A Novel Multi Social Communicative HHO Based Neural Networks with Quasi L1 – Regularization for ASD Bio Marker Identification
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
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.
Autism Spectrum Disorder, HHO, Artificial Neural networks, Breast Cancer, L1-regularization.
 M. C. Lai, M. V. Lombardo, and S. Baron-Cohen, Autism, in The Lancet, 383(2014) 9920 896–910.
 P. A. Filipek et al., The Screening and Diagnosis of Autistic Spectrum Disorders 1 (1999).
 C. Qu, W. He, X. Peng, and X. Peng., Harris Hawks optimization with information exchange, Appl. Math. Model., 84(2020) 52–75.
 B. Niu, H. Huang, L. Tan, and Q. Duan., Symbiosis-Based Alternative Learning Multi-Swarm Particle Swarm Optimization., IEEE/ACM Trans. Comput. Biol. Bioinforma., 14(1) (2017) 4–14.
 H. Xiong, B. Qiu, and J. Liu., An improved multi-swarm particle swarm optimizer for optimizing the electric field distribution of multichannel transcranial magnetic stimulation., Artif. Intell. Med. 104(2019) (2020) 101790.
 S. Rekha, D. Debahuti, and M. Minakhi, “A hybridized ELM using self-adaptive multi-population-based Jaya algorithm for currency exchange prediction : an empirical assessment., Neural Comput. Appl., 8(2018).
 S. S. Tirumala., Evolving deep neural networks using coevolutionary algorithms with multi-population strategy., Neural Comput. Appl., 32(16) (2020) 13051–13064.
 Y. Ma., A multi-population differential evolution with best-random mutation strategy for large-scale global optimization,( 2019).
 A. S. Heinsfeld, A. R. Franco, R. C. Craddock, A. Buchweitz, and F. Meneguzzi., Identification of autism spectrum disorder using deep learning and the ABIDE dataset., NeuroImage Clin., 17(2018) 16–23.
 A. El-Gazzar, M. Quaak, L. Cerliani, P. Bloem, G. van Wingen, and R. Mani Thomas., A Hybrid 3DCNN and 3DC-LSTM Based Model for 4D Spatio-Temporal fMRI Data: An ABIDE Autism Classification Study, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11796 LNCS, (2019) 95–102.
 B. Tran, B. Xue, and M. Zhang., Variable-Length Particle Swarm Optimisation for Feature Selection on High-Dimensional Classification, IEEE Trans.. Comput., 1(2018).
 H. Faris, I. Aljarah, M. A. Al-Betar, and S. Mirjalili, “Grey wolf optimizer: a review of recent variants and applications., Neural Computing and Applications, vol.. Springer London, 30(2) (2018) 413–435.
 S. Mirjalili and A. Lewis., The Whale Optimization Algorithm., Adv. Eng. Softw., 95(2016) 51–67.
 N. B. Arunekumar, A. Kumar, and K. S. Joseph., Hybrid bat-inspired algorithm for multiprocessor real-time scheduling preparation, 2016 Int. Conf. Commun. Signal Process., (2016) 2194–2198.
 C. Paper., An improved cuckoo search algorithm for parallel machine scheduling Metadata of the chapter that will be visualized in SpringerLink, (2015).
 J. Wu, Y. G. Wang, K. Burrage, Y. C. Tian, B. Lawson, and Z. Ding., An improved firefly algorithm for global continuous optimization problems, Expert Syst. Appl., 149( 2020).
 S. Mirjalili, A. H. Gandomi, S. Z. Mirjalili, S. Saremi, H. Faris, and S. M. Mirjalili, Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems, Adv. Eng. Softw., 114(2017) 163–191.
 A. Bedri Ozer and Ahmet., CIDE: Chaotically Initialized Differential Evolution, Expert Syst. Appl., 37(6) (2010) 4632–4641.
 Y. Li, Y. Zhao, and J. Liu., Dynamic sine cosine algorithm for large-scale global optimization problems, Expert Syst. Appl., (2021) 114950.
 Y. Wang, J. Wang, F. X. Wu, R. Hayrat, and J. Liu, AIMAFE: Autism spectrum disorder identification with multi-atlas deep feature representation and ensemble learning, J. Neurosci. Methods, 343 (2020).
 M. A. Reiter, A. Jahedi, A. R. J. Fredo, I. Fishman, B. Bailey, and R. A. Müller., Performance of machine learning classification models of autism using resting-state fMRI is contingent on sample heterogeneity, Neural Comput. Appl., 33(8) (2021) 3299–3310.
 S. Parisot et al., Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer’s disease., Med. Image Anal., 48(2018) 117–130.