Haar Wavelet Neural Network Model

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dc.authorscopusid57207452310
dc.authorscopusid15077642900
dc.authorscopusid23389512500
dc.authorscopusid25651286200
dc.authorscopusid35604815200
dc.contributor.authorPala, T.
dc.contributor.authorYücedağ, I.
dc.contributor.authorKahraman, H. T.
dc.contributor.authorGüvenç, U.
dc.contributor.authorSönmez, Y.
dc.date.accessioned2021-12-01T18:38:50Z
dc.date.available2021-12-01T18:38:50Z
dc.date.issued2019
dc.department[Belirlenecek]en_US
dc.description2018 International Conference on Artificial Intelligence and Data Processing, IDAP 2018 -- 28 September 2018 through 30 September 2018 -- -- 144523en_US
dc.description.abstractConvolutional neural networks, one of the most important methods of deep learning which is a popular and modern research topic. Nowadays, thismethod has been applied many problems in a short time and obtained successful results for science and the industry. The multi-layer structure adopted in the design of the convolutional neural network increases the network depth and thus leads to significant problems. In this study, Haar Wavelet Transform-based neural network structure is proposed. Proposed model reduces complexity and number of layers in the network structure. Performance ratios of the proposed model and the conventional model were tested on benchmark MNIST dataset. As a result, when the proposed Haar Wavelet Neural Network model and the convolutional neural network model are compared the accuracy is increased and running time is 6.5 times faster. © 2018 IEEE.en_US
dc.identifier.doi10.1109/IDAP.2018.8620855
dc.identifier.isbn9781538668788
dc.identifier.scopus2-s2.0-85062483616en_US
dc.identifier.urihttps://doi.org/10.1109/IDAP.2018.8620855
dc.identifier.urihttps://hdl.handle.net/20.500.12684/9866
dc.indekslendigikaynakScopusen_US
dc.language.isotren_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2018 International Conference on Artificial Intelligence and Data Processing, IDAP 2018en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectConvolutional neural network (CNN)en_US
dc.subjectDeep learningen_US
dc.subjectHaar Wavelet Transfromen_US
dc.titleHaar Wavelet Neural Network Modelen_US
dc.typeConference Objecten_US

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