A model for predicting drying time period of wool yarn bobbins using computational intelligence techniques

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dc.authorid0000-0003-2492-8690
dc.authorid0000-0003-4842-2635
dc.authorscopusid6506635786
dc.authorscopusid11539603200
dc.authorscopusid6602300270
dc.authorscopusid6603854266
dc.authorwosidAkyol, Ugur/ABA-8180-2020
dc.authorwosidTufekci, Pinar/ABA-5121-2020
dc.authorwosidKahveci, Kamil/A-2954-2016
dc.contributor.authorAkyol, Uğur
dc.contributor.authorTüfekçi, Pınar
dc.contributor.authorKahveci, Kamil
dc.contributor.authorCihan, Ahmet
dc.date.accessioned2022-05-11T14:10:27Z
dc.date.available2022-05-11T14:10:27Z
dc.date.issued2015
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Makine Mühendisliği Bölümü
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractIn this study, a predictive model has been developed using computational intelligence techniques for the prediction of drying time in the wool yarn bobbin drying process. The bobbin drying process is influenced by various drying parameters, 19 of which were used as input variables in the dataset. These parameters affect the drying time of yarn bobbins, which is considered as the target variable. The dataset, which consists of these input and target variables, was collected from an experimental yarn bobbin drying system. Firstly, the most effective input variables on the target variable, named as the best feature subset of the dataset, were investigated by using a filter-based feature selection method. As a result, the most important five parameters were obtained as the best feature subset. Afterwards, the most successful method that can predict the drying time of wool yarn bobbins with the highest accuracy was explored amongst the 16 computational intelligence methods for the best feature subset. Finally, the best performance has been found by the REP tree method, which achieved minimum error and time taken to build the model.
dc.description.sponsorshipTUBITAKTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [108M274]
dc.description.sponsorshipThis work was supported by TUBITAK (grant number 108M274).
dc.identifier.doi10.1177/0040517514553879
dc.identifier.endpage1380
dc.identifier.issn0040-5175
dc.identifier.issn1746-7748
dc.identifier.issue13en_US
dc.identifier.scopus2-s2.0-84930354858
dc.identifier.scopusqualityQ2
dc.identifier.startpage1367
dc.identifier.urihttps://doi.org/10.1177/0040517514553879
dc.identifier.urihttps://hdl.handle.net/20.500.11776/5408
dc.identifier.volume85
dc.identifier.wosWOS:000354440900005
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorAkyol, Uğur
dc.institutionauthorTüfekci, Pınar
dc.language.isoen
dc.publisherSage Publications Ltd
dc.relation.ispartofTextile Research Journal
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectprediction of drying time
dc.subjectwool
dc.subjectbobbin
dc.subjectfeature selection
dc.subjectmachine learning regression method
dc.subjectREP tree method
dc.subjectMoisture Transfer-Coefficients
dc.subjectRegression
dc.subjectSelection
dc.subjectDiffusivities
dc.subjectProducts
dc.subjectObjects
dc.subjectTrees
dc.titleA model for predicting drying time period of wool yarn bobbins using computational intelligence techniques
dc.typeArticle

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