http://ir.haramaya.edu.et//hru/handle/123456789/10 Mon, 07 Sep 2026 14:11:09 GMT 2026-09-07T14:11:09Z http://ir.haramaya.edu.et//hru/handle/123456789/8696 BAYESIAN ANALYSIS OF FACTORS INFLUENCING INTENTION NOT TO USE CONTRACEPTIVES AMONG WOMEN IN REPRODUCTIVE AGE IN JIGJIGA CITY, SOMALI REGION ETHIOPIA Mohamed Ayanle Hassen; Habtamu Kiros (Assist. Prof.); Kasahun Takele (PhD) Contraception is the prevention of conception intentional through various tools, sexual practices, chemicals, drugs, or surgical procedure. Any kind of tool or actions whose purpose is to prevent a woman from becoming pregnant can be considered contraception. The main objective of this study was to identify factors influencing intention not to use contraceptives among Women in Reproductive Age in Jigjiga City, Somali Region Ethiopia, Bayesian logistic regression approaches were used to meet the objective. The study used primary data which was collected from sampled respondents across the four subcities of Somali regional state capital Jigjiga. Among married women who were in reproductive age (15 – 49) in Jigjiga city, a sample of 352 was taken for this study. Out of the 352 married women in reproductive age, 21 % (74) were contraceptive user, and 79 %( 278) were non-users at the time of the data collection. Similarly, among non users, that is 278, about 199(71.6%) had intention not to use modern contraceptive method. Study found that significant association between intention on contraceptive method and the socio-economic and demographic variables: Religion, Living Son(s), Desire to have more children, women’s Education Level, Information about Family planning, Known Family planning methods, Past Experience on contraceptives and Access to Family Planning Service.Bayesian logistic regression procedure were used to make inference, the result of the model parameters, and the model revealed that Protestants (RELIG2) significantly lower odds of intending not to use contraceptives (OR=0.23, 95% CrI: 0.06-0.58), Orthodox Christians (RELIG3) show no statistically significant difference (OR=0.73, 95% CrI:0.30-1.83). The results also show that women who have no desire (DRCH2) for more children have a 91% lower odd of intending not to use contraceptives than the reference (has desire). The result also showed that the odds of not intending to use contraceptive was 4.18 times higher for women who had no past experience than their counter parts. By addressing religious, informational, and behavioral barriers, policymakers and health providers can significantly improve contraceptive uptake and maternal health outcomes 63p. Tue, 01 Apr 2025 00:00:00 GMT http://ir.haramaya.edu.et//hru/handle/123456789/8696 2025-04-01T00:00:00Z http://ir.haramaya.edu.et//hru/handle/123456789/8485 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. Fri, 01 Nov 2024 00:00:00 GMT http://ir.haramaya.edu.et//hru/handle/123456789/8485 2024-11-01T00:00:00Z http://ir.haramaya.edu.et//hru/handle/123456789/8465 Automatic Question Generation from Afaan Oromo Text UsingDeepLearning Jemal Abdela; Million Meshesha (PhD); Jemal Abate (MSc) In the modern digital era, the availability of electronic content in multiple languages has grownconsiderably. Nevertheless, generating questions from these materials remains a labor-intensiveand time-consuming process, particularly for low-resource languages like Afaan Oromo. Whilesignificant progress has been made in Automatic Question Generation (AQG) for languages suchas English, Chinese, Amharic and Somali. there is a notable lack of technology with NLPonAfaan Oromo QG because Afaan Oromo has the unique linguistic characteristics. This studybridges this gap by developing an AQG system for Afaan Oromo using deep learning models, specifically LSTM, Bi-LSTM, and GRU. To build the model a dataset of 5,000 paragraph-question-answer triples was meticulouslycurated from Afaan Oromo textbooks and educational resources. The dataset underwent preprocessing steps such as tokenization, normalization, and word embedding using Word2Vec. A deep learning model with an attention mechanism was employed to generate questions bydesign science research methodology, Among the models evaluated, the Bi-LSTMdemonstratedthe highest performance, achieving a training accuracy of 95.3% and a validation accuracyof 92.5%. The LSTM model also performed well, with a training accuracy of 92.31%and a validation accuracy of 91.02%, while the GRU model showed marked improvement after hyperparameter tuning, reaching 88.0% training accuracy and 87.0% validation accuracy.The results indicate that the Bi-LSTM model is the most effective for generating both factoidandnon-factoid questions in Afaan Oromo. Future research should explore transfer learning frompre-trained models, expand the dataset through collaborations with educational institutions, andintegrate advanced neural architectures like Transformers to further enhance performance andquestion quality. This study makes a significant contribution to the field of Natural LanguageProcessing (NLP) by pioneering AQG for Afaan Oromo, use as input for QA system, providingafoundation for future research and practical applications in education and language preservation. 138p. Sat, 01 Mar 2025 00:00:00 GMT http://ir.haramaya.edu.et//hru/handle/123456789/8465 2025-03-01T00:00:00Z http://ir.haramaya.edu.et//hru/handle/123456789/8464 Automatic Question Generation from Afaan Oromo Text UsingDeepLearning Jemal Abdela; Million Meshesha (PhD); Jemal Abate (MSc) In the modern digital era, the availability of electronic content in multiple languages has grownconsiderably. Nevertheless, generating questions from these materials remains a labor-intensiveand time-consuming process, particularly for low-resource languages like Afaan Oromo. Whilesignificant progress has been made in Automatic Question Generation (AQG) for languages suchas English, Chinese, Amharic and Somali. there is a notable lack of technology with NLPonAfaan Oromo QG because Afaan Oromo has the unique linguistic characteristics. This studybridges this gap by developing an AQG system for Afaan Oromo using deep learning models, specifically LSTM, Bi-LSTM, and GRU. To build the model a dataset of 5,000 paragraph-question-answer triples was meticulouslycurated from Afaan Oromo textbooks and educational resources. The dataset underwent preprocessing steps such as tokenization, normalization, and word embedding using Word2Vec. A deep learning model with an attention mechanism was employed to generate questions bydesign science research methodology, Among the models evaluated, the Bi-LSTMdemonstratedthe highest performance, achieving a training accuracy of 95.3% and a validation accuracyof 92.5%. The LSTM model also performed well, with a training accuracy of 92.31%and a validation accuracy of 91.02%, while the GRU model showed marked improvement after hyperparameter tuning, reaching 88.0% training accuracy and 87.0% validation accuracy The results indicate that the Bi-LSTM model is the most effective for generating both factoidandnon-factoid questions in Afaan Oromo. Future research should explore transfer learning frompre-trained models, expand the dataset through collaborations with educational institutions, andintegrate advanced neural architectures like Transformers to further enhance performance andquestion quality. This study makes a significant contribution to the field of Natural LanguageProcessing (NLP) by pioneering AQG for Afaan Oromo, use as input for QA system, providingafoundation for future research and practical applications in education and language preservation. 138p. Sat, 01 Mar 2025 00:00:00 GMT http://ir.haramaya.edu.et//hru/handle/123456789/8464 2025-03-01T00:00:00Z