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The use of multiple features combined using machine learning methods has also been studied [19, 20].
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.
One of the most commonly used machine learning methods in virtual screening is Support Vector Machines (SVM) [37, 38].
An alternative approach to text analysis, SIDE uses machine learning methods to analyze text responses.
Augmented analytics uses machine learning (ML) to transform the way analytics content is created, ingested and dispersed.
Support vector machine (SVM) is a widely used machine learning method in intelligent fault diagnosis.
Here, we focused on using the structured data to build predictive models using machine learning based methods.
Big data analytics can enable timely and accurate insights using machine learning and predictive analytics to make better decisions.
Computing curvature for volume of fluid methods using machine learning.
We will use the extracted features from signal processing analyses for classification using machine learning algorithms including: nearest neighbour methods, support vector machines, random forests and gradient boosting.
Amenity Analytics (New York, U.S .: text analytics platform to identify actionable signals from unstructured data using machine learning, sentiment analysis and predictive analytics.
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