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.

ABSTRUCT

In This Paper, some semi- parametric spatial models were estimated, these models are, the semi – parametric spatial error model (SPSEM), which suffer from the problem of spatial errors dependence, and the semi – parametric spatial auto regressive model (SPSAR). Where the method of maximum likelihood was used in estimating the parameter of spatial error          ( λ ) in the model (SPSEM), estimated  the parameter of spatial dependence ( ρ ) in the model ( SPSAR ), and using the non-parametric method in estimating the smoothing function m(x) for these two models, these non-parametric methods are; the local linear estimator (LLE) which require finding the smoo

In this paper  has been one study of autoregressive generalized conditional heteroscedasticity models existence of the seasonal component, for the purpose applied to the daily financial data at high frequency is characterized by Heteroscedasticity seasonal conditional, it has been depending on Multiplicative seasonal Generalized Autoregressive Conditional Heteroscedastic Models Which is symbolized by the Acronym (SGARCH) , which has proven effective expression of seasonal phenomenon as opposed to the usual GARCH models. The summarizing of the research work studying the daily data for the price of the dinar exchange rate against the dollar, has been used autocorrelation function to detect seasonal first, then was diagnosed wi

This research including lineament automated extraction by using PCI Geomatica program, depending on satellite image and lineament analysis by using GIS program. Analysis included density analysis, length density analysis and intersection density analysis. When calculate the slope map for the study area, found the relationship between the slope and lineament density.
The lineament density increases in the regions that have high values for the slope, show that lineament play an important role in the classification process as it isolates the class for the other were observed in Iranian territory, clearly, also show that one of the lineament hit shoulders of Galal Badra dam and the surrounding areas dam. So should take into consideration

The research aims to identify the factors that affect the quality of the product by using the Failure Mode and Effect Analysis (FMEA) tool and to suggest measures to reduce the deviations or defects in the production process. I used the case study approach to reach its goals, and the air filter product line was chosen in the air filters factory of Al-Zawraa General Company. The research sample was due to the emergence of many defects of different impact and the continuing demand for the product. I collected data and information from the factory records for two years (2018-2019) and used a scheme Pareto Fishbone Diagram as well as an FMEA tool to analyze data and generate results.

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