Browsing by Author "Kebede, Gashaw(PhD)"
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Item Application of Case Based Recommender System to Advise Students in Field of Study selection at Higher Education in Ethiopia(Addis Ababa University, 2013-01) Getnet, Biazen; Kebede, Gashaw(PhD)Using recommender systems with the help of computer systems technology to support the academic advising process offers many advantages over the traditional student advising system. One of the main problems faced by students is to take the right decision in relation to field of study selection process based on available information. The objective of this research is to develop a prototype case base recommender system that assists the students in their field of study selection process. The system provides recommendation to the students based on previously solved cases and new query given by the student. For this study, about 105 cases which are collected from successful students and 13 attributes which are collected from experts are used as case base. These attributes and cases are used as knowledge base to construct case base recommender. The system calculates similarity between existing case and new queries that are provided by the students and provides solution or recommendation by taking best cases to the new query. This recommendation enables students to make decision easily. In this study, JCOLIBRI case base development tool is used to develop the prototype of case based recommender system. JCOLIBRI contains both user interface which enables students to enter their query and programming codes with the help of Java script language. After developing the prototype of the system, testing of the prototype for case base recommender system was done to evaluate the performance of the system. Based on prototype testing, the average performance of the system is 77.2% and 80.2% by the domain experts and students respectively. Using field of study selection recommender system for students helps them to make decision easily to select appropriate field of study based on their interest and background information. The researcher recommends for the future work to improve the system that can recommend university as well as field of study selection by providing detail information about each selection.Item Application of Data Mining Techniques for Customers Segmentation and Prediction: the Case of Buusaa Gonofa Microfinance Institution(Addis Ababa University, 2013-01) Reganier, Belachew; Kebede, Gashaw(PhD)Identifying customers which are more likely potential to a product and service offering is an important issue. In customers identification data mining has been used extensively to predict potential customers for a product and service. The final goal of this thesis is to build a model that helps to classify customers for Buusaa Gonofa microfinance institution product and service. Since there are no predefined classes, that describe the customers of the institution, the researcher uses clustering techniques that resulted in the appropriate number of clusters. Then, a predictive model was developed to predict potential customers. This predictive model achieved an accuracy of 99.95%. For modeling purpose, data was gathered from the institution head office. Since irrelevant features result in bad model performance, data preprocessing was performed in order to determine the inputs to the model. Thus, various data mining techniques and algorithms were used to implement each step of the modeling process and alleviate related difficulties. K-means was used as a clustering algorithm to segment customers‟ record into clusters with similar characters. Different parameters were used to run the clustering algorithm before reaching at segment that made business sense. J48 decision tree algorithm was used for classification purpose. In addition to those attributes that are believed by the experts to have high impact on customer segmentation, attributes value of loan amount have a big influence. Generally, the result of the study was encouraging, which reinforces the possible application of data mining solution to the microfinance industry, particularly, in customer segmentation and prediction in Buusaa Gonofa microfinance institutionItem Application of Data Mining Technology to Identify Significant Patterns in Census or Survey Data: the Case of 2001 Child Labor Survey in Ethiopia(Addis Ababa University, 2003-06) Tefera, Helen; Kebede, Gashaw(PhD); Anagaw, ShegawKnowledge and understanding of a problem is always the first step in identifying effective solutions. Child labor is both a sign and cause of poverty that should be eliminated as soon as possible. In Ethiopia, there is no much statistical data on child labor practice. To fill this data gap, the FDRE, CSA carried out country wide child labor survey in 2001. This organization uses very simple statistical tools to show summary figures of different variables involved in 2001 child labor survey database. However traditional statistical methods are not good enough to discover complex relationships from large volume databases. The inefficiency of these tools necessitated the development of more powerful methods and techniques that can be used to study relationships and patterns through the large volumes of data collected for example for census and survey purposes. In developed world, government and non-government organizations which have access to censuses and surveys are making use of the relatively new and modern technology, data mining, to identify important patterns and relationships within the data that is accumulated in large database. The application of data mining techniques to official data such as the 2001 child labor survey has great potential in supporting good public policy. This research focused on identifying relationships between attributes within the 2001 child labor survey database that can be used to clearly understand the nature of child labor problem in Ethiopia. So the goal of the data mining process in this research was identifying interesting patterns and relationships in the 2001 child labor database. After the identification and understanding of the problem domain and the research objectives, the remaining stages of the research project focused on the following three major phases in data mining process. During the first phase, selection of the appropriate data mining tool which can be used to attain the defined data mining goal and the target dataset used in model building were the major tasks. The next phase, data cleaning and preparation, involved identifying and correcting mis-transmitted information, consolidating and combining records, transforming data from one form to another suitable for the selected data mining tool, handling missing attributes and selecting relevant attributes for generating meaningful association rules. As a final step for data preparation, the selected dataset was categorized into five classes using expectation maximization clustering algorithm implemented in knowledge studio version 3.0. A dataset of 2398 records with 63 attributes were used for clustering purpose. Apriori is an association rule algorithm which is implemented in Weka software. In the third phase, model building and evaluation, the apriori algorithm was used to generate association rules from the clustered as well as non-clustered selected dataset. Different attributes were given to apriori in an effort to generate meaningful