2019 |
Misman, M F; Samah, A A; Ezudin, F A; Majid, H A; Shah, Z A; Hashim, H; Harun, M F Classification of adults with autism spectrum disorder using deep neural network Conference Institute of Electrical and Electronics Engineers Inc., 2019, ISBN: 9781728130415, (cited By 0). Abstract | Links | BibTeX | Tags: Autism Spectrum Disorders, Brain Disorders, Classification (of information), Classification Accuracy, Classification Methods, Clinical Tests, Cognitive Skill, Computer Aided Diagnosis, Deep Learning, Deep Neural Networks, Diseases, Learning, Machine Learning Methods, Screening Data, Support Vector Machines @conference{Misman201929, title = {Classification of adults with autism spectrum disorder using deep neural network}, author = {M F Misman and A A Samah and F A Ezudin and H A Majid and Z A Shah and H Hashim and M F Harun}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079349811&doi=10.1109%2fAiDAS47888.2019.8970823&partnerID=40&md5=dd727e950667359680a6dbcc4855422f}, doi = {10.1109/AiDAS47888.2019.8970823}, isbn = {9781728130415}, year = {2019}, date = {2019-01-01}, journal = {Proceedings - 2019 1st International Conference on Artificial Intelligence and Data Sciences, AiDAS 2019}, pages = {29-34}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, abstract = {Autism Spectrum Disorder (ASD) is a developmental brain disorder that causes deficits in linguistic, communicative, and cognitive skills as well as social skills. Various application of Machine Learning has been applied apart from the clinical tests available, which has increased the performance in the diagnosis of this disorder. In this study, we applied the Deep Neural Network (DNN) architecture, which has been a popular method in recent years and proved to improve classification accuracy. This study aims to analyse the performance of DNN model in the diagnosis of ASD in terms of classification accuracy by using two datasets of adult ASD screening data. The results are then compared with the previous Machine Learning method by another researcher, which is Support Vector Machine (SVM). The accuracy achieved by the DNN model in the classification of ASD diagnosis is 99.40% on the first dataset and achieved 96.08% on the second dataset. Meanwhile, the SVM model achieved an accuracy of 95.24% and 95.08% using the first and second data, respectively. The results show that ASD cases can be accurately identified by implementing the DNN classification method using ASD adult screening data. © 2019 IEEE.}, note = {cited By 0}, keywords = {Autism Spectrum Disorders, Brain Disorders, Classification (of information), Classification Accuracy, Classification Methods, Clinical Tests, Cognitive Skill, Computer Aided Diagnosis, Deep Learning, Deep Neural Networks, Diseases, Learning, Machine Learning Methods, Screening Data, Support Vector Machines}, pubstate = {published}, tppubtype = {conference} } Autism Spectrum Disorder (ASD) is a developmental brain disorder that causes deficits in linguistic, communicative, and cognitive skills as well as social skills. Various application of Machine Learning has been applied apart from the clinical tests available, which has increased the performance in the diagnosis of this disorder. In this study, we applied the Deep Neural Network (DNN) architecture, which has been a popular method in recent years and proved to improve classification accuracy. This study aims to analyse the performance of DNN model in the diagnosis of ASD in terms of classification accuracy by using two datasets of adult ASD screening data. The results are then compared with the previous Machine Learning method by another researcher, which is Support Vector Machine (SVM). The accuracy achieved by the DNN model in the classification of ASD diagnosis is 99.40% on the first dataset and achieved 96.08% on the second dataset. Meanwhile, the SVM model achieved an accuracy of 95.24% and 95.08% using the first and second data, respectively. The results show that ASD cases can be accurately identified by implementing the DNN classification method using ASD adult screening data. © 2019 IEEE. |
