DocumentCode :
714663
Title :
Gender prediction based on the expiratory flow volume curve
Author :
Cosgun, Sema ; Ozbek, I. Yucel
Author_Institution :
Elektrik ve Elektron. Muhendisligi Bolumu, Ataturk Univ., Erzurum, Turkey
fYear :
2015
fDate :
16-19 May 2015
Firstpage :
2119
Lastpage :
2121
Abstract :
This study is performed estimated using the gender of the person is the expiration of the current-volume curve obtained from the test. Gender studies estimate is carried out using two different machine learning method. These methods Gaussian Mixture Model (GMM) and Support Vector Machines are (SVM). Gender prediction in both methods are performed using classification. The proposed methods have three main stages. These stages are feature extraction, training and gender of test person is detected. Performance evaluation is made according to the experimental results obtained. As a result of these studies, the gender prediction accuracy of 99.43 per cent are carried out.
Keywords :
Gaussian processes; feature extraction; gender issues; image classification; learning (artificial intelligence); mixture models; support vector machines; GMM; Gaussian mixture model; SVM; current-volume curve expiration; expiratory flow volume curve; feature extraction; gender prediction; image classification; machine learning method; support vector machines; Support vector machines; classification; gaussian mixture models; gender estimation; support vector machines; the expiratory flow volume curve;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing and Communications Applications Conference (SIU), 2015 23th
Conference_Location :
Malatya
Type :
conf
DOI :
10.1109/SIU.2015.7130290
Filename :
7130290
Link To Document :
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