Support vector machine (SVM) is a powerful machine learning technique relying on the
structural risk minimization principle. The applications of SVM in structural reliability analysis …

Smartphones and other consumer devices capable of capturing video content and sharing it
on social media in nearly real time are widely available at a reasonable cost. Thus, there is a …

Recognition of epileptic seizures from offline EEG signals is very important in clinical
diagnosis of epilepsy. Compared with manual labeling of EEG signals by doctors, machine …

Since the introduction of support vector machines, we have witnessed the huge
development in theory, models, and applications of what is so-called kernel-based methods …

Heating load prediction plays an important role in supporting the operation of a residential
district energy station. To find out the most suitable prediction algorithm, seven popular …

Recognition of epileptic electroencephalogram (EEG) signals using machine learning
techniques is becoming popular. In general, the construction of intelligent epileptic EEG …

Assessing rare event probabilities still suffers from its computational cost despite some
available methods widely accepted by researchers and engineers. For low to moderately …

The hyperparameters in support vector regression (SVR) determine the effectiveness of the
support vectors with fitting and predictions. However, the choice of these hyperparameters …

A robust machine learning methodology is used to generate a site-specific power-curve of a
full-scale isolated wind turbine operating in an atmospheric boundary layer to drastically …

The analysis and prediction of financial time-series data are difficult, and are the most
complicated tasks concerned with improving investment decisions. In this study, we …