Open Access

ARTICLE

Recommendation Learning System Model for Children with Autism

V. Balaji^*, S. Kanaga Suba Raja

Easwari Engineering College, Chennai, 600089, India

* Corresponding Author: V. Balaji. Email:

Intelligent Automation & Soft Computing 2022, 31(2), 1301-1315. https://doi.org/10.32604/iasc.2022.020287

Received 18 May 2021; Accepted 01 July 2021; Issue published 22 September 2021

Abstract

Autism spectrum disorder (ASD), is a neurological developmental disorder. It affects how people communicate and interact with others, as well as how they behave and learn. The symptoms and signs appear when a child is very young. Derived with increased usage of machine learning procedure in the medicinal analysis investigations. In this paper, our objective is to find out the most significant attributes and automate the process using classification techniques and pattern clustering using K-means clustering. We have analyzed ASD datasets of children towards determining the best performance of classifier for these binary datasets considering recall, precision, accuracy and classification errors. For this purpose only we use classifier along with a training model with the deep learning technique. In this deep neural network the different types of patterns have been obtained, and then the entire autism dataset has been trained using this classification technique. After that, the group of patterns has been formed using K- means clustering technique. Then these grouped patterns have been sent to the stochastic gradient descent (SGD) for obtaining the classification with enhanced accuracy, and after this, the regression process is done, then by this classified output, the recommendation learning system model is given for autism affected children. The need for recommendation for ASD is due to slow functioning of the brain and change in personal characteristic of the affected children, so it is necessary for recommendation learning model in ASD.

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