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.
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 ...
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 ...
A machine learning classifier approach for identifying the determinants of under-five child undernutrition in Ethiopian administrative zones. Haile Mekonnen Fenta1*, Temesgen …
Study area in central Ethiopia and PALSAR data from June 02, 2008 (HH and HV) : Land use classification accuracy matrix using Median de-speckled data at 27 27 window size
This paper aimed to explore the efficacy of machine learning (ML) approaches in predicting under-five undernutrition in Ethiopian administrative zones …
Classifiers used to make predictions about cardiac health have varying degrees of success. ... College of Computing and Informatics, Haramaya University, POB 138, Dire Dawa, Ethiopia. Gemmachis ...
Feature selection. Before developing the clinical prediction models, we performed data-driven feature selection. The goal of feature selection is to select a subset of features from the entire feature space that allows a classifier to achieve optimal performance, where it is a user-specified or adaptively parameter chosen [].In machine …
In this study, the classifier categories are normal and LBW. RF was the best classifier, predicting LBW with 91.60 percent accuracy, 91.60 percent Recall, 96.80 …
Method. This study implemented predictive LBW models based on the data obtained from the Ethiopia Demographic and Health Survey 2016. This study was employed to compare and identify the best-suited classifier for predictive classification among Logistic Regression, Decision Tree, Naive Bayes, K-Nearest Neighbor, Random …
Different machine-learning techniques have been used for effective classification of chronic kidney disease from patients' data. Charleonnan et al. [] did comparison of the predictive models such as K-nearest neighbors (KNN), support vector machine (SVM), logistic regression (LR), and decision tree (DT) on Indians Chronic …
Random forest, Decision tree pruned J48 and k-nearest neighbor algorithms have better classification and prediction performance for classifying and predicting …
Taking selected hydrological catchments of the Lake Haramaya Watershed in the East Hararghe Ethiopian highland as an example, we statistically compared the …
A machine learning classifier approach for identifying the determinants of under-five child undernutrition in Ethiopian administrative zones. BMC Med. Inf. Decis. Mak. 21 (1), 1–12 (2021).
A New Hybrid Convolutional Neural Network and eXtreme Gradient Boosting Classifier for Recognizing Handwritten Ethiopian Characters Abstract: Handwritten character recognition has been profoundly studied for many years in the field of pattern recognition. Due to its vast practical applications and financial implications, the handwritten ...
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 ...
Binary Classifiers: These are used when there are only two possible classes. For example, an email classifier might be designed to detect spam and non-spam emails. Multiclass Classifiers: These handle situations where there are more than two classes. For example, a classifier that categorizes news articles into 'sports', 'politics', 'technology ...
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 …
Naive Bayes classifiers were utilized, using a novel weight adjustment method. Mohebian et al. 22 looked at the feasibility of using ensemble learning to foretell cancer recurrence.
This paper aimed to explore the efficacy of machine learning (ML) approaches in predicting under-five undernutrition in Ethiopian administrative zones …
Request PDF | Multiclass classification of Ethiopian coffee bean using deep learning | Ethiopia is the homeland of Coffee Arabica. Coffee is the major export commodity and a high-income source of ...
The findings suggested that the RF classifier was the most accurate E. crassipes detection algorithm, and autumn was an appropriate season for E. crassipes detection in Lake Tana. ... 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 ...
Materials and methods. This study was carried out on the disparities of malnutrition in Ethiopia, with a surface area of 1.1 million km 2, 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.It is divided into 11 administrative units (regions) including …
The CNN classifier was used to subtract color, texture, and plant leaf arrangement geometries from the given images. ... The images were from Jimma and Zegie in Southern Ethiopia. Backpropagation artificial neural network (BPNN) and DT approaches were used. A total of 9100 images were collected. 70% of them are used for training, …
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.
As the review conducted Ethiopia has richened by medicinal plant species and traditional knowledge that have a significant role in the management of various human and livestock diseases.
Thus, in contrast to race, ancestry is a valid genomic classifier. To illustrate the distinctions among these group labels, we provide two examples from genetic epidemiology.
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.
Subtract 1 from the Ethiopian month (EM) to account for the Ethiopian month starting from 1. The Ethiopian day (ED) remains unchanged. If the Ethiopian year is a leap year, add 1 day to the converted Gregorian date. For example, if the Ethiopian date is, the conversion results in the Gregorian date .
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 …
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نشانی: شماره 1688، جاده شرقی گاوک، منطقه جدید پودونگ، شانگهای، چین.
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