Browsing by Author "Yifiru, Martha(Dr.)"

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    Predicting Under Nutrition Status of Under-Five Children Using Data Mining Techniques; The Case of 2011 Ethiopian Demographic and Health Survey.
    (Addis Ababa University, 2013-06) Markos, Zenebe; Yifiru, Martha(Dr.); Haidar, Jemal(Dr.)
    Background: under nutrition is one of the leading causes of morbidity and mortality in children under the age of five in most developing countries including Ethiopia. Objective: The general objective of this study was to design a model that predicts the nutritional status of under-five children using data mining techniques. Methodology: This study followed hybrid methodology of Knowledge Discovery Process to achieve the goal of building predictive model using data mining techniques and used secondary data from 2011 Ethiopia Demographic and Health Survey dataset. Hybrid process model was selected since it combines best features of Cross-Industry Standard Process for Data Mining and Knowledge Discovery in Database methodology to identify and describe several explicit feedback loops which are helpful in attaining the research objectives. WEKA 3.6.8 data mining tools and techniques such as J48decision tree, Naïve Bayes and PART rule induction classifiers were utilized as means to address the research problem. Result: In this particular study, the predictive model developed using PART pruned rule induction found to be best performing having 92.6% of accurate results and 97.8% WROC area.Promising result has been achieved from the rules regarding nutritional status prediction. Conclusion: The results from this study were encouraging and confirmed that applying data mining techniques could indeed support a predictive model building task that predicts nutritional status of under-five children in Ethiopia. In the future, integrating large demographic and health survey dataset and clinical dataset, employing other classification algorithms, tools and techniques could yield better results.
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    Proposing A Knowledge Management System (KMS) Architecture for Traditional Medicine
    (Addis Ababa University, 2015-06-05) Ayalew, Dereje; Yifiru, Martha(Dr.)
    Background: Traditional medicine knowledge is a medical knowledge developed by indigenous cultures that incorporates plant, animal and mineral-based medicines, and manual techniques designed to treat illness or maintain well being. The provisions of this service depend on managing its knowledge properly. Knowledge management is a process that deals with knowledge creation, storage, dissemination and application to improve the overall business performance. A knowledge management system (KMS) is the use of ICT platform that combines and integrates functions for the contextualized handling of both, explicit and tacit knowledge as part of knowledge management initiative. In Ethiopia, there exists gaps in managing traditional medicine knowledge and KMS has not been designed and implemented for traditional medicine practice. Objectives: The objective of the project is to propose knowledge management system architecture to promote knowledge management in traditional medicine practice in Ethiopia. Methods: Methodology for proposing knowledge management system architectures is used. A non- probabilistic purposive sampling technique is used to conduct key informant interview with experts working in the Ethiopian public health institute, Ethiopian biodiversity institutes, Addis Ababa University School of pharmacy, school of botany and school of anthropology. The data collected was analyzed to define requirements for designing KMS architecture for traditional medicine. Findings: Through assessment of the existing knowledge management practices for traditional medicine, Gaps in knowledge management practices were identified. KMS architecture was proposed as an artifact for the identified problems. The proposed KMS architecture, which is a base for designing and implementing the knowledge management system, adopted three stages in the knowledge management cycle which are knowledge capturing, knowledge storage and knowledge sharing. Conclusion and Recommendation: This study provides a guide for implementing a KMS in TM practice and leverage knowledge, which in turn helps the community and the whole country in terms of supporting the existing health system. Therefore, the recommendation of this project is that KMS for traditional medicine practice should be designed and implemented by the Ethiopian government (Ethiopian Public Health Institute) based on the proposed architecture.
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    Spontaneous Speech Recognition for Amharic Using HMM
    (Addis Ababa University, 2015-03-05) Deksiso, Adugna; Yifiru, Martha(Dr.)
    The ultimate goal of automatic speech recognition is towards developing a model that automatically converts speech utterance into a sequence of words. Having similar objective of transforming Amharic speech in to its equivalent sequence of words, this study explored the possibility of developing Amharic spontaneous speech recognition system using hidden Markov model (HMM). A spontaneous, speaker independent Amharic speech recognizer developed in this research work was done using conversational speeches between two or more speakers. This speech data are collected from web and transcribed manually. Among the collected data for training 2007 sentences uttered by 36 peoples from different age group and sex is used. This training data consists of 9460 unique words and it is around 3 hours and 10 minutes speech. For testing, 820 unique words which are from 104 utterances (sentences) uttered by 14 speakers are used. The collected conversational speech data contains different non-speech events both from speaker and from environment which causes the decrement of speech recognizer performance. Depending on these non-speech events frequencies, two data sets are prepared, the first data set prepared by including less frequent non-speech events in models and the second data set prepared by excluding them. Using the data sets, the acoustic model developed using word internal and cross word tied state tri-phones up to 11th Gaussian mixture. For this research, relatively the best recognizer performance is found to be 41.60% word accuracy for speakers involved in training, 39.86% for test data from both speakers which are involved and not involved in training and 23.25% for speakers those do not involved in training. The recognizer developed using cross-word tri-phone shows less performance than word internal tri-phone due to smallness of our data size. The recognizer developed and tested using the data which includes less frequent non-speech events showed less word accuracy than the one that include them. According to the finding of this research, the performance gained for Amharic spontaneous speech recognizer is less in accuracy. This is due to the nature of speech and the smallness of the size of data used; therefore, this result can be optimized by increasing the size of the data.