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. 2022:33:101081.
doi: 10.1016/j.imu.2022.101081. Epub 2022 Sep 24.

Swarming morlet wavelet neural network procedures for the mathematical robot system

Peerapongpat Singkibud  1 , Zulqurnain Sabir  2   3 , Irwan Fathurrochman  4 , Sharifah E Alhazmi  5 , Mohamed R Ali  6
Affiliations

Swarming morlet wavelet neural network procedures for the mathematical robot system

Peerapongpat Singkibud et al. Inform Med Unlocked. 2022.

Abstract

The task of this work is to present the solutions of the mathematical robot system (MRS) to examine the positive coronavirus cases through the artificial intelligence (AI) based Morlet wavelet neural network (MWNN). The MRS is divided into two classes, infected I ( θ ) and Robots R ( θ ) . The design of the fitness function is presented by using the differential MRS and then optimized by the hybrid of the global swarming computational particle swarm optimization (PSO) and local active set procedure (ASP). For the exactness of the AI based MWNN-PSOIPS, the comparison of the results is presented by using the proposed and reference solutions. The reliability of the MWNN-PSOASP is authenticated by extending the data into 20 trials to check the performance of the scheme by using the statistical operators with 10 hidden numbers of neurons to solve the MRS.

Keywords: Active set procedure; Artificial intelligence; Mathematical robot system; Morlet wavelet; Numerical solutions; Particle swarm optimization.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Workflow illustration of MWNN-PSOASP for the MRS.
Fig. 2
Fig. 2
Optimal weights, comparison performances and AE for I(θ) and R(θ) of the MRS.
Fig. 3
Fig. 3
Performances of the statistical operators for I(θ) and R(θ) of the MRS.
Fig. 4
Fig. 4
TIC operator convergence measures performances for I(θ) and R(θ) of the MRS.
Fig. 5
Fig. 5
MSE operator convergence measures performances for I(θ) and R(θ) of the MRS.
Image 1
See this image and copyright information in PMC

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