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Swarming Computational Efficiency to Solve a Novel Third-Order Delay Differential Emden-Fowler System

Computers, Materials & Continua 2022年第12期73卷 4833-4849页

作者： Wajaree Weera Zulqurnain Sabir Muhammad Asif Zahoor Raja sakda noinang Thongchai Botmart Department of Mathematics Faculty of ScienceKhon Kaen UniversityKhon Kaen40002Thailand Department of Mathematics and Statistics Hazara UniversityMansehraPakistan Future Technology Research Center National Yunlin University of Science and Technology123 University RoadSection 3DouliouYunlin64002Taiwan Department of Mathematics Statistics and Computer Faculty of ScienceUbon Ratchathani UniversityUbon Ratchathani34190Thailand

The purpose of this research is to construct an integrated neuro swarming scheme using the procedures of the artificial neural networks(ANNs)with the use of global search particle swarm optimization(PSO)along with the competent local search interior-point programming(IPP)called as ANN-PSOIPP.The proposed computational scheme is implemented for the numerical simulations of the third order nonlinear delay differential Emden-Fowler model(TON-DD-EFM).The TON-DD-EFM is based on two types along with the particulars of shape factor,delayed terms,and singular points.A merit function is performed using the optimization of PSOIPP to find the solutions to the TON-DD-EFM.The effectiveness of the ANN-PSOIPP is certified through the comparison with the exact results for solving four examples of the TON-DD-EFM.The scheme’s efficiency is observed by performing the absolute error in suitable measures found around 10−04 to 10−07.Furthermore,the statistical-based assessments for 100 trials are provided to compute the accuracy,stability,and constancy of the ANNPSOIPP for solving the TON-DD-EFM.

关键词： Third-order nonlinear emden-fowler system artificial neural network statistical results particle swarm optimization numerical experimentations local search programming

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Numerical Procedure for Fractional HBV Infection with Impact of Antibody Immune

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Computers, Materials & Continua 2023年第2期74卷 2575-2588页

作者： sakda noinang Zulqurnain Sabir Muhammad Asif Zahoor Raja Soheil Salahshour Wajaree Weera Thongchai Botmart Department of Mathematics Statistics and Computer Faculty of ScienceUbon Ratchathani UniversityUbon Ratchathani34190Thailand Department of Mathematics and Statistics Hazara UniversityMansehra21120Pakistan Future Technology Research Center National Yunlin University of Science and TechnologyDouliouYunlin64002Taiwan Faculty of Engineering and Natural Sciences Bahcesehir UniversityIstanbul34494Turkey Department of Mathematics Faculty of ScienceKhon Kaen UniversityKhon Kaen40002Thailand

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 to solve the FO-HBV-DIS with the response of antibody immune.The numerical observations are achieved using the stochastic LMBNNs procedures for soling the FO-HBV-DIS with the response of antibody immune and comparison of the results is presented through the database Adams-Bashforth-Moulton approach.To authenticate the validity,competence,consistency,capability and exactness of the LMBNNs,the numerical presentations using the mean square error(MSE),error histograms(EHs),state transitions(STs),correlation and regression are accomplished.

关键词： Fractional order HBV differential infection system artificial neural networks nonlinear Levenberg-Marquardt backpropagation Adams-Bashforth-Moulton

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A Novel Stochastic Framework for the MHD Generator in Ocean

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Computers, Materials & Continua 2022年第11期73卷 3383-3402页

作者： sakda noinang Zulqurnain Sabir Shumaila Javeed Muhammad Asif Zahoor Raja Dostdar Ali Wajaree Weera Thongchai Botmart Department of Mathematics Statistics and Computer Faculty of ScienceUbon Ratchathani UniversityUbon Ratchathani 34190Thailand Department of Mathematics and Statistics Hazara UniversityMansehraPakistan Department of Mathematics Comsats University Islamabad45550 Islamabad CampusIslamabadPakistan Future Technology Research Center National Yunlin University of Science and TechnologyYunlin 64002Taiwan Department of Mathematics Faculty of ScienceKhon Kaen UniversityKhon Kaen 40002Thailand

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.

关键词： MHD energy salinity levenberg-marquardt backpropagation Soret number nonlinear

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Swarming Computational Techniques for the Influenza Disease System

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Computers, Materials & Continua 2022年第12期73卷 4851-4868页

作者： sakda noinang Zulqurnain Sabir Gilder Cieza Altamirano Muhammad Asif Zahoor Raja Manuel Jesús Sànchez-Chero María-Verónica Seminario-Morales Wajaree Weera Thongchai Botmart Department of Mathematics Statistics and Computer Faculty of ScienceUbon Ratchathani UniversityUbon Ratchathani34190Thailand Department of Mathematics and Statistics Hazara UniversityMansehra21120Pakistan Universidad Nacional Autonoma de Chota Cajamarca06121Peru Future Technology Research Center National Yunlin University of Science and Technology123 University RoadSection 3DouliouYunlin64002Taiwan Universidad Nacional de Frontera SullanaPeru Department of Mathematics Faculty of ScienceKhon Kaen UniversityKhon Kaen40002Thailand

The current study relates to designing a swarming computational paradigm to solve the influenza disease system(IDS).The nonlinear system’s mathematical form depends upon four classes:susceptible individuals,infected people,recovered individuals and cross-immune people.The solutions of the IDS are provided by using the artificial neural networks(ANNs)together with the swarming computational paradigm-based particle swarmoptimization(PSO)and interior-point scheme(IPA)that are the global and local search approaches.The ANNs-PSO-IPA has never been applied to solve the IDS.Instead a merit function in the sense of mean square error is constructed using the differential form of each class of the IDS and then optimized by the PSOIPA.The correctness and accuracy of the scheme are observed to perform the comparative analysis of the obtained IDS results with the Adams solutions(reference solutions).An absolute error in suitable measures shows the precision of the proposed ANNs procedures and the optimization efficiency of the PSOIPA.Furthermore,the reliability and competence of the proposed computing method are enhanced through the statistical performances.

关键词： Disease influenza model reference results particle swarm optimization artificial neural networks interior-point scheme statistical investigations

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