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The Artificial Neural Network methodology is a very important & new subjects that build's the models for Analyzing, Data Evaluation, Forecasting & Controlling without depending on an old model or classic statistic method that describe the behavior of statistic phenomenon, the methodology works by simulating the data to reach a robust optimum model that represent the statistic phenomenon & we can use the model in any time & states, we used the Box-Jenkins (ARMAX) approach for comparing, in this paper depends on the received power to build a robust model for forecasting, analyzing & controlling in the sod power, the received power come from

The process of risk assessment in the build-operate transfer (BOT) project is very important to identify and analyze the risks in order to make the appropriate decision to respond to them. In this paper, AHP Technique was used to make the appropriate decision regarding response to the most prominent risks that were generated in BOT projects, which includes a comparison between the criteria for each risk as well as the available alternatives and by mathematical methods using matrices to reach an appropriate decision to respond to each risk.Ten common risks in BOT contracts are adopted for analysis in this paper, which is grouped into six main risk headings.The procedures followed in this paper are the questionnaire method

Lowpass spatial filters are adopted to match the noise statistics of the degradation seeking
good quality smoothed images. This study imply different size and shape of smoothing
windows. The study shows that using a window square frame shape gives good quality
smoothing and at the same time preserving a certain level of high frequency components in
comparsion with standard smoothing filters.

Groupwise non-rigid image alignment is a difficult non-linear optimization problem involving many parameters and often large datasets. Previous methods have explored various metrics and optimization strategies. Good results have been previously achieved with simple metrics, requiring complex optimization, often with many unintuitive parameters that require careful tuning for each dataset. In this chapter, the problem is restructured to use a simpler, iterative optimization algorithm, with very few free parameters. The warps are refined using an iterative Levenberg-Marquardt minimization to the mean, based on updating the locations of a small number of points and incorporating a stiffness constraint. This optimization approach is eff

<span>Dust is a common cause of health risks and also a cause of climate change, one of the most threatening problems to humans. In the recent decade, climate change in Iraq, typified by increased droughts and deserts, has generated numerous environmental issues. This study forecasts dust in five central Iraqi districts using machine learning and five regression algorithm supervised learning system framework. It was assessed using an Iraqi meteorological organization and seismology (IMOS) dataset. Simulation results show that the gradient boosting regressor (GBR) has a mean square error of 8.345 and a total accuracy ratio of 91.65%. Moreover, the results show that the decision tree (DT), where the mean square error is 8.965, c

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

Ferritin is a key organizer of protected deregulation, particularly below risky hyperferritinemia, by straight immune-suppressive and pro-inflammatory things. , We conclude that there is a significant association between levels of ferritin and the harshness of COVID-19. In this paper we introduce a semi- parametric method for prediction by making a combination between NN and regression models. So, two methodologies are adopted, Neural Network (NN) and regression model in design the model; the data were collected from مستشفى دار التمريض الخاص for period 11/7/2021- 23/7/2021, we have 100 person, With COVID 12 Female & 38 Male out of 50, while 26 Female & 24 Male non COVID out of 50. The input variables of the NN m

In this paper, the reliability and scheduling of maintenance of some medical devices were estimated by one variable, the time variable (failure times) on the assumption that the time variable for all devices has the same distribution as (Weibull distribution.

The method of estimating the distribution parameters for each device was the OLS method.

The main objective of this research is to determine the optimal time for preventive maintenance of medical devices. Two methods were adopted to estimate the optimal time of preventive maintenance. The first method depends on the maintenance schedule by relying on information on the cost of maintenance and the cost of stopping work and acc

Drag reduction (DR) techniques are used to improve the flow by spare the flow energy. The applications of DR are conduits in oil pipelines, oil well operations and flood water disposal, many techniques for drag reduction are used. One of these techniques is microbubbles.  In this work, reduce of drag percent occurs by using a small bubbles of air pumped in the fluid transported. Gasoil is used as liquid transporting in the pipelines and air pumped as microbubbles. This study shows that the maximum value of drag reduction is 25.11%.