EMG sinyallerinin aşırı ögrenme makinesi ile sınıflandırılması
| dc.authorid | 0000-0003-0710-0867 | en_US |
| dc.authorid | 0000-0001-7789-6376 | en_US |
| dc.authorid | 0000-0001-5167-1101 | en_US |
| dc.contributor.advisor | Batman Üniversitesi Mühendislik - Mimarlık Fakültesi Bilgisayar Mühendisliği Bölümü | en_US |
| dc.contributor.author | Ertuğrul, Ömer Faruk | |
| dc.contributor.author | Tağluk, Mehmet Emin | |
| dc.contributor.author | Kaya, Yılmaz | |
| dc.contributor.author | Tekin, Ramazan | |
| dc.date.accessioned | 2019-07-04T13:20:00Z | |
| dc.date.available | 2019-07-04T13:20:00Z | |
| dc.date.issued | 2013-06-13 | en_US |
| dc.department | Batman Üniversitesi Mühendislik - Mimarlık Fakültesi Elektrik-Elektronik Mühendisliği Bölümü | en_US |
| dc.description.abstract | From disease detection to action assessment EMG signals are used variety of field. Miscellaneous studies have been conducted toward analysis of EMG signals. In this study some statistical features of signal were derived, the best evocative features were selected via Linear Discriminant Analysis (LDA) and feature vectors were constructed. This analytic feature vectors were classified through Extreme Learning Machine (ELM). 8 channel EMG signals recorded from 10 normal and 10 aggressive actions were used as an example. By cross-comparison of the obtained results to the ones obtained via various feature identifying methods (AR coefficients, wavelet energy and entropy) and classification methods (NB, SVM, LR, ANN, PART, Jrip, J48 and LMT) the success of the proposed method was determined. | en_US |
| dc.identifier.citation | Ertuğrul, Ö F., Tağluk, M. E., Kaya, Y., Tekin, R. (2013). EMG sinyallerinin aşırı ögrenme makinesi ile sınıflandırılması. 2013 21st Signal Processing and Communications Applications Conference (SIU), 24-26 April 2013, Haspolat, Turkey. https://doi.org/10.1109/siu.2013.6531269 | en_US |
| dc.identifier.isbn | 978-1-4673-5563-6 | |
| dc.identifier.isbn | 978-1-4673-5562-9 | |
| dc.identifier.isbn | 978-1-4673-5561-2 | |
| dc.identifier.scopusquality | N/A | en_US |
| dc.identifier.uri | https://doi.org/10.1109/siu.2013.6531269 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12402/2198 | |
| dc.identifier.wosquality | N/A | en_US |
| dc.indekslendigikaynak | Web of Science | en_US |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.language.iso | tr | en_US |
| dc.publisher | IEEE | en_US |
| dc.relation.isversionof | 10.1109/siu.2013.6531269 | en_US |
| dc.relation.journal | 2013 21st Signal Processing and Communications Applications Conference (SIU) | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.rights | Attribution-NonCommercial-ShareAlike 3.0 United States | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/3.0/us/ | * |
| dc.subject | EMG | en_US |
| dc.subject | Ayırma Analizi | en_US |
| dc.subject | Aşırı Öğrenme Makineleri | en_US |
| dc.subject | İstatistiki Parametreler | en_US |
| dc.subject | Discriminant Analysis | en_US |
| dc.subject | Extreme Learning Machine | en_US |
| dc.subject | Statistical Parameters | en_US |
| dc.title | EMG sinyallerinin aşırı ögrenme makinesi ile sınıflandırılması | en_US |
| dc.title.alternative | EMG signal classification by extreme learning machine | en_US |
| dc.type | Conference Object | en_US |
