Road accident prediction and model interpretation using a
Traffic accidents occurred daily in the capital city of Addis Ababa—Ethiopia. Human beings' life and property damage with a fraction of seconds. ... 99.86%, 73.13% and 68.58% respectively. The experiment revealed performances of each classifier on various classifier metric on both data set showed all classification algorithms perform well ...
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Water | Free Full-Text | Detection of Water Hyacinth (Eichhornia …
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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Automatic skin disease diagnosis using deep learning from …
The feature extractor outputs 1280 image feature maps to the classifier. The model is suitable for resource limited environments including smartphones. ... A total of 1880 skin images of top five diseases were collected from the Southwest of Ethiopia (Dr. Gerbi Medium Clinic, Jimma), Eastern Amhara, and Afar region (Boru‐Meda General Hospital ...
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Classifieds and Directories in Ethiopia | SearchEthio
SearchEthio is your daily feed of a wide variety of Classifieds and Directories listed in Ethiopia with a Quick and Easy search tool.
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Classification of Ethiopian Coffee Beans Using Imaging Techniques
The results of this study have revealed that imaging technique could be used as the most effective method to determine coffee bean qualities for export, however, it is suggested that the repeatability of this coffee quality testing method be validated using a large data set before employing the algorithm for the purpose of classifying coffee beans as a daily …
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Performance Evaluation of Multiple Classifiers for Predicting …
We propose to introduce a platform based on the background study men-tioned above, where fourteen different classifiers will be utilized to train a data-set to distinguish fake or authentic news. Accuracy, precision, recall, and F1 score will all be used to evaluate performance in this model.
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(PDF) Grading Ethiopian Coffee Raw Quality Using …
This paper is an extension of the previous article wherein the classifier used are the 23 machine learning algorithms of MATLAB's Classification Learner App. The same dataset was used from the …
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A systematic method for diagnosis of hepatitis disease using
Hepatitis is among the deadliest diseases on the planet. Machine learning approaches can contribute toward diagnosing hepatitis disease based on a few characteristics. On the UCI dataset, authors assessed distinct classifiers' performance in order to develop a systematic strategy for hepatitis disease diagnosis. The classifiers …
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An active learning machine technique based prediction of
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 ...
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Effects of land use land cover change on streamflow of Akaki
In this study, GEE was used for two purposes: (i) to access Landsat images, and (ii) to classify LULC of the Akaki catchment, Ethiopia, using machine learning classifiers. This study aims to quantify the effects of historical (1990–2020) LULC change on streamflow in the Akaki catchment.
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Artificial intelligence models for prediction of monthly rainfall
Ethiopia is one of the countries whose economy is mainly dependent on rain-fed agriculture and also faces periodic floods and drought. Current climate variability is ... Casanova-Mateo C. Accurate precipitation prediction with support vector classifiers: a study including novel predictive variables and observational data. Atmos Res. 2014;139: ...
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Full article: Assessing land use and land cover change detection …
Ethiopia is facing huge LULC change mostly change of natural resources to the farming system and human settlement (Birhane et al., ... the entire image has classified by supervised classification method using maximum likelihood classifier (algorithm) environment. In this study, downloading Landsat images of the required years for the …
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[Retracted] An Image Processing Approach for Detection of
According to the statistics, naïve Bayes classifier has the highest overall accuracy. [Retracted] An Image Processing Approach for Detection of Prenatal Heart Disease ... Ethiopia for the research and preparation of the manuscript. The authors thank the AIMST University and Saveetha Institute of Medical and Technical Sciences, …
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Applying Image Processing for Malt-barley Seed Identification
Those vectors are then used to train classifiers such as Linear Discriminant Analysis (Zapotoczny et al. 2008), artificial neural networks (Nowakowski et al. 2012), hyperellipsoidal decision ...
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(PDF) A machine learning classifier approach for identifying the
This paper aimed to explore the efficacy of machine learning (ML) approaches in predicting under-five undernutrition in Ethiopian administrative zones and to identify ... Ridge regression: biased estimation for non- 36. Behroozi M, Sami A. A multiple-classifier framework for Parkinson's orthogonal problems. Technometrics. 1970;12(1):55–67. ...
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Analysis and Detection of Road Traffic Accident Severity via Data
Around the world, road traffic accidents are the leading cause of serious injuries and deaths. Ethiopia is one of the countries that suffer the most from traffic accidents. Every government in every country wants to keep its citizens safe from accidents. To keep people safe from accidents, it is necessary to conduct a detailed analysis of the …
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Machine Learning Algorithms for understanding the determinants …
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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Machine Learning Hybrid Model for the Prediction of Chronic
5. Results and Discussion. Machine learning algorithms such as gradient boosting, Gaussian Naïve Bayes, decision tree, and random forest classifier were used in the proposed hybrid model. These different machine learning classifiers were used as a combination for the chronic kidney disease predictions.
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Woody species diversity and regeneration challenges in Ethiopia: …
Introduction Ethiopian Forest resources. In the Horn of Africa, Ethiopia is a biodiversity hotspot (Wang et al., 2019), with a diverse woody flora and a substantial number of unique species.Despite the fact that anthropogenic activities have reduced the overall vegetation cover of woodlands in Ethiopia (Mesfin et al., 2020), it remains a key source …
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Features Extraction and Dataset Preparation for Grading of Ethiopian
From the total production of coffee beans in Ethiopia is 50% from gardens, 30% from semi-forest, 10% from plantation and 10% from forest . According to, from the total export of coffee beans, 70–80% covers unwashed and 20–30% washed. An organization established by the Government of Ethiopia is Ethiopian Commodity …
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Grading Ethiopian Coffee Raw Quality Using Image
A. R. Baleker, "Raw Quality Value Classification of Ethiopian Coffee Using Image Processing Techniques: In the case of Wollega region," Addis Ababa University, Addis Ababa, Ethiopia, 2011.
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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. ... Accuracy Accuracy of classifier refers to the ability of classifier to predict ...
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Developing Ethiopian Yirgacheffe Coffee Grading …
The total number of images taken was 684 containing 6138 coffee beans. To extract coffee bean features and build a classification model for grading coffee, the state of art deep learning algorithm ...
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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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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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Multiclass classification of Ethiopian coffee bean using
Image processing and the state-of-the-art deep-learning techniques were employed to automatically classify coffee bean images into nine different classes grown in six different regions of Ethiopia ...
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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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[PDF] A machine learning classifier approach for identifying the
This paper aimed to explore the efficacy of machine learning (ML) approaches in predicting under-five undernutrition in Ethiopian administrative zones and to identify the most …
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Proposing a machine-learning based method to predict stillbirth …
Background Stillbirth is defined as fetal loss in pregnancy beyond 28 weeks by WHO. In this study, a machine-learning based method is proposed to predict stillbirth from livebirth and discriminate stillbirth before and during delivery and rank the features. Method A two-step stack ensemble classifier is proposed for classifying the instances …
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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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