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Design and interactive performance of human resource management system based on artificial intelligence

Author

Listed:
  • Yangda Gong
  • Min Zhao
  • Qin Wang
  • Zhihan Lv

Abstract

The purpose is to strengthen Human Resources Management (HRM) through information management using Artificial Intelligence (AI) technology. First, the selection criteria of the applicant’s resume during recruitment and the formulation standards of the contract salary are analyzed. Then, the resume information is extracted and converted into the data-type format. Besides, the salary forecast model in the HRM system (HRMS) is designed based on the Back Propagation Neural Network (BPNN), and network structure, parameter initialization, and activation function of the BPNN are selected and optimized. The experimental results demonstrate that the algorithm optimized by the Nadm has shown improved convergence speed and forecast effect, with 187 iterations. Moreover, compared with other regression algorithms, the designed algorithm achieves the best test scores. The above results can provide references for designing the AI-based HRMS.

Suggested Citation

  • Yangda Gong & Min Zhao & Qin Wang & Zhihan Lv, 2022. "Design and interactive performance of human resource management system based on artificial intelligence," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-20, January.
    • Handle: RePEc:plo:pone00:0262398
    DOI: 10.1371/journal.pone.0262398
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    References listed on IDEAS

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    1. Samuel Roscoe & Nachiappan Subramanian & Charbel J.C. Jabbour & Tao Chong, 2019. "Green human resource management and the enablers of green organisational culture: Enhancing a firm's environmental performance for sustainable development," Business Strategy and the Environment, Wiley Blackwell, vol. 28(5), pages 737-749, July.
    2. Bilal Bin Saeed & Bilal Afsar & Shakir Hafeez & Imran Khan & Muhammad Tahir & Muhammad Asim Afridi, 2019. "Promoting employee's proenvironmental behavior through green human resource management practices," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 26(2), pages 424-438, March.
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