Sakda Noinang¹, Zulqurnain Sabir², Muhammad Asif Zahoor Raja³, Soheil Salahshour⁴, Wajaree Weera^5,*, Thongchai Botmart⁵

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2575-2588, 2023, DOI:10.32604/cmc.2023.029046 - 31 October 2022

Abstract The current investigations are presented to solve the fractional order HBV differential infection system (FO-HBV-DIS) with the response of antibody immune using the optimization based stochastic schemes of the Levenberg-Marquardt backpropagation (LMB) neural networks (NNs), i.e., LMBNNs. The FO-HBV-DIS with the response of antibody immune is categorized into five dynamics, healthy hepatocytes (H), capsids (D), infected hepatocytes (I), free virus (V) and antibodies (W). The investigations for three different FO variants have been tested numerically to solve the nonlinear FO-HBV-DIS. The data magnitudes are implemented 75% for training, 10% for certification and 15% for testing More >

Muneerah Al Nuwairan^1,*, Zulqurnain Sabir², Muhammad Asif Zahoor Raja³, Maryam Alnami¹, Hanan Almuslem¹

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4441-4454, 2022, DOI:10.32604/cmc.2022.029432 - 16 June 2022

Abstract In this paper, a fractional order model based on the management of waste plastic in the ocean (FO-MWPO) is numerically investigated. The mathematical form of the FO-MWPO model is categorized into three components, waste plastic, Marine debris, and recycling. The stochastic numerical solvers using the Levenberg-Marquardt backpropagation neural networks (LMQBP-NNs) have been applied to present the numerical solutions of the FO-MWPO system. The competency of the method is tested by taking three variants of the FO-MWPO model based on the fractional order derivatives. The data ratio is provided for training, testing and authorization is 77%, More >