Computer Science

Permanent URI for this collectionhttps://etd.hu.edu.et/handle/123456789/76

Browse

Filters

20232

Settings

Subject: search.filters.subject.Machine learning

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Predictions of the Status of Undernutrition for Children below Five Using Ensemble Metho
    (Hawassa University, 2023-08-02) Natnael Abate Choreno
    Undernutrition is one of the main causes of morbidity and mortality in children under five in most developing countries, including Ethiopia. It increases the risk of infectious diseases, impairs cognitive and physical development, reduces school performance and productivity, and perpetuates intergenerational cycles of poverty and malnutrition. The primary goal of this thesis is to build an ensemble model that predicts the undernutrition status of children under five using data from the 2019 EMDHS. The experiments covered 15082 instances and 20 attributes. Ensemble methods combine several models to deliver better results. Typically, results from an ensemble approach are more accurate than those from a single model. The selected method consists of preprocessing, feature selection, k-fold cross-validation, model building, an ensemble classifier, and final prediction steps. In this work, different machine learning classification models such as the Decision Tree, Support Vector Machine, K-Nearest Neighbors, and Naive Bayes classifiers have been used as base model algorithms with an accuracy rate of 0.92%, 0.94%, 0.92%, and 0.75% respectively. The final result was combined by the stacking ensemble method with logistic regression. The most accurate predictive model, with a 96 % accuracy rate was created using the stacking ensemble method. HAZ, WAZ, WHZ, age in 5 years groups, region, source of drinking water, education level, type of toilet facility, wealth index, total children born, number of antenatal visits, vaccination, breastfeeding duration, ever had nutritious food and plain water has given are the major features that contribute to undernutrition in children under-five. The findings of this study provided encouraging evidence that using the ensemble method could support the development of a predictive model that predicts the nutritional status of children under five in Ethiopia. Future research could produce better results by combining large datasets from clinical and hospital datasets. Future research may also include children over the age of five and children with obesity as a malnutrition status
  • No Thumbnail Available
    Item
    Maize Crop Yield Prediction Using Machine Learning Techniques
    (Hawassa University, 2023-11) Kedija Abdurhman
    Maize is one of the main crops cultivated all throughout the world, including in Ethiopia. However, the production of maize changes widely based on many factors, such as weather, soil quality, and fertilizer usage. Predicting maize yields is crucial for farmers because it allows them to make informed crop management decisions. Machine learning approaches have shown promise in predicting crop productivity in recent years. The goal of this thesis is to explore the utilization of ensemble methods, namely Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Decision Trees (DT), in the context of maize yield prediction. Ensemble methods involve combining predictions from multiple models to enhance accuracy and fortify the reliability of the forecasting process. The dataset used in this study was compiled from data collected between 2003 and 2022 on several aspects such as weather, soil quality, and maize production. Before developing the ensemble techniques, the dataset was preprocessed and the features were normalized. The results of this research show that ensemble techniques can potentially be employed for predicting maize yields with great performance. The MAE was 0. 0025, the MSE was 0. 0032, the RMSE was 0. 0057, and the R2 was 0. 9928. The results show that meteorological factors like rainfall and temperature have a considerable impact on maize yields. Soil quality was also recognized as an important factor influencing maize crop production by the model. The research demonstrates that ensemble techniques could potentially be used to accurately predict maize yields. Farmers can use the study's findings to informed decisions about their agricultural practices. The research also emphasizes the significance of meteorological conditions and soil quality in predicting maize yields.