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Fractional Order Nonlinear Bone Remodeling Dynamics Using the Supervised Neural Network

Narongsak Yotha1, Qusain Hiader2, Zulqurnain Sabir3, Muhammad Asif Zahoor Raja4, Salem Ben Said5, Qasem Al-Mdallal5, Thongchai Botmart6, Wajaree Weera6,*

1 Department of Applied Mathematics and Statistics, Rajamangala University of Technology Isan, Nakhon Ratchasima, 30000, Thailand
2 Department of Mathematics, University of Gujrat, Gujrat, 50700, Pakistan
3 Department of Mathematics, Hazara University, Mansehra, 21120, Pakistan
4 Future Technology Research Center, National Yunlin University of Science and Technology, Douliou, Yunlin, 64002, Taiwan
5 Department of Mathematical Science, College of Science, United Arab Emirates University, Al Ain, Abu Dhabi, UAE
6 Department of Mathematics, Faculty of Science, Khon Kaen University, Khon Kaen, 40002, Thailand

* Corresponding Author: Wajaree Weera. Email: email

Computers, Materials & Continua 2023, 74(2), 2415-2430.


This study aims to solve the nonlinear fractional-order mathematical model (FOMM) by using the normal and dysregulated bone remodeling of the myeloma bone disease (MBD). For the more precise performance of the model, fractional-order derivatives have been used to solve the disease model numerically. The FOMM is preliminarily designed to focus on the critical interactions between bone resorption or osteoclasts (OC) and bone formation or osteoblasts (OB). The connections of OC and OB are represented by a nonlinear differential system based on the cellular components, which depict stable fluctuation in the usual bone case and unstable fluctuation through the MBD. Untreated myeloma causes by increasing the OC and reducing the osteoblasts, resulting in net bone waste the tumor growth. The solutions of the FOMM will be provided by using the stochastic framework based on the Levenberg-Marquardt backpropagation (LVMBP) neural networks (NN), i.e., LVMBPNN. The mathematical performances of three variations of the fractional-order derivative based on the nonlinear disease model using the LVMPNN. The static structural performances are 82% for investigation and 9% for both learning and certification. The performances of the LVMBPNN are authenticated by using the results of the Adams-Bashforth-Moulton mechanism. To accomplish the capability, steadiness, accuracy, and ability of the LVMBPNN, the performances of the error histograms (EHs), mean square error (MSE), recurrence, and state transitions (STs) will be provided.


Cite This Article

N. Yotha, Q. Hiader, Z. Sabir, M. A. Z. Raja, S. B. Said et al., "Fractional order nonlinear bone remodeling dynamics using the supervised neural network," Computers, Materials & Continua, vol. 74, no.2, pp. 2415–2430, 2023.

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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