Abdullah, A A; Rijal, S; Dash, S R Evaluation on Machine Learning Algorithms for Classification of Autism Spectrum Disorder (ASD) Conference 1372 (1), Institute of Physics Publishing, 2019, ISSN: 17426588, (cited By 0). Abstract | Links | BibTeX | Tags: Autism Spectrum Disorders, Behaviour Evaluations, Biomedical Engineering, Brain Mapping, Classification (of information), Decision Trees, Diseases, Feature Extraction, Feature Selection Methods, K Fold Cross Validations, Learning, Least Absolute Shrinkage and Selection Operators, Least Squares Approximations, Logistic Regressions, Machine Learning, Machine Learning Methods, Magnetic Resonance Imaging, Nearest Neighbor Search, Regression Analysis, Supervised Learning, Supervised Machine Learning @conference{Abdullah2019, title = {Evaluation on Machine Learning Algorithms for Classification of Autism Spectrum Disorder (ASD)}, author = {A A Abdullah and S Rijal and S R Dash}, editor = {Rahim Mustafa Zaaba Norali Noor S B A N B S K A N B A B M Fook C.Y. Yazid H.B.}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076493636&doi=10.1088%2f1742-6596%2f1372%2f1%2f012052&partnerID=40&md5=2ec1bd9f6cf1e3afe965cc9e3792f536}, doi = {10.1088/1742-6596/1372/1/012052}, issn = {17426588}, year = {2019}, date = {2019-01-01}, journal = {Journal of Physics: Conference Series}, volume = {1372}, number = {1}, publisher = {Institute of Physics Publishing}, abstract = {Autism Spectrum Disorder (ASD) was characterized by delay in social interactions development, repetitive behaviors and narrow interest, which usually diagnosed with standard diagnostic tools such as Autism Diagnostic Observation Schedule (ADOS) and Autism Diagnostic Interview-Revised (ADIR-R). Previous work has implemented machine-learning methods for the classification of ASD, however they used different types of dataset such as brain images for MRI and EEG, risk genes in genetic profiles and behavior evaluation based on ADOS and ADI-R. Here a trial on using Autism Spectrum Questions (AQ) to build models that have higher potential to classify ASD was developed. In this research, Chi-square and Least Absolute Shrinkage and Selection Operator (LASSO) have been selected as feature selection methods to select the most important features for 3 supervised machine learning algorithms, which are Random Forest, Logistic Regression and K-Nearest Neighbors with K-fold cross validation. The performance was evaluated in which results Logistic Regression scored the highest accuracy with 97.541% using model with 13 selected features based on Chi-square selection method. © 2019 IOP Publishing Ltd. All rights reserved.}, note = {cited By 0}, keywords = {Autism Spectrum Disorders, Behaviour Evaluations, Biomedical Engineering, Brain Mapping, Classification (of information), Decision Trees, Diseases, Feature Extraction, Feature Selection Methods, K Fold Cross Validations, Learning, Least Absolute Shrinkage and Selection Operators, Least Squares Approximations, Logistic Regressions, Machine Learning, Machine Learning Methods, Magnetic Resonance Imaging, Nearest Neighbor Search, Regression Analysis, Supervised Learning, Supervised Machine Learning}, pubstate = {published}, tppubtype = {conference} } Autism Spectrum Disorder (ASD) was characterized by delay in social interactions development, repetitive behaviors and narrow interest, which usually diagnosed with standard diagnostic tools such as Autism Diagnostic Observation Schedule (ADOS) and Autism Diagnostic Interview-Revised (ADIR-R). Previous work has implemented machine-learning methods for the classification of ASD, however they used different types of dataset such as brain images for MRI and EEG, risk genes in genetic profiles and behavior evaluation based on ADOS and ADI-R. Here a trial on using Autism Spectrum Questions (AQ) to build models that have higher potential to classify ASD was developed. In this research, Chi-square and Least Absolute Shrinkage and Selection Operator (LASSO) have been selected as feature selection methods to select the most important features for 3 supervised machine learning algorithms, which are Random Forest, Logistic Regression and K-Nearest Neighbors with K-fold cross validation. The performance was evaluated in which results Logistic Regression scored the highest accuracy with 97.541% using model with 13 selected features based on Chi-square selection method. © 2019 IOP Publishing Ltd. All rights reserved. |
2018 |
