introduction: brain computer interface (bci) systems based on movement imagination (mi) are widely used in recent decades. separate feature extraction methods are employed in the mi data sets and classified in virtual reality (vr) environments for real-time applications. methods: this study applied wide variety of features on the recorded data using linear discriminant analysis (lda) classifier...

The goal of supervised feature selection is to find a subset of input features that are responsible for predicting output values. The least absolute shrinkage and selection operator (Lasso) allows computationally efficient feature selection based on linear dependency between input features and output values. In this paper, we consider a feature-wise kernelized Lasso for capturing non-linear inp...

In this paper, a new feature transformation method is introduced to decrease misclassification rate. Linear classifiers in general are not able to classify feature vectors which lie in a high dimensional feature space. When the feature vectors from difference classes have underlying distributions which are severely overlapped, it is even more difficult to classify those feature vectors with des...

the non-linear order of revelations is often considered by many scholars to signify lack of coherence, disunity, and fragmentariness. it seems that 'coherence' and the related terms have veiled a significant linguistic feature of the qur’an, namely, the non-linear nature of revelations, which might be used to make a distinction between ordinary and quranic discourse. we will argue that to unvei...

This paper considers the problem of effective feature extraction acoustic signals from oil and gas pipelines under different working conditions. A pipeline leakage detection method is proposed based on multi-feature entropy fusion local linear embedding (LLE). First, seven kinds commonly used which can reflect characteristics signal better are extracted through experiments, including permutatio...