D. Pavithra, K. Padmanaban, V. Kumararaja, S. Sujanthi
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An In-Depth Analysis of Autism Spectrum Disorder Using Optimized Deep Recurrent Neural Network
Autism spectrum disease is one of the severe neuro developmental disorders that are currently present worldwide (ASD). It is a chronic disorder that has an impact on a person’s behaviour and communication abilities. The world health organization’s 2019 study states that an increasing number of people are being diagnosed with ASD, which poses a risk because it is comparable to high medical expenses. Early detection can significantly lessen the impact. Traditional techniques are costly and time-consuming. This paper offers a Novel Deep Recurrent Neural Network (NDRNN) algorithm for the detection of the level of autism to address the aforementioned problems. The deep recurrent neural network is developed with several hidden recurrent network layers with Long-Short Term Memory (LSTM) units. In this work, Artificial Algae Algorithm (AAA) is used as a feature extraction algorithm, to obtain the best optimal features among the listed feature set. An Intelligent Water Droplet (IWD) algorithm is used for obtaining optimal weight and bias value for the recurrent neural network. The algorithm was evaluated for the dataset obtained by the Indian scale for assessment of autism. Experimental results shows that this proposed model produces the 91% of classification accuracy and 92% of sensitivity and reduces the cost.
期刊介绍:
The International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems is a forum for research on various methodologies for the management of imprecise, vague, uncertain or incomplete information. The aim of the journal is to promote theoretical or methodological works dealing with all kinds of methods to represent and manipulate imperfectly described pieces of knowledge, excluding results on pure mathematics or simple applications of existing theoretical results. It is published bimonthly, with worldwide distribution to researchers, engineers, decision-makers, and educators.