Implementing Gated Recurrent Units and Generative Adversarial Networks to Enhance Effectiveness in Human Resource Management and Organizational Efficiency

This study investigates the implementation of Gated Recurrent Units (GRU) and Generative Adversarial Networks (GAN) to enhance effectiveness in Human Resource Management (HRM) and organizational efficiency. Across 10 experimental trials, the GRU model achieved an average accuracy of 85.7%, with precision, recall, and F1 score values averaging 0.86, 0.82, and 0.84, respectively. Meanwhile, the GAN model demonstrated an average accuracy of 93.2%, with precision, recall, and F1 score values averaging 0.93, 0.90, and 0.91, respectively. These results highlight the potential of neural network technologies to optimize HRM processes, including recruitment, performance evaluation, and workforce planning. By providing more accurate predictions and insights, GRU and GAN offer valuable decision support tools for organizations aiming to improve HRM practices and enhance organizational performance. This study contributes to the growing body of literature on the application of artificial intelligence in HRM. It underscores the importance of leveraging advanced technologies to drive innovation and efficiency in modern workplaces.