parties, the magistrate, the dispute, argument and procedure with all its features at the hearing principally means the application of law on facts in order to resolution of conflicts, but it needs by itself to be organized with some techniques as a result of organizing the litigation process. some general norms are the means for achieving this goal which called principles of law. we can introd...

Color image segmentation Entropy k-means algorithm W-k-means algorithm a b s t r a c t In this paper, a weight selection procedure in the W-k-means algorithm is proposed based on the statistical variation viewpoint. This approach can solve the W-k-means algorithm's problem that the clustering quality is greatly affected by the initial value of weight. After the statistics of data, the weights o...

Due to the progressive growth of the amount of data available in a wide variety of scientific fields, it has become more difficult to manipulate and analyze such information. In spite of its dependency on the initial settings and the large number of distance computations that it can require to converge, the K-means algorithm remains as one of the most popular clustering methods for massive data...

To verify the process of laser-cleaning on metal cathodes we performed a combined photoemission (PE) and scanning electron microscope (SEM) study. We followed the laser-cleaning procedure on thin Lead films arc-deposited on Mo-substrates by means of photoemission in the photon energy range of 16 – 140 eV and determined the surface roughness before and after laser irradiationwith whitelight-inte...

We introduce a novel fuzzy learning vector quantization algorithm for image compression. The design procedure of this algorithm encompasses two basic issues. Firstly, a modified objective function of the fuzzy c-means algorithm is reformulated and then is minimized by means of an iterative gradient-descent procedure. Secondly, the training procedure is equipped with a systematic strategy to acc...

The k-means++ algorithm is the state of the art algorithm to solve k-Means clustering problems as the computed clusterings are O(log k) competitive in expectation. However, its seeding step requires k inherently sequential passes through the full data set making it hard to scale to massive data sets. The standard remedy is to use the k-means‖ algorithm which reduces the number of sequential rou...

The issue discussed in this paper is a bi-level problem in which two rivals compete in attracting customers and maximizing their profits which means that competitors competing for market share must compete in the centers that are going to be located in the near future. In this paper, a nonlinear model presented in the literature considering customer preferences is linearized. Customer behavior ...

We introduce an adaptive algorithm to estimate the uncertain parameter of a stochastic optimization problem. The procedure estimates the one-step-ahead means, variances and covariances of a random process in a distribution-free and multidimensional framework when these means, variances and covariances are slowly varying on a given past interval. The quality of the approximate problem obtained w...