@article{Liang2020421,
title = {Autism Spectrum Disorder Classification in Videos: A Hybrid of Temporal Coherency Deep Networks and Self-organizing Dual Memory Approach},
author = {S Liang and C K Loo and A Q Md Sabri},
editor = {Kim H -Y Kim K.J.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077496650&doi=10.1007%2f978-981-15-1465-4_42&partnerID=40&md5=8d885d212faf9e5a9d686c58a2e4eecd},
doi = {10.1007/978-981-15-1465-4_42},
issn = {18761100},
year = {2020},
date = {2020-01-01},
journal = {Lecture Notes in Electrical Engineering},
volume = {621},
pages = {421-430},
publisher = {Springer},
abstract = {Autism is at the moment, a common disorder. Prevalence of Autism Spectrum Disorder (ASD) is reported to be 1 in every 88 individuals. Early diagnosis of ASD has a significant impact to the livelihood of autistic children and their parents, or their caregivers. In this paper, we have developed an unsupervised online learning model for ASD classification. The proposed approach is a hybrid approach, consisting, the temporal coherency deep networks approach, and, the self-organizing dual memory approach. The primary objective of the research is, to have a scalable system that can achieve online learning, and, is able to avoid the catastrophic forgetting phenomena in neural networks. We have evaluated our approach using an ASD specific dataset, and obtained promising results that are well inclined in supporting the overall objective of the research. © Springer Nature Singapore Pte Ltd 2020.},
note = {cited By 0},
keywords = {Artificial Intelligence, Autism Spectrum Disorders, Autistic Children, Catastrophic Forgetting, Children with Autism, Diagnosis, Diseases, E-learning, Hybrid Approach, Learning, Neural Networks, Primary Objective, Scalable Systems, Temporal Coherency, Unsupervised Online Learning},
pubstate = {published},
tppubtype = {article}
}

@article{Razali2020495,
title = {Intelligent Autism Screening Using Fuzzy Agent},
author = {N N C Razali and N A Ghani and S I Hisham},
editor = {Najib Abdul Wahab Othman Abd Ghani Irawan Khatun Raja Ismail Saari Daud Mohd Faudzi M S Y N A N M A S R M T M M M R A A Kasruddin Nasir A.N. Ahmad M.A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083080538&doi=10.1007%2f978-981-15-2317-5_42&partnerID=40&md5=c37fc825e41384f79ae2dd3361a504a7},
doi = {10.1007/978-981-15-2317-5_42},
issn = {18761100},
year = {2020},
date = {2020-01-01},
journal = {Lecture Notes in Electrical Engineering},
volume = {632},
pages = {495-503},
publisher = {Springer},
abstract = {In the diagnosis of diseases, either physical or psychological, there are situations causing reaching for second independent opinion very hard. This is especially true in the diagnosis of Autism due to the complex process of diagnosis. Apart from the complex process, the challenges include cost and the availability of experts. This, however, does not change the fact that having regular independent second opinions is crucial. Hence, this study proposes an intelligent autism screening model using fuzzy agent, to assist the expert and non-expert in making the diagnosis. In this study, the fuzzy inputs are assigned based on five categories, which are Communication, Gross Motor, Fine Motor, Problem Solving, and Personal Social, and is specifically for three-year-old children only. The proposed model will be able to produce output in the form of sequences based on lowest to highest mark of the scores for each category. This output will then relate to the suggestion of activities to autistic children by priority (based on the scores obtained). © 2020, Springer Nature Singapore Pte Ltd.},
note = {cited By 0},
keywords = {Autistic Children, Children with Autism, Complex Processes, Diagnosis, Diseases, Fine Motors, Fuzzy Agents, Fuzzy Input, Screening Models, Second Opinions},
pubstate = {published},
tppubtype = {article}
}

@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}
}

@article{Masiran2018b,
title = {Autism and trichotillomania in an adolescent boy},
author = {R Masiran},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053164449&doi=10.1136%2fbcr-2018-226270&partnerID=40&md5=7eed3f6af717df527dce73838feab571},
doi = {10.1136/bcr-2018-226270},
issn = {1757790X},
year = {2018},
date = {2018-01-01},
journal = {BMJ Case Reports},
volume = {2018},
publisher = {BMJ Publishing Group},
abstract = {An adolescent with autism spectrum disorder and improperly treated attention deficit hyperactivity disorder presented with recurrent hair pulling. Treatment with selective serotonin reuptake inhibitor and stimulant improved these conditions. © © BMJ Publishing Group Limited 2018.},
note = {cited By 0},
keywords = {Adolescent, Alopecia, Anxiety, Article, Attention Deficit Disorder, Attention Deficit Hyperactivity Disorder, Autism, Autism Spectrum Disorders, Behaviour Disorder, Body Mass, Case Report, Central Nervous System Stimulants, Child Behaviour Checklist, Clinical Article, Comorbidity, Complication, Diagnosis, Differential, Differential Diagnosis, Drug Dose Titration, Drug Tolerance, DSM-5, Echolalia, Fluvoxamine, Follow Up, Human, Hyperactivity, Intellectual Impairment, Male, Methylphenidate, Obesity, Occupational Therapy, Perceptual Reasoning Index, Priority Journal, Processing Speed Index, Psychiatric Status Rating Scales, Psychological Rating Scale, Rating Scale, Restlessness, Reward, Serotonin Uptake Inhibitor, Serotonin Uptake Inhibitors, Special Education, Speech Delay, Speech Disorder, Speech Therapy, Trichotillomania, Verbal Comprehension Index, Wechsler Intelligence Scale, Working Memory Index},
pubstate = {published},
tppubtype = {article}
}