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This study aims to explain the purposes of thinking in light of the Holy Qur’an. The study adopted the deductive method by analyzing the meanings of the eighteen thinking verses mentioned in the Holy Qur’an That is by referring to the ancient and contemporary book interpretations and inferring the purposes of thinking from the context of those verses. The study concluded that there are seven main purposes, for thinking in the light of the Holy Qur’an, are understanding the universal regulations, contemplating the social codes, inferring, extracting maxims and meanings from the Qur’anic proverbs, analyzing the situation objectively, balancing between interests and evils, and freeing the mind from the authority of the paranormal. T

The purpose of the search definition on factors superior this whoever  impact on the improvement the exploitation  decision this  detect on stock exchange markets  assumption , and using  questioners to data accumulate  and the Iraq market of financial papers  battleground to search annoy chose the eyeball extension (47) peoples from peoples employees on the market , and using the bought statistic program (SPSS) on input and analysis search  data , and machinate the search outputs to their group of factors internal and external which impact on exploitation  decision on financial papers markets , and the search commend on necessary rise  the incumbency direction  in the stock exchange

  In Iraq,  government contributions to the public companies have become a very important aspect which contributes to the survival and sustainability of these institutions as it consider  one of the main sources of funding, if not it consider the basis of funding.

  According to  the vital roles assigned to these institutions to follow up, which usually  include important activities in the national economy, the research focused on studying the field reality of the method used in evaluating the stock of total production and purchases of goods for the purpose of selling the strategic commodities of the General Company for Grain Trade.  As a result, the aim of this study came to came to highlight&n

Classification of imbalanced data is an important issue. Many algorithms have been developed for classification, such as Back Propagation (BP) neural networks, decision tree, Bayesian networks etc., and have been used repeatedly in many fields. These algorithms speak of the problem of imbalanced data, where there are situations that belong to more classes than others. Imbalanced data result in poor performance and bias to a class without other classes. In this paper, we proposed three techniques based on the Over-Sampling (O.S.) technique for processing imbalanced dataset and redistributing it and converting it into balanced dataset. These techniques are (Improved Synthetic Minority Over-Sampling Technique (Improved SMOTE),  Border

      In this study, different methods were used for estimating location parameter  and scale parameter for extreme value distribution, such as maximum likelihood estimation (MLE) , method of moment  estimation (ME),and approximation  estimators based on percentiles which is called white method in estimation, as the extreme value distribution is one of exponential distributions. Least squares estimation (OLS) was used, weighted least squares estimation (WLS), ridge regression estimation (Rig), and adjusted ridge regression estimation (ARig) were used. Two parameters for expected value to the percentile  as estimation for distribution f

Two unsupervised classifiers for optimum multithreshold are presented; fast Otsu and k-means. The unparametric methods produce an efficient procedure to separate the regions (classes) by select optimum levels, either on the gray levels of image histogram (as Otsu classifier), or on the gray levels of image intensities(as k-mean classifier), which are represent threshold values of the classes. In order to compare between the experimental results of these classifiers, the computation time is recorded and the needed iterations for k-means classifier to converge with optimum classes centers. The variation in the recorded computation time for k-means classifier is discussed.