Revolutionizing Human Resources Management Using Deep Belief Learning Approach For Talent Acquisition And Retention In The Digital Era

Background: In the modern digital age, Human Resources (HR) is faced with unique challenges in attracting and retaining skilled individuals. Traditional HR methods may struggle to keep up with the evolving needs of today's workforce. Methods: This study aims to address these challenges by proposing a new approach - integrating Deep Belief Learning (DBL) into HR management. The main issue is the limitations of traditional techniques in predicting and meeting the diverse needs of employees in the digital era, leading to a loss of talented individuals. Our research fills this gap by using DBL, an advanced machine learning method, to analyze large datasets and uncover important patterns in employee behavior. To ensure comprehensive results, we combine quantitative analysis of past HR data with qualitative insights from employee surveys and interviews. Results: The DBL model is specifically designed to identify complex patterns, allowing for accurate predictions of employee preferences and potential turnover risks. The results of this study show a significant improvement in talent acquisition and retention rates, highlighting the potential of DBL in HR management.