Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: A Review

Autism spectrum disorder (ASD) research has yet to leverage big data on the same scale as other fields; however, advancements in easy, affordable data collection and analysis may soon make this a reality. Indeed, there has been a notable increase in research literature evaluating the effectiveness of machine learning for diagnosing ASD, exploring its genetic underpinnings, and designing effective interventions. This paper provides a comprehensive review of 45 papers utilizing supervised machine learning in ASD, including algorithms for classification and text analysis. The goal of the paper is to identify and describe supervised machine learning trends in ASD literature as well as inform and guide researchers interested in expanding the body of clinically, computationally, and statistically sound approaches for mining ASD data

Automatic Autism Spectrum Disorder Detection Using Artificial Intelligence Methods with MRI Neuroimaging: A Review

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, the process of diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist the specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We conclude by suggesting future approaches to detecting ASDs using AI techniques and MRI neuroimaging

An Application of the Autism Management Platform to Tracking Student Progress in the Special Education Environment

In the age of online courses and digital textbooks, several areas of academia, such as special education, are far behind in the technological revolution. Some teachers use long unstructured digital documents, while others maintain large physical files for students containing every piece of information or coursework they have ever received. Could these extremely unstructured approaches to data collection and aggregation be streamlined with a software platform built specifically for this purpose? Could this platform also be built to accommodate multiple integrations and practical new features? Most importantly, in terms of usability, would this software be enjoyable to use? The Autism Management Platform was initially built for parents of children with Autism to be able to post and view various events and activities throughout their child’s life. The platform now allows for communication on posts between parents, teachers, physicians, and anyone else involved in the child’s life who is authorized to do so. What started as a general platform built for Autism was able to adopt an integration specific to special education. The Autism Management Platform was then further enriched by new features, such as the addition of appointments and visual schedules, proving that it could serve beyond the platform it was intended to be. Through several semester-long user trials in high school special education programs, it was found that real people could use an integration of this platform in an enjoyable and meaningful way

The Design And Evaluation Of A Video Game To Help Train Perspective-taking And Empathy In Children With Autism Spectrum Disorder

This paper discusses the design, implementation, and evaluation of a serious game intended to reinforce applied behavior analysis (ABA) techniques used with children with autism spectrum disorder (ASD) by providing a low cost and easily accessible supplement to traditional methods. Past and recent research strongly supports the use of computer assisted instruction in the education of individuals with ASD (Moore & Calvert, 2000; Noor, Shahbodin, & Pee, 2012). Computer games have been shown to boost confidence and provide calming mechanisms (Griffiths, 2003) while being a safe environment for social exploration and learning (Moore, Cheng, McGrath, & Powell, 2005). Games increase children\u27s motivation and thus increase the rate of learning in computer mediated environments (Moore & Calvert, 2000). Furthermore, children with ASD are able to understand basic emotions and facial expressions in avatars more easily than in real-world interactions (Moore, Cheng, McGrath, & Powell, 2005). Perspective-taking (also known as role-taking) has been shown to be a crucial component and antecedent to empathy (Gomez-Becerra, Martin, Chavez-Brown, & Greer, 2007; Peng, Lee, & Heeter, 2010). Though symptoms vary across children with ASD, perspective-taking and empathy are abilities that have been shown to be limited across a wide spectrum of individuals with ASD and Asperger\u27s disorder (Gomez-Becerra, Martin, Chavez-Brown, & Greer, 2007). A game called WUBeeS was developed to aid young children with ASD in perspective taking and empathy by placing the player in the role of a caregiver to a virtual avatar. It is hypothesized that through the playing of this game over a series of trials, children with ASD will show an iv increase in the ability to discriminate emotions, provide appropriate responses to basic needs (e.g. feeding the avatar when it is hungry), and be able to communicate more clearly about emotions

Face Image and Video Analysis in Biometrics and Health Applications

Computer Vision (CV) enables computers and systems to derive meaningful information from acquired visual inputs, such as images and videos, and make decisions based on the extracted information. Its goal is to acquire, process, analyze, and understand the information by developing a theoretical and algorithmic model. Biometrics are distinctive and measurable human characteristics used to label or describe individuals by combining computer vision with knowledge of human physiology (e.g., face, iris, fingerprint) and behavior (e.g., gait, gaze, voice). Face is one of the most informative biometric traits. Many studies have investigated the human face from the perspectives of various different disciplines, ranging from computer vision, deep learning, to neuroscience and biometrics. In this work, we analyze the face characteristics from digital images and videos in the areas of morphing attack and defense, and autism diagnosis. For face morphing attacks generation, we proposed a transformer based generative adversarial network to generate more visually realistic morphing attacks by combining different losses, such as face matching distance, facial landmark based loss, perceptual loss and pixel-wise mean square error. In face morphing attack detection study, we designed a fusion-based few-shot learning (FSL) method to learn discriminative features from face images for few-shot morphing attack detection (FS-MAD), and extend the current binary detection into multiclass classification, namely, few-shot morphing attack fingerprinting (FS-MAF). In the autism diagnosis study, we developed a discriminative few shot learning method to analyze hour-long video data and explored the fusion of facial dynamics for facial trait classification of autism spectrum disorder (ASD) in three severity levels. The results show outstanding performance of the proposed fusion-based few-shot framework on the dataset. Besides, we further explored the possibility of performing face micro- expression spotting and feature analysis on autism video data to classify ASD and control groups. The results indicate the effectiveness of subtle facial expression changes on autism diagnosis

Relationship between window and view factors in the workplace: A SEM approach

Office occupants’ have always preferred workplaces that have windows that connects them to the outside. Window access to the outside can influence occupants’ satisfaction with the combination of other workplace features. This study aims to identify the window and view factors relationship in the workplace, to confirm the reliability and validity of the measurement and structural model. Adopting a cross-sectional survey design, primary data from five offices in the Kogi State of Nigeria with 267 respondents were collected by using the convenience sampling method and analysis was performed with the Statistical Package for Social Science version 23 and AMOS 22.0 version as the modelling tool. The study identified eleven vital factors that are interrelated in the relationship between windows and view in the workplace. They are referred to as latent construct namely; Window distance (WDB), Seating arrangement (SAB), Room height (FHB), Office size (OSB), Window position (WPB), Window Sill level (WLC), Window size (SWC), Window type (TWC), View content (CVC), View satisfaction (VSC), and Occupants’ satisfaction (SAT). The result showed a valid model using the Structural Equation Model, and the effect of the current workplace negligence on occupants’. This study improves the existing knowledge on the window and view relationship in the workplace, and provide suggestions for Facility Managers, Architects, and Interior Designers on maintaining a healthy workplace environment