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

Shibiao Mu

International Journal of Information Technology and Management, 2022, vol. 21, issue 2/3, 294-310

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.

Keywords: morphological gradient; enterprise innovation behaviour; data characteristics; feature extraction model. (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=123407 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijitma:v:21:y:2022:i:2/3:p:294-310

Access Statistics for this article

More articles in International Journal of Information Technology and Management from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().