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Dynamics of Fractional Differential Model for Schistosomiasis Disease

Computers, Materials & Continua 2022年第10期73卷 981-999页

作者： Thongchai Botmart Wajaree Weera Muhammad Asif Zahoor Raja Zulqurnain Sabir Qusain Hiader Gilder Cieza Altamirano Plinio Junior Muro Solano Alfonso Tesen Arroyo Department of Mathematics Faculty of ScienceKhon Kaen UniversityKhon Kaen40002Thailand Future Technology Research Center National Yunlin University of Science and TechnologyDouliouYunlin64002Taiwan Department of Mathematics and Statistics Hazara UniversityMansehra21120Pakistan Department of Mathematics University of GujratGujrat50700Pakistan Universidad Nacional Autónoma de Chota Chota06121Peru Universidad Nacional Pedro Ruiz Gallo Lambayeque14013Peru

In the present study,a design of a fractional order mathematical model is presented based on the schistosomiasis *** observe more accurate performances of the results,the use of fractional order derivatives in the mathematical model is introduce based on the schistosomiasis disease is *** preliminary design of the fractional order mathematical model focused on schistosomiasis disease is classified as follows:uninfected with schistosomiasis,infected with schistosomiasis,recovered from infection,susceptible snail unafflicted with schistosomiasis disease and susceptible snail afflicted with this *** solutions to the proposed system of the fractional order mathematical model will be presented using stochastic artificial neural network(ANN)techniques in conjunction with the LevenbergMarquardt backpropagation(LMBP),referred to as *** illustrate the preciseness of the ANN-LMBP method,mathematical presentations of three different values focused on fractional order will be *** statics performances are taken in these investigations are 78%and 11%for both learning and *** accuracy of the ANN-LMBP method is determined by comparing the values obtained by the database Adams-Bash forth-Moulton *** simulation-based error histograms(EHs),MSE,recurrence,and state transitions(STs)will be offered to achieve the capability,accuracy,steadiness,abilities,and finesse of the ANN-LMBP method.

关键词： Biological model fractional order schistosomiasis disease artificial neural networks reference results Levenberg-Marquardt backpropagation

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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 *** 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 *** ANNs-PSO-IPA has never been applied to solve the *** 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 *** 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 ***,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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