A neural networks approach to predict call center calls of an internet service provider

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dc.contributor.authorNamli, Özge H.
dc.contributor.authorYanik, Seda
dc.contributor.authorNouri, Faranak
dc.contributor.authorSerap Şengör, N.
dc.contributor.authorKoyuncu, Yusuf Mertkan
dc.contributor.authorUçar, Ömer Berk
dc.date.accessioned2025-02-20T08:46:31Z
dc.date.available2025-02-20T08:46:31Z
dc.date.issued2022
dc.departmentTürk-Alman Üniversitesien_US
dc.description.abstractIn today's competitive business environment, companies are striving to reduce costs and workload of call centers while improving customer satisfaction. In this study, a framework is presented that predicts and encourages taking proactive actions to solve customer problems before they lead to a call to the call center. Machine learning techniques are implemented and models are trained with a dataset which is collected from an internet service provider's systems in order to detect internet connection problems of the customers proactively. Firstly, two classification techniques which are multi perceptron neural networks and radial basis neural networks are applied as supervised techniques to classify whether the internet connection of customers is problematic or not. Then, by using unsupervised techniques, namely Kohonnen's neural networks and Adaptive Resonance Theory neural networks, the same data set is clustered and the clusters are used for the customer problem prediction. The methods are then integrated with an ensemble technique bagging. Each method is implemented with bagging in order to obtain improvement on the estimation error and variation of the accuracy. Finally, the results of the methods applied for classification and clustering with and without bagging are evaluated with performance measures such as recall, accuracy and Davies-Bouldin index, respectively. © 2022 - IOS Press. All rights reserved.
dc.description.sponsorshipTUBITAK-TEYDEB, (5190002); Türkiye Bilimsel ve Teknolojik Araştirma Kurumu, TÜBITAK
dc.identifier.doi10.3233/JIFS-2191207
dc.identifier.endpage515en_US
dc.identifier.issn1064-1246
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85122791287
dc.identifier.scopusqualityQ1
dc.identifier.startpage503en_US
dc.identifier.urihttps://doi.org/10.3233/JIFS-2191207
dc.identifier.urihttps://hdl.handle.net/20.500.12846/1774
dc.identifier.volume42en_US
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIOS Press BV
dc.relation.ispartofJournal of Intelligent and Fuzzy Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250220
dc.subjectartificial neural networksen_US
dc.subjectbaggingen_US
dc.subjectCall center problem predictionen_US
dc.subjectclassificationen_US
dc.subjectclusteringen_US
dc.titleA neural networks approach to predict call center calls of an internet service provider
dc.typeArticle

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