Banknote classification using artificial neural network approach

Basit Öğe Kaydı
dc.contributor.authorKaya, Esra
dc.contributor.authorYasar, Ali
dc.contributor.authorSaritas, Ismail
dc.date.accessioned2020-03-26T19:09:26Z
dc.date.available2020-03-26T19:09:26Z
dc.date.issued2016
dc.departmentSelçuk Üniversitesi, Teknoloji Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractIn this study, clustering process has been performed using artificial neural network (ANN) approach on the pictures belonging to our dataset to determine if the banknotes are genuine or counterfeit. Four input parameters, one hidden layer with 10 neurons and one output has been used for the ANN. All of these parameters were real-valued continuous. Data were extracted from images that were taken from genuine and forged banknote-like specimens. For digitization, an industrial camera usually used for print inspection was used. The final images have 400x 400 pixels. Due to the object lens and distance to the investigated object gray-scale pictures with a resolution of about 660 dpi were gained. Wavelet Transform tool were used to extractfeatures from images. Four input parameters are processed in the hidden layer with 10 neurons and the output realizes the clustering process. The classification process of 1372 unit data by using ANN approach is sure to be a success as much as the actual data set. The regression results of the clustering process is considerably well. It is determined that the training regression is 0,99914, testing regression is 0,99786 and the validation regression is 0,9953, respectively. Based on the results obtained, it is seen that classification process using ANN is capable of achieving outstanding successen_US
dc.identifier.citationKaya, E., Yasar, A., Saritas, I. (2016). Banknote Classification Using Artificial Neural Network Approach. International Journal of Intelligent Systems and Applications in Engineering, 4(1), 16-19.
dc.identifier.endpage19en_US
dc.identifier.issn2147-6799en_US
dc.identifier.issn2147-6799en_US
dc.identifier.issue1en_US
dc.identifier.startpage16en_US
dc.identifier.urihttp://www.trdizin.gov.tr/publication/paper/detail/TWpFeU9UUXdNQT09
dc.identifier.urihttps://hdl.handle.net/20.500.12395/33026
dc.identifier.volume4en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.institutionauthorKaya, Esra
dc.institutionauthorYasar, Ali
dc.institutionauthorSaritas, Ismail
dc.language.isoenen_US
dc.relation.ispartofInternational Journal of Intelligent Systems and Applications in Engineeringen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectANNen_US
dc.subjectBanknoteen_US
dc.subjectClassification
dc.subjectMachine Learning Database
dc.titleBanknote classification using artificial neural network approachen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
Esra KAYA.pdf
Boyut:
1009.33 KB
Biçim:
Adobe Portable Document Format
Açıklama:
Full Text Access
İndir

Koleksiyon

TR-Dizin İndeksli Yayınlar Koleksiyonu
Elektrik Elektronik Mühendisliği/Makale Koleksiyonu