Hariharan, M; Sindhu, R; Vijean, V; Yazid, H; Nadarajaw, T; Yaacob, S; Polat, K Improved binary dragonfly optimization algorithm and wavelet packet based non-linear features for infant cry classification Journal Article Computer Methods and Programs in Biomedicine, 155 , pp. 39-51, 2018, ISSN: 01692607, (cited By 21). Abstract | Links | BibTeX | Tags: Accidents, Algorithms, Article, Artificial Neural Network, Asphyxia, Binary Dragonfly Optimization Aalgorithm, Classification (of information), Classification Algorithm, Classifier, Coding, Computer-Assisted, Constants and Coefficients, Crying, Database Systems, Databases, Deafness, Diagnosis, Energy, Entropy, Extraction, Extreme Learning Machine, Factual, Factual Database, Feature Extraction, Feature Selection Methods, Fuzzy System, Hearing Impairment, Human, Hunger, Infant, Infant Cry, Infant Cry Classifications, Jaundice, Kernel Method, Learning, Linear Predictive Coding, Machine Learning, Mathematical Transformations, Mel Frequency Cepstral Coefficient, Mel Frequency Cepstral Coefficients, Multi-Class Classification, Neural Networks, Nonlinear Dynamics, Nonlinear System, Optimization, Pain, Pathophysiology, Prematurity, Reproducibility, Reproducibility of Results, Signal Processing, Speech Recognition, Wavelet Analysis, Wavelet Packet, Wavelet Packet Transforms @article{Hariharan201839, title = {Improved binary dragonfly optimization algorithm and wavelet packet based non-linear features for infant cry classification}, author = {M Hariharan and R Sindhu and V Vijean and H Yazid and T Nadarajaw and S Yaacob and K Polat}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85036611215&doi=10.1016%2fj.cmpb.2017.11.021&partnerID=40&md5=1f3b17817b00f07cadad6eb61c0f4bf9}, doi = {10.1016/j.cmpb.2017.11.021}, issn = {01692607}, year = {2018}, date = {2018-01-01}, journal = {Computer Methods and Programs in Biomedicine}, volume = {155}, pages = {39-51}, publisher = {Elsevier Ireland Ltd}, abstract = {Background and objective Infant cry signal carries several levels of information about the reason for crying (hunger, pain, sleepiness and discomfort) or the pathological status (asphyxia, deaf, jaundice, premature condition and autism, etc.) of an infant and therefore suited for early diagnosis. In this work, combination of wavelet packet based features and Improved Binary Dragonfly Optimization based feature selection method was proposed to classify the different types of infant cry signals. Methods Cry signals from 2 different databases were utilized. First database contains 507 cry samples of normal (N), 340 cry samples of asphyxia (A), 879 cry samples of deaf (D), 350 cry samples of hungry (H) and 192 cry samples of pain (P). Second database contains 513 cry samples of jaundice (J), 531 samples of premature (Prem) and 45 samples of normal (N). Wavelet packet transform based energy and non-linear entropies (496 features), Linear Predictive Coding (LPC) based cepstral features (56 features), Mel-frequency Cepstral Coefficients (MFCCs) were extracted (16 features). The combined feature set consists of 568 features. To overcome the curse of dimensionality issue, improved binary dragonfly optimization algorithm (IBDFO) was proposed to select the most salient attributes or features. Finally, Extreme Learning Machine (ELM) kernel classifier was used to classify the different types of infant cry signals using all the features and highly informative features as well. Results Several experiments of two-class and multi-class classification of cry signals were conducted. In binary or two-class experiments, maximum accuracy of 90.18% for H Vs P, 100% for A Vs N, 100% for D Vs N and 97.61% J Vs Prem was achieved using the features selected (only 204 features out of 568) by IBDFO. For the classification of multiple cry signals (multi-class problem), the selected features could differentiate between three classes (N, A & D) with the accuracy of 100% and seven classes with the accuracy of 97.62%. Conclusion The experimental results indicated that the proposed combination of feature extraction and selection method offers suitable classification accuracy and may be employed to detect the subtle changes in the cry signals. © 2017 Elsevier B.V.