http://ir.haramaya.edu.et//hru/handle/123456789/49 2026-06-08T13:25:26Z 2026-06-08T13:25:26Z Suleiman Mohamed Abdi Wondwossen Mulugeta (Ph.D) Faizur Rashid (Ph.D) http://ir.haramaya.edu.et//hru/handle/123456789/8485 2026-05-28T06:33:31Z 2024-11-01T00:00:00Z

A DEEP LEARNING APPROACH FOR CLASSIFICATION OF SIX CASES OF CHEST X-RAYS Suleiman Mohamed Abdi; Wondwossen Mulugeta (Ph.D); Faizur Rashid (Ph.D) Artificial Intelligence (AI), particularly deep learning, is transforming healthcare by enabling automated analysis and diagnosis from medical images, addressing critical challenges such as the shortage of radiologists and the demand for accurate diagnostic systems. This study focuses on the classification of six distinct chest X-ray conditions: Normal, Pneumonia, Tuberculosis, Lung Mass, Rib Fracture, and Enlarged Heart. Using a dataset of 10,200 chest X-ray images collected from Hargeisa Group Hospital, two pretrained convolutional neural network (CNN) architectures, DenseNet and GoogleNet, were fine-tuned and evaluated. Comprehensive preprocessing, including noise removal, image enhancement, and augmentation techniques, ensured high-quality and balanced training data. The models demonstrated exceptional performance, achieving classification accuracies of 97% and 96%, respectively, surpassing benchmarks in multi-class medical image classification Despite these promising results, the study encountered limitations. The dataset size, while sufficient for this research, remains relatively small for broader generalizability. Additionally, processing sensitive personal data required compliance with Somaliland’s Data Protection Act, posing challenges in accessing and utilizing X-ray images. These limitations highlight the need for expanded datasets and improved data access protocols for future research.This research establishes a robust framework for automating chest X-ray diagnostics, empowering radiologists with timely and accurate decision support. The findings contribute to advancing AIdriven solutions for healthcare, addressing both global and region-specific challenges 83p.

2024-11-01T00:00:00Z Feyisa Abebe Abebe Belay Adege (PhD) Mr. Tadesse Kebede http://ir.haramaya.edu.et//hru/handle/123456789/8419 2026-05-20T07:28:23Z 2025-04-01T00:00:00Z

MACHINE LEARNING-BASED PREDICTION OF UNDER-FIVEMORTALITY USING HEALTH,SOCIO-DEMOGRAPHIC,ANDCLIMATE DATA Feyisa Abebe; Abebe Belay Adege (PhD); Mr. Tadesse Kebede Health is a state of full well-being and a cornerstone of international development, withconsiderable investments made over the last three decades to reduce morbidity and mortality. Under-five mortality, which is generally defined as the death of children under the age of five, is still a critical public health challenge in developing countries. The study here proposesamachine learning-based approaches for predicting under-five mortality using health, socio-demographic, and climate data from Eastern Hararghe, Ethiopia. The data used in this study were collected from the Hararghe Health DemographicSurveillance System and Ethiopian National Meteorology Agency. The followingeight supervised machine learning algorithms were considered: Naïve Bayes (NB), Support VectorMachine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT), RandomForest (RF), eXtreme Gradient Boosting (XGBoost), Attentive Tabular Network (TabNet), andConvolutional Neural Network (CNN). The proposed framework covers data preprocessing, exploratory data analysis, model training, prediction, performance evaluation, andidentification of the key determinants of under-five mortality. It was observed that an80:20split produced an optimum performance in the models. Preprocessing techniques werethenapplied to enhance data quality before training the machine learning models. There weretwoexperimental setups: one with a data-balancing technique and the other without.Results indicated that balanced datasets always outperformed. Amongst all the modelsdeveloped, the XGBoost recorded the highest accuracy, having testing accuracy scoreof97.9%, precision of 98%, F1-score of 98%, and recall of 98%. The determinants of under-fivemortality identified in this study were antenatal care, child gender, wealth index, total numberof alive children, preceding child alive, physical healthy ,birth place and weight of baby. Intheend, the XGBoost algorithm emerged as the best among other models, proving to be themost reliable predictive model for under-five mortality. This study has shown the potential ofmachine learning providing helps in tackling critical public health challenges by leveragingdiverse datasets to enhance decision-making and interventions. Household-level climatedatawere not utilized in this thesis, which would be taken into account by future researchers. 129p.

