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DOI: 10.14569/IJACSA.2023.0140778
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Criminal Law Risk Management and Prediction Method based on Echo State Network

Author 1: Zhe Li

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 7, 2023.

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Abstract: Criminal law plays an important role in maintaining social security and achieving effective social control. However, criminal law has hidden risks that cannot be ignored at the legislative, judicial and theoretical levels. This paper starts from all aspects of criminal law, analyzes criminal law risk and its management measures, and predicts and analyzes criminal law risk through echo state network model. The prediction results of the echo state network model fit well with the actual situation, and its verification can provide reference for the study of criminal law risk prediction and management systems. Legislative risk and theoretical risk belong to social factors and are also fundamental risks of criminal law. Judicial risk is mainly manifested in the level of judicial power. Criminal law is closely related to the political environment, social system, economic system, etc. In criminal law legislation, we should pay attention to the balance between criminal law rules and realistic social functions, and properly control social risks, so as to avoid the criminal law risks brought by the establishment of risky criminal law, and provide the necessary guarantee for the national security system.

Keywords: Echo state network; model; criminal law; risk prediction; risk prediction

Zhe Li, “Criminal Law Risk Management and Prediction Method based on Echo State Network” International Journal of Advanced Computer Science and Applications(IJACSA), 14(7), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140778

@article{Li2023,
title = {Criminal Law Risk Management and Prediction Method based on Echo State Network},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140778},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140778},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {7},
author = {Zhe Li}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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