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- Amharic language, Hate speech, Social media, Multimodal Learning, deep learning, Word2vec, MFCC, LSTM, BILSTM, GRU, BIGRU. [1]
Allium fitsulosum, Red Onion Seed, Visual Geometry Group, GoogleNet, ResNet, Pretrained Models, Transfer Learning [1]
Amharic language , Fake news, Social media, Content-based feature, Social-context feature, N-gram,Word2vec, Logistic Regression, Random Forest, Support vector machine. [1]
Amharic language, hate speech detection, social media, deep learning [1]
Amharic language, Hate speech, Social media, Multimodal Learning, deep learning, Word2vec, MFCC, LSTM, BILSTM, GRU, BIGRU [2]
Amharic speech recognition, ESL, Rule-based Machine translation [1]
Amharic-Ge’ez Cross-Lingual Information Retrieval, Information Retrieval, neural machine translation, Amharic, Ge’ez [1]
Anemia, Homogeneous Ensemble Machine learning, Flask, Heroku, Class Decomposition [1]
childbirth mode, ensemble machine learning, predictive model, pregnant women. [1]
childes that have nutritional problem [1]
Chronic non-communicable diseases, diabetes, hypertension, multi-label predictive model, multi-label smote, multi-label feature selection. [1]
Cororna virus(Covid-19), occlusion, face mask, InceptionV3, MobileNetV2, DenseNet121, World Health Organization(WHO), Deep learning, Center of Diseases control(CDC), Computer vision. [1]
psychiatric symptoms, Social media, N-gram, Word2vec, Polarity, Subjectivity Logistic Regression, Random Forest, Support vector machine, Naïve Bayes [1]
the proposed ensemble model which is EEOTCSIRNET is 99.5% and a total of 3 misclassified images. So the proposed model performance having a promising result and it will have significant when deploy in mobile application for different users including saint icon painters, historians, religion follower, and other users to know easily the type of saint icons. Keywords: [1]
The researcher finally recommended other researchers to use more than three multispectral images provided by satellite rather than RGB bands and may extend this work to include prediction of green area change [1]
This research offers recommendationsthatwill assist the Ethiopian government, stakeholders such as ISPs, as well asowners/managersofbanks, in resolving the problems confronting banks in Ethiopia [1]

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