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Computing curvature for volume of fluid methods using machine learning.
Methods using machine learning and Natural Language Processing (NLP) techniques exist to automate the process to some extent.
Some existing methods use machine learning and statistical approaches to predict binding sites.
Several computational methods have been developed to predict DNA- or RNA-binding residues in protein sequences using machine learning methods such as support vector machines (SVM) as classifiers.
We will use the extracted features from signal processing analyses for classification using machine learning algorithms such as nearest neighbour methods, support vector machines or random forests.
One of the most commonly used machine learning methods in virtual screening is Support Vector Machines (SVM) [37, 38].
These features use machine learning methods for predicting and detecting malicious files.
An alternative approach to text analysis, SIDE uses machine learning methods to analyze text responses.
Support vector machine (SVM) is a widely used machine learning method in intelligent fault diagnosis.
More recently, Regina Barzilay from MIT decoded a long-dead language using machine learning assuming similarity with a known language.
The use of multiple features combined using machine learning methods has also been studied [19, 20].
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