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Multimedia Technology and Enhanced Learning. 5th EAI International Conference, ICMTEL 2023, Leicester, UK, April 28-29, 2023, Proceedings, Part III

Research Article

Research on Performance Evaluation of Industrial Economic Management Based on Improved Machine Learning

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-50577-5_27,
        author={Jie Gao and Aiqing Wang},
        title={Research on Performance Evaluation of Industrial Economic Management Based on Improved Machine Learning},
        proceedings={Multimedia Technology and Enhanced Learning. 5th EAI International Conference, ICMTEL 2023, Leicester, UK, April 28-29, 2023, Proceedings, Part III},
        proceedings_a={ICMTEL PART 3},
        year={2024},
        month={2},
        keywords={Improving machine learning algorithm Industrial economic management Storage architecture Performance evaluation},
        doi={10.1007/978-3-031-50577-5_27}
    }
    
  • Jie Gao
    Aiqing Wang
    Year: 2024
    Research on Performance Evaluation of Industrial Economic Management Based on Improved Machine Learning
    ICMTEL PART 3
    Springer
    DOI: 10.1007/978-3-031-50577-5_27
Jie Gao1, Aiqing Wang1,*
  • 1: Department of Building Management, Chongqing College of Architecture and Technology
*Contact email: wangaiqing111@126.com

Abstract

Aiming at the problem of poor self-protection ability caused by too many iterations in the performance evaluation system of industrial economic management, a study on performance evaluation of industrial economic management based on improved machine learning is proposed. In terms of hardware design, design and deploy the system storage architecture and network environment for system operation; In software design, the improved machine learning algorithm is used to screen out appropriate evaluation indicators from the set of factors that affect the performance evaluation of industrial economic management. After the evaluation indicators are determined, the rating threshold is divided, and the total score of performance evaluation is calculated according to the score and weight of each indicator, which is compared with the rating table one by one to achieve the performance evaluation of industrial economic management. So far, the overall design of the system has been completed. The test results show that the industrial economic management performance evaluation system designed based on improved machine learning has low sensitivity, high fluency and high self-protection ability.

Keywords
Improving machine learning algorithm Industrial economic management Storage architecture Performance evaluation
Published
2024-02-21
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-50577-5_27
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