Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6512
Full metadata record
DC FieldValueLanguage
dc.contributor.authorÜbeyli, Elif Derya-
dc.contributor.authorGüler, İnan-
dc.date.accessioned2021-09-11T15:37:02Z-
dc.date.available2021-09-11T15:37:02Z-
dc.date.issued2008en_US
dc.identifier.issn1051-2004-
dc.identifier.issn1095-4333-
dc.identifier.urihttps://doi.org/10.1016/j.dsp.2007.02.002-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/6512-
dc.description.abstractThis paper illustrates the use of modified mixture of experts (MME) network structure to guide model selection for classification of Doppler ultrasound signals with diverse features. The MME is a modular neural network architecture for supervised learning. Expectation-maximization (EM) algorithm was used for training the MME so that the learning process is decoupled in a manner that fits well with the modular structure. In practical applications of pattern recognition, there are often diverse features extracted from raw data which needs recognizing. Decision making was performed in two stages: feature extraction by spectral analysis methods (fast Fourier transform and model-based methods) and classification using the classifiers trained on the extracted features. In order to discriminate the Doppler ultrasound signals, the ability of designed and trained MME network structure combined with spectral analysis methods was explored. The MME achieved accuracy rates which were higher than that of the mixture of experts (ME) and feedforward neural network models (multilayer perceptron neural network-MLPNN). The proposed MME approach can be useful in classifying the Doppler ultrasound signals for early detection of arterial diseases. (C) 2007 Elsevier Inc. All rights reserved.en_US
dc.language.isoenen_US
dc.publisherAcademic Press Inc Elsevier Scienceen_US
dc.relation.ispartofDigital Signal Processingen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectmodified mixture of expertsen_US
dc.subjectexpectation-maximization algorithmen_US
dc.subjectspectral analysis methodsen_US
dc.subjectDoppler ultrasound signalsen_US
dc.subjectdiverse featuresen_US
dc.titleDetecting Variabilities of Doppler Ultrasound Signals by a Modified Mixture of Experts With Diverse Featuresen_US
dc.typeArticleen_US
dc.departmentFaculties, Faculty of Engineering, Department of Electrical and Electronics Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümütr_TR
dc.identifier.volume18en_US
dc.identifier.issue2en_US
dc.identifier.startpage267en_US
dc.identifier.endpage279en_US
dc.identifier.wosWOS:000254781300015en_US
dc.institutionauthorÜbeyli, Elif Derya-
dc.identifier.doi10.1016/j.dsp.2007.02.002-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ2-
item.openairetypeArticle-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Show simple item record



CORE Recommender

SCOPUSTM   
Citations

3
checked on Dec 21, 2024

WEB OF SCIENCETM
Citations

3
checked on Sep 21, 2024

Page view(s)

78
checked on Dec 16, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.