Tamilvizhi Thanarajan¹, Youseef Alotaibi², Surendran Rajendran^3,*, Krishnaraj Nagappan⁴

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1995-2013, 2023, DOI:10.32604/cmc.2023.039644

Abstract Autism spectrum disorder (ASD) can be defined as a neurodevelopmental condition or illness that can disturb kids who have heterogeneous characteristics, like changes in behavior, social disabilities, and difficulty communicating with others. Eye tracking (ET) has become a useful method to detect ASD. One vital aspect of moral erudition is the aptitude to have common visual attention. The eye-tracking approach offers valuable data regarding the visual behavior of children for accurate and early detection. Eye-tracking data can offer insightful information about the behavior and thought processes of people with ASD, but it is important to be aware of its limitations… More >

Mohammed Al Jaffal^*

International Journal of Mental Health Promotion, Vol.25, No.8, pp. 881-890, 2023, DOI:10.32604/ijmhp.2023.028982

Abstract Many individuals with autism spectrum disorder (ASD) experience delays in the development of social and communications skills, which can limit their opportunities in higher education and employment resulting in an overall negative impact to their quality of life. This systematic review identifies 15 studies that explored the effectiveness of Video-Based Interventions (VBIs) for those with ASD during the critical years of adolescence and young adulthood. The 15 studies described herein found this to be an effective intervention for this population for the improvement of their vocational, daily living, and academic skills. In addition, VBIs allow for the maintenance and generalization… More >

Hanan Abdullah Mengash¹, Hamed Alqahtani², Mohammed Maray³, Mohamed K. Nour⁴, Radwa Marzouk¹, Mohammed Abdullah Al-Hagery⁵, Heba Mohsen⁶, Mesfer Al Duhayyim^7,*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5251-5265, 2023, DOI:10.32604/cmc.2023.032729

Abstract Autism Spectrum Disorder (ASD) refers to a neuro-disorder where an individual has long-lasting effects on communication and interaction with others. Advanced information technology which employs artificial intelligence (AI) model has assisted in early identify ASD by using pattern detection. Recent advances of AI models assist in the automated identification and classification of ASD, which helps to reduce the severity of the disease. This study introduces an automated ASD classification using owl search algorithm with machine learning (ASDC-OSAML) model. The proposed ASDC-OSAML model majorly focuses on the identification and classification of ASD. To attain this, the presented ASDC-OSAML model follows min-max… More >

Taher M. Ghazal^1,2, Sundus Munir^3,4, Sagheer Abbas³, Atifa Athar⁵, Hamza Alrababah¹, Muhammad Adnan Khan^6,*

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 11-22, 2023, DOI:10.32604/iasc.2023.030125

Abstract Autism spectrum disorder (ASD) is a challenging and complex neuro-development syndrome that affects the child’s language, speech, social skills, communication skills, and logical thinking ability. The early detection of ASD is essential for delivering effective, timely interventions. Various facial features such as a lack of eye contact, showing uncommon hand or body movements, babbling or talking in an unusual tone, and not using common gestures could be used to detect and classify ASD at an early stage. Our study aimed to develop a deep transfer learning model to facilitate the early detection of ASD based on facial features. A dataset… More >

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

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 Learning Driven ASD Detection and… More >

G. Anurekha^*, P. Geetha

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

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 classification. To overcome the challenges,… 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

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 upon a time-variant factor. The… 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

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 training data. Deep learning on… 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

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 investigate the possible changes in… 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

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 patterns for the ASD diagnosis.… More >