A Research on Enterprise Technical Risk Threshold Activation Model Construction in ICV Industry

DOI

10.2991/978-94-6463-498-3_6How to use a DOI?

Keywords

Enterprise Technology Risk; Threshold Activation; Classification Prediction; Data Mining; Intelligent and Connected Vehicle (ICV)

Abstract

Addressing the critical need for enhanced industrial risk monitoring, this research advances the analytical capabilities of management entities and policy advisors in scrutinizing enterprise technological risks in specific sectors. It introduces a machine learning-assisted approach to systematically comprehend the triggers and mitigators of technological risks. The research develops a Machine Learning-based Enterprise Technology Risk Threshold Activation (ETRTA) Model. The model, grounded in a multi-dimensional classification of enterprise risks, is adept at delving into the nuances of these risks in industry-specific contexts. Employing a suite of eight machine learning techniques, including Random Forest, XGBoost, etc. the model trains on various parameters to discern the characteristics of enterprise technological risks. Additionally, automated processes are employed to uncover consistent patterns in the activation of these risks. The efficacy of the model is highlighted by the classification prediction accuracy of three gradient boosting ensemble models, which stands at 82.59%. The accuracy facilitates the identification of enterprises at potential technological risk using extensive datasets. The future scope includes enhancing the prediction precision and robustness of the models and broadening their applicability in assessing enterprise technological risks in diverse industries.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Zhang Yue
AU  - Cao Yue
AU  - Bai Chen
PY  - 2024
DA  - 2024/08/22
TI  - A Research on Enterprise Technical Risk Threshold Activation Model Construction in ICV Industry
BT  - Proceedings of 2023 China Science and Technology Information Resource Management and Service Annual Conference (COINFO 2023)
PB  - Atlantis Press
SP  - 49
EP  - 63
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-498-3_6
DO  - 10.2991/978-94-6463-498-3_6
ID  - Yue2024
ER  -