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The global problems of child malnutrition and mortality in different world regions. J Health Soc Policy. 2003;16(4):1–26. 4. Fenta HM, et al. Determinants of stunting among under-five years children in Ethiopia from the 2016 Ethiopia demographic and Health Survey: application of ordinal logistic regression model using complex sampling designs.
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This paper aimed to explore the efficacy of machine learning (ML) approaches in predicting under-five undernutrition in Ethiopian administrative zones and …
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Random forest, Decision tree pruned J48 and k-nearest neighbor algorithms have better classification and prediction performance for classifying and predicting …
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Fenta et al. BMC Med Inform Decis Mak (2021) 21:291 Page 2 of 12 the last 2 decades in Ethiopia. Particularly, it has been found that the prevalence of under- ve children under-weight in Ethiopia ...
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This paper aimed to explore the efficacy of machine learning (ML) approaches in predicting under-five undernutrition in Ethiopian administrative zones and …
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CNN-SVM classifier .The experimental results showed that SVM performs better than softmax classifier in terms of performance and computational time. Our proposed model with SVM classifier achieved an overall classification accuracy of 96.5%. Keywords: Ethiopian coffee, SVM, CNN, coffee disease 1. INTRODUCTION
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The results showed that the considered machine learning classification algorithms can effectively predict the under-five undernutrition status in Ethiopian administrative zones. Background Undernutrition is the main cause of child death in developing countries. This paper aimed to explore the efficacy of machine learning (ML) …
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the e cacy of machine learning (ML) approaches in predicting under- ve undernutrition in Ethiopian administrative zones and to identify the most important predictors.
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The RF predicted the occurrence of LBW more accurately and effectively than other classifiers in Ethiopia Demographic Health Survey. Gender of the child, marriage to birth interval, mother's occupation and mother's age were Ethiopia's top four critical predictors of low birth weight in Ethiopia.
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In predicting unintended pregnancy factors in Ethiopia, the ExtraTrees classifier has a somewhat higher predictive ability than other selected machine learning classifiers. By using the ExtraTrees classifier to choose the desired features related to unintended pregnancy, we found that region, the ideal number of children, religion, …
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The descriptive results show that there are considerable regional variations in under-five mortality rates in Ethiopia and the best predictive model shows that size, time to the source of water, breastfeeding status, number of births in the preceding 5 years, of a child, birth intervals, antenatal care, birth order, type of water source, and …
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Persistent under-five undernutrition status was found in the northern part of Ethiopia. The identification of such high-ri … A machine learning classifier approach for identifying the determinants of under-five child undernutrition in Ethiopian administrative zones BMC Med Inform Decis Mak. 2021 Oct 24 ...
DetailsDifferent parts of plants are used in traditional medicine for. the treatmen t of human and livestock diseases in Ethiopia. However, roots and leaves are the most commonly used plant. parts, with ...
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spatial resolution images in Benishangul (BG), Gambella (GM), Oromia (OR), Ethiopia. Performance of the classifiers were compared through analyzing the classification results. Multi-variate linear regression models were built to explore the relationships between factors ... classifiers resulting in lower accuracies (Hay and Castilla 2006). Also ...
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The RF predicted the occurrence of low birth weight more accurately and effectively than other classifiers in Ethiopia Demographic Health Survey, and was the best classifier for predictive classification. Background Birth weight is a significant determinant of the likelihood of survival of an infant. Babies born at low birth weight are 25 times more …
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The use of fully polarized radar data has the potential to further improve the proposed land use classification in tropical countries. Study area in central Ethiopia and PALSAR data from June 02 ...
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The classifier also consists of three fully connected layers (FC1, FC2 and F3) and dropout is included after the first two fully connected layers to prevent the problem of overfitting. Flattening layer is also included to convert the output of feature extractor in to 1D feature vectors for the classifiers. The output of the
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A machine learning classifier approach for identifying the determinants of under-five child undernutrition in Ethiopian administrative zones. Haile Mekonnen Fenta1*, Temesgen …
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The ee.Classifier.smileRandomForest function is part of the GEE JavaScript Application Programming Interface (API) and creates a RF classifier. The function ee.Classifier.smileRandomForest was used to train the RF classifier, and then classify function was used to apply the trained classifier to the target imagery. Support ...
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The classifier also consists of three fully connected layers (FC1, FC2 and F3) and dropout is included after the first two fully connected layers to prevent the problem of overfitting. Flattening layer is also included to convert the output of feature extractor in to 1D feature vectors for the classifiers. ... Images of Ethiopian coffee bean ...
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The classifier in the classification of pepper leaf and fruit disease images is a fully connected layer. Feature extraction of the VGG16 and AlexNet based CNN concatenation model The process of analyzing and extracting attributes like image color, texture, edges, and segmentation of pepper leaves and fruit rot disease is known as …
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The long-tail distribution of the visual world poses great challenges for deep learning based classification models on how to handle the class imbalance problem.
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The decision tree classifier achieved a sensitivity, precision, and f1-score of 99.80%, 96.41%, and 98.08%, respectively. The K-Nearest Neighbors Classifier achieved the highest sensitivity performance but lower precision and f1-score as compared to the random forest classifier.
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A study by Ethiopian provides evidence of J48 machine learning and artificial neural network (ANN) techniques to find the causes of child mortality . Another study showed that the machine learning model effectively predicted the under-nutrition status of under-five children in the Ethiopian administrative zones [ 5 ].
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Abstract. This research probed how classifiers marking an object's membership in the grammar of classifier languages like Mandarin Chinese and Korean may influence their speakers to categorize objects differently compared to speakers of non-classifier languages like English. Surveys in multiple-choice format were given to native …
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In Ethiopia, undernutrition in the form of under-five stunting (low height for age) decreased from 58 % in 2000 to 38 % in 2016, a reduction of about one-third. ... The κ statistic is used not only to evaluate a single classifier but also several classifiers amongst themselves. The calculation of the Observed Accuracy and Expected Accuracy is ...
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Ethiopia, known as the birthplace of coffee, relies on coffee exports as a major source of foreign currency. This research paper focuses on developing a hybrid feature mining technique to automatically classify Ethiopian coffee beans based on their provenance: Harrar, Jimma, Limu, Sidama, and Wellega, which correspond to their …
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In Ethiopia, many studies ... The classifiers were found to have accuracies within the range of 67-70% and performed comparable to or even better than the diagnostic rule on the available data. In ...
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