rules. The results from this study were encouraging, which strengthened the hypothesis that interesting patterns can be generated from census and survey database by applying one of the data mining techniques: association rule mining. Key words:Data mining , knowledge discovery, association rule, apriori algorithm.Item Application of KDD on Crime Data to Support the Advocacy and Awareness Raising Program of Forum on Street Children Ethiopia(Addis Ababa University, 2003-07) Kifle, Woldekidan; Kebede, Gashaw(PhD); Anagaw, ShegawThis thesis work gives an account of the process followed to determine the application of KDD to support the advocacy and awareness raising program of FSCE and Addis Ababa Police Commission, and the potential of a data mining learning scheme to discover regularities that underlie the crime dataset. The KDD process as described by Fayyad, Piatetsky-Shapiro and Gregory (1996) that consists of five major phases, namely understanding of the problem domain, data selection, data preprocessing, data mining, and discussion and interpretation was adopted. The discovery task was run on the crime database that consists of 10,878 records/tuples in 17 tables describing a total of 25 attributes. Association rule mining, an exploratory data mining technique was applied to accomplish the goal of the research. To this effect, the Apriori algorithm, which is an implementation of the Association rule in the Weka software, was used. The KDD process can be applied on the crime database to good effect since it can result in rules that can serve as input for the advocacy and awareness raising program. On the basis of subjective (opinions of domain experts) and objective (support and confidence) measures of interestingness, a number of rules having practical relevance or that can add to the current knowledge in the problem domain were identified.Item Information Needs and Seeking Behavior among Health Professionals Working at Governmental Hospital and Health Centers in Bahir Dar Town, Amhara Region, Ethiopia.(Addis Ababa University, 2012-06) Andualem, Mulusew; Kumie, Abera(PhD); Kebede, Gashaw(PhD)Background: Universal access to information for health professionals is a pre requisite for meeting the MDGs and achieving health for all strategy. In developing countries, a large proportion of the population, including health professionals have no or only poor access to health information resources due to poor infrastructures, economic related, poor attention, etc Objective: The purpose of this study is to assess information needs and seeking behavior of health professionals working at Governmental Hospitals and Health Centers in Bahir Dar town, Amhara Region, Ethiopia. Methods: A cross sectional study design using quantitative and qualitative approaches was carried out to achieve the research objectives using 350 study participants. Self-administered questionnaire and observation checklist were instruments to collect the required data. Manually edited data were entered in to computer using Epi-info version 3.5.1; further cleaned and exported to SPSS statistics version19; then cleaned again and analyzed as needed. Frequencies, cross tabulation, chi-square, Odds ratio with 95%CI, and Binary logistic regression analysis were done to describe and assess associations among variables of interest. Results: Nearly all (97.3%) of respondents reported that they need health information to update themselves and support daily activities. More than half (54%) encountered problems on their daily activities due to information limitation. Major barriers to access information were geographical, organizational, personal, economic related, educational status and time. Only 145 (42.8%) respondents have access to internet at different places with various frequencies and have shown statistically significant association (p <0.05) with age, sex, monthly income, computer literacy and access, patient seen per day, working experience, and working site. Majority of study participants have too much limited access to different information resources,especially library and internet. More than half (57.7%) respondents seek information by consulting their hard copies when there is a need. About 151(44.5%) respondents prefer to access on job trainings and soft copies next to hardcopies. Conclusions and recommendations: Almost all respondents need to access health information and more than 80% of study areas have no library, internet and computer services. Therefore, great attentions and efforts must be done to help those starved health professionals working at those areas.Item Prototype Knowledge Based System for Anxiety Mental Disorder Diagnosis(Addis Ababa University, 2011-06) Esseynew, Seblewongel; Kebede, Gashaw(PhD)Mental health is the basic building block for the entire healthy life of a person. There is a motto which explains the significance of mental health as “Without mental health no health!” Mental health problems touch every aspects of human life such as humans’ general health condition, work, family life, social relations, etc. However, mental health issue is one of the neglected issues throughout the world. Particularly, in developing countries, mental health has the least attention it deserves. Ethiopia is one of the developing countries. In Ethiopia, mental health issue is not getting sufficient attentions. The major challenge for mental health service in the country is shortage of skilled mental health professionals. In Ethiopia, the number of mental disorder patients and mental health professionals are disproportionate too. Due to this the distribution of mental health professionals is greatly unfair. Lacks of knowledge among primary health care workers, the allocation of insufficient budgets for mental health issue, and the absence of adequate awareness about mental illnesses are the other challenges that are creating obstacles to address mental health services satisfactorily. In an attempt to address such problems, the objective of this research work is to look into the possibility of applying knowledge based systems technology to diagnose patients with anxiety mental disorders by developing prototype knowledge based system that can mimic /simulate the services of psychiatrists and psychologists. To achieve this objective, knowledge is acquired using both structured and unstructured extensive interviews with three experts, which are selected purposively from Amanuel Mental Specialized Hospital and Rank Higher Clinic. Additionally, knowledge is acquired from secondary sources by using document analysis method of knowledge elicitation. The knowledge acquired is modelled using decision tree structure that represents concepts, parameters and procedures involved in anxiety disorders diagnoses. Based on the model, the prototype is developed with SWI Prolog by using ‘if – then’ rules. The prototype developed uses backward chaining to infer the rules and extract conclusions and recommendations. XI Domain experts evaluate the prototype and satisfactory result is found; about 86% of system evaluators accept the prototype. Additionally, the performance of the system is evaluated by using predictive validation technique with twenty test cases. The result of the validation revealed the accuracy of the prototype to be 85%. The prototype knowledge based system needs further studies to expand its scope and to enhance the performance of it by integrating with other knowledge representation techniques.