IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/3145947.html
   My bibliography  Save this article

A New Generalized Deep Learning Framework Combining Sparse Autoencoder and Taguchi Method for Novel Data Classification and Processing

Author

Listed:
  • Ahmad M. Karim
  • Mehmet S. Güzel
  • Mehmet R. Tolun
  • Hilal Kaya
  • Fatih V. Çelebi

Abstract

Deep autoencoder neural networks have been widely used in several image classification and recognition problems, including hand-writing recognition, medical imaging, and face recognition. The overall performance of deep autoencoder neural networks mainly depends on the number of parameters used, structure of neural networks, and the compatibility of the transfer functions. However, an inappropriate structure design can cause a reduction in the performance of deep autoencoder neural networks. A novel framework, which primarily integrates the Taguchi Method to a deep autoencoder based system without considering to modify the overall structure of the network, is presented. Several experiments are performed using various data sets from different fields, i.e., network security and medicine. The results show that the proposed method is more robust than some of the well-known methods in the literature as most of the time our method performed better. Therefore, the results are quite encouraging and verified the overall performance of the proposed framework.

Suggested Citation

  • Ahmad M. Karim & Mehmet S. Güzel & Mehmet R. Tolun & Hilal Kaya & Fatih V. Çelebi, 2018. "A New Generalized Deep Learning Framework Combining Sparse Autoencoder and Taguchi Method for Novel Data Classification and Processing," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-13, June.
  • Handle: RePEc:hin:jnlmpe:3145947
    DOI: 10.1155/2018/3145947
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2018/3145947.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/MPE/2018/3145947.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2018/3145947?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Afshin Shoeibi & Marjane Khodatars & Navid Ghassemi & Mahboobeh Jafari & Parisa Moridian & Roohallah Alizadehsani & Maryam Panahiazar & Fahime Khozeimeh & Assef Zare & Hossein Hosseini-Nejad & Abbas K, 2021. "Epileptic Seizures Detection Using Deep Learning Techniques: A Review," IJERPH, MDPI, vol. 18(11), pages 1-33, May.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:jnlmpe:3145947. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.