}, note = {cited By 21}, keywords = {Accidents, Algorithms, Article, Artificial Neural Network, Asphyxia, Binary Dragonfly Optimization Aalgorithm, Classification (of information), Classification Algorithm, Classifier, Coding, Computer-Assisted, Constants and Coefficients, Crying, Database Systems, Databases, Deafness, Diagnosis, Energy, Entropy, Extraction, Extreme Learning Machine, Factual, Factual Database, Feature Extraction, Feature Selection Methods, Fuzzy System, Hearing Impairment, Human, Hunger, Infant, Infant Cry, Infant Cry Classifications, Jaundice, Kernel Method, Learning, Linear Predictive Coding, Machine Learning, Mathematical Transformations, Mel Frequency Cepstral Coefficient, Mel Frequency Cepstral Coefficients, Multi-Class Classification, Neural Networks, Nonlinear Dynamics, Nonlinear System, Optimization, Pain, Pathophysiology, Prematurity, Reproducibility, Reproducibility of Results, Signal Processing, Speech Recognition, Wavelet Analysis, Wavelet Packet, Wavelet Packet Transforms}, pubstate = {published}, tppubtype = {article} } Background and objective Infant cry signal carries several levels of information about the reason for crying (hunger, pain, sleepiness and discomfort) or the pathological status (asphyxia, deaf, jaundice, premature condition and autism, etc.) of an infant and therefore suited for early diagnosis. In this work, combination of wavelet packet based features and Improved Binary Dragonfly Optimization based feature selection method was proposed to classify the different types of infant cry signals. Methods Cry signals from 2 different databases were utilized. First database contains 507 cry samples of normal (N), 340 cry samples of asphyxia (A), 879 cry samples of deaf (D), 350 cry samples of hungry (H) and 192 cry samples of pain (P). Second database contains 513 cry samples of jaundice (J), 531 samples of premature (Prem) and 45 samples of normal (N). Wavelet packet transform based energy and non-linear entropies (496 features), Linear Predictive Coding (LPC) based cepstral features (56 features), Mel-frequency Cepstral Coefficients (MFCCs) were extracted (16 features). The combined feature set consists of 568 features. To overcome the curse of dimensionality issue, improved binary dragonfly optimization algorithm (IBDFO) was proposed to select the most salient attributes or features. Finally, Extreme Learning Machine (ELM) kernel classifier was used to classify the different types of infant cry signals using all the features and highly informative features as well. Results Several experiments of two-class and multi-class classification of cry signals were conducted. In binary or two-class experiments, maximum accuracy of 90.18% for H Vs P, 100% for A Vs N, 100% for D Vs N and 97.61% J Vs Prem was achieved using the features selected (only 204 features out of 568) by IBDFO. For the classification of multiple cry signals (multi-class problem), the selected features could differentiate between three classes (N, A & D) with the accuracy of 100% and seven classes with the accuracy of 97.62%. Conclusion The experimental results indicated that the proposed combination of feature extraction and selection method offers suitable classification accuracy and may be employed to detect the subtle changes in the cry signals. © 2017 Elsevier B.V. |
2017 |
Ilias, S; Tahir, N M; Jailani, R Institute of Electrical and Electronics Engineers Inc., 2017, ISBN: 9781509009251, (cited By 0). Abstract | Links | BibTeX | Tags: Classification (of information), Discriminant Analysis, Diseases, Extraction, Feature Extraction, Gait Analysis, Gait Classification, Image Retrieval, Industrial Electronics, Kernel Function, Kinematic Parameters, Kinematics, Learning, Linear Discriminant Analysis, Machine Learning Approaches, Motion Analysis System, Polynomial Functions, Principal Component Analysis, Support Vector Machines, SVM Classifiers @conference{Ilias2017275, title = {Feature extraction of autism gait data using principal component analysis and linear discriminant analysis}, author = {S Ilias and N M Tahir and R Jailani}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034081031&doi=10.1109%2fIEACON.2016.8067391&partnerID=40&md5=7deaef6538413df7bfaf7cf723001d72}, doi = {10.1109/IEACON.2016.8067391}, isbn = {9781509009251}, year = {2017}, date = {2017-01-01}, journal = {IEACon 2016 - 2016 IEEE Industrial Electronics and Applications Conference}, pages = {275-279}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, abstract = {In