Abstract: Internet enterprises, as the representative enterprises of technology-based enterprises, contribute more and more to the growth of the world economy. To ensure the sustainable development of enterprises, it is necessary to predict the risks in the operation of Internet enterprises. An accurate risk prediction model can not only safeguard the interests of enterprises but also provide certain references for investors. Therefore, this study designed a Convolutional Neural Network (CNN) model based on the Kepler optimization algorithm (KOA) for risk prediction of Internet enterprises, aiming to maximize the accuracy of the prediction model, and to help Internet enterprises carry out risk management. Firstly, we select the indicators related to the financial risk of Internet enterprises, and predict the risk based on the traditional statistical analysis of Logistic regression model. On this basis, KOA was improved based on evolutionary strategies and fish foraging strategies, and the improved algorithm was applied to optimize CNN. Based on improved KOA and CNN algorithms, an IKOA-CNN risk prediction model is proposed. Finally, by comparing traditional statistical analysis-based models and other learning-based models, the results show that the IKOA-CNN algorithm proposed in this study has the highest prediction accuracy.

Bin Liu, Fengjiao Zhou, Haitong Jiang and Rui Ma. “A Kepler Optimization Algorithm-Based Convolutional Neural Network Model for Risk Management of Internet Enterprises”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.7 (2024). http://dx.doi.org/10.14569/IJACSA.2024.0150720

@article{Liu2024,
title = {A Kepler Optimization Algorithm-Based Convolutional Neural Network Model for Risk Management of Internet Enterprises},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150720},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150720},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {7},
author = {Bin Liu and Fengjiao Zhou and Haitong Jiang and Rui Ma}
}