Predictive modeling for breast cancer classification in the …
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.
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(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. October 2021; ... In Ethiopia, about 35% of ...
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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. Haile Mekonnen Fenta1*, Temesgen …
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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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Human ancestry correlates with language and reveals that …
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.
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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 ...
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Water | Free Full-Text | Detection of Water Hyacinth …
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 ...
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A New Hybrid Convolutional Neural Network and eXtreme …
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 ...
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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 ...
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(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 …
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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 …
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A machine learning classifier approach for identifying the …
Our results showed that the considered machine learning classification algorithms can effectively predict the under-five undernutrition status in Ethiopian …
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Classifier Definition
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 ...
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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 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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Machine-learning algorithms for land use dynamics in Lake …
Taking selected hydrological catchments of the Lake Haramaya Watershed in the East Hararghe Ethiopian highland as an example, we statistically compared the …
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(PDF) Grading Ethiopian Coffee Raw Quality Using Image …
Classifier is a program that takes input feature vectors and assigns it to one of a set of design ated classes [10] . Artificial neural network (ANN) was used for developing
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(PDF) Analysis of Medicinal Plants and Traditional Knowledge
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.
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(PDF) A machine learning classifier approach for identifying …
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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(PDF) Grading Ethiopian coffee raw quality using image …
Experimental outcomes confirm that Artificial Neural Network classifier generated the highest performance of 89.45% accuracy as compared to support vector machine (with 83.75%) and K-Nearest ...
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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. Mak. 21 (1), 1–12 (2021).
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Ethiopian Calendar Converter, Ethiopian Date Converter, EC …
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 .
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Developing Ethiopian Yirgacheffe Coffee Grading Model …
Developing Ethiopian Yirgacheffe Coffee Grading Model using a Deep Learning Classifier International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-9 ...
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Machine learning algorithms for predicting low birth weight in Ethiopia
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 …
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Machine learning algorithms for predicting low birth weight …
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 …
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Land Use Classification and Analysis Using Radar Data Mining in Ethiopia
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
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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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[PDF] Enhancing Classifier Accuracy in Ayurvedic Medicinal …
This paper proposes efficient accurate classifier for ayurvedic medical plant identification (EAC-AMP) utilizing using hybrid optimal machine learning techniques to increase the accuracy of classifier. Identification of right medicinal plants that goes in to the formation of a medicine is significant in ayurvedic medicinal industry. This paper focuses …
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Plant disease detection and classification techniques: a …
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, …
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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 …
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