Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/44813
Title: Identification of Alcoholism Based on Wavelet Renyi Entropy and Three-Segment Encoded Jaya Algorithm
Authors: Wang, Shui-Hua
Muhammad, Khan
Lv, Yiding
Sui, Yuxiu
Han, Liangxiu
Zhang, Yu-Dong
First Published: 17-Jan-2018
Publisher: Hindawi, Wiley
Citation: Complexity, 2018, Article ID 3198184,
Abstract: The alcohol use disorder (AUD) is an important brain disease, which could cause the damage and alteration of brain structure. The current diagnosis of AUD is mainly done manually by radiologists. This study proposes a novel computer-vision-based method for automatic detection of AUD based on wavelet Renyi entropy and three-segment encoded Jaya algorithm from MRI scans. The wavelet Renyi entropy is proposed to provide multiresolution and multiscale analysis of features, describe the complexity of the brain structure, and extract the distinctive features. Grid search method was used to select the optimal wavelet decomposition level and Renyi order. The classifier was constructed based on feedforward neural network and a three-segment encoded (TSE) Jaya algorithm providing parameter-free training of the weights, biases, and number of hidden neurons. We have conducted the experimental evaluation on 235 subjects (114 are AUDs and 121 healthy). -fold cross validation has been used to avoid overfitting and report out-of-sample errors. The results showed that the proposed method outperforms four state-of-the-art approaches in terms of accuracy. The proposed TSE-Jaya provides a better performance, compared to the conventional approaches including plain Jaya, multiobjective genetic algorithm, particle swarm optimization, bee colony optimization, modified ant colony system, and real-coded biogeography-based optimization.
DOI Link: 10.1155/2018/3198184
ISSN: 1076-2787
eISSN: 1099-0526
Links: https://www.hindawi.com/journals/complexity/2018/3198184/
http://hdl.handle.net/2381/44813
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © the authors, 2018. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Appears in Collections:Published Articles, Dept. of Computer Science

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