Published Paper Details:

Machine learning is an artificial intelligence (AI) application that provides systems with the ability to automatically learn and improve based on experiences without explicit programming. Machine learning is aimed at developing computer programs that can access data and use it for self-study. The main goal is to give computers the opportunity to learn automatically without human intervention and help, and adjust actions accordingly An Intellect of concept drift poses an additional challenge to existing learning algorithms. Detecting concept changes, such as changing customer preferences for telecommunications services, is very important in terms of forecasting and decision-making applications in a dynamic environment. Specifically, for case-based reasoning systems. This paper presents a novel method for detecting concept drift in a case-based reasoning system. Rather than measuring the actual case distribution, we introduce a new competency model that detects differences through changes incompetence. Eight sets of experiments in three categories show that our method is effective in detecting drift concepts and accurately identifying drift efficiency. These results directly contribute to the research that tackles concept drift in case-based reasoning and competence model studies.