You can view the full text of this article for free using the link below.

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

Authors: Shibiao Mu

Addresses: School of Electro-mechanical and Information Technology, Yiwu Industrial and Commercial College, Yi'wu 322000, China

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.

DOI: 10.1504/IJITM.2022.123407

International Journal of Information Technology and Management, 2022 Vol.21 No.2/3, pp.294 - 310

Received: 24 Nov 2019
Accepted: 12 May 2020

Published online: 20 Jun 2022 *

Full-text access for editors Full-text access for subscribers Free access Comment on this article