Large group activity security risk assessment and risk early warning based on random forest algorithm

•With the continuous development of artificial intelligence, machine learning, as an indispensable means to realize artificial intelligence, is constantly improving, and deep learning is one of the contents. This article aims to evaluate and warn the security risks of large-scale group activities ba...

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Published in:	Pattern recognition letters Vol. 144; pp. 1 - 5
Main Authors:	Chen, Yanyu, Zheng, Wenzhe, Li, Wenbo, Huang, Yimiao
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier B.V 01.04.2021 Elsevier Science Ltd
Subjects:	Algorithms Artificial intelligence Deep learning Environmental protection Forest management Large-scale group activities Learning algorithms Machine learning Optimization Random forest algorithm Random variables Risk analysis Risk assessment Risk warning Security Security risk assessment Security risk assessment Random forest algorithm Risk warning Large-scale group activities
ISSN:	0167-8655, 1872-7344
Online Access:	Get full text
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Abstract	•With the continuous development of artificial intelligence, machine learning, as an indispensable means to realize artificial intelligence, is constantly improving, and deep learning is one of the contents. This article aims to evaluate and warn the security risks of large-scale group activities based on the random forest algorithm.•In this paper, the computational random forest algorithm is used to calculate the importance of variables and the security risk index weight, and combined with the model parameters of the random forest algorithm, optimization experiments and random forest model training experiments are carried out respectively. At the same time, an international youth environmental protection festival is taken as an example to analyze, which has verified the feasibility and effectiveness of this article.•This article mainly evaluates the risks in large-scale group activities, but it can be further improved in future applications. On the basis of it, if the activities want to achieve better results, they must also satisfy the people who participate in the activities. Thereby, it can better help resolve some unnecessary risks and ensure the safety of people in their activities. With the continuous development of artificial intelligence, machine learning, the necessary way to achieve artificial intelligence, is also constantly improving, of which deep learning is one of the contents. The purpose of this paper is to evaluate and warn the security risk of large-scale group activities based on the random forest algorithm. This paper uses the methods of calculating the importance of the random forest algorithm to variables and the calculation formula of the weight of the security risk index, and combining the model parameters of the random forest algorithm The optimization experiment and the random forest model training experiment are used for risk analysis, and the classification accuracy rate reaches a maximum of 0.86, which leads to the conclusion that the random forest algorithm has good predictive ability in the risk assessment of large-scale group activities. This article takes a certain international youth environmental protection festival as an example for analysis, and better verifies the feasibility and effectiveness of this article.
AbstractList	With the continuous development of artificial intelligence, machine learning, the necessary way to achieve artificial intelligence, is also constantly improving, of which deep learning is one of the contents. The purpose of this paper is to evaluate and warn the security risk of large-scale group activities based on the random forest algorithm. This paper uses the methods of calculating the importance of the random forest algorithm to variables and the calculation formula of the weight of the security risk index, and combining the model parameters of the random forest algorithm The optimization experiment and the random forest model training experiment are used for risk analysis, and the classification accuracy rate reaches a maximum of 0.86, which leads to the conclusion that the random forest algorithm has good predictive ability in the risk assessment of large-scale group activities. This article takes a certain international youth environmental protection festival as an example for analysis, and better verifies the feasibility and effectiveness of this article. •With the continuous development of artificial intelligence, machine learning, as an indispensable means to realize artificial intelligence, is constantly improving, and deep learning is one of the contents. This article aims to evaluate and warn the security risks of large-scale group activities based on the random forest algorithm.•In this paper, the computational random forest algorithm is used to calculate the importance of variables and the security risk index weight, and combined with the model parameters of the random forest algorithm, optimization experiments and random forest model training experiments are carried out respectively. At the same time, an international youth environmental protection festival is taken as an example to analyze, which has verified the feasibility and effectiveness of this article.•This article mainly evaluates the risks in large-scale group activities, but it can be further improved in future applications. On the basis of it, if the activities want to achieve better results, they must also satisfy the people who participate in the activities. Thereby, it can better help resolve some unnecessary risks and ensure the safety of people in their activities. With the continuous development of artificial intelligence, machine learning, the necessary way to achieve artificial intelligence, is also constantly improving, of which deep learning is one of the contents. The purpose of this paper is to evaluate and warn the security risk of large-scale group activities based on the random forest algorithm. This paper uses the methods of calculating the importance of the random forest algorithm to variables and the calculation formula of the weight of the security risk index, and combining the model parameters of the random forest algorithm The optimization experiment and the random forest model training experiment are used for risk analysis, and the classification accuracy rate reaches a maximum of 0.86, which leads to the conclusion that the random forest algorithm has good predictive ability in the risk assessment of large-scale group activities. This article takes a certain international youth environmental protection festival as an example for analysis, and better verifies the feasibility and effectiveness of this article.
Author	Zheng, Wenzhe Chen, Yanyu Huang, Yimiao Li, Wenbo
Author_xml	– sequence: 1 givenname: Yanyu surname: Chen fullname: Chen, Yanyu email: cyy100628@126.com organization: College of Economics and Management, Zhejiang Normal University, Jinhua 321004, Zhejiang, China – sequence: 2 givenname: Wenzhe surname: Zheng fullname: Zheng, Wenzhe email: zwzzjnu@sina.com organization: College of Economics and Management, Zhejiang Normal University, Jinhua 321004, Zhejiang, China – sequence: 3 givenname: Wenbo surname: Li fullname: Li, Wenbo email: 420612906@qq.com organization: College of Economics and Management, Zhejiang Normal University, Jinhua 321004, Zhejiang, China – sequence: 4 givenname: Yimiao surname: Huang fullname: Huang, Yimiao email: 236285736@qq.com organization: School of Public Administration, Changchun University of Technology, Changchun 130012, Jilin, China
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SubjectTerms	Algorithms Artificial intelligence Deep learning Environmental protection Forest management Large-scale group activities Learning algorithms Machine learning Optimization Random forest algorithm Random variables Risk analysis Risk assessment Risk warning Security Security risk assessment
Title	Large group activity security risk assessment and risk early warning based on random forest algorithm
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