Sentence examples for useful features from the from inspiring English sources

The new algorithms can extract useful features from the data corrupted by impulsive noises (or outliers) in a more effective way.

The first module, referred to as the precipitation pattern classification system based on deep learning in this paper, applies deep learning techniques to automatically extract useful features from the matrix of polarimetric measurements.

As the performance of ANN depends upon the input and output characteristics, so, it is essential to pre-process and extract the useful features from the input data to train the ANN.

Useful features from the sEMG signals are then correlated with the VAS scores.

The merit is that a nonlinear relationship is represented by the spectrum of a spectrum, so only the useful features from the frequency domain in addition to other strong statistical features from the time-domain are encoded into the multidimensional vector which of course is limited in space.

The Runge Kutta discontinuous Galerkin (RKDG) method for solving hyperbolic conservation laws is a high order finite element method, which utilizes the useful features from high resolution finite volume schemes, such as the exact or approximate Riemann solvers, TVD Runge Kutta time discretizations, and limiters.

We therefore ask the following question: can we use machine-learning techniques to automatically extract useful features from human DNA sequences and achieve state-of-the-art poly(A) motif classification results?

The local discontinuous Galerkin (LDG) method is a spatial discretization procedure for convection diffusion equations, which employs useful features from high resolution finite volume schemes, such as the exact or approximate Riemann solvers serving as numerical fluxes and limiters, which is termed as Runge Kutta LDG (RKLDG) when TVD Runge Kutta method is applied for time discretization.

Runge Kutta discontinuous Galerkin (RKDG) method is a high order finite element method for solving hyperbolic conservation laws employing useful features from high resolution finite volume schemes, such as the exact or approximate Riemann solvers serving as numerical fluxes, TVD Runge Kutta time discretizations, and limiters.

We exact useful features from sequences and develop machine learning algorithms for the above task.

In this study, the effectiveness of the deep learning (DL) approaches to extract useful features from bispectral satellite information, infrared (IR), and water vapor (WV) channels and to produce rain/no-rain (R/NR) detection is explored [16].