New human identification method using Tietze graph-based feature generation

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dc.authorid0000-0003-1765-7474en_US
dc.contributor.authorTuncer, Türker
dc.contributor.authorAydemir, Emrah
dc.contributor.authorDoğan, Şengül
dc.contributor.authorKobat, Mehmet Ali
dc.contributor.authorKaya, Muhammed Çağrı
dc.contributor.authorMetin, Serkan
dc.date.accessioned2021-08-30T19:58:06Z
dc.date.available2021-08-30T19:58:06Z
dc.date.issued2021en_US
dc.departmentMTÖ Üniversitesi, Sosyal ve Beşeri Bilimler Fakültesi, Yönetim Bilişim Sistemleri Bölümüen_US
dc.description.abstractElectrocardiogram (ECG) signals have been widely used for disease diagnosis. Besides, the ECG signals can be used for human identification. In this work, a Tietze pattern and neighborhood component analysis (NCA)-based human identification method is proposed. Our model uses two feature generation methods to extract both statistical and textural features. The Tietze graph is considered to create a pattern of the presented local graph structure (LGS). Both statistical and textural feature generations are not enough to present a high-accurate model. Therefore, a multileveled structure must be created. Tunable Q-factor wavelet transform (TQWT) is employed as a decomposer. The generated/extracted features in each level are merged, and the merged features are selected using NCA. The k-nearest neighbors (kNN) classifier is deployed on the chosen features in the classification phase to obtain predicted values. The recommended method was tested on two ECG signal corpora called ECGID and MIT-BIH. The model achieved 99.12% and 99.94% accuracies on the used ECGID and MIT-BIH datasets, respectively.en_US
dc.identifier.citationTuncer, T., Aydemir, E., Dogan, S., Kobat, M. A., Kaya, M. C., & Metin, S. (2021). New human identification method using Tietze graph-based feature generation. Soft Computing, 1-13.en_US
dc.identifier.doi10.1007/s00500-021-06094-5
dc.identifier.endpage13en_US
dc.identifier.issn1432-7643en_US
dc.identifier.issn1433-7479en_US
dc.identifier.scopus2-s2.0-85112595858en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.1007/s00500-021-06094-5
dc.identifier.urihttps://link.springer.com/article/10.1007/s00500-021-06094-5
dc.identifier.urihttps://hdl.handle.net/20.500.12899/374
dc.identifier.wosWOS:000682428800004en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorMetin, Serkan
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofSoft Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectECG signal classificationen_US
dc.subjectTietze graphen_US
dc.subjectTunable Q-factor wavelet transformen_US
dc.subjectMachine learningen_US
dc.titleNew human identification method using Tietze graph-based feature generationen_US
dc.typeArticleen_US

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