The great scientific progress has led to widespread Information as information accumulates in large databases is important in trying to revise and compile this vast amount of data and, where its purpose to extract hidden information or classified data under their relations with each other in order to take advantage of them for technical purposes.

      And work with data mining (DM) is appropriate in this area because of the importance of research in the (K-Means) algorithm for clustering data in fact applied with effect can be observed in variables by changing the sample size (n) and the number of clusters (K)

In this paper, the Monte-Carlo simulation method was used to compare the robust circular S estimator with the circular Least squares method in the case of no outlier data and in the case of the presence of an outlier in the data through two trends, the first is contaminant with high inflection points that represents contaminant in the circular independent variable, and the second the contaminant in the vertical variable that represents the circular dependent variable using three comparison criteria, the median standard error (Median SE), the median of the mean squares of error (Median MSE), and the median of the mean cosines of the circular residuals (Median A(k)). It was concluded that the method of least squares is better than the

Geotechnical engineers have always been concerned with the stabilization of slopes. For this purpose,
various methods such as retaining walls, piles, and geosynthetics may be used to increase the safety factor of slopes prone to failure. The application of stone columns may also be another potential alternative for slope stabilization. Such columns have normally been used for cohesive soil improvement. Most slope analysis and design is based on deterministic approach i.e a set of single valued design parameter are adopted and a set of single valued factor of safety (FOS) is determined. Usually the FOS is selected in view of the understanding and knowledge of the material parameters, the problem geometry, the method of analysis and the

The aim of this work is study the partical distribution function g(r12,r1) for Carbon ion cases (C+2,C+3,C+4) in the position space using Hartree-Fock's Wave function, and the partitioning technique for each shell which is represented by Carbon Ions [C+2 (1s22s2)], [C+3 (1s22s)] and [C+4 (1s2)]. A comparision has been made among the three Carbon ions for each shell. A computer programs (MATHCAD ver. 2001i) has been used texcute the results.

The process of evaluating data (age and the gender structure) is one of the important factors that help any country to draw plans and programs for the future. Discussed the errors in population data for the census of Iraqi population of 1997. targeted correct and revised to serve the purposes of planning. which will be smoothing the population databy using nonparametric regression estimator (Nadaraya-Watson estimator) This estimator depends on bandwidth (h) which can be calculate it by two ways of using Bayesian method, the first when observations distribution is Lognormal Kernel and the second is when observations distribution is Normal Kernel

Big data analysis has important applications in many areas such as sensor networks and connected healthcare. High volume and velocity of big data bring many challenges to data analysis. One possible solution is to summarize the data and provides a manageable data structure to hold a scalable summarization of data for efficient and effective analysis. This research extends our previous work on developing an effective technique to create, organize, access, and maintain summarization of big data and develops algorithms for Bayes classification and entropy discretization of large data sets using the multi-resolution data summarization structure. Bayes classification and data discretization play essential roles in many learning algorithms such a

The Weibull distribution is considered one of the Type-I Generalized Extreme Value (GEV) distribution, and it plays a crucial role in modeling extreme events in various fields, such as hydrology, finance, and environmental sciences. Bayesian methods play a strong, decisive role in estimating the parameters of the GEV distribution due to their ability to incorporate prior knowledge and handle small sample sizes effectively. In this research, we compare several shrinkage Bayesian estimation methods based on the squared error and the linear exponential loss functions. They were adopted and compared by the Monte Carlo simulation method. The performance of these methods is assessed based on their accuracy and computational efficiency in estimati

This paper shews how to estimate the parameter of generalized exponential Rayleigh (GER) distribution by three estimation methods. The first one is maximum likelihood estimator method the second one is moment employing estimation method (MEM), the third one is rank set sampling estimator method (RSSEM)The simulation technique is used for all these estimation methods to find the parameters for generalized exponential Rayleigh distribution. Finally using the mean squares error criterion to compare between these estimation methods to find which of these methods are best to the others

This paper presents a hybrid genetic algorithm (hGA) for optimizing the maximum likelihood function ln(L(phi(1),theta(1)))of the mixed model ARMA(1,1). The presented hybrid genetic algorithm (hGA) couples two processes: the canonical genetic algorithm (cGA) composed of three main steps: selection, local recombination and mutation, with the local search algorithm represent by steepest descent algorithm (sDA) which is defined by three basic parameters: frequency, probability, and number of local search iterations. The experimental design is based on simulating the cGA, hGA, and sDA algorithms with different values of model parameters, and sample size(n). The study contains comparison among these algorithms depending on MSE value. One can conc

The logistic regression model regarded as the important regression Models ,where of the most interesting subjects in recent studies due to taking character more advanced in the process of statistical analysis .                                                

The ordinary estimating methods is failed in dealing with data that consist of the presence of outlier values and hence on the absence of such that have undesirable effect on the result.    &nbs