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dc.contributor.authorCieza Altamirano, Gilderes_ES
dc.contributor.authorSakda Noinanges_ES
dc.contributor.authorZulqurnain Sabires_ES
dc.contributor.authorMuhammad Asif Zahoor Rajaes_ES
dc.contributor.authorManuel Jesús Sànchez-Cheroes_ES
dc.contributor.authorSeminario-Morales, María-Verónicaes_ES
dc.contributor.authorWajaree Weeraes_ES
dc.contributor.authorThongchai Botmartes_ES
dc.description.abstractThe 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 ndividuals, 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 swarm optimization (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.es_ES
dc.publisherTech Science Presses_ES
dc.relation.ispartofComputers, Materials & Continuaes_ES
dc.sourceCMC, 2022, vol.73, no.3es_ES
dc.subjectinfluenza modeles_ES
dc.subjectreference resultses_ES
dc.subjectparticle swarm optimizationes_ES
dc.subjectartificial neural networkses_ES
dc.subjectinterior-point schemees_ES
dc.subjectstatistical investigationses_ES
dc.titleSwarming Computational Techniques for the Influenza Disease Systemes_ES

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