Artificial intelligence knacks-based stochastic paradigm to study the dynamics of plant virus propagation model with impact of seasonality and delays

Abstract

The presented study deals with the exploitation of the artificial intelligence knacksbased stochastic paradigm for the numerical treatment of the nonlinear delay differential system for dynamics of plant virus propagation with the impact of seasonality and delays (PVP-SD) model by implementing neural networks backpropagation with Bayesian regularization scheme (NNs-BBRS). The PVP-SD model is represented with five classes-based ODEs systems for the interaction between insects and plants. The nonlinear PVP-SD model governs with five populations: S(t) susceptible plants, I(t) infected plants, X(t) susceptible insect vectors, Y (t) infected insect vectors and P(t) predators. Adams numerical procedure is adopted to generate the reference solutions of the nonlinear PVP-SD model based on the variety of cases by varying the plants bite rate due to vectors, vector bite rate due to plants, plant's recovery rate, predator contact rate with healthy insects, predator contact rate with infected insects and death rate caused by insecticides. The approximate solutions of the nonlinear PVP-SD model are determined by executing the designed NNs-BBRS through different target and inputs arbitrary selected samples for the training and testing data. Validation of the consistent precision and convergence of the designed NNs-BBRS is efficaciously substantiated through exhaustive simulations and analyses on mean square error-based merit function, index of regression and error histogram illustrations.

Reliable numerical treatment with Adams and BDF methods for plant virus propagation model by vector with impact of time lag and density

“…Numerous implicit approaches have been created over time and are the subject of in-depth literature discussion; see [37][38][39][40][41][42][43]. To that end, the goal of this study is to apply the precise and stable ADM [44][45][46][47][48] and BDF methods to determine an initial value problem solution.…”

Stability analysis of a time-delayed Van der Pol–Helmholtz–Duffing oscillator in fractal space with a non-perturbative approach

“…In addressing the challenges posed by nonlinear delays, an innovative approach was introduced, conceptualizing an intelligent computational model that leverages both the strengths and weaknesses of the established method through dual-layered network frameworks [25]. For the computational analysis of the nonlinear delay system, particularly concerning the dynamics of plant virus propagation influenced by seasonality and delays, advanced stochastic models rooted in artificial intelligence were utilized [26].…”

Stochastic Computational Heuristic for the Fractional Biological Model Based on Leptospirosis

“…The solution of this novel fraction order model will be presented first by using the stochastic ANNs-SCG scheme. The stochastic numerical performances have been applied to solve the corneal shape of model [38], human immunodeficiency virus (HIV) system [39], plant virus propagation model [40], coronavirus mathematical model [41], travelling wave solution-based new Riccati equation [42], solar energy model [43], Schrödinger equation [44], dusty plasma model [45] and relation extraction [46,47]. These applications enhance the motivations and inspirations for the authors to perform the exhaustive frameworks in the field of artificial intelligence (AI) through the stochastic computational structure to provide a substitute, stable, precise, robust, and accurate solutions to the mathematical form of the biological Leptospirosis model.…”