The Performance Impact of Human Resources Recruitment System for Business Process Management Using K-Means and SVM

In this proposed approach to take a critical look at the literature on human resource management and diversity management. The proposed study describes the primary concerns and desired outcomes of diversity management and assess the current level of diversity management practices in enterprises with respect to human resources. The findings show that bias and inequality persist in many workplaces despite global efforts from HR departments. There has been a dismissal of the value of diversity in all its forms. The findings highlight a knowledge gap in the area of human resource management's potential for improving organizations' handling of diversity. Data preprocessing, feature extraction, and model training were all utilized in this proposed approach. Normalization of fault detection and fault identification are achieved by data preprocessing. The data will be extracted after a preliminary processing step. The proposed system employed PCA and K-means for feature extraction. The training process will be assessed using support vector machine, k-means, and k-SVM. The accuracy of the proposed approach improves to around 98.42%.