Browsing by Author "Tadesse, Ethiopia"

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    Application of Case-based Reasoning for Amharic Legal Precedent Retrieval: A Case Study with the Ethiopian Labor Law
    (Addis Ababa University, 2002-07) Tadesse, Ethiopia; Biru, Tesfaye; Amsalu, Saba
    This Research is concerned with the development of a Case-Based Reasoning (CBR) based precedent retrieval system in the domain of Ethiopian Labor Law. The requirement for the system was to build a knowledge base in which complete decided cases could be entered and then recalled when similar cases arose again. Standard case representation to the original knowledge source (legal cases) has been used to store legal cases. Legal cases have a predefined case structure with a number of features. The features are extracted to reflect the important aspects of a legal case. Given a new case, the feature values are used to do the search for a similar case from the casebase. Content based matching mechanism is used in the retrieval process. Content based matching matches the equivalent parts of the target and the source cases and calculates the degree of similarity according to the number of features matched, and feature weights. To increase the retrieval effectiveness, a mechanism for feature importance value (weight) assignment was required. The approach adopted takes into account domain experts' opinions to assign weights to the features. A Case-Based Reasoning prototype has been implemented by using the CBR-Works toolkit. To facilitate the insertion of additional cases and searching, an online interface has also been included.
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    The Application of Data Mining in Crime Prevention: the Case of Oromia Police Commission
    (Addis Ababa University, 2003-06) Woldu, Leul; Tadesse, Ethiopia; Teferi, Dereje
    Law enforcement agencies like that of police today are faced with large volume of data that must be processed and transformed into useful information and hence data mining can greatly improve crime analysis and aid in reducing and preventing crime. The purpose of this study is to explore the applicability of data mining technique in the efforts of crime prevention with particular emphasis to the Oromia Police Commission and to build a model that could help to extract crime patterns. With this objective decision trees and neural network were employed to classify crime records on the basis of the values of attributes crime label (CrimeLabel) and crime scene (SceneLabel). Results of the experiments have shown that decision tree has classified crime records at an accuracy rate of 94 percent when the attribute CrimeLabel is used as a basis for classification. Where as, in the same experiment, the accuracy rate of neural networks is 92.5 percent. On the other hand, in the case of classification of records on the values of the attribute SceneLabel decision tree has shown an accuracy rate of 85 percent while neural network revealed 80 percent. In both experiments the output indicated that decision tree performed better. Besides, decision tree generated understandable rules that could be easily presented in human language and thus police officers can make use of these rules for designing crime prevention strategies. Thus, this experiment has proved that data mining is valuable to support the crime prevention process and particularly, decision trees seem more appropriate for the domain problem.