this research, the application of machine learning approach specifically support vector machine along with principal component analysis and linear discriminant analysis as feature extractions are evaluated and validated in discriminating gait features between normal subjects and autism children. Gait features of 32 normal and 12 autism children were recorded and analyzed using VICON motion analysis system and a force platform during normal walking. Here, twenty one gait features describing the three types of gait characteristics namely basic, kinetic and kinematic in these children are extracted. Further, with these gait features as input during classification, the ability of SVM as classifier are investigated using three different kernel functions specifically linear, polynomial, and radial basis. Results showed that LDA as feature extraction is the highest accuracy with kinematic parameters as gait features along with polynomial function as kernel for the SVM classifier. This finding proven that LDA is suitable as feature extraction and SVM is indeed apt as gait classifier in classifying the gait pattern autism and normal children. © 2016 IEEE.}, note = {cited By 0}, keywords = {Classification (of information), Discriminant Analysis, Diseases, Extraction, Feature Extraction, Gait Analysis, Gait Classification, Image Retrieval, Industrial Electronics, Kernel Function, Kinematic Parameters, Kinematics, Learning, Linear Discriminant Analysis, Machine Learning Approaches, Motion Analysis System, Polynomial Functions, Principal Component Analysis, Support Vector Machines, SVM Classifiers}, pubstate = {published}, tppubtype = {conference} } In this research, the application of machine learning approach specifically support vector machine along with principal component analysis and linear discriminant analysis as feature extractions are evaluated and validated in discriminating gait features between normal subjects and autism children. Gait features of 32 normal and 12 autism children were recorded and analyzed using VICON motion analysis system and a force platform during normal walking. Here, twenty one gait features describing the three types of gait characteristics namely basic, kinetic and kinematic in these children are extracted. Further, with these gait features as input during classification, the ability of SVM as classifier are investigated using three different kernel functions specifically linear, polynomial, and radial basis. Results showed that LDA as feature extraction is the highest accuracy with kinematic parameters as gait features along with polynomial function as kernel for the SVM classifier. This finding proven that LDA is suitable as feature extraction and SVM is indeed apt as gait classifier in classifying the gait pattern autism and normal children. © 2016 IEEE. |
Hameed, S S; Hassan, R; Muhammad, F F Selection and classification of gene expression in autism disorder: Use of a combination of statistical filters and a GBPSO-SVM algorithm Journal Article PLoS ONE, 12 (11), 2017, ISSN: 19326203, (cited By 11). Abstract | Links | BibTeX | Tags: Accuracy, Algorithms, Article, Autism, Autism Spectrum Disorders, CAPS2 Gene, Classification (of information), Classifier, Experimental Study, Gene, Gene Expression, Gene Identification, Genetic Association, Genetic Procedures, Genetic Risk, Genetics, Geometric Binary Particle Swarm Optimization Support Vector Machine Algorithm, Human, RIsk Assessment, Standardization, Statistical Filter, Statistical Parameters, Statistics, Support Vector Machines @article{Hameed2017, title = {Selection and classification of gene expression in autism disorder: Use of a combination of statistical filters and a GBPSO-SVM algorithm}, author = {S S Hameed and R Hassan and F F Muhammad}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033361187&doi=10.1371%2fjournal.pone.0187371&partnerID=40&md5=f9260d41165145f229a3cf157699635e}, doi = {10.1371/journal.pone.0187371}, issn = {19326203}, year = {2017}, date = {2017-01-01}, journal = {PLoS ONE}, volume = {12}, number = {11}, publisher = {Public Library of Science}, abstract = {In this work, gene expression in autism spectrum disorder (ASD) is analyzed with the goal of selecting the most attributed genes and performing classification. The objective was achieved by utilizing a