Controlling public expenditures is one of the main objectives of the public budget. The public budget often suffers from a deficit, whether in developed or developing countries, because expenditures are usually greater than the revenues generated. This requires the existence of financial rules that are adhered to by the government, which in turn leads to discipline. Fiscal policy leads to a reduction in the obligations incumbent on the government.  Adhering to the financial rules would correct the course of fiscal policy in Iraq, with the need to direct oil revenues in the years of financial abundance when global oil prices rise to sovereign funds similar to other rentier countries, which contributes to maintaining the stabi

A three-stage learning algorithm for deep multilayer perceptron (DMLP) with effective weight initialisation based on sparse auto-encoder is proposed in this paper, which aims to overcome difficulties in training deep neural networks with limited training data in high-dimensional feature space. At the first stage, unsupervised learning is adopted using sparse auto-encoder to obtain the initial weights of the feature extraction layers of the DMLP. At the second stage, error back-propagation is used to train the DMLP by fixing the weights obtained at the first stage for its feature extraction layers. At the third stage, all the weights of the DMLP obtained at the second stage are refined by error back-propagation. Network structures an

Within the framework of big data, energy issues are highly significant. Despite the significance of energy, theoretical studies focusing primarily on the issue of energy within big data analytics in relation to computational intelligent algorithms are scarce. The purpose of this study is to explore the theoretical aspects of energy issues in big data analytics in relation to computational intelligent algorithms since this is critical in exploring the emperica aspects of big data. In this chapter, we present a theoretical study of energy issues related to applications of computational intelligent algorithms in big data analytics. This work highlights that big data analytics using computational intelligent algorithms generates a very high amo

In this paper, the speed control of the real DC motor is experimentally investigated using nonlinear PID neural network controller. As a simple and fast tuning algorithm, two optimization techniques are used; trial and error method and particle swarm optimization PSO algorithm in order to tune the nonlinear PID neural controller's parameters and to find best speed response of the DC motor. To save time in the real system, a Matlab simulation package is used to carry out these algorithms to tune and find the best values of the nonlinear PID parameters. Then these parameters are used in the designed real time nonlinear PID controller system based on LabVIEW package. Simulation and experimental results are compared with each other and showe

Nurse scheduling problem is one of combinatorial optimization problems and it is one of NP-Hard problems which is difficult to be solved as optimal solution. In this paper, we had created an proposed algorithm which it is hybrid simulated annealing algorithm to solve nurse scheduling problem, developed the simulated annealing algorithm and Genetic algorithm. We can note that the proposed algorithm (Hybrid simulated Annealing Algorithm(GS-h)) is the best method among other methods which it is used in this paper because it satisfied minimum average of the total cost and maximum number of Solved , Best and Optimal problems. So we can note that the ratios of the optimal solution are 77% for the proposed algorithm(GS-h), 28.75% for Si

In this article, the high accuracy and effectiveness of forecasting global gold prices are verified using a hybrid machine learning algorithm incorporating an Adaptive Neuro-Fuzzy Inference System (ANFIS) model with Particle Swarm Optimization (PSO) and Gray Wolf Optimizer (GWO). The hybrid approach had successes that enabled it to be a good strategy for practical use. The ARIMA-ANFIS hybrid methodology was used to forecast global gold prices. The ARIMA model is implemented on real data, and then its nonlinear residuals are predicted by ANFIS, ANFIS-PSO, and ANFIS-GWO. The results indicate that hybrid models improve the accuracy of single ARIMA and ANFIS models in forecasting. Finally, a comparison was made between the hybrid foreca

Abstract

Multivariate GARCH Models take several forms , the most important DCC dynamic conditional correlation, and CCC constant conditional correlation , The Purpose of this research is the Comparison for both Models.Using three  financial time series which is a series of daily Iraqi dinar exchange rate indollar, Global daily Oil price in dollar and Global daily gold price in dollarfor the period from 01/01/2014 till 01/01/2016, Where it has been transferred to the three time series returns to get the Stationarity, some tests were conducted including Ljung-Box , JarqueBera  , Multivariate ARCH to Returns Series and Residuals Series for both models In Comparison

The study of the dynamic behavior of packed distillation column was studied by frequency response analysis using Matlab program. A packed distillation column (80 mm diameter) (2000 mm height) filled with glass packing (Raschig Rings 10mm), packing height (1500 mm) has been modified for separation of methanol-water mixture (60 vol%). The column dynamic behavior was studied experimentally under different step changes in, feed rate (±30%), reflux rate (±22%), and reboiler heat duty (±150%), the top and bottom concentration of methanol were measured. A frequency response analysis for the above step response was carried out using Bode diagram, the log modulus and the phase angle were  used to analyze the process model. A Matlab progra

Maximizing the net present value (NPV) of oil field development is heavily dependent on optimizing well placement. The traditional approach entails the use of expert intuition to design well configurations and locations, followed by economic analysis and reservoir simulation to determine the most effective plan. However, this approach often proves inadequate due to the complexity and nonlinearity of reservoirs. In recent years, computational techniques have been developed to optimize well placement by defining decision variables (such as well coordinates), objective functions (such as NPV or cumulative oil production), and constraints. This paper presents a study on the use of genetic algorithms for well placement optimization, a ty