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:
- 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
- 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.
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. |
2011 |
Iradah, Siti I; Rabiah, A K EduTism: An assistive educational system for the treatment of autism children with intelligent approach Journal Article Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7067 LNCS (PART 2), pp. 193-204, 2011, ISSN: 03029743, (cited By 3). Abstract | Links | BibTeX | Tags: Algorithms, Assistive, Autism Intervention, Data Collection, Diseases, E-learning, Education, Educational Software, Educational Systems, High-Functioning Autism, Information Science, Intelligent Approach, Malaysia, Multimedia Systems, Rule Based, Software Testing, Student Performance, Students @article{SitiIradah2011193, title = {EduTism: An assistive educational system for the treatment of autism children with intelligent approach}, author = {I Siti Iradah and A K Rabiah}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-81255214646&doi=10.1007%2f978-3-642-25200-6_19&partnerID=40&md5=85447136ace048f4543c86a103c8a786}, doi = {10.1007/978-3-642-25200-6_19}, issn = {03029743}, year = {2011}, date = {2011-01-01}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {7067 LNCS}, number = {PART 2}, pages = {193-204}, abstract = {This paper presents the development of an assistive educational system with intelligent approach which can be a basic electronic training and treatment tool to assist children with high-functioning autism. The plan is to bring these changes through the use of rules based algorithm as an approach to decide which level difficulty of the system should go according to the autism student performance based on the percentage of score. By applying this approach, the system will be able to monitor and analyze the performance of intelligent of autism student's capabilities. The system is capable to control the particular level of the autism students should play. It is capable to replace the teacher's responsibilities in terms of monitoring the student's progress and performance. Testing was conducted in Autism Intervention Programme of The National Autism Society of Malaysia (NASOM) at Malacca branch. Results and findings from this testing support the idea that educational software may be one of an effective and practical tool for teaching academic skills to autism children. Having programssuch asEduTism can improve effectiveness and efficiency of data collection tracking and reporting for the teachers and parents. © 2011 Springer-Verlag.}, note = {cited By 3}, keywords = {Algorithms, Assistive, Autism Intervention, Data Collection, Diseases, E-learning, Education, Educational Software, Educational Systems, High-Functioning Autism, Information Science, Intelligent Approach, Malaysia, Multimedia Systems, Rule Based, Software Testing, Student Performance, Students}, pubstate = {published}, tppubtype = {article} } This paper presents the development of an assistive educational system with intelligent approach which can be a basic electronic training and treatment tool to assist children with high-functioning autism. The plan is to bring these changes through the use of rules based algorithm as an approach to decide which level difficulty of the system should go according to the autism student performance based on the percentage of score. By applying this approach, the system will be able to monitor and analyze the performance of intelligent of autism student's capabilities. The system is capable to control the particular level of the autism students should play. It is capable to replace the teacher's responsibilities in terms of monitoring the student's progress and performance. Testing was conducted in Autism Intervention Programme of The National Autism Society of Malaysia (NASOM) at Malacca branch. Results and findings from this testing support the idea that educational software may be one of an effective and practical tool for teaching academic skills to autism children. Having programssuch asEduTism can improve effectiveness and efficiency of data collection tracking and reporting for the teachers and parents. © 2011 Springer-Verlag. |
Razali, N; Wahab, A 2D Affective Space Model (ASM) for detecting autistic children Conference 2011, ISBN: 9781612848433, (cited By 8). Abstract | Links | BibTeX | Tags: Autistic Children, Brain Disorders, Brain Imaging, Brain Imaging Techniques, Brain Signals, Children with Autism, Consumer Electronics, Data Collection, Diseases, Electroencephalogram, Electroencephalography, Feature Extraction, Frequency Domains, Functional Magnetic Resonance Imaging, Gaussian Mixture Model, Magnetic Resonance Imaging, Multi Layer Perceptron, Multilayer Perceptron, Multilayers, Positron Emission Tomography, Resonance, Space Models, Verification Results @conference{Razali2011536, title = {2D Affective Space Model (ASM) for detecting autistic children}, author = {N Razali and A Wahab}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-80052392399&doi=10.1109%2fISCE.2011.5973888&partnerID=40&md5=f6ea401148e6558b861e4df6407e527e}, doi = {10.1109/ISCE.2011.5973888}, isbn = {9781612848433}, year = {2011}, date = {2011-01-01}, journal = {Proceedings of the International Symposium on Consumer Electronics, ISCE}, pages = {536-541}, abstract = {There are many research works have been done on autism cases using brain imaging techniques. In this paper, the Electroencephalogram (EEG) was used to understand and analyze the functionality of the brain to identify or detect brain disorder for autism in term of motor imitation. Thus, the portability and affordability of the EEG equipment makes it a better choice in comparison with other brain imaging device such as functional magnetic resonance imaging (fMRI), positron emission tomography (PET) and megnetoencephalography (MEG). Data collection consists of both autistic and normal children with the total of 6 children for each group. All subjects were asked to clinch their hand by following video stimuli which presented in 1 minute time. Gaussian mixture model was used as a method of feature extraction for analyzing the brain signals in frequency domain. Then, the extraction data were classified using multilayer perceptron (MLP). According to the verification result, the percentage of discriminating between both groups is up to 85% in average by using k-fold validation. © 2011 IEEE.