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.

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%.

The advancement of the present day technology enables the production of huge amount of information. Retrieving useful information out of these huge collections necessitates proper organization and structuring. Automatic text classification is an inevitable solution in this regard. However, the present approach focuses on the flat classification, where each topic is treated as a separate class, which is inadequate in text classification where there are a large number of classes and a huge number of relevant features needed to distinguish between them. This paper aimed to explore the use of hierarchical structure for classifying a large, heterogeneous collection of Amharic News Text. The approach utilizes the hierarchical topic structure to decompose the classification task into a set of simpler problems, one at each node in the classification tree. An experiment had been conducted using a categorical data collected from Ethiopian News Agency (ENA) using SVM to see the performances of the hierarchical classifiers on Amharic News Text. The findings of the experiment show the accuracy of flat classification decreases as the number of classes and documents (features) increases. Moreover, the accuracy of the flat classifier decreases at an increasing number of top feature set. The peak accuracy of the flat classifier was 68.84 % when the top 3 features were used. The findings of the experiment done using hierarchical classification show an increasing performance of the classifiers as we move down the hierarchy. The maximum accuracy achieved was 90.37% at level-3(last level) of the category tree. Moreover, the accuracy of the hierarchical classifiers increases at an increasing number of top feature set compared to the flat classifier. The peak accuracy was 89.06% using level three classifier when the top 15 features were used. Furthermore, the performance between flat classifier and hierarchical classifiers are compared using the same test data. Thus, it shows that use of the hierarchical structure during classification has resulted in a significant improvement of 29.42 % in exact match precision when compared with a flat classifier.

Knowledge based system can be designed to solve complex medical problems. It incorporates the expert’s knowledge that has been coded into facts, rules, heuristics and procedures. Incorporation of local languages with the knowledge based system allows end-users communicate with the system in a simpler and easier way. In this study a localized knowledge based system is developed for TB disease diagnosis using Ethiopian national language. To develop the localized knowledge based system, tacit knowledge is acquired from domain experts using interviewing techniques and explicit knowledge is captured from documented sources using relevant documents analysis method. Then the acquired knowledge is modeled using decision tree structure that represents concepts and procedures involved in diagnosis of disease. Production rules are used to represent domain knowledge.  The localized knowledge based system is developed using SWI Prolog version 6.4.1 programming language. Prolog supports natural language processing feature to localize the system. As a result, the system is implemented using Amharic language (the national language of Ethiopia) user interface.  With Localization, users at remote areas and users who are not good in foreign languages are benefited enormously. The system is tested and evaluated to ensure that whether the performance of the system is accurate and the system is usable by physicians and patients. The average performance of the localized knowledge based system has registered 81.5%.