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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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. |
2015 |
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. |
2010 |
Othman, M; Wahab, A Understanding autistic children perception through EEG Conference 2010, ISBN: 9781617820267, (cited By 0). Abstract | Links | BibTeX | Tags: Autism Spectrum Disorders, Autistic Children, Behavioral Research, Children with Autism, Computer Applications, Control Subject, Electroencephalography, Emotion, Emotional State, Empirical Studies, Facial Expression, Mel Frequency Cepstral Coefficients, Multilayer-Percheptron (MLP), Speech Recognition @conference{Othman2010315, title = {Understanding autistic children perception through EEG}, author = {M Othman and A Wahab}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84883660524&partnerID=40&md5=df9dac75053fbfa693b4823d5a0a77ad}, isbn = {9781617820267}, year = {2010}, date = {2010-01-01}, journal = {23rd International Conference on Computer Applications in Industry and Engineering 2010, CAINE 2010 - Including SNA 2010 Workshop}, pages = {315-320}, abstract = {Autistic children are known as having difficulties understanding human's facial expressions, making them incapable of interpreting the emotional states of others. This paper seeks to understand autistic children perception by analyzing brain signals using MFCC and MLP. An empirical study was conducted on 6 autistic and 6 typically developing children. Subjects' brainwaves were monitored while watching calm, happy and sad faces. Experimental results show that it is possible to discriminate the emotions of autistic children against control subjects with the accuracy of 76.61%. Brainwaves of autistic children also showed the trend of reversed emotions compared to normal children while watching happy and sad faces.}, note = {cited By 0}, keywords = {Autism Spectrum Disorders, Autistic Children, Behavioral Research, Children with Autism, Computer Applications, Control Subject, Electroencephalography, Emotion, Emotional State, Empirical Studies, Facial Expression, Mel Frequency Cepstral Coefficients, Multilayer-Percheptron (MLP), Speech Recognition}, pubstate = {published}, tppubtype = {conference} } Autistic children are known as having difficulties understanding human's facial expressions, making them incapable of interpreting the emotional states of others. This paper seeks to understand autistic children perception by analyzing brain signals using MFCC and MLP. An empirical study was conducted on 6 autistic and 6 typically developing children. Subjects' brainwaves were monitored while watching calm, happy and sad faces. Experimental results show that it is possible to discriminate the emotions of autistic children against control subjects with the accuracy of 76.61%. Brainwaves of autistic children also showed the trend of reversed emotions compared to normal children while watching happy and sad faces. |