NUMERICAL COMPUTING TO SOLVE THE NONLINEAR CORNEAL SYSTEM OF EYE SURGERY USING THE CAPABILITY OF MORLET WAVELET ARTIFICIAL NEURAL NETWORKS

Bo Wang, J. F. Gã“mez-Aguilar, Zulqurnain Sabir, Muhammad Asif Zahoor Raja, Wei-Feng Xia, Hadi Jahanshahi, Madini O. Alassafi and Fawaz E. Alsaadi
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Bo Wang: School of Electronic Information and Automation, Aba Teachers University, Wenchuan 623002, P. R. China†School of Applied Mathematics, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
J. F. Gã“mez-Aguilar: ��CONACyT-Tecnológico Nacional de México/CENIDET, Interior Internado Palmira S/N, Col. Palmira, C.P. 62490, Cuernavaca, Morelos, México
Zulqurnain Sabir: �Department of Mathematics and Statistics, Hazara University, Mansehra, Pakistan
Muhammad Asif Zahoor Raja: �Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road Section 3, Douliou, Yunlin 64002, Taiwan, R.O.C.
Wei-Feng Xia: ��School of Engineering, Huzhou University, Huzhou 313000, P. R. China**Institute for Advanced Study Honoring Chen Jian Gong, Hangzhou Normal University, Hangzhou 311121, P. R. China
Hadi Jahanshahi: ��†Department of Mechanical Engineering, University of Manitoba, Winnipeg, Canada R3T 5V6, Canada
Madini O. Alassafi: ��‡Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
Fawaz E. Alsaadi: ��‡Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

FRACTALS (fractals), 2022, vol. 30, issue 05, 1-19

Abstract: In this study, a novel heuristic computing technique is presented to solve bioinformatics problem for the corneal shape model of eye surgery using Morlet wavelet artificial neural network optimized by the global search schemes, i.e. genetic algorithm (GA), local search technique, i.e. sequential quadratic programming (SQP) and the hybrid of GA-SQP. To measure the performance of the design network configuration, different cases based on nonlinear second-order differential equations governing the corneal model have been solved effectively. The numerical procedure of Adams method is implemented for the comparison purpose of the presented outcomes of the stochastic solver, which shows the worth of the present scheme based on accuracy and convergence with negligible values of absolute error in the range 10−7 to 10−8. Furthermore, statistical measures are presented based on “mean absolute error†, “root mean square error†and “coefficient of Theil’s inequality†which additionally endorsed consistently accurate performance of integrated intelligent computing framework for solving the corneal shape model.

Keywords: Morlet Wavelet Kernel; Corneal Model; Nonlinear; Genetic Algorithm; Artificial Neural Network; Statistical Analysis; Sequential Quadratic Programming (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0218348X22401478

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