Alemu Kumilachew Tegegne

Work place: Faculty of Computing, Bahir Dar Institute of Technology, Bahir Dar, Ethiopia



Research Interests: Computer systems and computational processes, Artificial Intelligence, Computer Architecture and Organization, Information Systems, Information Retrieval, Information Storage Systems, Data Structures and Algorithms


Mr. Alemu Kumilachew obtained his M.S.c degree in Information Science from Addis Ababa University and B.S.c degree in Information Technology from Jimma University, Ethiopia. He is currently working as a lecturer at the faculty of computing, Bahir Dar University. His area of research interests include information systems and retrieval, Artificial intelligence, question answering systems, machine learning.

Author Articles
Recommender System in Tourism Using Case based Reasoning Approach

By Tamir Anteneh Alemu Alemu Kumilachew Tegegne Adane Nega Tarekegn

DOI:, Pub. Date: 8 Sep. 2017

Using recommender systems with the help of computer systems technology to support the Tourist advising process offers many advantages over the traditional system. A knowledge based recommender reasons about the fit between a user’s need and the features of available products. Providing an effective service in Ethiopian Tourism sector is critical to attract more foreign and local tourists. However, there are major problems that need immediate solution. First, the difficulty of getting fast, reliable, and consistent expert advice in the sector that is suitable to each visitor’s characteristics and capabilities. Second, inadequacy of the number of experienced experts and consulting individuals who can give advice on tourism issues in the country. Therefore, this paper aims to design a recommender system for tourist attraction area and visiting time selection that can assist experts and tourists to make timely decisions that helps them to get fast and consistent advisory service. So that, visitors can identify tourist attraction areas that have the highest potential of success/satisfaction and that match their personal characteristics. The system provides recommendation to visitors based on previously solved cases and new query given by the tourist. For this study, about 615 cases which are collected from National Tour operation and 10 attributes which are collected from experts are used as case base. These attributes and cases are used as knowledge base to construct case based recommender. The system calculates similarity between existing cases and new queries that are provided by the visitors, and provide solution or recommendation by taking best cases to the new query. In this study, JCOLIBRI case base development tool is used to develop the prototype. JCOLIBRI contains both user interface which enables visitors to enter their query and programming codes with the help of Java script language. To decide the applicability of the prototype in the domain area, the system has been evaluated by involving domain experts and visitors through visual interaction using the criteria of easiness to use, time efficiency, applicability in the domain area and providing correct recommendation. Based on prototype user acceptance testing, the average performance of the system is 80% and 82% by domain experts and visitors respectively. The performance of the system is also measured using the standard measure of relevance (IR system) recall, precision and accuracy measures, where the system registers 83% recall, 61% precision and 85.4% accuracy.

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Data Mining Based Hybrid Intelligent System for Medical Application

By Adane Nega Tarekegn Alemu Kumilachew Tegegne

DOI:, Pub. Date: 8 Jul. 2017

Hybrid intelligent system is a combination of artificial intelligence (AI) techniques that can be applied in healthcare to solve complex medical problems. Case-based reasoning (CBR) and rule based reasoning (RBR) are the two more popular AI techniques which can be easily combined. Both techniques deal with medical data and domain knowledge in diagnosing patient conditions. This paper proposes a hybrid intelligent system that uses data mining technique as a tool for knowledge acquisition process. Data Mining solves the knowledge acquisition problem of rule based reasoning by supplying extracted knowledge to rule based reasoning system. We use WEKA for model construction and evaluation, Java NetBeans for integrating data mining results with rule based reasoning and Prolog for knowledge representation. To select the best model for disease diagnosis, four experiments were carried out using J48, BFTree, JRIP and PART. The PART classification algorithm is selected as best classification algorithm and the rules generated from the PART classifier are used for the development of knowledge base of hybrid intelligent system. In this study, the proposed system measured an accuracy of 87.5% and usability of 89.2%.

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