Machine learning to predict virological failure among HIV …
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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Water | Free Full-Text | Detection of Water Hyacinth …
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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CNN-HOG based hybrid feature mining for classification of
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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Land Use Classification and Analysis Using Radar Data Mining in Ethiopia
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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RESEARCH Open Access A machine learning classi er …
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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Water | Free Full-Text | Detection of Water …
Lake Tana is Ethiopia's largest lake and is infested with invasive water hyacinth (E. crassipes), which endangers the lake's biodiversity and habitat. Using appropriate remote sensing detection …
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(PDF) Detection and Classification of Coffee Leaf
Ethiopia is the leading coffee exporter in Africa which accounts for 22% of the country's commodity exports. Coffee is one of the crucial agricultural product in the global economy, particularly ...
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Machine learning algorithms for predicting low birth weight in Ethiopia …
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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Detection of Water Hyacinth (Eichhornia crassipes) in Lake …
Lake Tana is Ethiopia's largest lake and is infested with invasive water hyacinth (E. crassipes), which endangers the lake's biodiversity and habitat. ... Different classifiers were used to perform a pixel-based detection of this invasive species in both datasets. From the different classifiers used, the results were achieved by the random ...
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[PDF] A machine learning classifier approach for identifying …
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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A machine learning classifier approach for identifying the
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 ...
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A New Hybrid Convolutional Neural Network and eXtreme …
These scripts have been used to write ancient histories, science, and arts of Ethiopia and Eritrea. In this study, a hybrid model of two super classifiers: Convolutional Neural Network (CNN), as well as eXtreme Gradient Boosting (XGBoost), are proposed for classification.
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(PDF) Data Mining of Access to Tetanus Toxoid
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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Employing supervised machine learning algorithms for
In developing countries, one-quarter of young women have suffered from anemia. However, the available studies in Ethiopia have been usually used the traditional stastical methods. Therefore, this study aimed to employ multiple machine learning algorithms to identify the most effective model for the …
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DECOUPLING REPRESENTATION AND CLASSIFIER FOR LONG …
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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Predicting the risk of hypertension using machine learning …
Background and objectives Hypertension (HTN), a major global health concern, is a leading cause of cardiovascular disease, premature death and disability, worldwide. It is important to develop an automated system to diagnose HTN at an early stage. Therefore, this study devised a machine learning (ML) system for predicting …
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Classifiers in competition for categorization | Language and …
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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CPD-CCNN: classification of pepper disease using a …
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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Machine-learning algorithms for land use dynamics in …
As OBIA classifier performed poorly for our study in the eastern Ethiopian highland (i.e., probably because of the classified land use maps based on medium resolution Landsat-8 OLI image), higher classification accuracy was achieved by previous studies using a similar method elsewhere in the world (Myint et al. 2011; Varga et al. …
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Application of supervised machine learning algorithms for
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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Classification of Ethiopian Coffee Beans Using Imaging …
categorize the coffee beans according to their provenance. Four classification setups ( 1, 2, 3 and 4) were. employed based on the features used for colou r, morphology, texture, and combination ...
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Machine Learning Algorithms for understanding the …
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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ETHIOPIAN COFFEE LEAF DISEASES IDENTIFICATION …
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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(PDF) Grading Ethiopian coffee raw quality using image …
In Ethiopia, Coffee Grading is performed by using two methods. These. grading methods are raw quality or green analysis and liquor value or cup. test [7]. Raw quality is computed out of forty ...
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RESEARCH Open Access A machine learning classi er …
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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Machine learning prediction of adolescent HIV testing services in Ethiopia
1 Department of Public Health, School of Public Health, College of Medicine and Health Science, Mizan-Tepi University, Mizan-Aman, Ethiopia; 2 Department of Public Health, College of Medicine and Health Science, Debre-Markos University, Gojjam, Ethiopia; Background: Despite endeavors to achieve the Joint United Nations …
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A machine learning classifier approach for identifying the …
A machine learning classifier approach for identifying the determinants of under-five child undernutrition in Ethiopian administrative zones. BMC Med. Inf. Decis. …
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Object-based Classification of High Spatial Resolution …
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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(PDF) Analysis of Medicinal Plants and Traditional Knowledge
Different 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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Machine learning to predict unintended pregnancy among …
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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