Rapturous Chimp Optimization-based Feed-Forward Neural Networks for Autism Spectrum Disorder Classification
Rapturous Chimp Optimization-based Feed-Forward Neural Networks for Autism Spectrum Disorder Classification |
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| © 2023 by IJETT Journal | ||
| Volume-71 Issue-3 |
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| Year of Publication : 2023 | ||
| Author : B. Suresh kumar, D. Jayaraj |
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| DOI : 10.14445/22315381/IJETT-V71I3P216 | ||
How to Cite?
B. Suresh kumar, D. Jayaraj, "Rapturous Chimp Optimization-based Feed-Forward Neural Networks for Autism Spectrum Disorder Classification," International Journal of Engineering Trends and Technology, vol. 71, no. 3, pp. 155-167, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I3P216
Abstract
Observational and interview-based evaluations are used to diagnose autism spectrum disorder (ASD), but these methods are labor-intensive, highly subjective, and fraught with doubts about their validity and reliability. Artificial intelligence’s deep learning subfield focuses on creating useful new programmes in many fields. Its great classification accuracy makes it a popular tool for mining large datasets for previously unseen patterns and insights. This paper proposes a bio-inspired optimization-based deep learning technique to perform robust classification of ASD with neuroimaging data, namely Rapturous Chimp Optimization-based Feed-Forward Neural Networks (RCO-FFNN). Fitness evaluation, exploration, and exploitation play a vibrant role in RCO-FFNN to achieve better accuracy. In RCO-FFNN, social incentives are preferred to detect ASD and non-ASD. The Autism Brain Imaging Data Exchange II (ABIDE-II) dataset, a worldwide multisite collection of functional and structural brain imaging data, is used to test the RCO-FFNN’s efficacy. Results make an indication that the RCO-FFNN outperforms current classifiers in terms of Classification Accuracy, F- Measure, FowlkesMallows Index, and Matthews Correlation Coefficient.
Keywords
Autism, Chimp, Classification, Feed-Forward Neural Network, Optimization.
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