Deep Learning Based Framework for Human Resource Practices on Employment Outcomes at Universities

Abstract

Human resource management (HRM) strategies have a significant impact on hiring outcomes in the higher education sector. Conventional approaches to human resource management fall short, however, when confronted with the complex dynamics of university employment. The advent of deep learning techniques in the last few years has opened up exciting new avenues for the analysis of diverse and massive datasets, opening the door to better human resource management approaches. This study proposes a novel deep learning-based architecture to improve HRM procedures and enhance employment outcomes in educational institutions. The suggested system uses a trio of deep learning architectures—deep belief networks (DBNs), recurrent neural networks (RNNs), and convolutional neural networks (CNNs)—to examine multidimensional data associated with university employment. Included in this data collection are applicant qualifications, performance evaluation standards for teachers, institutional resources, and job market trends. The actionable insights obtained from these many data sources may help universities enhance their talent development, recruitment, and retention initiatives.