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https://hdl.handle.net/20.500.11851/6331
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Güler, İnan | - |
dc.contributor.author | Übeyli, Elif Derya | - |
dc.date.accessioned | 2021-09-11T15:35:52Z | - |
dc.date.available | 2021-09-11T15:35:52Z | - |
dc.date.issued | 2006 | en_US |
dc.identifier.issn | 0018-9294 | - |
dc.identifier.issn | 1558-2531 | - |
dc.identifier.uri | https://doi.org/10.1109/TBME.2005.863929 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/6331 | - |
dc.description.abstract | In this paper, we present the automated diagnostic systems for Doppler ultrasound signals classification with diverse and composite features and determine their accuracies. We compared the classification accuracies of six different classifiers, namely multilayer perceptron neural network (MLP), combined neural network (CNN), mixture of experts (ME)9 modified mixture of experts (MME), probabilistic neural network (PNN), and support vector machine (SVM), which were trained on diverse or composite features. The present study was conducted with the purpose of answering the question of whether the automated diagnostic systems improve the capability of classification of ophthalmic arterial (OA) and internal carotid arterial (ICA) Doppler signals. Our research demonstrated that the SVM trained on composite feature and the MME trained on diverse features achieved accuracy rates which were higher than that of the other automated diagnostic systems. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE-Inst Electrical Electronics Engineers Inc | en_US |
dc.relation.ispartof | IEEE Transactions On Biomedical Engineering | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | combined neural network (CNN) | en_US |
dc.subject | composite feature | en_US |
dc.subject | diverse features | en_US |
dc.subject | Doppler ultrasound signals | en_US |
dc.subject | mixture of experts (ME) | en_US |
dc.subject | modified mixture of experts (MME) | en_US |
dc.subject | multilayer perceptron neural network (MLP) | en_US |
dc.subject | probabilistic neural network (PNN) | en_US |
dc.subject | support vector machine (SVM) | en_US |
dc.title | Automated Diagnostic Systems With Diverse and Composite Features for Doppler Ultrasound Signals | en_US |
dc.type | Article | en_US |
dc.department | Faculties, Faculty of Engineering, Department of Electrical and Electronics Engineering | en_US |
dc.department | Fakülteler, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü | tr_TR |
dc.identifier.volume | 53 | en_US |
dc.identifier.issue | 10 | en_US |
dc.identifier.startpage | 1934 | en_US |
dc.identifier.endpage | 1942 | en_US |
dc.identifier.wos | WOS:000240698800009 | en_US |
dc.identifier.scopus | 2-s2.0-33749519590 | en_US |
dc.institutionauthor | Übeyli, Elif Derya | - |
dc.identifier.pmid | 17019857 | en_US |
dc.identifier.doi | 10.1109/TBME.2005.863929 | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.scopusquality | Q1 | - |
item.openairetype | Article | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
Appears in Collections: | Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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