FIGS-DEAF: an novel implementation of hybrid deep learning algorithm to predict autism spectrum disorders using facial fused gait features

2021 ◽  
Author(s):  
A. Saranya ◽  
R. Anandan
Keyword(s):  
Autism Spectrum Disorders ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Autism Spectrum ◽  
Deep Learning Algorithm ◽  
Gait Features ◽  
Spectrum Disorders

scholarly journals Exploring the Structural and Strategic Bases of Autism Spectrum Disorders With Deep Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 153341-153352 ◽  
Author(s):  
Fengkai Ke ◽  
Seungjin Choi ◽  
Young Ho Kang ◽  
Keun-Ah Cheon ◽  
Sang Wan Lee
Keyword(s):  
Autism Spectrum Disorders ◽  
Deep Learning ◽  
Autism Spectrum ◽  
Spectrum Disorders

scholarly journals Deep learning for automatic stereotypical motor movement detection using wearable sensors in autism spectrum disorders

2018 ◽  
Vol 144 ◽  
pp. 180-191 ◽  
Author(s):  
Nastaran Mohammadian Rad ◽  
Seyed Mostafa Kia ◽  
Calogero Zarbo ◽  
Twan van Laarhoven ◽  
Giuseppe Jurman ◽  
...  
Keyword(s):  
Autism Spectrum Disorders ◽  
Deep Learning ◽  
Wearable Sensors ◽  
Autism Spectrum ◽  
Movement Detection ◽  
Motor Movement ◽  
Spectrum Disorders

scholarly journals Autism spectrum disorder classification on electroencephalogram signal using deep learning algorithm

2020 ◽  
Vol 9 (1) ◽  
pp. 91
Author(s):  
Nur Alisa Ali
Keyword(s):  
Autism Spectrum Disorder ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Research Work ◽  
Autism Spectrum ◽  
Performance Measure ◽  
Spectrum Disorder ◽  
Font Size ◽  
Deep Learning Algorithm

<span style="color: black; font-family: 'Times New Roman',serif; font-size: 9pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-US">Autism Spectrum Disorder (ASD) is a neurodevelopmental that impact the social interaction and communication skills. Diagnosis of ASD is one of the difficult problems facing researchers. This research work aimed to reveal the different pattern between autistic and normal children via electroencephalogram (EEG) by using the deep learning algorithm. The brain signal database used pattern recognition where the extracted features will undergo the multilayer perceptron network for the classification process. The promising method to perform the classification is through a deep learning algorithm, which is currently a well-known and superior method in the pattern recognition field. The performance measure for the classification would be the accuracy. The higher percentage means the more effectiveness for the ASD diagnosis. </span><span style="color: black; font-family: 'Times New Roman',serif; font-size: 9pt; mso-fareast-font-family: 'Times New Roman+FPEF'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-US">This can be seen as the ground work for applying a new algorithm for further development diagnosis of autism to see how the treatment is working as well in future.</span>

Disfluency Characteristics Observed in Young Children With Autism Spectrum Disorders: A Preliminary Report

2010 ◽  
Vol 20 (2) ◽  
pp. 42-50 ◽  
Author(s):  
Laura W. Plexico ◽  
Julie E. Cleary ◽  
Ashlynn McAlpine ◽  
Allison M. Plumb
Keyword(s):  
Autism Spectrum Disorders ◽  
Young Children ◽  
Children With Autism ◽  
Descriptive Study ◽  
Autism Spectrum ◽  
Typically Developing ◽  
Children With Asd ◽  
Young Children With Autism ◽  
Spectrum Disorders ◽  
Developmental Stuttering

This descriptive study evaluates the speech disfluencies of 8 verbal children between 3 and 5 years of age with autism spectrum disorders (ASD). Speech samples were collected for each child during standardized interactions. Percentage and types of disfluencies observed during speech samples are discussed. Although they did not have a clinical diagnosis of stuttering, all of the young children with ASD in this study produced disfluencies. In addition to stuttering-like disfluencies and other typical disfluencies, the children with ASD also produced atypical disfluencies, which usually are not observed in children with typically developing speech or developmental stuttering. (Yairi & Ambrose, 2005).

Aging and Autism

2012 ◽  
Vol 17 (2) ◽  
pp. 69-75 ◽  
Author(s):  
Pamela A. Smith
Keyword(s):  
Health Care ◽  
Autism Spectrum Disorders ◽  
Patient Group ◽  
Older Patients ◽  
Geriatric Patients ◽  
Recent Literature ◽  
Communication Disorders ◽  
Autism Spectrum ◽  
Need For Support ◽  
Spectrum Disorders

In this article, I will review the available recent literature about the aging population with autism, a patient group that researchers know little about and a group that is experiencing a growing need for support from communication disorders professionals. Speech-language pathologists working with geriatric patients should become familiar with this issue, as the numbers of older patients with autism spectrum disorders is likely to increase. Our profession and our health care system must prepare to meet the challenge these patients and residents will present as they age.

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