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

Abstract

      In this study, we compare between the autoregressive approximations (Yule-Walker equations, Least Squares , Least Squares ( forward- backword ) and Burg’s (Geometric and Harmonic ) methods, to determine the optimal approximation to the time series generated from the first - order moving Average non-invertible process, and fractionally - integrated noise process, with several values for d (d=0.15,0.25,0.35,0.45) for different sample sizes (small,median,large)for two processes . We depend on figure of merit function which proposed by author Shibata in 1980, to determine the theoretical optimal order according to min

Biological activity of the carotenoids which are produced fromchemically-mutaed local isolate of Rhodotorula mucilaginosawas studied. The results showed variation of inhibition activity of caritenoids against different types of pathogenic bacteria include, Staph aureus, E. coli, B. subtilis and Salmo. typh., the number declined from 2×107cell/ml to 2×104, 5×104, 2×105, 9×105 cell/ml respectively after 24hour. The produced carotenoids from alocal mutant Rhodotorula mucilaginosa revealed an antioxidant activity as free radical removal of 85.6%. Carotinoides revealed a highest stability in petroleum ether solvent for 30 days at room temperature. It found that the pigment was more stability in sesame oil compared with sun flower and coc

Background: Evaluation and measurement of primary stability could be achieved by several methods, including the resonance frequency analysis (RFA) and implant insertion torque (IT) values. The need for a sufficient primary stability, guaranteed by an adequate insertion torque and implant stability quotient values, increased its importance mainly in one stage implants or in immediate loading protocols. The aims of this study was to find if there is a correlation between the peak insertion torque (PIT) and ISQ values of implants inserted in the jaws of different bone quality which regarded as two important clinical determinant factors for prediction of implant primary stability, and to evaluate and compare whether an experienced clinician cou

The art of calligraphy is regarded as one of the tremendous and distinct products in the history of Islamic arts through the unparalleled high ranks it reached to, until it gained Islam's indication and became a symbol for it, in terms of its function linked to the calligraphy of the holy Qur'anic texts, where this merit formed a strong impulse in seeking its development, the emergence of the calligraphic constructs, including the calligraphic straps, is one of this continual seeking products, and an advanced stage at the level of technical and expressive development, and also one of the natural results generated by the development of fonts and the increase of their employing methods through what they recorded at the level of formal char

This paper study two stratified quantile regression models of the marginal and the conditional varieties. We estimate the quantile functions of these models by using two nonparametric methods of smoothing spline (B-spline) and kernel regression (Nadaraya-Watson). The estimates can be obtained by solve nonparametric quantile regression problem which means minimizing the quantile regression objective functions and using the approach of varying coefficient models. The main goal is discussing the comparison between the estimators of the two nonparametric methods and adopting the best one between them

The banking system considered as one of the most important intermediate circle between creditor and debtors it is mean the most important funding rings in economic activity, whether finance takes the a consumer or investment form and therefore it is the main base to stimulate economic activity both on the demand side, both consumption and investment and therefore of the main motivating factors for economic growth.

The banking system depends in achieve its goals on the grants and loan recovery, or what is known credit process and according to what the importance referred to the role of the banking system, it is important to ensure the safety and efficiency of the mechanisms of banking device and safety is

As the process of  estimate for model and variable selection significant is a crucial process in the semi-parametric modeling At the beginning of the modeling process often At there are many explanatory variables to Avoid the loss of any explanatory elements may be important as a result , the selection of significant variables become necessary , so the process of variable selection is not intended to simplifying  model complexity explanation , and also predicting. In this research was to use some of the semi-parametric methods (LASSO-MAVE , MAVE and The proposal method (Adaptive LASSO-MAVE) for variable selection and estimate semi-parametric single index model (SSIM) at the same time .

Methods of estimating statistical distribution have attracted many researchers when it comes to fitting a specific distribution to data. However, when the data belong to more than one component, a popular distribution cannot be fitted to such data. To tackle this issue, mixture models are fitted by choosing the correct number of components that represent the data. This can be obvious in lifetime processes that are involved in a wide range of engineering applications as well as biological systems. In this paper, we introduce an application of estimating a finite mixture of Inverse Rayleigh distribution by the use of the Bayesian framework when considering the model as Markov chain Monte Carlo (MCMC). We employed the Gibbs sampler and