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Browsing Information Science by Subject

Now showing items 1-20 of 34

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Academic libraries, Ethiopia, Current and future trends in LIS, Developing countries, Ethiopian universities, Higher learning institutions, Current and future trends in LIS [1]
Academic library, Consortia, Digital, Documentation services, Ethiopia, University library [1]
Amharic hate speech detection, meme, hate speech, deep learning, hyperparameter tuning [1]
Anemia, Machine Learning, Flask, Heroku, EDHS Dataset, Predictive Model [1]
Apriori algorithm, data mining, grade inflation, practicum course grade inflation [1]
Brain Stroke, Deep Convolutional Neural Network, Gabor Filter and Magnetic Resonance Imaging [1]
CDEO, Distance education, Ethiopia, Amhara, Programs, University [1]
Child under-five mortality, Climate change, Machine learning, Tabnet, Determinant factors [1]
Class imbalance, ensemble algorithms, neonatal mortality, rural area [1]
Classification, Deep Learning, Malaria, Transfer Learning, hyper tuning parameters [1]
College libraries; library automation; e-library/Internet; software package, information and communication technology (ICT [1]
Crop Selection, Machine Learning, Knowledge based system, Rule based reasoning, case based reasoning, NeuralProphet [1]
Data portal, Data sets, Ethiopia, Handbook, Open data, Statistics [1]
digital literacy, lifelong learning, information technology [1]
Digitization, Distance Education, Library and Documentation Service, Open Learning. [1]
Distance education, Higher education, Information and communication technology, Open learning. [1]
Embedded random forest is used as feature selection techniques. Binning is used to discretizing numerical variables into fewer categorical counterparts. Then, different experiments are done using homogenous ensemble methods of machine learning algorithms. To make preprocessing and build a model Python 3.7.4 programing language with anaconda distribution is used. The accuracy of decision tree and naïve Bayes is 91.79% and 85.25% respectively. A maximum increase of 4% accuracy for weak classifiers is achieved with the help of ensemble classification. Accuracy of decision tree with bagging and AdaBoost is 94.34% and 94.79% and area under ROC is 86% and 87% respectively. Naïve Bayes achieves 87.60% and 89.5% with bagging and AdaBoost. The best performing classifier is a decision tree with AdaBoost ensemble method with highest F-measure and recall or true positive rate which is and 97.19% and 99.92% respectively [1]
Ethiopian Tourism site, Recommender system, Collaborative Filtering, Clustering [1]
Fetus, Stillbirths, Cardiotocogram, Fetal health rate, Uterine contractions, Machine learning [1]
Health systems, Delivery Date, Machine Learning, Prediction model, Linear regression, Multilayer Perceptron, Random Forest, evaluation metrics [1]

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