S. Rama Sree¹, Inderjeet Kaur², Alexey Tikhonov³, E. Laxmi Lydia⁴, Ahmed A. Thabit⁵, Zahraa H. Kareem⁶, Yousif Kerrar Yousif⁷, Ahmed Alkhayyat^8,*

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2195-2209, 2023, DOI:10.32604/cmc.2023.032586 - 22 September 2022

Abstract Autism spectrum disorder (ASD) is regarded as a neurological disorder well-defined by a specific set of problems associated with social skills, recurrent conduct, and communication. Identifying ASD as soon as possible is favourable due to prior identification of ASD permits prompt interferences in children with ASD. Recognition of ASD related to objective pathogenic mutation screening is the initial step against prior intervention and efficient treatment of children who were affected. Nowadays, healthcare and machine learning (ML) industries are combined for determining the existence of various diseases. This article devises a Jellyfish Search Optimization with Deep… More >

G. Anurekha^*, P. Geetha

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3049-3064, 2023, DOI:10.32604/iasc.2023.029127 - 17 August 2022

Abstract Autism Spectrum Disorder (ASD) is a complicated neurodevelopmental disorder that is often identified in toddlers. The microarray data is used as a diagnostic tool to identify the genetics of the disorder. However, microarray data is large and has a high volume. Consequently, it suffers from the problem of dimensionality. In microarray data, the sample size and variance of the gene expression will lead to overfitting and misclassification. Identifying the autism gene (feature) subset from microarray data is an important and challenging research area. It has to be efficiently addressed to improve gene feature selection and… More >

S. P. Abirami^1,*, G. Kousalya¹, R. Karthick²

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 595-607, 2023, DOI:10.32604/iasc.2023.027562 - 06 June 2022

Abstract Autism Spectrum Disorder (ASD) is found to be a major concern among various occupational therapists. The foremost challenge of this neurodevelopmental disorder lies in the fact of analyzing and exploring various symptoms of the children at their early stage of development. Such early identification could prop up the therapists and clinicians to provide proper assistive support to make the children lead an independent life. Facial expressions and emotions perceived by the children could contribute to such early intervention of autism. In this regard, the paper implements in identifying basic facial expression and exploring their emotions… More >

K. Chola Raja^1,*, S. Kannimuthu²

Computer Systems Science and Engineering, Vol.44, No.1, pp. 741-755, 2023, DOI:10.32604/csse.2023.025331 - 01 June 2022

Abstract Autism Spectrum Disorder (ASD) requires a precise diagnosis in order to be managed and rehabilitated. Non-invasive neuroimaging methods are disease markers that can be used to help diagnose ASD. The majority of available techniques in the literature use functional magnetic resonance imaging (fMRI) to detect ASD with a small dataset, resulting in high accuracy but low generality. Traditional supervised machine learning classification algorithms such as support vector machines function well with unstructured and semi structured data such as text, images, and videos, but their performance and robustness are restricted by the size of the accompanying… More >

Mengxian Zhao¹, Yonghao You², Jinming Li³, Sean Healy⁴, Alyx Taylor⁵, Zhihao Zhang³, Linlin Li⁶, Liye Zou^7,*

International Journal of Mental Health Promotion, Vol.24, No.4, pp. 475-489, 2022, DOI:10.32604/ijmhp.2022.021361 - 27 May 2022

Abstract Therapeutic horseback riding (THR) as an animal-assisted intervention is one of the innovative approaches emerging in the treatment for children with autism spectrum disorder (ASD). The current study was designed to investigate the effects of a 12-week, twice a week THR program on motor skills in sixty-eight children with ASD aged 5–10 years old. All participants selected met the DSM-V criteria for ASD, and a total of fifty-three participants completed the study. A randomized controlled trial design was utilized for the study. Data was collected via a pre-THR test, interim-THR test, and post-THR test to More >

