The Effect of Adaptive Neuro-fuzzy Inference System (ANFIS) on Determining the Leadership Perceptions of Construction Employees

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dc.authoridHaznedar, Bulent/0000-0003-0692-9921
dc.contributor.authorKeles, Abdullah Emre
dc.contributor.authorHaznedar, Bulent
dc.contributor.authorKeles, Muemine Kaya
dc.contributor.authorArslan, Mustafa Turan
dc.date.accessioned2024-09-18T19:54:21Z
dc.date.available2024-09-18T19:54:21Z
dc.date.issued2023
dc.departmentHatay Mustafa Kemal Üniversitesien_US
dc.description.abstractIn the construction industry, which is Turkey's locomotive and the strategic sector, determining the kind of leadership that impacts employees' productivity is directly related to the success of the business. The identification of leadership types that will motivate and support employees has great importance in terms of construction businesses where the human element is at the forefront. From the point of view of the site chiefs, it is thought that it will benefit all the stakeholders in the construction sector to determine which leader type will motivate which employees. In this study, the productivity relations between the engineers working in construction companies constructing buildings in Adana Province and the employees who are the hierarchically lower-level employees of these persons were investigated using bi-directional surveys. The impact of leadership types on the employees' productivity has been investigated using machine learning. The effects of ANFIS method and the use of genetic algorithm (GA) on the training of ANFIS for the classification are investigated. The data set, which was prepared within the scope of the study, was classified by ANFIS-genetic algorithm (ANFIS-GA), ANFIS-backpropagation algorithm (ANFIS-BP), and ANFIS-hybrid algorithm (ANFIS-HB) algorithms after the required preprocesses. The 10-fold cross-validation technique is used to test the performance of the classification methods. According to the obtained results, the highest accuracy rate of 82.18% is obtained when ANFIS-GA algorithm is used as a classifier. As a result of the study, it is concluded that for this data set, ANFIS, an artificial neural network-based algorithm, is more successful in determining the leadership perceptions of construction employees when it is trained by GA.en_US
dc.description.sponsorshipScientific Research Projects Commission Unit of Adana Science and Technology University [17103018]en_US
dc.description.sponsorshipAcknowledgementsThis study was supported by Scientific Research Projects Commission Unit of Adana Science and Technology University under Grant Number: 17103018. Please address any correspondence to mkaya@atu.edu.tr, which is the e-mail address of the corresponding author.en_US
dc.identifier.doi10.1007/s40996-023-01146-2
dc.identifier.endpage4157en_US
dc.identifier.issn2228-6160
dc.identifier.issn2364-1843
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85160846661en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage4145en_US
dc.identifier.urihttps://doi.org/10.1007/s40996-023-01146-2
dc.identifier.urihttps://hdl.handle.net/20.500.12483/7677
dc.identifier.volume47en_US
dc.identifier.wosWOS:000999002600001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Int Publ Agen_US
dc.relation.ispartofIranian Journal of Science and Technology-Transactions of Civil Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectANFIS algorithmen_US
dc.subjectConstruction managementen_US
dc.subjectFeature selectionen_US
dc.subjectGenetic algorithmen_US
dc.subjectMachine learningen_US
dc.subjectLeadership perceptionen_US
dc.titleThe Effect of Adaptive Neuro-fuzzy Inference System (ANFIS) on Determining the Leadership Perceptions of Construction Employeesen_US
dc.typeArticleen_US

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