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

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Artificial Intelligence (AI) enhances Human Resources Management (HRM) by optimizing recruitment and salary forecasting. An AI-driven system using Back Propagation Neural Networks (BPNN) improved accuracy and efficiency in HR processes.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Human Resources Management

Background:

  • Traditional Human Resources Management (HRM) faces challenges in efficient data processing and predictive analytics.
  • Integrating Artificial Intelligence (AI) offers potential solutions for optimizing HRM functions.
  • Automating recruitment and salary forecasting can improve accuracy and reduce manual effort.

Purpose of the Study:

  • To enhance Human Resources Management (HRM) through improved information management utilizing Artificial Intelligence (AI).
  • To develop and validate an AI-based salary forecasting model within an HRM system (HRMS).
  • To analyze applicant resume selection criteria and contract salary formulation standards.

Main Methods:

  • Resume information extraction and conversion into a data-type format.
  • Design of a salary forecasting model using Back Propagation Neural Network (BPNN).
  • Optimization of BPNN network structure, parameter initialization, and activation function, utilizing the Nadm optimizer.

Main Results:

  • The Nadm-optimized algorithm demonstrated superior convergence speed and forecasting accuracy.
  • The model achieved optimal performance with 187 iterations.
  • The designed AI algorithm outperformed other regression algorithms in test scores.

Conclusions:

  • AI-driven HRMS can significantly improve recruitment and salary forecasting processes.
  • The developed BPNN model provides a robust framework for AI-based HRM.
  • The findings offer valuable insights for the practical implementation of AI in HR departments.