Machine learning approach for detection of nonTor traffic

There are several popular content filters such as: Bayesian filters, Rule Based Filters, Support Vector Machines (SVM) and Artificial Neural Network (ANN)

List of abbreviations: support vector machine (SVM), artificial neural networks (ANN), non-negative matrix factorization (NMF), principal component analysis (PCA), extreme

Random Forest (RF), Artificial Neural Network (ANN) and Support Vector Machine (SVM). The final outcome of our experiment indicated that the proposed computerized

Several machine learning algorithms such as Support Vector Machines(SVM), Naïve Bayes (NB) Classifier, Clustering techniques, Neural Networks (NN) were used for the

This paper proposes Support Vector Machine (SVM), Artificial Neural Network (ANN) Multilayer Perceptron, (Recurrent Neural Network – Long Short Term Memory

Among the machine learning techniques, there are Support Vector Machine (SVM), Artificial Neural Network (ANN), Fuzzy Neural Network (FNN), Decision Trees (DT),

We use the following machine learning models: linear support vector machines (SVM-linear), support vector machines with radial basis function kernel (SVM-RBF),

We developed two models to screen for prediabetes using an artificial neural network (ANN) and support vector machine (SVM) and performed a systematic evaluation of the models