534&doi=10.1109%2fACCESS.2020.2986608&partnerID=40&md5=266b4a1de057baa6582f13eb62483811}, doi = {10.1109/ACCESS.2020.2986608}, issn = {21693536}, year = {2020}, date = {2020-01-01}, journal = {IEEE Access}, volume = {8}, pages = {78779-78807}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, abstract = {This paper presents a systematic review of relevant primary studies on the use of augmented reality (AR) to improve various skills of children and adolescents diagnosed with autism spectrum disorder (ASD) from years 2005 to 2018 inclusive in eight bibliographic databases. This systematic review attempts to address eleven specific research questions related to the learing skills, participants, AR technology, research design, data collection methods, settings, evaluation parameters, intervention outcomes, generalization, and maintenance. The social communication skill was the highly targeted skill, and individuals with ASD were part of all the studies. Computer, smartphone, and smartglass are more frequently used technologies. The commonly used research design was pre-test and post-test. Almost all the studies used observation as a data collection method, and classroom environment or controlled research environment were used as a setting of evaluation. Most of the evaluation parameters were human-assisted. The results of the studies show that AR benefited children with ASD in learning skills. The generalization test was conducted in one study only, but the results were not reported. The results of maintenance tests conducted in five studies during a short-term period following the withdrawal of intervention were positive. Although the effect of using AR towards the learning of individuals was positive, given the wide variety of skills targeted in the studies, and the heterogeneity of the participants, a summative conclusion regarding the effectiveness of AR for teaching or learning of skills related to ASD based on the existing literature is not possible. The review also proposes the research taxonomy for ASD. Future research addressing the effectiveness of AR among more participants, different technologies supporting AR for the intervention, generalization, and maintenance of learning skills, and the evaluation in the inslusive classroom environment and other settings is warranted. © 2013 IEEE.}, note = {cited By 0}, keywords = {Adolescent, Augmented Reality, Autism Spectrum Disorders, Bibliographic Database, Children, Classroom Environment, Data Acquisition, Data Collection, Diseases, Evaluation Parameters, Information Services, Maintenance, Parameter Estimation, Research, Social Communications}, pubstate = {published}, tppubtype = {article} } This paper presents a systematic review of relevant primary studies on the use of augmented reality (AR) to improve various skills of children and adolescents diagnosed with autism spectrum disorder (ASD) from years 2005 to 2018 inclusive in eight bibliographic databases. This systematic review attempts to address eleven specific research questions related to the learing skills, participants, AR technology, research design, data collection methods, settings, evaluation parameters, intervention outcomes, generalization, and maintenance. The social communication skill was the highly targeted skill, and individuals with ASD were part of all the studies. Computer, smartphone, and smartglass are more frequently used technologies. The commonly used research design was pre-test and post-test. Almost all the studies used observation as a data collection method, and classroom environment or controlled research environment were used as a setting of evaluation. Most of the evaluation parameters were human-assisted. The results of the studies show that AR benefited children with ASD in learning skills. The generalization test was conducted in one study only, but the results were not reported. The results of maintenance tests conducted in five studies during a short-term period following the withdrawal of intervention were positive. Although the effect of using AR towards the learning of individuals was positive, given the wide variety of skills targeted in the studies, and the heterogeneity of the participants, a summative conclusion regarding the effectiveness of AR for teaching or learning of skills related to ASD based on the existing literature is not possible. The review also proposes the research taxonomy for ASD. Future research addressing the effectiveness of AR among more participants, different technologies supporting AR for the intervention, generalization, and maintenance of learning skills, and the evaluation in the inslusive classroom environment and other settings is warranted. © 2013 IEEE. |
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. |
Jamil, N; Khir, N H M; Ismail, M; Razak, F H A Gait-Based Emotion Detection of Children with Autism Spectrum Disorders: A Preliminary Investigation Conference 76 , Elsevier B.V., 2015, ISSN: 18770509, (cited By 4). Abstract | Links | BibTeX | Tags: Autism Spectrum Disorders, Children with Autism, Data Acquisition, Diseases, Emotion, Emotion Detection, Emotion Recognition, Emotional State, Facial Expression, Gait Analysis, Intelligent Control, Nonverbal Communication, Pattern Recognition, Robotics, Smart Sensors, Social Communications, Speech Recognition @conference{Jamil2015342, title = {Gait-Based Emotion Detection of Children with Autism Spectrum Disorders: A Preliminary Investigation}, author = {N Jamil and N H M Khir and M Ismail and F H A Razak}, editor = {Miskon M F Yussof H.}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962833568&doi=10.1016%2fj.procs.2015.12.305&partnerID=40&md5=6893678f1ed83b87147ff9183b94428b}, doi = {10.1016/j.procs.2015.12.305}, issn = {18770509}, year = {2015}, date = {2015-01-01}, journal = {Procedia Computer Science}, volume = {76}, pages = {342-348}, publisher = {Elsevier B.V.}, abstract = {With the disturbing increase of children with Autism Spectrum Disorder (ASD) in Malaysia, a lot of efforts and studies are put forward towards understanding and managing matters related to ASD. One way is to find means of easing the social communications among these children and their caretakers, particularly during intervention. If the caretaker is able to comprehend the children emotional state of mind prior to therapy, some sort of trust and attachment will be developed. However, regulating emotions is a challenge to these children. Nonverbal communication such as facial expression is difficult for ASD children. Therefore, we proposed the use of walking patterns (i.e. gait) to detect the type of emotions of ASD children. Even though using gait for emotion recognition is common among normal individuals, none can be found done on children with ASD. Thus, the aim of this paper is to conduct a preliminary review on the possibilities of carrying out gait-based emotion detection among ASD children with regards to the emotional types, gait parameters and methods of gait data acquisition. © 2015 The Authors.}, note = {cited By 4}, keywords = {Autism Spectrum Disorders, Children with Autism, Data Acquisition, Diseases, Emotion, Emotion Detection, Emotion Recognition, Emotional State, Facial Expression, Gait Analysis, Intelligent Control, Nonverbal Communication, Pattern Recognition, Robotics, Smart Sensors, Social Communications, Speech Recognition}, pubstate = {published}, tppubtype = {conference} } With the disturbing increase of children with Autism Spectrum Disorder (ASD) in Malaysia, a lot of efforts and studies are put forward towards understanding and managing matters related to ASD. One way is to find means of easing the social communications among these children and their caretakers, particularly during intervention. If the caretaker is able to comprehend the children emotional state of mind prior to therapy, some sort of trust and attachment will be developed. However, regulating emotions is a challenge to these children. Nonverbal communication such as facial expression is difficult for ASD children. Therefore, we proposed the use of walking patterns (i.e. gait) to detect the type of emotions of ASD children. Even though using gait for emotion recognition is common among normal individuals, none can be found done on children with ASD. Thus, the aim of this paper is to conduct a preliminary review on the possibilities of carrying out gait-based emotion detection among ASD children with regards to the emotional types, gait parameters and methods of gait data acquisition. © 2015 The Authors. |