ABSTRACT
The advancement of human resource management (HRM) research and practices is hindered by absence of big data-based methodologies. Although academics have acknowledged the value of using a big data strategy in HRM practices, there is still a dearth of clear instructions on how to combine the two.
The goal of this chapter is to examine the practicability of big data extensive mining and analysis innovation in the general HRM work of extended business in order to significantly improve the cumulative base business strategy of the organizational structure and raise the overall level of enterprise HRM. We propose a novel data mining algorithm for the management of human capital big data that assists in all enterprise-related data management activities.
The big data is collected and preprocessed using z-score normalization. The features are extracted using linear discriminant analysis (LDA). Feature selection is done using hybrid sine cosine genetic algorithm (HSCGA). Classification is done using the proposed binary crow search convolutional neural network (BCS-CNN).
Finally, the experiment's findings show that big data mining and analysis technologies may be used to address the problems tiny and midsized firms have with improving the standard of their human resources. Regarding the prevention of current skill loss in business entities, the quality assessment monitoring system of companies, and the assessment of the kinds of human resources in those businesses, the implementation impact of the proposed data mining analysis model has significantly improved.
