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Enterprise Human Resource Management Model by Artificial Intelligence Digital Technology.

Yu Jiaping1

  • 1School of Public Policy and Administration, Nanchang University, Nanchang 330031, Jiangxi, China.

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This summary is machine-generated.

Artificial intelligence optimizes human resource management by predicting salaries using a backpropagation neural network. This AI-driven salary prediction model improves efficiency and reduces HR workload.

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Area of Science:

  • Management Science
  • Artificial Intelligence
  • Data Mining

Background:

  • Artificial intelligence (AI) is transforming management practices and organizational structures.
  • AI impacts key corporate processes like knowledge management and consumer satisfaction.
  • Optimizing human resource management (HRM) is crucial for efficiency and workload reduction.

Purpose of the Study:

  • To optimize the human resource management (HRM) process using AI.
  • To reduce the workload of human resource managers and enhance work efficiency.
  • To develop and validate an AI-based salary prediction model (SPM).

Main Methods:

  • Designed a salary prediction model (SPM) utilizing backpropagation neural network (BPNN) and AI digitization technology.
  • Integrated Nesterov and Adaptive Moment Estimation (Nadam) algorithms for model optimization.
  • Used resume content to predict candidate hiring salaries and validate the SPM.

Main Results:

  • The Nadam optimization algorithm demonstrated the best optimization effect and fastest convergence speed.
  • The Nadam algorithm achieved a final predicted result score of 0.75% with a training period of 186 seconds.
  • The BPNN-based SPM optimized by Nadam achieved an accuracy rate of 79.4% in the learning process.

Conclusions:

  • The AI-driven SPM validated in this study effectively predicts hiring salaries.
  • The Nadam-optimized BPNN offers a robust solution for salary prediction in HRM.
  • Findings provide a valuable reference for implementing data mining in HRM systems.