Yin Liang^1,*, Gaoxu Xu¹, Sadaqat ur Rehman²

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4645-4661, 2022, DOI:10.32604/cmc.2022.026999 - 21 April 2022

Abstract Whole brain functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used in the diagnosis of brain disorders such as autism spectrum disorder (ASD). Recently, an increasing number of studies have focused on employing deep learning techniques to analyze FC patterns for brain disease classification. However, the high dimensionality of the FC features and the interpretation of deep learning results are issues that need to be addressed in the FC-based brain disease classification. In this paper, we proposed a multi-scale attention-based deep neural network (MSA-DNN) model to classify FC… More >

A. Saranya^1,*, R. Anandan²

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1167-1182, 2022, DOI:10.32604/iasc.2022.023445 - 08 February 2022

Abstract Hereditary Autism Spectrum Disorder (ASD) is a neuron disorder that affects a person's ability for communication, interaction, and also behaviors. Diagnostics of autism are available throughout all stages of life, from infancy through adolescence and adulthood. Facial Emotions detection is considered to be the most parameter for the detection of Autismdisorders among the different categories of people. Propelled with a machine and deep learning algorithms, detection of autism disorder using facial emotions has reached a new dimension and has even been considered as the precautionary warning system for caregivers. Since Facial emotions are limited to… More >

Zhiyuan Sun^1,2, Fabian Herold^3,4, Kelong Cai^1,2, Qian Yu⁵, Xiaoxiao Dong^1,2, Zhimei Liu^1,2, Jinming Li⁶, Aiguo Chen^1,2,* , Liye Zou^7,*

International Journal of Mental Health Promotion, Vol.24, No.2, pp. 143-158, 2022, DOI:10.32604/ijmhp.2022.020075 - 18 January 2022

Abstract In recent years evidence has emerged suggesting that Mini-basketball training program (MBTP) can be an effective intervention method to improve social communication (SC) impairments and restricted and repetitive behaviors (RRBs) in preschool children suffering from autism spectrum disorder (ASD). However, there is a considerable degree if interindividual variability concerning these social outcomes and thus not all preschool children with ASD profit from a MBTP intervention to the same extent. In order to make more accurate predictions which preschool children with ASD can benefit from an MBTP intervention or which preschool children with ASD need additional… More >

MAKOTO NABETANI^1,*, TAKEO MUKAI²

BIOCELL, Vol.46, No.4, pp. 873-879, 2022, DOI:10.32604/biocell.2022.018218 - 15 December 2021

Abstract Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by impairments in social communication, abnormal to absent verbal communication, the presence of repetitive stereotypic verbal and non-verbal behaviors and restricted interests, with onset early in life. We showed cognitive and behavioral characteristics of ASD by impairment of communication, cognition, perception, motor skills, executive function, theory of mind and emotion control. Recently, pathogenesis of immune pathology in the brains of individuals with ASD has been focused. New therapeutic approaches in the viewpoints of immune modulation and microglial function are logical for novel treatments for individuals More >

Anwer Mustafa Hilal¹, Imène ISSAOUI², Marwa Obayya³, Fahd N. Al-Wesabi⁴, Nadhem NEMRI⁵, Manar Ahmed Hamza^1,*, Mesfer Al Duhayyim⁶, Abu Sarwar Zamani¹

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3853-3867, 2022, DOI:10.32604/cmc.2022.022663 - 07 December 2021

Abstract The abundant existence of both structured and unstructured data and rapid advancement of statistical models stressed the importance of introducing Explainable Artificial Intelligence (XAI), a process that explains how prediction is done in AI models. Biomedical mental disorder, i.e., Autism Spectral Disorder (ASD) needs to be identified and classified at early stage itself in order to reduce health crisis. With this background, the current paper presents XAI-based ASD diagnosis (XAI-ASD) model to detect and classify ASD precisely. The proposed XAI-ASD technique involves the design of Bacterial Foraging Optimization (BFO)-based Feature Selection (FS) technique. In addition, More >