2025-04-01T00:00:00Z Elias Debelo Dr. Wondwossen Mulugeta Akubazgi Gebremariam http://ir.haramaya.edu.et//hru/handle/123456789/8138 2025-01-08T06:59:50Z 2019-08-01T00:00:00Z

AUTOMATIC ANAPHORA RESOLUTION FOR AFAAN OROMOO LANGUAGE Elias Debelo; Dr. Wondwossen Mulugeta; Akubazgi Gebremariam Anaphora Resolution is a process of finding an entity introduced earlier in the discourse referred to by current entity back in discourse. The referenced entity is called antecedent, of referring entity which is called anaphor. There are number of anaphora types in a text, pronominal anaphora is the most prevalent one. Anaphora resolution is an important subtask that can be used in many Natural Language Processing applications. This study aims at developing Anaphora Resolution model based on Machine Learning approach for Afaan Oromoo language. The language is morphologically complex in that pronoun itself exist hidden inside the verbs. Input data cleaning, tokenization and part of speech tagging, noun phrase extraction and hidden pronoun extraction are useful steps toward Anaphora resolution. From every valid antecedent-anaphor pairs in training and testing sets, feature vectors will be generated. Machine Learning classifier trained using positive and negative instances generated from training set. Sklearn python package was used for as a trainer using fit function and as a predictor using predict function. Sklearn is set of packages consisting of implementations of Machine Learning algorithms. Three types of dataset, gathered from Afaan Oromoo News, Bible verses and Oromo Fictions, were used for training and testing. Five top best features were used for training and testing out of 14 features extracted from the text. Using 10-fold cross validation technique, the three datasets were divided into 10% testing and 90% training at each run. Each test sets of datasets were tested by range of 1 to 10 sentence distance between antecedent and anaphor on three Machine Learning algorithm Decision Tree (DT), Support Vector Machine (SVM) and Naïve Bayes (NB). Performance of the models on the three datasets were represented as mean average of the 10-folds on ten sentence range. Generally, average precision achieved for Bible-Fiction dataset 52.3% on DT, 51.25% using SVM and 53.57% using NB, for News dataset 57.67% using DT, 47.77% using SVM and 57.5% using NB and for compiled dataset 47.62% using DT, 46.82% using SVM and 50.15% using NB was achieved for combined independent and hidden anaphors. This result could be enhanced primarily by finding better ways of getting feature values for antecedent-anaphor pairs 96p.

2019-08-01T00:00:00Z Elias Debelo Dr. Wondwossen Mulugeta http://ir.haramaya.edu.et//hru/handle/123456789/7904 2024-11-08T11:59:56Z 2019-08-01T00:00:00Z

AUTOMATIC ANAPHORA RESOLUTION FOR AFAAN OROMOO LANGUAGE Elias Debelo; Dr. Wondwossen Mulugeta Anaphora Resolution is a process of finding an entity introduced earlier in the discourse referred to by current entity back in discourse. The referenced entity is called antecedent, of referring entity which is called anaphor. There are number of anaphora types in a text, pronominal anaphora is the most prevalent one. Anaphora resolution is an important subtask that can be used in many Natural Language Processing applications. This study aims at developing Anaphora Resolution model based on Machine Learning approach for Afaan Oromoo language. The language is morphologically complex in that pronoun itself exist hidden inside the verbs. Input data cleaning, tokenization and part of speech tagging, noun phrase extraction and hidden pronoun extraction are useful steps toward Anaphora resolution. From every valid antecedent-anaphor pairs in training and testing sets, feature vectors will be generated. Machine Learning classifier trained using positive and negative instances generated from training set. Sklearn python package was used for as a trainer using fit function and as a predictor using predict function. Sklearn is set of packages consisting of implementations of Machine Learning algorithms. Three types of dataset, gathered from Afaan Oromoo News, Bible verses and Oromo Fictions, were used for training and testing. Five top best features were used for training and testing out of 14 features extracted from the text. Using 10-fold cross validation technique, the three datasets were divided into 10% testing and 90% training at each run. Each test sets of datasets were tested by range of 1 to 10 sentence distance between antecedent and anaphor on three Machine Learning algorithm Decision Tree (DT), Support Vector Machine (SVM) and Naïve Bayes (NB). Performance of the models on the three datasets were represented as mean average of the 10-folds on ten sentence range. Generally, average precision achieved for Bible-Fiction dataset 52.3% on DT, 51.25% using SVM and 53.57% using NB, for News dataset 57.67% using DT, 47.77% using SVM and 57.5% using NB and for compiled dataset 47.62% using DT, 46.82% using SVM and 50.15% using NB was achieved for combined independent and hidden anaphors. This result could be enhanced primarily by finding better ways of getting feature values for antecedent-anaphor pairs 96p.

2019-08-01T00:00:00Z