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Issue title: Theory and Applications of Fractional Fourier Transform and its Variants
Guest editors: Yudong Zhang, Xiao-Jun Yang, Carlo Cattani, Zhengchao Dong, Ti-Fei Yuan and Liang-Xiu Han
Article type: Research Article
Authors: Wang, Shuihuaa; * | Li, Pengb | Chen, Pengc | Phillips, Preethad | Liu, Gee | Du, Sidanf | Zhang, Yudongg; †
Affiliations: [a] School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210046, China | [b] School of Control Science and Engineering, Shandong University, Jinan, 205100, China | [c] Department of Electrical Engineering, Columbia University, New York, NY 10027, USA | [d] School of Natural Sciences and Mathematics, Shepherd University, Shepherdstown, WV 25443, USA | [e] Department of Psychiatry, College of Physicians & Surgeons, Columbia University, New York, NY 10032, USA | [f] School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210046, China. coff128@nju.edu.cn | [g] School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210046, China. zhangyudong@njnu.edu.cn
Correspondence: [†] Address for correspondence: School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210046, China
Note: [*] Also works: School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu 210023, China
Abstract: (Aim) In order to detect pathological brains in a more efficient way, (Method) we proposed a novel system of pathological brain detection (PBD) that combined wavelet packet Tsallis entropy (WPTE), feedforward neural network (FNN), and real-coded biogeography-based optimization (RCBBO). (Results) The experiments showed the proposed WPTE + FNN + RCBBO approach yielded an average accuracy of 99.49% over a 255-image dataset. (Conclusions) The WPTE + FNN + RCBBO performed better than 10 state-of-the-art approaches.
Keywords: pathological brain detection, feed-forward neural network, wavelet packet Tsallis entropy, real-coded biogeography-based optimization
DOI: 10.3233/FI-2017-1492
Journal: Fundamenta Informaticae, vol. 151, no. 1-4, pp. 275-291, 2017
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