Estimation of Number of Flight Using Particle Swarm Optimization and Artificial Neural Network

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dc.authoridPEKEL, Engin / 0000-0002-5295-8013
dc.contributor.authorOzmen, Ebru Pekel
dc.contributor.authorPekel, Engin
dc.date.accessioned2021-11-01T15:01:58Z
dc.date.available2021-11-01T15:01:58Z
dc.date.issued2019
dc.department[Belirlenecek]
dc.description.abstractThe number of flight (NF) is one of the key factors for the administration of the airport to evaluate the apron capacity and airline companies to fix the size of the flight. This paper aims to estimate the monthly NF by performing particle swarm optimization (PSO) and artificial neural network (ANN). Performed PSO-ANN algorithm aims to minimize the proposed evaluation criterion in the training stage. PSO-ANN based on the proposed evaluation criterion offers satisfying fitness values with respect to correlation coefficient and mean absolute percentage error in the training and testing stage.
dc.identifier.doi10.14201/ADCAIJ2019832733
dc.identifier.endpage33en_US
dc.identifier.issn2255-2863
dc.identifier.issue3en_US
dc.identifier.startpage27en_US
dc.identifier.urihttps://doi.org/10.14201/ADCAIJ2019832733
dc.identifier.urihttps://hdl.handle.net/11491/6785
dc.identifier.volume8en_US
dc.identifier.wosWOS:000535788500003
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.institutionauthor[Belirlenecek]
dc.language.isoen
dc.publisherEdiciones Univ Salamanca
dc.relation.ispartofAdcaij-Advances In Distributed Computing And Artificial Intelligence Journal
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectartificial neural networken_US
dc.subjectairporten_US
dc.subjectparticle swarm optimizationen_US
dc.subjectestimationen_US
dc.titleEstimation of Number of Flight Using Particle Swarm Optimization and Artificial Neural Network
dc.typeArticle

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