combination of various statistical filters and a wrapper-based geometric binary particle swarm optimization-support vector machine (GBPSO-SVM) algorithm. The utilization of different filters was accentuated by incorporating a mean and median ratio criterion to remove very similar genes. The results showed that the most discriminative genes that were identified in the first and last selection steps included the presence of a repetitive gene (CAPS2), which was assigned as the gene most highly related to ASD risk. The merged gene subset that was selected by the GBPSO-SVM algorithm was able to enhance the classification accuracy. © 2017 Hameed et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.}, note = {cited By 11}, keywords = {Accuracy, Algorithms, Article, Autism, Autism Spectrum Disorders, CAPS2 Gene, Classification (of information), Classifier, Experimental Study, Gene, Gene Expression, Gene Identification, Genetic Association, Genetic Procedures, Genetic Risk, Genetics, Geometric Binary Particle Swarm Optimization Support Vector Machine Algorithm, Human, RIsk Assessment, Standardization, Statistical Filter, Statistical Parameters, Statistics, Support Vector Machines}, pubstate = {published}, tppubtype = {article} } In this work, gene expression in autism spectrum disorder (ASD) is analyzed with the goal of selecting the most attributed genes and performing classification. The objective was achieved by utilizing a combination of various statistical filters and a wrapper-based geometric binary particle swarm optimization-support vector machine (GBPSO-SVM) algorithm. The utilization of different filters was accentuated by incorporating a mean and median ratio criterion to remove very similar genes. The results showed that the most discriminative genes that were identified in the first and last selection steps included the presence of a repetitive gene (CAPS2), which was assigned as the gene most highly related to ASD risk. The merged gene subset that was selected by the GBPSO-SVM algorithm was able to enhance the classification accuracy. © 2017 Hameed et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
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} } 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. |
2013 |
Shams, W K; Wahab, A Source-temporal-features for detection EEG behavior of autism spectrum disorder Conference 2013, ISBN: 9781479901340, (cited By 1). Abstract | Links | BibTeX | Tags: ASD, Autism Spectrum Disorders, Brain Activity, Children with Autism, Classification (of information), Communication, Diseases, Electroencephalography, Electronic Document, Information Technology, Multi-Layer Perception, Temporal Features, Time Difference of Arrival @conference{Shams2013, title = {Source-temporal-features for detection EEG behavior of autism spectrum disorder}, author = {W K Shams and A Wahab}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84879037124&doi=10.1109%2fICT4M.2013.6518913&partnerID=40&md5=db31715811e1e8fdf62c9d61daf8e6f6}, doi = {10.1109/ICT4M.2013.6518913}, isbn = {9781479901340}, year = {2013}, date = {2013-01-01}, journal = {2013 5th International Conference on Information and Communication Technology for the Muslim World, ICT4M 2013}, abstract = {This study introduces a new model to capture the abnormal brain activity of children with Autism Spectrum Disorder (ASD) during eyes open and eyes closed resting conditions. EEG data was collected from normal subjects' ages (4 to 9) years and ASD subjects match group. Time Difference of Arrival (TDOA) approach was applied with EEG data raw for feature extracted at time domain. The neural network, Multilayer Perception (MLP) was used to distinguish between the two groups during the two tasks. Results show significant accuracy around 98% for both tasks and clearly discriminate for the features in z-dimension his electronic document is a "live" template and already defines the components of your paper [title, text, heads, etc.] in its style sheet. © 2013 IEEE.