Generalisability of fetal ultrasound deep learning models to low
To that end, a classifier trained with 1792 patients from Spain is first evaluated on a new centre in Denmark in optimal conditions with 1008 patients and is …
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Novel WkNN-based technique to improve instantaneous
Improving satellite rainfall estimation from MSG data in Northern Algeria by using a multi-classifier model based on machine learning
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(PDF) Predicting Forest Fire in Algeria Using Data
Predicting forest fire in Algeria using data mining techniques using a Decision tree model is investigated for predicting wildfires [3]. The objective is to embody the decision tree algorithm in ...
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Text Classification with Extremely Small Datasets
"We tried building a classifier with a small dataset. You Wont Believe What Happens Next!" "We love these 11 techniques to build a text classifier. # 7 will SHOCK you." "Smart Data Scientists use these techniques to work with small datasets. Click to know what they are" These types of catchy titles are all over the internet.
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Improving satellite rainfall estimation from MSG data in Northern
To estimate precipitation from data from the MSG satellite, a multi-classifier model was developed and applied in North-East Algeria (see Fig. 1). For learning and …
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How to Create a Machine Learning Decision Tree Classifier Using …
DecisionTree dt = new DecisionTree (7, 3); dt.BuildTree (dataX, dataY); The constructor creates a tree with seven empty nodes except for the nodeID field. Method BuildTree () uses the training data to determine the source rows, split column, split value, class counts, and predicted class for each node.
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New AI classifier for indicating AI-written text
Our classifier is a language model fine-tuned on a dataset of pairs of human-written text and AI-written text on the same topic. We collected this dataset from a variety of sources that we believe to be written by humans, such as the pretraining data and human demonstrations on prompts submitted to InstructGPT.We divided each text into a prompt …
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Towards an Automatic Dialect Identification System for …
classifier trained with a feature vector incorporating textual features beat the other systems, achieving an accuracy of 51.36%. (Shon et al.,2020) sup-plied vast dialectal Arabic …
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Binary Classification Using a scikit Decision Tree
Figure 1: Binary Classification Using a scikit Decision Tree. After training, the model is applied to the training data and the test data. The model scores 81.00 percent accuracy (162 out of 200 correct) on the training data, and 77.50 percent accuracy (31 out of 40 correct) on the test data. The demo concludes by displaying the model in pseudo ...
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Sustainability | Free Full-Text | Prediction of Groundwater Quality
This study aimed to assess the water quality for irrigation purposes in the region of Adrar and to develop a classification model to predict the irrigation water …
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Comparative Analysis of Machine Learning Algorithms for Early
The limitation of this study was the small PID dataset used to evaluate the findings of this research. ... We build the prediction classifiers and the diabetes class attribute is used as the target variable. Ten machine ... Algeria. Allaoua Chaoui . Faculty of New Information and Communication Technologies, University of Constantine 2 ...
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Rainfall detection over northern Algeria by combining MSG
In this paper, a new method to delineate rain areas in northern Algeria is presented. The proposed approach is based on the blending of the geostationary meteosat second generation (MSG), infrared channel with the low-earth orbiting passive tropical rainfall measuring mission (TRMM). To model the system designed, we use an artificial …
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"Classifiers" American Sign Language (ASL)
A classifier (in ASL) is a sign that represents a general category of things, shapes, or sizes. A predicate is the part of a sentence that modifies (says something about or describes) the topic of the sentence or some other noun or noun phrase in the sentence. (Valli & Lucas, 2000) Example: JOHN HANDSOME.
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Machine Learning Classifiers
A classifier in machine learning is an algorithm that automatically orders or categorizes data into one or more of a set of "classes.". One of the most common examples is an email classifier that …
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Classification in Machine Learning: A Guide for …
Classification is a supervised machine learning method where the model tries to predict the correct label of a given input data. In classification, the model is fully trained using the training data, and then it is evaluated on …
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ACM
ACM. The ACM classifier mill was the first classifier mill on the market in 1962. Since then, the ACM has been continuously developed and adapted to the changing needs of the market. With over 1000 references worldwide, it is used in every industry: Chemicals: bisphenol, tartaric acid, E-PVC, fungicides, herbicides, stearates.
