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Dynamics of Fractional Differential Model for Schistosomiasis Disease

Thongchai Botmart1, Wajaree Weera1,*, Muhammad Asif Zahoor Raja2, Zulqurnain Sabir3, Qusain Hiader4, Gilder Cieza Altamirano5, Plinio Junior Muro Solano6, Alfonso Tesen Arroyo6

1 Department of Mathematics, Faculty of Science, Khon Kaen University, Khon Kaen, 40002, Thailand
2 Future Technology Research Center, National Yunlin University of Science and Technology, Douliou, Yunlin, 64002, Taiwan
3 Department of Mathematics and Statistics, Hazara University, Mansehra, 21120, Pakistan
4 Department of Mathematics, University of Gujrat, Gujrat, 50700, Pakistan
5 Universidad Nacional Autónoma de Chota, Chota, 06121, Peru
6 Universidad Nacional Pedro Ruiz Gallo, Lambayeque, 14013, Peru

* Corresponding Author: Wajaree Weera. Email: email

Computers, Materials & Continua 2022, 73(1), 981-999. https://doi.org/10.32604/cmc.2022.028921

Abstract

In the present study, a design of a fractional order mathematical model is presented based on the schistosomiasis disease. To observe more accurate performances of the results, the use of fractional order derivatives in the mathematical model is introduce based on the schistosomiasis disease is executed. The preliminary design of the fractional order mathematical model focused on schistosomiasis disease is classified as follows: uninfected with schistosomiasis, infected with schistosomiasis, recovered from infection, susceptible snail unafflicted with schistosomiasis disease and susceptible snail afflicted with this disease. The solutions to the proposed system of the fractional order mathematical model will be presented using stochastic artificial neural network (ANN) techniques in conjunction with the Levenberg-Marquardt backpropagation (LMBP), referred to as ANN-LMBP. To illustrate the preciseness of the ANN-LMBP method, mathematical presentations of three different values focused on fractional order will be performed. These statics performances are taken in these investigations are 78% and 11% for both learning and certification. The accuracy of the ANN-LMBP method is determined by comparing the values obtained by the database Adams-Bash forth-Moulton scheme. The simulation-based error histograms (EHs), MSE, recurrence, and state transitions (STs) will be offered to achieve the capability, accuracy, steadiness, abilities, and finesse of the ANN-LMBP method.

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APA Style
Botmart, T., Weera, W., Raja, M.A.Z., Sabir, Z., Hiader, Q. et al. (2022). Dynamics of fractional differential model for schistosomiasis disease. Computers, Materials & Continua, 73(1), 981-999. https://doi.org/10.32604/cmc.2022.028921
Vancouver Style
Botmart T, Weera W, Raja MAZ, Sabir Z, Hiader Q, Altamirano GC, et al. Dynamics of fractional differential model for schistosomiasis disease. Comput Mater Contin. 2022;73(1):981-999 https://doi.org/10.32604/cmc.2022.028921
IEEE Style
T. Botmart et al., "Dynamics of Fractional Differential Model for Schistosomiasis Disease," Comput. Mater. Contin., vol. 73, no. 1, pp. 981-999. 2022. https://doi.org/10.32604/cmc.2022.028921



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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