}, note = {cited By 1}, keywords = {ASD, Autism Spectrum Disorders, Brain Activity, Children with Autism, Classification (of information), Communication, Diseases, Electroencephalography, Electronic Document, Information Technology, Multi-Layer Perception, Temporal Features, Time Difference of Arrival}, pubstate = {published}, tppubtype = {conference} } This study introduces a new model to capture the abnormal brain activity of children with Autism Spectrum Disorder (ASD) during eyes open and eyes closed resting conditions. EEG data was collected from normal subjects' ages (4 to 9) years and ASD subjects match group. Time Difference of Arrival (TDOA) approach was applied with EEG data raw for feature extracted at time domain. The neural network, Multilayer Perception (MLP) was used to distinguish between the two groups during the two tasks. Results show significant accuracy around 98% for both tasks and clearly discriminate for the features in z-dimension his electronic document is a "live" template and already defines the components of your paper [title, text, heads, etc.] in its style sheet. © 2013 IEEE. |
2012 |
Shams, W K; Wahab, A; Qidwai, U A Fuzzy model for detection and estimation of the degree of autism spectrum disorder Journal Article Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7666 LNCS (PART 4), pp. 372-379, 2012, ISSN: 03029743, (cited By 2). Abstract | Links | BibTeX | Tags: Autism Spectrum Disorders, Classification (of information), Data Processing, Detection and Estimation, Diseases, Early Intervention, EEG Signals, Electrophysiology, Fuzzy Approach, Fuzzy Modeling, Spectrum Energy, Subtractive Clustering, Time-Frequency Transformation, Treatment Process @article{Shams2012372, title = {Fuzzy model for detection and estimation of the degree of autism spectrum disorder}, author = {W K Shams and A Wahab and U A Qidwai}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84869038189&doi=10.1007%2f978-3-642-34478-7_46&partnerID=40&md5=98929aba468010a02f652994b0da2a54}, doi = {10.1007/978-3-642-34478-7_46}, issn = {03029743}, year = {2012}, date = {2012-01-01}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {7666 LNCS}, number = {PART 4}, pages = {372-379}, abstract = {Early detection of autism spectrum disorder (ASD) is of great significance for early intervention. Besides, knowing the degree of severity in ASD and how it changes with the intervention is imperative for the treatment process. This study proposes Takagi- Sugeno-Kang (TSK) fuzzy modeling approach that is based on subtractive clustering to classify autism spectrum disorder and to estimate the degree of prognosis. The study has been carried out using Electroencephalography (EEG) signal on two groups of control and ASD children age-matched between seven to nine years old. EEG signals are quantized to temporal-time domain using Short Time Frequency Transformation (STFT). Spectrum energy is extracted as features for alpha band. The proposed system is modeled to estimate the degree in which subject is autistic, normal or uncertain. The results show accuracy in range (70-97) % when using fuzzy model .Also this system is modeled to generate crisp decision; the results show accuracy in the range (80-100) %. The proposed model can be adapted to help psychiatrist for diagnosis and intervention process. © 2012 Springer-Verlag.}, note = {cited By 2}, keywords = {Autism Spectrum Disorders, Classification (of information), Data Processing, Detection and Estimation, Diseases, Early Intervention, EEG Signals, Electrophysiology, Fuzzy Approach, Fuzzy Modeling, Spectrum Energy, Subtractive Clustering, Time-Frequency Transformation, Treatment Process}, pubstate = {published}, tppubtype = {article} } Early detection of autism spectrum disorder (ASD) is of great significance for early intervention. Besides, knowing the degree of severity in ASD and how it changes with the intervention is imperative for the treatment process. This study proposes Takagi- Sugeno-Kang (TSK) fuzzy modeling approach that is based on subtractive clustering to classify autism spectrum disorder and to estimate the degree of prognosis. The study has been carried out using Electroencephalography (EEG) signal on two groups of control and ASD children age-matched between seven to nine years old. EEG signals are quantized to temporal-time domain using Short Time Frequency Transformation (STFT). Spectrum energy is extracted as features for alpha band. The proposed system is modeled to estimate the degree in which subject is autistic, normal or uncertain. The results show accuracy in range (70-97) % when using fuzzy model .Also this system is modeled to generate crisp decision; the results show accuracy in the range (80-100) %. The proposed model can be adapted to help psychiatrist for diagnosis and intervention process. © 2012 Springer-Verlag. |
