The logistic regression model of the most important regression models a non-linear which aim getting estimators have a high of efficiency, taking character more advanced in the process of statistical analysis for being a models appropriate form of Binary Data.                                                          

Among the problems that appear as a result of the use of some statistical methods I

      The activity concentration of natural radioactivity levels, of artificial cesium and transfer factor from soil to plants in agricultural areas at Al- Yusiefya region were determined by using NaI (Tl) detector spectrometer. Ten species of leafy plants have been selected: Spinach, Parsley, Watercress, Lettuce, Rashad, Radish, Green onion, Turnip green, Green beet and Mint. The mean activity concentrations of ²³⁸U, ²³²Th, ⁴⁰K and ¹³⁷Cs in leafy vegetable samples were  12.4±3.8, 14.8±4.7, 283±93 and 1.06±0.99  Bg/kg, and in soil samples were 15.9±4.3, 16.1±5.2, 298.5±3.9, and 1.11±0.37 Bq/kg. The radiation hazard indices were evaluated (radium

By optimizing the efficiency of a modular simulation model of the PV module structure by genetic algorithm, under several weather conditions, as a portion of recognizing the ideal plan of a Near Zero Energy Household (NZEH), an ideal life cycle cost can be performed. The optimum design from combinations of NZEH-variable designs, are construction positioning, window-to-wall proportion, and glazing categories, which will help maximize the energy created by photovoltaic panels. Comprehensive simulation technique and modeling are utilized in the solar module I-V and for P-V output power. Both of them are constructed on the famous five-parameter model.  In addition, the efficiency of the PV panel is established by the genetic algorithm

    It is so much noticeable that initialization of architectural parameters has a great impact on whole learnability stream so that knowing  mathematical properties of dataset results in providing neural network architecture a better expressivity and capacity. In this paper, five random samples of the Volve field dataset were taken. Then a training set was specified and the persistent homology of the dataset was calculated to show impact of data complexity on selection of multilayer perceptron regressor (MLPR) architecture. By using the proposed method that provides a well-rounded strategy to compute data complexity. Our method is a compound algorithm composed of the t-SNE method, alpha-complexity algorithm, and a persistence barcod

One of the serious problems in any wireless communication system using multi carrier modulation technique like Orthogonal Frequency Division Multiplexing (OFDM) is its Peak to Average Power Ratio (PAPR).It limits the transmission power due to the limitation of dynamic range of Analog to Digital Converter and Digital to Analog Converter (ADC/DAC) and power amplifiers at the transmitter, which in turn sets the limit over maximum achievable rate.

        This issue is especially important for mobile terminals to sustain longer battery life time. Therefore reducing PAPR can be regarded as an important issue to realize efficient and affordable mobile communication services.

      Detecting and subtracting the Motion objects from backgrounds is one of the most important areas. The development of cameras and their widespread use in most areas of security, surveillance, and others made face this problem. The difficulty of this area is unstable in the classification of the pixels (foreground or background). This paper proposed a suggested background subtraction algorithm based on the histogram. The classification threshold is adaptively calculated according to many tests. The performance of the proposed algorithms was compared with state-of-the-art methods in complex dynamic scenes.

We have studied Bayesian method in this paper by using the modified exponential growth model, where this model is more using to represent the growth phenomena. We focus on three of prior functions (Informative, Natural Conjugate, and the function that depends on previous experiments) to use it in the Bayesian method. Where almost of observations for the growth phenomena are depended on one another, which in turn leads to a correlation between those observations, which calls to treat such this problem, called Autocorrelation, and to verified this has been used Bayesian method.

The goal of this study is to knowledge the effect of Autocorrelation on the estimation by using Bayesian method. F

In this paper a measure of linear local dependence has been used between two
random variables and a study is conducted for the properties of this measure where
two examples of bivariate probability distributions has been considered, which are
bivariate Gumbel distribution and bivariate Beta-Stacy distribution, and applied on
data collected by using a questionnaire conducted to study the reasons for the
increase of application in private collages in Iraq. Five elements has been considered
as random variables and the dependence has been measured between every two
elements to estimate how correlated these elements are and their effect on the
application in private collages of Iraq generally and Baghdad specifically.<