Mohammed Abbas Kadhim

Work place: Department of Computer Science, College of Computer Science and IT, University of Al-Qadisiyah, Iraq



Research Interests: Computer systems and computational processes, Neural Networks, Network Architecture, Data Mining, Data Structures and Algorithms


Mohammed Abbas Kadhim is Assistant Professor in College of Computer Science and IT, University of Al-Qadisiyah, Iraq. He gained his Ph.D. in Computer Science Dept., Hamdard University, New Delhi, India on the topic of a multi-intelligent agent for automatic construction of expert system. He received his B.Sc. and M.Sc. degree in Computer Science from Babylon University, Babylon, Iraq in 1996 and 1999 respectively. He has published many research papers in Artificial Intelligence field. His research interest includes expert systems, intelligent agents, text mining, data mining, neural network, and genetic algorithm.

Author Articles
A Multi-intelligent Agent System for Automatic Construction of Rule-based Expert System

By Mohammed Abbas Kadhim M. Afshar Alam Harleen Kaur

DOI:, Pub. Date: 8 Sep. 2016

The main general purpose of this research is the automatic construction of rule-based expert system in diagnosis domain based on an expert system tool and a multi-intelligent agent system. The first goal is used an expert system tool (shell) which is called Diagnosis Domain Tool for Rule-based Expert System (DDTRES) [1]. The second goal is used a multi-intelligent agent architecture for knowledge extraction to elicit knowledge from its resources (domain experts, text documents, databases) for automatic construction of a knowledge base. That means, instead of using traditional methods for knowledge base construction, we used automatic way for that job. In order to achieve second objective, the following agents have been used: The Expert Mining Intelligent Agent (EMIA), The Text Mining Intelligent Agent (TMIA) [2], and The Multi-Intelligent Agent for Knowledge Discovery in Database (MIAKDD) [3]. We are aim to produce an effective final knowledge base by cooperation between EMIA, TMIA, and MIAKDD approaches and integrated with the diagnosis domain tool (DDTRES) to produce a complete rule-based expert system in diagnosis domain. We applied the captured rule-based expert system on heart diseases diagnosis, we found system performance is between a good and a very good range.

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