• 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