The aim of the research is to study the comparison between (ARIMA) Auto Regressive Integrated Moving Average and(ANNs) Artificial Neural Networks models and to select the best one for prediction the monthly relative humidity values depending upon the standard errors between estimated and observe values . It has been noted that both can be used for estimation and the best on among is (ANNs) as the values (MAE,RMSE, R2) is )0.036816,0.0466,0.91) respectively for the best formula for model (ARIMA) (6,0,2)(6,0,1) whereas the values of estimates relative to model (ANNs) for the best formula (5,5,1) is (0.0109, 0.0139 ,0.991) respectively. so that model (ANNs) is superior than (ARIMA) in a such evaluation.
This study aims to measure the basic foundations of organizational health in the General Company for Food Products and to indicate the extent of its presence or not within the company under investigation.
This research was completed using a descriptive and analytical approach using a sample of 97 employees from the General Company for Petroleum Products. Calculating the arithmetic mean, standard deviation, coefficient of variation, and confirmatory factor analysis are all part of the data processing process.
The aim of this research is to study the effects of the North Atlantic Oscillation (NAO) on the temperature and precipitation patterns in Baghdad city. Data of the monthly means of the NAO index,the monthlymeans of temperature, and the monthly total of precipitation were analyzed for the period 1900-2008. Non-parametric tests were used to investigate the correlations between these variables. The time series of temperature and precipitation showed no trends. The results indicated that is a slight correlation between the NAO and temperature and precipitation suggesting that NAO has no major effects on the temperature and precipitation patterns in Baghdad city.
To achieve the goals, the researcher followed the design of equal and independent groups of partial control and post-test . The research has chosen the Institute of Fine Arts in the area Almansour area as deliberate sample where three sections of students have been chosen and the number of students is (69) students. The researcher conducted equivalence in the variables (age, and IQ , and the overall rate for grade III). in diagnostic phase, (21) concepts of alternative image out of (46) concepts have been identified in addition to the goals of formulation of acquisition concepts according to the three processes (definition, discrimination and application). Achievement test has been
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The research aim is to determine the relation between governmental consumption expenditure (GCE) & GDP in Iraq for the period 1981-2006.
The research has determined the scale of optimization for (GCE) & try to know the extent productivity of this expenditure and using the long run &short run model to test .The results clarify the following
1-The marginal productivity for the (GCE) is positive so it is productive.
2-The (GCE) in Iraq is too high because the marginal productivity for the expenditure less than 1.
3- The (GCE) percentage to GNP is
... Show MoreThe special core analysis tests were accomplished on a set of core plugs for Mishrif Formation (mA, mB1, and mB2cde/mC units) in West Qurna/1 oilfield, southern Iraq. Oil relative permeability (Kro) data and the Corey-type fit of the data as functions of the brine saturation at the core outlet face for individual samples in the water-oil imbibition process to estimate relative permeability measurements by the centrifuge method were utilized. Identical correlations for oil and water relative permeabilities were extracted by steady-state and unsteady-state methods. For the mA samples, the gas-water capillary pressure curves were within a narrow range (almost identical) indicating that mA is a homogeneous unit. Kro curves for three mB2
... Show MoreThe special core analysis tests were accomplished on a set of core plugs for Mishrif Formation (mA, mB1, and mB2cde/mC units) in West Qurna/1 oilfield, southern Iraq. Oil relative permeability (Kro) data and the Corey-type fit of the data as functions of the brine saturation at the core outlet face for individual samples in the water-oil imbibition process to estimate relative permeability measurements by the centrifuge method were utilized. Identical correlations for oil and water relative permeabilities were extracted by steady-state and unsteady-state methods. For the mA samples, the gas-water capillary pressure curves were within a narrow range (almost identical) indicating that mA is a homogeneous unit. Kro curves for thr
... Show MoreA reliability system of the multi-component stress-strength model R(s,k) will be considered in the present paper ,when the stress and strength are independent and non-identically distribution have the Exponentiated Family Distribution(FED) with the unknown shape parameter α and known scale parameter λ equal to two and parameter θ equal to three. Different estimation methods of R(s,k) were introduced corresponding to Maximum likelihood and Shrinkage estimators. Comparisons among the suggested estimators were prepared depending on simulation established on mean squared error (MSE) criteria.
In this paper, we derived an estimator of reliability function for Laplace distribution with two parameters using Bayes method with square error loss function, Jeffery’s formula and conditional probability random variable of observation. The main objective of this study is to find the efficiency of the derived Bayesian estimator compared to the maximum likelihood of this function and moment method using simulation technique by Monte Carlo method under different Laplace distribution parameters and sample sizes. The consequences have shown that Bayes estimator has been more efficient than the maximum likelihood estimator and moment estimator in all samples sizes
Software-defined networking (SDN) presents novel security and privacy risks, including distributed denial-of-service (DDoS) attacks. In response to these threats, machine learning (ML) and deep learning (DL) have emerged as effective approaches for quickly identifying and mitigating anomalies. To this end, this research employs various classification methods, including support vector machines (SVMs), K-nearest neighbors (KNNs), decision trees (DTs), multiple layer perceptron (MLP), and convolutional neural networks (CNNs), and compares their performance. CNN exhibits the highest train accuracy at 97.808%, yet the lowest prediction accuracy at 90.08%. In contrast, SVM demonstrates the highest prediction accuracy of 95.5%. As such, an
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