Predicting the network traffic of web pages is one of the areas that has increased focus in recent years. Modeling traffic helps find strategies for distributing network loads, identifying user behaviors and malicious traffic, and predicting future trends. Many statistical and intelligent methods have been studied to predict web traffic using time series of network traffic. In this paper, the use of machine learning algorithms to model Wikipedia traffic using Google's time series dataset is studied. Two data sets were used for time series, data generalization, building a set of machine learning models (XGboost, Logistic Regression, Linear Regression, and Random Forest), and comparing the performance of the models using (SMAPE) and

In this paper, the series solution is applied to solve third order fuzzy differential equations with a fuzzy initial value. The proposed method applies Taylor expansion in solving the system and the approximate solution of the problem which is calculated in the form of a rapid convergent series; some definitions and theorems are reviewed as a basis in solving fuzzy differential equations. An example is applied to illustrate the proposed technical accuracy. Also, a comparison between the obtained results is made, in addition to the application of the crisp solution, when the-level equals one.

There are many methods of forecasting, and these methods take data only, analyze it, make a prediction by analyzing, neglect the prior information side and do not considering the fluctuations that occur overtime. The best way to forecast oil prices that takes the fluctuations that occur overtime and is updated by entering prior information is the Bayesian structural time series (BSTS) method. Oil prices fluctuations have an important role in economic so predictions of future oil prices that are crucial for many countries whose economies depend mainly on oil, such as Iraq. Oil prices directly affect the health of the economy. Thus, it is necessary to forecast future oil price with models adapted for emerging events. In this article, we st

    In this paper we introduce a brief review about Box-Jenkins models. The acronym ARIMA stands for “autoregressive integrated moving average”. It is a good method to forecast for stationary and non stationary time series. According to the data which obtained from Baghdad Water Authority, we are modelling two series, the first one about pure water consumption and the second about the number of participants. Then we determine an optimal model by depending on choosing minimum MSE as criterion.

        Artificial Neural networks (ANN) are powerful and effective tools in time-series applications. The first aim of this paper is to diagnose better and more efficient ANN models (Back Propagation, Radial Basis Function Neural networks (RBF), and Recurrent neural networks) in solving the linear and nonlinear time-series behavior. The second aim is dealing with finding accurate estimators as the convergence sometimes is stack in the local minima. It is one of the problems that can bias the test of the robustness of the ANN in time series forecasting. To determine the best or the optimal ANN models, forecast Skill (SS) employed to measure the efficiency of the performance of ANN models. The mean square error and

Abstract
This study aimed to kmow the effect of food on appearance of ovaries cyst in women aged 15-54 year in Baghdad. City and its relation ship with reproductive health Woman samples was divided to four aged groups;15-24 , 25-34 , 35-44 and 45-54 years.
Results demonstrate that all samples of women has varied level of obesity.
Also we are noticed that all samples of women has varied level of obesity.
Also we are noticed tgat is a relation ship between obesity and marriagestatas with the highest proportion of ovarian cystsin obese marriage woman reached to37.90% The percent of un married women which have obesity class // with ovarian cysts reached50% Results refer to found that %19-24 of married women had obortians and

The binary cluster model (BCM) and the two-frequency shell model (TFSM) have been used to study the ground state matter densities of neutron-rich ⁶He and ¹¹Li halo nuclei. Calculations show that both models provide a good description on the matter density distribution of above nuclei. The root-mean square (rms) proton, neutron and matter radii of these halo nuclei obtained by TFSM have been successfully obtained. The elastic charge form factors for these halo nuclei are studied through combining the charge density distribution obtained by TFSM with the plane wave Born approximation (PWBA).

The charge density distributions (CDD) and the elastic electron
scattering form factors F(q) of the ground state for some even mass
nuclei in the 2s 1d shell ( Ne Mg Si 20 24 28 , , and S 32 ) nuclei have
been calculated based on the use of occupation numbers of the states
and the single particle wave functions of the harmonic oscillator
potential with size parameters chosen to reproduce the observed root
mean square charge radii for all considered nuclei. It is found that
introducing additional parameters, namely 1 , and , 2  which
reflect the difference of the occupation numbers of the states from
the prediction of the simple shell model leads to a remarkable
agreement between the calculated an

The ground state proton, neutron and matter densities and
corresponding root mean square radii of unstable proton-rich 17Ne
and 27P exotic nuclei are studied via the framework of the twofrequency
shell model. The single particle harmonic oscillator wave
functions are used in this model with two different oscillator size
parameters core b and halo , b the former for the core (inner) orbits
whereas the latter for the halo (outer) orbits. Shell model calculations
for core nucleons and for outer (halo) nucleons in exotic nuclei are
performed individually via the computer code OXBASH. Halo
structure of 17Ne and 27P nuclei is confirmed. It is found that the
structure of 17Ne and 27P nuclei have 2
5 / 2 (1d ) an