A Novel Multi Social Communicative HHO Based Neural Networks with Quasi L1 – Regularization for ASD Bio Marker Identification

A Novel Multi Social Communicative HHO Based Neural Networks with Quasi L1 – Regularization for ASD Bio Marker Identification
  IJETT-book-cover           
  
© 2021 by IJETT Journal
Volume-69 Issue-9
Year of Publication : 2021
Authors : Nimita Gajjar, Tejas Zaveri, Naimish Zaveri
DOI :  10.14445/22315381/IJETT-V69I9P226

How to Cite?

N B Arunekumar, K Suresh Joseph, "A Novel Multi Social Communicative HHO Based Neural Networks with Quasi L1 – Regularization for ASD Bio Marker Identification," International Journal of Engineering Trends and Technology, vol. 69, no. 9, pp. 220-229, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I9P226

Abstract
The Autism spectrum disorder being a spectrum of the syndrome, may cause impairments in children’s cognition capabilities. The study aims to find a biomarker for ASD based on the activation patterns exhibited by the brain image using fMRI. The functional connectivity of various regions in the brain is investigated from the functional image to obtain a pattern that will classify the presence of autism. The functional imaging data were acquired from 17 sites around the globe, totaling 1112 fMRI images in the ABIDE dataset. Based on the HHO algorithm, a Multi Social Communicative HHO is proposed. This algorithm uses sub swarms of HHO with cooperation among the sub swarms. The proposed algorithm is tested over benchmark functions. To elevate the disadvantages of backpropagation in training the ANN over complex datasets such as ABIDE, a NN trained based upon the MSCHHO is proposed. The MSCHHO-ANN is also tested over the Wisconsin breast cancer dataset, which is another generic medical dataset.

Keywords
Autism Spectrum Disorder, HHO, Artificial Neural networks, Breast Cancer, L1-regularization.

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