1995 |
Kasmini, K; Zasmani, S Asperger's syndrome: a report of two cases from Malaysia. Journal Article Singapore medical journal, 36 (6), pp. 641-643, 1995, ISSN: 00375675, (cited By 2). Abstract | Links | BibTeX | Tags: Article, Autism, Autism Spectrum Disorders, Case Report, Child Development Disorders, Children, Classification (of information), Human, Language Development Disorders, Language Disability, Malaysia, Male, Pervasive, Psychiatric Status Rating Scales, Psychological Aspect, Psychological Rating Scale, Social Behaviour, Stereotyped Behaviour, Stereotypy, Syndrome @article{Kasmini1995641, title = {Asperger's syndrome: a report of two cases from Malaysia.}, author = {K Kasmini and S Zasmani}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-0029445569&partnerID=40&md5=6280382e5c679f84eea178a916b2e19f}, issn = {00375675}, year = {1995}, date = {1995-01-01}, journal = {Singapore medical journal}, volume = {36}, number = {6}, pages = {641-643}, abstract = {Asperger's Syndrome is a distinct variant of autism, with a prevalence rate of 10 to 26 per 10,000 of normal intelligence, and 0.4 per 10,000 in those with mild mental retardation. The syndrome now has its own clinical entity and diagnostic criteria. It is being officially listed in the ICD-10 under pervasive developmental disorder. Two such cases are described in this article. Case One lacked the ability to relate to others, was excessively preoccupied with the late actor P. Ramlee and demonstrated a peculiar behaviour of holding on to toothbrushes in his early childhood. Cognitively, he was unable to synthesise words into meaningful sentences. Similarly, Case Two was unable to relate well to others and was preoccupied with the planets and its constellations. Though he appeared intelligent with an IQ score of 101, he was unable to follow instructions at school. Both children had motor clumsiness and fulfilled the criteria for the diagnosis of Asperger's Syndrome.}, note = {cited By 2}, keywords = {Article, Autism, Autism Spectrum Disorders, Case Report, Child Development Disorders, Children, Classification (of information), Human, Language Development Disorders, Language Disability, Malaysia, Male, Pervasive, Psychiatric Status Rating Scales, Psychological Aspect, Psychological Rating Scale, Social Behaviour, Stereotyped Behaviour, Stereotypy, Syndrome}, pubstate = {published}, tppubtype = {article} } Asperger's Syndrome is a distinct variant of autism, with a prevalence rate of 10 to 26 per 10,000 of normal intelligence, and 0.4 per 10,000 in those with mild mental retardation. The syndrome now has its own clinical entity and diagnostic criteria. It is being officially listed in the ICD-10 under pervasive developmental disorder. Two such cases are described in this article. Case One lacked the ability to relate to others, was excessively preoccupied with the late actor P. Ramlee and demonstrated a peculiar behaviour of holding on to toothbrushes in his early childhood. Cognitively, he was unable to synthesise words into meaningful sentences. Similarly, Case Two was unable to relate well to others and was preoccupied with the planets and its constellations. Though he appeared intelligent with an IQ score of 101, he was unable to follow instructions at school. Both children had motor clumsiness and fulfilled the criteria for the diagnosis of Asperger's Syndrome. |
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2019 |
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2018 |
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2017 |
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2016 |
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2013 |
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2012 |
Fuzzy model for detection and estimation of the degree of autism spectrum disorder Journal Article Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7666 LNCS (PART 4), pp. 372-379, 2012, ISSN: 03029743, (cited By 2). |
1995 |
Asperger's syndrome: a report of two cases from Malaysia. Journal Article Singapore medical journal, 36 (6), pp. 641-643, 1995, ISSN: 00375675, (cited By 2). |