}, note = {cited By 8}, keywords = {Autistic Children, Brain Disorders, Brain Imaging, Brain Imaging Techniques, Brain Signals, Children with Autism, Consumer Electronics, Data Collection, Diseases, Electroencephalogram, Electroencephalography, Feature Extraction, Frequency Domains, Functional Magnetic Resonance Imaging, Gaussian Mixture Model, Magnetic Resonance Imaging, Multi Layer Perceptron, Multilayer Perceptron, Multilayers, Positron Emission Tomography, Resonance, Space Models, Verification Results}, pubstate = {published}, tppubtype = {conference} } There are many research works have been done on autism cases using brain imaging techniques. In this paper, the Electroencephalogram (EEG) was used to understand and analyze the functionality of the brain to identify or detect brain disorder for autism in term of motor imitation. Thus, the portability and affordability of the EEG equipment makes it a better choice in comparison with other brain imaging device such as functional magnetic resonance imaging (fMRI), positron emission tomography (PET) and megnetoencephalography (MEG). Data collection consists of both autistic and normal children with the total of 6 children for each group. All subjects were asked to clinch their hand by following video stimuli which presented in 1 minute time. Gaussian mixture model was used as a method of feature extraction for analyzing the brain signals in frequency domain. Then, the extraction data were classified using multilayer perceptron (MLP). According to the verification result, the percentage of discriminating between both groups is up to 85% in average by using k-fold validation. © 2011 IEEE. |
2010 |
Razali, N; Rahman, A W A Motor movement for autism spectrum disorder (ASD) detection Conference 2010, ISBN: 9789791948913, (cited By 3). Abstract | Links | BibTeX | Tags: Autism, Autism Spectrum Disorders, Autistic Children, Children with Autism, Data Collection, Diseases, Early Detection, Early Intervention, Finger Tapping, Gaussian Mixture Model, Information Technology, Motor Movements, Multi Layer Perceptron, Multilayer Perceptron (MLP), Multilayers @conference{Razali2010, title = {Motor movement for autism spectrum disorder (ASD) detection}, author = {N Razali and A W A Rahman}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-80052346152&doi=10.1109%2fICT4M.2010.5971921&partnerID=40&md5=234cdd8f3906ad980ed163a1036215ee}, doi = {10.1109/ICT4M.2010.5971921}, isbn = {9789791948913}, year = {2010}, date = {2010-01-01}, journal = {Proceeding of the 3rd International Conference on Information and Communication Technology for the Moslem World: ICT Connecting Cultures, ICT4M 2010}, pages = {E90-E95}, abstract = {In this paper, we are looking at the differences between autistic and normal children in term of fine motor movement. Previous findings have shown that there are differences between autistic children and normal children when performing a simple motor movement tasks. Imitating a finger tapping and clinching a hand are two examples of a simple motor movement tasks. Our study had adopted one of the video stimuli for clinching the hand from Brainmarkers. 6 selected autistic children and 6 selected normal children were involved in this study. The data collection is using EEG device and will be analyzed using Gaussian mixture model (GMM) and Multilayer perceptron (MLP) as classifier to discriminate between autistic and normal children. Experimental result shows the potential of verifying between autistic and normal children with accuracy of 92%. The potential of using these techniques to identify autistic children can help early detection for the purpose of early intervention. Moreover, the spectrums of the signals also present big differences between the two groups. © 2010 IEEE.}, note = {cited By 3}, keywords = {Autism, Autism Spectrum Disorders, Autistic Children, Children with Autism, Data Collection, Diseases, Early Detection, Early Intervention, Finger Tapping, Gaussian Mixture Model, Information Technology, Motor Movements, Multi Layer Perceptron, Multilayer Perceptron (MLP), Multilayers}, pubstate = {published}, tppubtype = {conference} } In this paper, we are looking at the differences between autistic and normal children in term of fine motor movement. Previous findings have shown that there are differences between autistic children and normal children when performing a simple motor movement tasks. Imitating a finger tapping and clinching a hand are two examples of a simple motor movement tasks. Our study had adopted one of the video stimuli for clinching the hand from Brainmarkers. 6 selected autistic children and 6 selected normal children were involved in this study. The data collection is using EEG device and will be analyzed using Gaussian mixture model (GMM) and Multilayer perceptron (MLP) as classifier to discriminate between autistic and normal children. Experimental result shows the potential of verifying between autistic and normal children with accuracy of 92%. The potential of using these techniques to identify autistic children can help early detection for the purpose of early intervention. Moreover, the spectrums of the signals also present big differences between the two groups. © 2010 IEEE. |