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Performance of machine learning methods in predicting water
Study area General setting. With 284,618 km 2 Illizi county is the third largest wilayah by area. It is located in the extreme southeast of Algeria, and it borders with three countries on a 1,233 km border with: Tunisia and Libya from the east and Niger from the south, where Ouargla county and Tamanrasset county border it from the north and the …
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Deep rule-based classifier for finger knuckle pattern recognition
In this paper, we proposed a novel finger knuckle pattern (FKP) based personal authentication system using multilayer deep rule based (DRB) classifier. The presented approach is completely data-driven and fully automatic. However, the DRB classifier is generic and can be used in variety of classification or prediction problems. In …
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Incremental supervised learning: algorithms and applications in …
IRDB use two classifiers: the Nearest Neighbor (NN) classifier and the SVM (Support Vector Machines) classifier, with radial basis function kernel. Mańdziuk and Shastri propose a new approach ICL (Incremental Class Learning) which is a supervised learning procedure for neural networks. This approach attempts to address the …
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2023/sbm algeria gold ore spiral classifier.md at master
sbm algeria gold ore spiral classifierGold Gold Ore Spiral Classifier Gold Gold Ore Spiral Classifier.Introduction gold cil process carbon in leach is an efficient method of extracting and recovering gold from its orey cyaniding and carbon leaching crushed gold ore slurry simultaneously,cil process lowers the gold mining operation cost and increases gold …
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Predicting wildfires in Algerian forests using machine
The F1-score is defined as the harmonic mean of precision and recall, i.e., The F1-score is a metric that measures the model's ability to predict wildfires well, both in terms of precision and recall. This score is between 0 and 1, and the closer it is to 1, the more efficient the model is. 4.2.
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Automated Transformer fault diagnosis using infrared …
DT is lowest after ELM classifier both in the average CA and standard deviation by 0.896 and 0.039 successively followed by the rest classifiers SOF, KNN, RF, DRB where are their averages classification is between 0.956 and 0.967; and standard deviation between 0.032 and 0.015.
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Classifier Definition | DeepAI
A classifier in machine learning is an algorithm that automatically orders or categorizes data into one or more of a set of "classes.". The process of categorizing or classifying information based on certain characteristics is known as classification. Classifiers are typically used in supervised learning systems where the correct class for ...
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E-word of mouth sentiment analysis for user behavior studies
In this paper, we use the self-training approach which is a popular learning method among the semi-supervised to construct the training model.In the self-training methods, the classifier should be trained using a small number of labeled training samples firstly, then the trained classifier will be trained with the set of all training samples ...
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Circulating small extracellular vesicle-based miRNA classifier for
Given the clinical application, the circulating sEV miRNA (CirsEV-miR) classifier was developed from five miRNAs based on qRT‒PCR data, which could well identify FTC patients (area under curve ...
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Application of Dempster-Shafer theory for optimization of …
In this work, a technique based on Dempster-Shafer Theory (DST) is proposed to combine three classifiers, namely Artificial Neural Network (ANN), Support Vector …
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An Efficient Prediction System for Diabetes Disease …
2.3.5. Random Forest. RF is one of the most common uses of classifier integration. As shown in Figure 4, RF is made up of numerous separate Decision Tree classifiers that vote on test samples according …
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Explainable, data-efficient text classification
Classifier heads based on "Branching Attention" have an advantage in case of relatively small datasets, and if just the classifier head is being optimized. Optimizing only a classifier head, on top of a pre-trained language model, can be a viable strategy if the labeled training data is scarce and/or many classifiers operating on similar ...
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Not another MNIST tutorial with TensorFlow – O'Reilly
As in the case of y = x^2, you can think of this as moving toward X = 0, which is also called the minimum. If the learning rate is too small, the classifier will take very small steps when learning; if it's too high, the steps it takes will be too large, and it may figuratively "overshoot" the true minimum. Figure 7.
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A satellite rainfall retrieval technique over northern Algeria based …
The ΔT 3.9-10.8 reaches the highest values in presence of optically thin clouds that consists of small or large particles. Medium to high difference values are obtained for large particles with a high optical thickness. These ΔT 3.9-10.8 values are lower for optically thin clouds. Small ΔT 3.9–10.8 is obtained with small particles for ...
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