sis 24(3):

Research Article

A Performance Prediction Method for Talent Team Building Based on Integrated ISA-BP Neural Networks

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  • @ARTICLE{10.4108/eetsis.4890,
        author={Shusheng Shen and Yansheng Deng},
        title={A Performance Prediction Method for Talent Team Building Based on Integrated ISA-BP Neural Networks},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={11},
        number={3},
        publisher={EAI},
        journal_a={SIS},
        year={2024},
        month={1},
        keywords={human workforce performance assessment prediction, neural network, internal search algorithm, integrated learning},
        doi={10.4108/eetsis.4890}
    }
    
  • Shusheng Shen
    Yansheng Deng
    Year: 2024
    A Performance Prediction Method for Talent Team Building Based on Integrated ISA-BP Neural Networks
    SIS
    EAI
    DOI: 10.4108/eetsis.4890
Shusheng Shen1,*, Yansheng Deng1,*
  • 1: School of Civil Engineering and Architecture Zhejiang University of Science & Technology, Hangzhou 310023, Zhejiang, China
*Contact email: 105043@zust.edu.cn, 105043@zust.edu.cn

Abstract

INTRODCTION: Objective, accurate and fair development of research and effective performance prediction methodology for the construction of the talent team is the current needs of the new era of innovation and reform and development of university management, as well as the need to improve the quality of scientific research and teaching level of the talent team.OBJCTIVES: To address the problems of irrational principle of indicator selection, incomplete system and imprecise methodology in the current research on performance prediction of talent team building.METHODS:This paper proposes a talent team construction performance prediction method based on intelligent optimization algorithm improving neural network with integrated learning as the framework. First of all, through the analysis of the current talent team construction performance prediction influencing factors selection principles, analyze the talent team construction performance management process, select the talent team construction performance prediction influencing factors, and construct the talent team construction performance analysis system; then, with the integrated learning as a framework, improve the neural network through the internal search optimization algorithm to construct the talent team construction performance prediction model; finally, through the simulation experiments to analyze and verify the effectiveness and superiority of the proposed method. The effective type and superiority of the proposed method are verified.RESULTS: The results show that the proposed method satisfies the real-time requirements while improving the prediction accuracy.CONCLUSION: This paper addresses the lack of precision in forecasting the performance of the talent pipeline and the lack of a sound analytical system.