Theatrical performances began with the Greeks when the theatrical scenes and skeletal figures were encoded, where the large wall of the Alskina, which contains three doors, the middle of them with a high height, and the two sides took the natural size, where the middle door indicated a symbolism of the god or demigods, as we find the condensation of the symbol in the architecture of the theater, and the symbol was taken In the theatrical scene, the development semantically and aesthetically, and interpreting and interpreting the current day, where the laser light formed the symbolism of the contemporary virtual scene, and in order to identify the aesthetics of the symbol in the theatrical scene, the current research was evaluated into fo

The monogenean Gyrodactylus taimeni Ergens, 1971 was recorded in this study for the first time in Iraq from gills of the common carp Cyprinus carpio Linnaeus, 1758. The description and measurements of this parasite as well as illustration were given. In addition, a list of species of Gyrodactylus so far recorded from C. carpio in Iraq is also included together with a list of all other hosts recorded for each gyrodactylid species.

The goal of the study is to discover the best model for forecasting the exchange rate of the US dollar against the Iraqi dinar by analyzing time series using the Box Jenkis approach, which is one of the most significant subjects in the statistical sciences employed in the analysis. The exchange rate of the dollar is considered one of the most important determinants of the relative level of the health of the country's economy. It is considered the most watched, analyzed and manipulated measure by the government. There are factors affecting in determining the exchange rate, the most important of which are the amount of money, interest rate and local inflation global balance of payments. The data for the research that represents the exchange r

        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

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

Abstract

In this research we study the wavelet characteristics for the important time series known as Sunspot, on the aim of verifying the periodogram that other researchers had reached by the spectral transform, and noticing the variation in the period length on one side and the shifting on another.

A continuous wavelet analysis is done for this series and the periodogram in it is marked primarily. for more accuracy, the series is partitioned to its the approximate and the details components to five levels, filtering these components by using fixed threshold on one time and independent threshold on another, finding the noise series which represents the difference between

Using photo electrochemical etching technique (PEC), porous silicon (PS) layers were produced on n-type silicon (Si) wafers to generate porous silicon for n-type with an orientation of (111) The results of etching time were investigated at: (5,10,15 min). X-ray diffraction experiments revealed differences between the surface of the sample sheet and the synthesized porous silicon. The largest crystal size is (30 nm) and the lowest crystal size is (28.6 nm) The analysis of Atomic Force Microscopy (AFM) and Field Emission Scanning Electron Microscope (FESEM) were used to research the morphology of porous silicon layer. As etching time increased, AFM findings showed that root mean square (RMS) of roughness and po

In this paper, the time-fractional Fisher’s equation (TFFE) is considered to exam the analytical solution using the Laplace q-Homotopy analysis method (Lq-HAM)”. The Lq-HAM is a combined form of q-homotopy analysis method (q-HAM) and Laplace transform. The aim of utilizing the Laplace transform is to outdo the shortage that is mainly caused by unfulfilled conditions in the other analytical methods. The results show that the analytical solution converges very rapidly to the exact solution.

    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