A New Generalized Deep Learning Framework Combining Sparse Autoencoder and Taguchi Method for Novel Data Classification and Processing

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dc.authoridTolun, Mehmet Resit -- 0000-0002-8478-7220
dc.contributor.authorKarim, Ahmad M.
dc.contributor.authorGüzel, Mehmet S.
dc.contributor.authorTolun, Mehmet R.
dc.contributor.authorKaya, Hilal
dc.contributor.authorÇelebi, Fatih V.
dc.date.accessioned13.07.201910:50:10
dc.date.accessioned2019-07-16T09:14:52Z
dc.date.available13.07.201910:50:10
dc.date.available2019-07-16T09:14:52Z
dc.date.issued2018
dc.departmentMühendislik Fakültesi
dc.description.abstractDeep autoencoder neural networks have been widely used in several image classification and recognition problems, including hand-writing recognition, medical imaging, and face recognition. The overall performance of deep autoencoder neural networks mainly depends on the number of parameters used, structure of neural networks, and the compatibility of the transfer functions. However, an inappropriate structure design can cause a reduction in the performance of deep autoencoder neural networks. A novel framework, which primarily integrates the Taguchi Method to a deep autoencoder based system without considering to modify the overall structure of the network, is presented. Several experiments are performed using various data sets from different fields, i.e., network security and medicine. The results show that the proposed method is more robust than some of the well-known methods in the literature as most of the time our method performed better. Therefore, the results are quite encouraging and verified the overall performance of the proposed framework.
dc.description.sponsorshipAnkara Yildirim Beyazit University
dc.description.sponsorshipThe authors gratefully acknowledge the support to this work by Ankara Yildirim Beyazit University.
dc.identifier.doi10.1155/2018/3145947
dc.identifier.issn1024-123X
dc.identifier.issn1563-5147
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1155/2018/3145947
dc.identifier.urihttps://hdl.handle.net/20.500.12451/4191
dc.identifier.wosWOS:000435820300001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherHindawi Publishing Corporation
dc.relation.ispartofMathematical Problems in Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.titleA New Generalized Deep Learning Framework Combining Sparse Autoencoder and Taguchi Method for Novel Data Classification and Processing
dc.typeArticle

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