Open Access

ARTICLE

An Artificial Approach for the Fractional Order Rape and Its Control Model

Wajaree Weera¹, Zulqurnain Sabir², Muhammad Asif Zahoor Raja³, Salem Ben Said⁴, Maria Emilia Camargo⁵, Chantapish Zamart¹, Thongchai Botmart^1,*

1 Department of Mathematics, Faculty of Science, Khon Kaen University, Khon Kaen, 40002, Thailand
2 Department of Mathematics, Near East University, Nicosia, 99138, Cyprus
3 Future Technology Research Center, National Yunlin University of Science and Technology, Douliou, Yunlin, 64002, Taiwan
4 Department of Mathematical Science, College of Science, United Arab Emirates University, Al Ain, Abu Dhabi, 15551, UAE
5 Graduate Program in Administration, Federal University of Santa Maria, Santa Maria, 93458, Brazil

* Corresponding Author: Thongchai Botmart. Email:

Computers, Materials & Continua 2023, 74(2), 3421-3438. https://doi.org/10.32604/cmc.2023.030996

Received 07 April 2022; Accepted 11 May 2022; Issue published 31 October 2022

Abstract

The current investigations provide the solutions of the nonlinear fractional order mathematical rape and its control model using the strength of artificial neural networks (ANNs) along with the Levenberg-Marquardt backpropagation approach (LMBA), i.e., artificial neural networks-Levenberg-Marquardt backpropagation approach (ANNs-LMBA). The fractional order investigations have been presented to find more realistic results of the mathematical form of the rape and its control model. The differential mathematical form of the nonlinear fractional order mathematical rape and its control model has six classes: susceptible native girls, infected immature girls, susceptible knowledgeable girls, infected knowledgeable girls, susceptible rapist population and infective rapist population. The rape and its control differential system using three different fractional order values is authenticated to perform the correctness of ANNs-LMBA. The data is used to present the rape and its control differential system is designated as 70% for training, 14% for authorization and 16% for testing. The obtained performances of the ANNs-LMBA are compared with the dataset of the Adams-Bashforth-Moulton scheme. To substantiate the consistency, aptitude, validity, exactness, and capability of the LMBA neural networks, the obtained numerical values are provided using the state transitions (STs), correlation, regression, mean square error (MSE) and error histograms (EHs).

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Keywords

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