ARTIFICIAL INTELLIGENCE-ENABLED KNOWLEDGE MANAGEMENT USING A MULTIDIMENSIONAL ANALYTICAL FRAMEWORK OF VISUALIZATIONS

To better manage human resources (HR), companies are increasingly incorporating artificial intelligence (AI) and other AI-based tools into their HR management (HRM) strategies, at a universal scale. Companies on a global scale, highlight the employment prospects and use of resources, business judgment, and make predictions using machine learning approaches. This work aims at the situation that the human resource department faces high employee turnover in the company especially some experienced employees leave. The termination of an employee is predicted by using an enhanced ID3 decision tree with ABC rule miner. The best-classifying attributes are chosen by ID3 and association rules are mined to generate an enhanced decision tree to perform classification. It is then passed to the regressor model to make prediction. Gradient descent optimizer is used for optimizing the proposed machine learning model. Predictive analysis is done in HR dataset v-14 by visualizing and analyzing and exploiting the behavioral relationship among the attributes. The variables of employee termination are predicted by a data-driven predictive analysis from the performance measure metrics. © 2023