Classifier In Ethiopia
Machine learning algorithms for predicting low birth weight in Ethiopia
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 likely …
Machine learning algorithms for predicting low birth weight …
This study was employed to compare and identify the best-suited classifier for predictive classification among Logistic Regression, Decision Tree, Naive Bayes, K …
Land | Free Full-Text | Scenario-Based Land Use and Land …
Land use and land cover (LULC) change detection and prediction studies are crucial for supporting sustainable watershed planning and management. Hence, this study aimed to detect historical LULC changes from 1985 to 2019 and predict future changes for 2035 (near future) and 2065 (far future) in the Gumara watershed, Upper …
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, …
Haile Mekonnen Fenta
A machine learning classifier approach for identifying the determinants of under-five child undernutrition in Ethiopian administrative zones. HM Fenta, T Zewotir, EK Muluneh ... Community and individual level determinants of infant mortality in rural Ethiopia using data from 2016 Ethiopian demographic and health survey.
Ethiopian Calendar Converter, Ethiopian Date Converter, EC …
Effortlessly convert between Gregorian and Ethiopian dates with our user-friendly Ethiopian Calendar Converter.
The Classifier's Handbook
The Classifier's Handbook TS-107 August 1991 . CHAPTER 1, POSITION CLASSIFICATION STANDARDS . Title 5, United States Code, governs the classification of positions in the Federal service.
Chronic kidney disease prediction using machine learning …
Kidney disease is serious public health problem in Ethiopia affecting hundreds of thousands of people irrespective of age, ... we applied machine learning classifiers without feature selection for both binary class and five classes. The machine learning models used in this study are Random Forest, Support Vector Machine, and …
Chronic kidney disease prediction based on machine …
Almasoud and Ward 3 found 98.5% accuracy with RF classifier. Islam et al. 18 obtained slightly greater accuracy with RF classifier. SVM outperformed than other classifiers conducted by Gudeti et al. 14 Aljaaf et al. 2 got as usual accuracy shown in Fig. 11. In a nutshell, the previous studies were conducted based on kNN, RF, NB, GB, …
Predicting the risk of hypertension using machine learning …
Conclusions The proposed framework provides an effective tool for accurately predicting individuals in Ethiopia who are at risk for developing HTN at an early stage and may help with early prevention and individualized treatment. ... By adding classifiers on top of each other iteratively, the next classifier can modify the errors of …
Object-based Classification of High Spatial Resolution …
Images in Ethiopia Using Machine Learning Approaches By Chuying Lu A thesis submitted in partial fulfillment of the requirements for the degree of ... spatial resolution images in Benishangul (BG), Gambella (GM), Oromia (OR), Ethiopia. Performance of the classifiers were compared through analyzing the classification results. Multi-variate ...
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 ...
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 ...
Employing supervised machine learning algorithms for
A machine learning classifier approach for identifying the determinants of under-five child undernutrition in Ethiopian administrative zones. BMC Med. Inf. Decis.
Modeling water hyacinth (Eichhornia crassipes) distribution …
Semantic Scholar extracted view of "Modeling water hyacinth (Eichhornia crassipes) distribution in Lake Tana, Ethiopia, using machine learning" by Matiwos Belayhun et al.
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. …
(PDF) Grading Ethiopian coffee raw quality using image …
classifier model is used to classify new images, but when labeled training data . ... More specifically, we analyze to what extent the Ethiopian Commodity Exchange (ECX) in combination with ...
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.
Classifiers in competition for categorization | Language and …
Classifiers that are grammaticalized in classifier languages may be providing their speakers with a powerful cognitive tool to notice diverse characteristics shared between objects, which is usually unavailable to non-classifier languages. Therefore, the strength of classifier-based strategy in the minds of classifier language speakers is ...
Grading Ethiopian Coffee Raw Quality Using Image
Ethiopian coffee, Grading Coffee Raw Qua ... classification process and procedures and image classification techniques and explains two common techniques K-means Classifier and Support Vector ...
Classification of Ethiopian Coffee Beans Using Imaging …
Ethiopian coffee beans are distinct from each other in terms of quality based on their geographic origins. The quality of export coffee beans is usually determined by visual inspection, which is ...
Developing Ethiopian Yirgacheffe Coffee Grading Model …
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. ... Images of Ethiopian according to a serious of experiments carried on the whole coffee bean with different grade values were captured from dataset that give ...
(PDF) Grading Ethiopian Coffee Raw Quality Using Image …
The summary of algorithm performance is shown in the below Table 5. Table 5: Summary of model grading performance Classifiers K-Nearest Neighbor (KNN) classifier and its output Using aggregated Features In this experimentation, the classification input features were sixteen, by combining ten morphological features and …
Developing Ethiopian Yirgacheffe Coffee Grading Model …
affects greatly the performance of the classifier and hence they are the future research direction that needs further investigations of noise removal techniques. Keywords: Coffee Grade, Image processing, deep learning, ... in Ethiopian coffee processing industries as a base to support the domain experts during coffee bean grading. And also, it ...
Water | Free Full-Text | Detection of Water Hyacinth …
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 methods and determining the seasonal distribution of the weed is important for decision-making, water resource management, and …
Predictive modeling for breast cancer classification in the
Naive Bayes classifiers were utilized, using a novel weight adjustment method. ... (KAP) toward cervical cancer screening among adama science and technology university students, Ethiopia.
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 …
A machine learning classifier approach for identifying the …
The study employed ML techniques using retrospective cross-sectional survey data from Ethiopia, a national-representative data collected in the year (2000, …
A machine learning classifier approach for identifying …
in childhood CIAF among 72 Ethiopian administrative zones. Materials and methods is study was carried out on the disparities of malnutri-tion in Ethiopia, with a surface area of 1.1 million km2, the country shares borders with Eritrea in the north, Djibouti and Somali in the east, Sudan and South Sudan in the west, and Kenya in the south.
(PDF) A machine learning classifier approach for identifying …
This paper aimed to explore the efficacy of machine learning (ML) approaches in predicting under-five undernutrition in Ethiopian administrative zones …
(PDF) A machine learning classifier approach for identifying …
A machine learning classifier approach for identifying the determinants of under-five child undernutrition in Ethiopian administrative zones.
(PDF) A machine learning classifier approach for identifying …
A machine learning classifier approach for identifying the determinants of under-five child undernutrition in Ethiopian administrative zones
Land Use Classification and Analysis Using Radar Data Mining in Ethiopia
Land use classification in tropical areas, is hindered by frequent cloud cover which limits the availability of optical satellite data. Satellite-borne radar is a possible alternative to optical ...
Machine learning approach for predicting under-five …
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 …
Classical Image Based Classification of Coffee Beans on Their …
Ethiopia is a homeland of coffee. Coffee is a major export commodity of Ethiopia, which has a significant role in earning foreign currency. This research was conducted with the objective of developing an appropriate computer routine algorithm that can characterize different varieties of Beneshanguel coffee based on their growing …
(PDF) A machine learning classifier approach for identifying …
This paper aimed to explore the efficacy of machine learning (ML) approaches in predicting under-five undernutrition in Ethiopian administrative zones and to identify Background …
Application of supervised machine learning algorithms for
Ethiopia has been challenged by the growing magnitude of diabetes in general and type-2 diabetes in particular. ... The best three classifier and predictor models were selected for prediction of ...
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.
(PDF) Detection and Classification of Coffee Leaf
PDF | Ethiopia is the leading coffee exporter in Africa which accounts for 22% of the country's commodity exports. Coffee is one of the crucial... | Find, read and cite all the research you need ...