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

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dc.authorid0000-0001-7461-1304
dc.contributor.authorDağ, Özge Hüsniye Namlı
dc.contributor.authorYanık, Seda
dc.contributor.authorNouri, Faranak
dc.contributor.authorŞengör, Neslihan Serap
dc.contributor.authorKoyuncu, Yusuf Mertkan
dc.contributor.authorUçar, Ömer Berk
dc.date.accessioned2022-05-18T08:46:34Z
dc.date.available2022-05-18T08:46:34Z
dc.date.issued2022
dc.departmentTAÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.description.abstractn 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.
dc.identifier.citationNamlı, Ö. H., Yanık, S., Nouri, F., Serap Şengör, N., Koyuncu, Y. M., & Uçar, Ö. B.(2022) A neural networks approach to predict call center calls of an internet service provider. Journal of Intelligent & Fuzzy Systems, 1-13.
dc.identifier.doi10.3233/JIFS-219207
dc.identifier.endpage515en_US
dc.identifier.issn1875-8967
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85168744864
dc.identifier.scopusqualityQ2
dc.identifier.startpage503en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12846/648
dc.identifier.volume42en_US
dc.identifier.wosWOS:000741363900038
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorDağ, Özge Hüsniye Namlı
dc.language.isoen
dc.publisherIOS Press
dc.relation.ispartofJournal Of Intelligent & Fuzzy Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectCall Center Problem Predictionen_US
dc.subjectClusteringen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectBaggingen_US
dc.subjectÇağrı Merkezi Sorun Tahminien_US
dc.subjectKümelemeen_US
dc.subjectYapay Sinir Ağlarıen_US
dc.subjectKünstliche Neurale Netzwerkeen_US
dc.subjectAbsackenen_US
dc.titleA neural networks approach to predict call center calls of an internet service provider
dc.typeArticle

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