A Novel Technique on Autism Spectrum Disorders Using Classification Techniques

Over the last few years, academic institutions have conducted a number of programmes to help school boards, colleges, and schools of autism spectrum educating pupils (ASD). Autism spectrum disorder (ASD) is a complicated neurological disorder which affects many skills over a lifetime. The main aim of the chapter is to examine the topic of autism and identify autism levels with furious logic classification algorithms using the artificial neural network. Data mining has generally been recognized as a method of decision making to promote higher use of resources for autism students.

ML-PASD

Autism spectrum disorder (ASD) is growing faster than ever before. Autism detection is costly and time intensive with screening procedures. Autism can be detected at an early stage by the development of artificial intelligence and machine learning (ML). While a number of experiments using many approaches were conducted, these studies provided no conclusion as to the prediction of autism characteristics in various age groups. This chapter is therefore intended to suggest an accurate MLASD predictive model based on the ML methodology to prevent ASD for people of all ages. It is a method for prediction. This survey was conducted to develop and assess ASD prediction in an artificial neural network (ANN). AQ-10 data collection was used to test the proposed pattern. The findings of the evaluation reveal that the proposed prediction model has improved results in terms of consistency, specificity, sensitivity, and dataset accuracy.

Mouse-Less Cursor Control for Quadriplegic and Autistic Patients Using Artificial Intelligence

Artificial intelligence is a field within computer science that attempts to simulate and build enhanced human intelligence into computers, mobiles, and various other machines. It can be termed as a powerful tool that has the capability to process huge sums of information with ease and assess patterns created over a period of time to give significant results or suggestions. It has garnered focus from almost every field from education to healthcare. Broadly, AI applications in healthcare include early detection and diagnosis, suggesting treatments, evaluating progress, medical history, and predicting outcomes. This chapter discussed AI, ASD, and what role AI currently plays in advancing autistic lives including detection, analysis, and treatment of ASD and how AI has been improving healthcare and the existing medical and technology aids available for autistic people. Current and future advancements are discussed and suggested in the direction of improving social abilities and reducing the communication and motor difficulties faced by people with ASD.

Machine Learning Techniques for Analysing and Identifying Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neuro disorder in which a person's contact and connection with others has a lifetime impact. In all levels of development, autism can be diagnosed as a “behavioural condition,” since signs generally occur within the first two years of life. The ASD problem begins with puberty and goes on in adolescence and adulthood. In this chapter, an effort is being made to use the supporting vector machine (SVM) and the convolutionary neural network (CNN) for prediction and interpretation of children's ASD problems based on the increased use of machine learning methodology in the research dimension of medical diagnostics. On freely accessible autistic spectrum disorder screening dates in children's datasets, the suggested approaches are tested. Using different techniques of machine learning, the findings clearly conclude that CNN-based prediction models perform more precisely on the dataset for autistic spectrum disorders.

A Novel Neuro-Fuzzy System-Based Autism Spectrum Disorder

The major purpose of these research works has been for the rapid, reliable diagnosis of autism disorder by a new neuro-fuzzy autism identification technique. The highly affected region for each person is highlighted by this procedure. This research, which involves autism and regular group, included two classes of adolescents. This neuro-fuzzy method was developed by the experts using fuzzy-logical principles. The two classes were checked on the system. The developed method has been confirmed to distinguish easily between autistic participants and normal participants with increased precision. The engineered device has also been found to be 97.3% precise and 98.9% specific. The engineered instrument can be used by physicians to diagnose autism in conjunction with the seriousness of autism and to precisely and immediately illuminate the highly affected region.

A Novel Automated Approach for Deep Learning on Stereotypical Autistic Motor Movements

Autism spectrum disorder (ASD) is an ongoing neurodevelopmental disorder, with repeated behavior called stereotypical movement autism (SMM). Some recent experiments with accelerometer features as feedback to computer classifiers demonstrate positive findings in persons with autistic motor disorders for the automobile detection of stereotypical motor motions (SMM). To date, several methods for detecting and recognizing SMMs have been introduced. In this context, the authors suggest an approach of deep learning for recognition of SMM, namely deep convolution neural networks (DCNN). They also implemented a robust DCNN model for the identification of SMM in order to solve stereotypical motor movements (SMM), which thus outperform state-of-the-art SMM classification work.

Optimization of Machine Learning Models for Early Diagnosis of Autism Spectrum Disorder

Autism spectrum disorder is a syndrome related to interaction with people and repetitive behavior. ASD is diagnosed by health experts with the help of special practices that can be prolonged and costly. Researchers developed several ASD detection techniques by utilizing machine learning tools. ML provides the advanced algorithms that build automatic classification models. But disease prediction is a challenge for ML models due to the majority of the medical datasets including irrelevant features. Feature selection is a critical job in the predictive modeling for selecting a subset of significant features from the dataset. Recent feature selection techniques are using the optimization algorithms to improve the prediction rate of classification models. Most of the optimization algorithms make use of several controlling parameters that have to be tuned for improved productivity. In this chapter, a novel feature selection technique is proposed using binary teaching learning-based optimization algorithm that requires standard controlling parameters to acquire optimum features from ASD data.

Application of Image Processing for Autism Spectrum Disorder

Autism spectrum disorder (ASD) is one of the most common diseases that cause difficulties for an individual to express his/her emotions or to understand other's emotions. ASD has become a challenging problem as its symptoms are unpredictable. The main symptoms of ASD include problems such as abnormal social reciprocity, nonverbal communication, sensory abnormalities, etc. To understand such abnormalities, there is a requirement of some learning tools. It has been witnessed that facial expression images, eye tracking, and neuroimage have been shown as effective tools for analysis of abnormalities that had occurred in both grey and white matter of the brain. Many researchers focused their work on the classification problem of ASD disorder from healthy subjects but still didn't reach effective diagnosis and healing tools. As with the advancement of digital image processing, it has become feasible to use these technologies for accurate diagnosis of ASD subjects. These technologies are integrated with deep learning for the identification and treatment of ASD.

Existing Assistive Techniques for Dyslexics

Reading is an essential skill for literacy development in children. But it is a challenge for children with dyslexia because of phonological-core deficits. Poor reading skills have an impact on vocabulary development and to exposure to relevant background knowledge. It affects the ability to interpret what one sees and hears or the ability to link information from different parts of the brain. Dyslexic children face many challenges in their educational life due to reading difficulty. Support to dyslexic children include computer-based applications and multi-sensory methods like text-to-speech and character animation techniques. Some applications provide immediate reading intervention facility. Automatic speech recognition (ASR) is a new platform with immediate intervention for assisting dyslexic children to improve their reading ability. Findings contribute to develop a suitable approach to correct the reading mistakes of dyslexic children. Speech recognition technology provides the most interactive environment between human and machine.