A Novel Fractional Dengue Transmission Model in the Presence of Wolbachia Using Stochastic Based Artificial Neural Network

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(S_(h)),exposed humans(E_(h)),infectious humans(I_(h)),and recovered humans(R_(h)).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(η),namely when Wolbachia-free mosquitoes persist only(η=0.6),when both types of mosquitoes persist(η=0.8),and when Wolbachia-carrying mosquitoes persist only(η=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(α=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).

A Novel Stochastic Framework for the MHD Generator in Ocean

This work aims to study the nonlinear ordinary differential equations(ODEs)system of magnetohydrodynamic(MHD)past over an inclined plate using Levenberg-Marquardt backpropagation neural networks(LMBNNs).The stochastic procedures LMBNNs are provided with three categories of sample statistics,testing,training,and verification.The nonlinear MHD system past over an inclined plate is divided into three profiles,dimensionless momentum,species(salinity),and energy(heat)conservations.The data is applied 15%,10%,and 75%for validation,testing,and training to solve the nonlinear system of MHD past over an inclined plate.A reference data set is designed to compare the obtained and proposed solutions for the MHD system.The plots of the absolute error(AE)are provided to check the accuracy and precision of the considered nonlinear system of MHD.The obtained numerical solutions of the nonlinear magnetohydrodynamic system have been considered to reduce the mean square error(MSE).For the capability,dependability,and aptitude of the stochastic LMBNNs procedure,the numerical performances are provided to authenticate the relative arrangements of MSE,error histograms(EHs),state transitions(STs),correlation,and regression.

Application of Modified Extended Tanh Technique for Solving Complex Ginzburg-Landau Equation Considering Kerr Law Nonlinearity

The purpose of this work is to find new soliton solutions of the complex Ginzburg–Landau equation(GLE)with Kerr law non-linearity.The considered equation is an imperative nonlinear partial differential equation(PDE)in the field of physics.The applications of complex GLE can be found in optics,plasma and other related fields.The modified extended tanh technique with Riccati equation is applied to solve the Complex GLE.The results are presented under a suitable choice for the values of parameters.Figures are shown using the three and two-dimensional plots to represent the shape of the solution in real,and imaginary parts in order to discuss the similarities and difference between them.The graphical representation of the results depicts the typical behavior of soliton solutions.The obtained soliton solutions are of different forms,such as,hyperbolic and trigonometric functions.The results presented in this paper are novel and reported first time in the literature.Simulation results establish the validity and applicability of the suggested technique for the complex GLE.The suggested method with symbolic computational software such as,Mathematica and Maple,is proven as an effective way to acquire the soliton solutions of nonlinear partial differential equations(PDEs)as well as complex PDEs.