Browsing by Author "Faiz, Zeshan"

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Citation - WoS: 6
Citation - Scopus: 6
A Novel Fractional Dengue Transmission Model in the Presence of Wolbachia Using Stochastic Based Artificial Neural Network
(Tech Science Press, 2024) Faiz, Zeshan; Ahmed, Iftikhar; Baleanu, Dumitru; Javeed, Shumaila; 56389; Matematik
The purpose of this research work is to investigate the numerical solutions of the fractional dengue transmission model (FDTM) in the presence of Wolbachia using the stochastic-based Levenberg-Marquardt neural network (LM-NN) technique. The fractional dengue transmission model (FDTM) consists of 12 compartments. The human population is divided into four compartments; susceptible humans (Sh), exposed humans (Eh), infectious humans (Ih), and recovered humans (Rh). Wolbachia-infected and Wolbachia-uninfected mosquito population is also divided into four compartments: aquatic (eggs, larvae, pupae), susceptible, exposed, and infectious. We investigated three different cases of vertical transmission probability (77), namely when Wolbachia-free mosquitoes persist only (77 = 0.6), when both types of mosquitoes persist (77 = 0.8), and when Wolbachia-carrying mosquitoes persist only (77 = 1). The objective of this study is to investigate the effectiveness of Wolbachia in reducing dengue and presenting the numerical results by using the stochastic structure LM-NN approach with 10 hidden layers of neurons for three different cases of the fractional order derivatives (alpha = 0.4, 0.6, 0.8). LM-NN approach includes a training, validation, and testing procedure to minimize the mean square error (MSE) values using the reference dataset (obtained by solving the model using the Adams-Bashforth-Moulton method (ABM). The distribution of data is 80% data for training, 10% for validation, and, 10% for testing purpose) results. A comprehensive investigation is accessible to observe the competence, precision, capacity, and efficiency of the suggested LM-NN approach by executing the MSE, state transitions findings, and regression analysis. The effectiveness of the LM-NN approach for solving the FDTM is demonstrated by the overlap of the findings with trustworthy measures, which achieves a precision of up to 10-4.
Citation - WoS: 14
Citation - Scopus: 15
Numerical solutions of the Wolbachia invasive model using Levenberg-Marquardt backpropagation neural network technique
(Elsevier, 2023) Faiz, Zeshan; Javeed, Shumaila; Ahmed, Iftikhar; Baleanu, Dumitru; Riaz, Muhammad Bilal; Sabir, Zulqurnain; 56389; Matematik
The current study presents the numerical solutions of the Wolbachia invasive model (WIM) using the neural network Levenberg-Marquardt (NN-LM) backpropagation technique. The dynamics of the Wolbachia model is categorized into four classes, namely Wolbachia-uninfected aquatic mosquitoes (A*n), Wolbachia-uninfected adult female mosquitoes (Fn*), Wolbachia-infected aquatic mosquitoes (A*w), and Wolbachia-infected adult female mosquitoes (F*w). A reference dataset for the proposed NN-LM technique is created by solving the Wolbachia model using the Runge-Kutta (RK) numerical method. The reference dataset is used for validation, training, and testing of the proposed NN-LM technique for three different cases. The obtained numerical results from the proposed neural network technique are compared with the results obtained from the RK method for accuracy, correctness, and efficiency of the designed methodology. The validation of the proposed solution methodology is checked through the mean square error (MSE), error histograms, error plots, regression plots, and fitness plots.