文章基本信息

标题：Enabling Legal Risk Management Model for International Corporation with Deep Learning and Self Data Mining
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作者：Guiling Wang ; Yimin Chen
期刊名称：Computational Intelligence and Neuroscience
印刷版ISSN：1687-5265
电子版ISSN：1687-5273
出版年度：2022
卷号：2022
DOI：10.1155/2022/6385404
语种：English
出版社：Hindawi Publishing Corporation
摘要：In uncertain times, risk management is critical in keeping companies from acting rashly and wrongly, allowing them to become more flexible and resilient. International cooperative production project investment and operational risks are different from domestic projects. It has a larger likelihood of occurrence, severe damage ramifications, and greater difficulty in prevention and control. As a result, companies must develop a scientific, logical, and comprehensive risk management system and procedure when “reaching out” to perform international joint production projects. We utilize machine learning (ML) to build a legal risk assessment model for international cooperative production projects, evaluate its validity, divide it into five risk categories, and suggest countermeasures for the risk variables discovered at each risk level in this work. The output of a single classifier is then fused using an SDM (self-organizing data mining) approach at the decision level, resulting in a multiclassifier early-warning model. In the context of the sustainable development goals, this methodology also allows for a sustainability assessment through risk evaluation. The experimental results show that the MCFM-SDM model outperforms a single classifier and other MCFMs in terms of early warning accuracy and stability, confirming the model’s use and superiority.