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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 Botmart1,*

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: email

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

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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APA Style
Weera, W., Sabir, Z., Raja, M.A.Z., Said, S.B., Camargo, M.E. et al. (2023). An artificial approach for the fractional order rape and its control model. Computers, Materials & Continua, 74(2), 3421-3438. https://doi.org/10.32604/cmc.2023.030996
Vancouver Style
Weera W, Sabir Z, Raja MAZ, Said SB, Camargo ME, Zamart C, et al. An artificial approach for the fractional order rape and its control model. Comput Mater Contin. 2023;74(2):3421-3438 https://doi.org/10.32604/cmc.2023.030996
IEEE Style
W. Weera et al., "An Artificial Approach for the Fractional Order Rape and Its Control Model," Comput. Mater. Contin., vol. 74, no. 2, pp. 3421-3438. 2023. https://doi.org/10.32604/cmc.2023.030996



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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