Esme, EnginKarlik, Bekir2024-03-132024-03-1320161568-49461872-9681https://doi.org/10.1016/j.asoc.2016.05.030https://hdl.handle.net/20.500.12662/3611Identification of more than three perfumes is very difficult for the human nose. It is also a problem to recognize patterns of perfume odor with an electronic nose that has multiple sensors. For this reason, a new hybrid classifier has been presented to identify type of perfume from a closely similar data set of 20 different odors of perfumes. The structure of this hybrid technique is the combination of unsupervised fuzzy clustering c-mean (FCM) and supervised support vector machine (SVM). On the other hand this proposed soft computing technique was compared with the other well-known learning algorithms. The results show that the proposed hybrid algorithm's accuracy is 97.5% better than the others. (C) 2016 Elsevier B.V. All rights reserved.eninfo:eu-repo/semantics/closedAccessClassifierSoft computingPerfume recognitionFuzzy c-meansSupport vector machinesArtificial neural networkArticle10.1016/j.asoc.2016.05.0302-s2.0-84971264764458Q145246WOS:000377999900033Q1