IDEAS home Printed from https://ideas.repec.org/a/ids/ijitma/v21y2022i2-3p294-310.html
   My bibliography  Save this article

Feature extraction modelling of enterprise innovation behaviour data based on morphological gradient

Author

Listed:
  • Shibiao Mu

Abstract

Aiming at the problem of slow speed and low accuracy of traditional feature extraction model for enterprise behaviour data, a feature extraction model for enterprise innovation behaviour data based on morphological gradient is constructed. The model is divided into two parts: the virtual method is used to integrate the data of enterprise innovation behaviour, and the data are synthesised and filtered; the morphological gradient operator is used to extract the features of the integrated data of enterprise innovation behaviour. The simulation results show that using the proposed model to extract the characteristics of enterprise innovation behaviour data, the extraction process only takes 15.68 min, and the average extraction accuracy can reach 96.68%. This result is much better than the three traditional models and achieves the expected goal.

Suggested Citation

  • Shibiao Mu, 2022. "Feature extraction modelling of enterprise innovation behaviour data based on morphological gradient," International Journal of Information Technology and Management, Inderscience Enterprises Ltd, vol. 21(2/3), pages 294-310.
    • Handle: RePEc:ids:ijitma:v:21:y:2022:i:2/3:p:294-310
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=123407
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:ids:ijitma:v:21:y:2022:i:2/3:p:294-310. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=18 .

    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.