The Evaluation of Electronic Human Resources (eHR) Management based Internet of Things using Machine Learning Techniques

Abstract

The eHR solutions are frequently employed in large organizations and sectors. For the company, such eHR is extremely competent, congruent, affordable, and committed. These days, eHR is greatly impacted by the Internet of Things (IoT), which provides eHR functions like standards, privacy, and security with a variety of facilities and supports. There are several uses for eHR and IoT together to execute plans, rules, and practices inside the company. The five essential components of an eHR are e-Selection, e-Recruitment, e-Performance, e-Compensation, and e-Learning. The suggested system in this study consists of two components. The first section covered a discussion and detailed explanation of the different eHR tasks using examples. The second section describes and provides examples of analytics of data using IoT for every eHR task. The four parts of the data analytics section are as follows: (a) preparing the data; (b) choosing features; (c) classifying the data; and (d) assessing performance. Four HR analytic datasets gathered at the Kaggle site were used to conduct extensive experimentation for each eHR activity. Ultimately, each dataset was used to perform performance with appropriate reasons for each eHR activity. CART outperformed kNN and SVM classifiers in terms of performance.