A Numerical Study of the Dynamics of Vector-Born Viral Plant Disorders Using a Hybrid Artificial Neural Network Approach

Abstract

Most plant viral infections are vector-borne. There is a latent period of disease inside the vector after obtaining the virus from the infected plant. Thus, after interacting with an infected vector, the plant demonstrates an incubation time before becoming diseased. This paper analyzes a mathematical model for persistent vector-borne viral plant disease dynamics. The backpropagated neural network based on the Levenberg–Marquardt algorithm (NN-BLMA) is used to study approximate solutions for fluctuations in natural plant mortality and vector mortality rates. A state-of-the-art numerical technique is utilized to generate reference data for obtaining surrogate solutions for multiple cases through NN-BLMA. Curve fitting, regression analysis, error histograms, and convergence analysis are used to assess accuracy of the calculated solutions. It is evident from our simulations that NN-BLMA is accurate and reliable.

Stability analysis of GO‐EG and GO‐blood base nanofluids by implementing of thermal radiation phenomena

“…Hu et al [30] unveil the role visible light-based photodynamic cancer treatment mechanism of GO/TiO 2 hybrid. Alhakami et al [31] used hybrid artificial neural network approach and provided dynamical study of vector-borne viral plant diseases. Alarfaj et al [32] investigated computed stiff fractional modeling through a numerical technique for machine learning and a numerical quantitative study was carried out by Alshammari et al [33] in their paper having diffusion equations for nonlinear convection.…”

“…Alhakami et al. [31] used hybrid artificial neural network approach and provided dynamical study of vector‐borne viral plant diseases. Alarfaj et al.…”

A design of novel Gudermannian neural networks for the nonlinear multi-pantograph delay differential singular model

“…The heuristic techniques using the stochastic solvers are presented by functioning the strengths of neural networks along with the practical versions of evolutionary computing submissions [30][31][32][33]. For instance, some of the recent possible applications are Thomas-Fermi atom's model [34], polytropic gas spheres and electric circuits [35], heat conduction model of human head [36], prey-predator models [37], heat transfer in micropolar fluid [38], vector-born viral plant disorders [39], singular Lane-Emden equation [40], Fredholm integral models [41], spherical cloud of gas model [42], nonlinear model for financial market forecasting [43], control systems [44], bilinear programming models [45], doubly singular model [46], wetted longitudinal porous heat exchanger model [47], singular functional differential model [48,49], plasma physics problems [50], mosquito dispersal model in a heterogeneous environment [51], Bagley-Torvik models [52], marine ecosystems [53], inclined longitudinal porous fin of trapezoidal [54], singular periodic model [55], power [56] and HIV infection model of CD4+ T cells [57]. These influences have been proven the stochastic solver in terms of convergence, robustness, and accuracy.…”

Mathematical Modeling in Systems Biology

“…The model incorporates the saturated incidence rate to accurately represent the transmission dynamics of the disease. The article by Hosam Alhakami et al uses a deterministic mathematical model of vector-borne viral plant disease dynamics to train a feed-forward neural network using Levenberg-Marquardt backpropagation algorithm [9]. The neural network is then used to study the implication of fluctuations on natural plant mortality and vector mortality rates.…”