A novel median filter based on crow optimization algorithms (OMF) is suggested to reduce the random salt and pepper noise improve quality of RGB-colored gray images. The fundamental idea approach that first, algorithm detects pixels, replacing them with an optimum value depending a criterion maximization fitness function. Finally, standard measure peak signal-to-noise ratio (PSNR), Structural S...

Discretization refers to splitting the range of continuous values into intervals so as to pro vide useful information about classes This is usually done by minimizing a goodness mea sure subject to constraints such as the maxi mal number of intervals the minimal number of examples per interval or some stopping cri terion for splitting We take a di erent ap proach by searching for minimum splits...

This paper presents an unsupervised discretization method that performs density estimation for univariate data. The subintervals that the discretization produces can be used as the bins of a histogram. Histograms are a very simple and broadly understood means for displaying data, and our method automatically adapts bin widths to the data. It uses the log-likelihood as the scoring function to se...

In this paper we present entropy driven methodology for discretization. Recently, the original entropy based discretization was enhanced by including two options of selecting the best numerical attribute. In one option, Dominant Attribute, an attribute with the smallest conditional entropy of the concept given the attribute is selected for discretization and then the best cut point is determine...

A new method for the spatial discretization of complex multi-scale systems described by partial differential equations is presented. This method allows to preserve the global power balance equation and the geometric structure of the system. The modelling of the adsorption column is based on a network approach. The key notions are the energy function and the description of the power transfers wi...

An efficientMonte Carlomethod for random sample generation fromhigh dimensional distributions of complex structures is developed. The method is based on random discretization of the sample space and direct inversion of the discretized cumulative distribution function. It requires only the knowledge of the target density function up to a multiplicative constant and applies to standard distributi...

We approached the problem as learning how to order documents by estimated relevance with respect to a user query. Our support vector machines based classifier learns from the relevance judgments available with the standard test collections and generalizes to new, previously unseen queries. For this, we have designed a representation scheme, which is based on the discrete representation of the l...

Abstract. Bridges and Reich suggested the idea of multi-symplectic spectral discretization on Fourier space [4]. Based on their theory, we investigate the multi-symplectic Fourier pseudospectral discretization of the nonlinear Schrödinger equation (NLS) on real space. We show that the multi-symplectic semi-discretization of the nonlinear Schrödinger equation with periodic boundary conditions ha...