Coronavirus disease (COVID-19) is an acute disease that affects the respiratory system which initially appeared in Wuhan, China. In Feb 2019 the sickness began to spread swiftly throughout the entire planet, causing significant health, social, and economic problems. Time series is an important statistical method used to study and analyze a particular phenomenon, identify its pattern and factors, and use it to predict future values. The main focus of the research is to shed light on the study of SARIMA, NARNN, and hybrid models, expecting that the series comprises both linear and non-linear compounds, and that the ARIMA model can deal with the linear component and the NARNN model can deal with the non-linear component. The models were applied in the health sector to predict the numbers of people infected with the Covid-19 virus in Iraq where the data were collected via the website of the Iraqi Ministry of Health through the daily epidemiological situation of all Iraqi provinces for the period (2021\3\28 to 2021\8\15). When analyzing, studying, and comparing these models, the researcher noted that the hybrid model outperformed other models because it had the lowest value for the MSE, RMSE, MAE, and MAPE so it was used to predict future values.
The purpose of this research is to find the estimator of the average proportion of defectives based on attribute samples. That have been curtailed either with rejection of a lot finding the kth defective or with acceptance on finding the kth non defective.
The MLE (Maximum likelihood estimator) is derived. And also the ASN in Single Curtailed Sampling has been derived and we obtain a simplified Formula All the Notations needed are explained.
Novel artificial neural network (ANN) model was constructed for calibration of a multivariate model for simultaneously quantitative analysis of the quaternary mixture composed of carbamazepine, carvedilol, diazepam, and furosemide. An eighty-four mixing formula where prepared and analyzed spectrophotometrically. Each analyte was formulated in six samples at different concentrations thus twentyfour samples for the four analytes were tested. A neural network of 10 hidden neurons was capable to fit data 100%. The suggested model can be applied for the quantitative chemical analysis for the proposed quaternary mixture.
A Novel artificial neural network (ANN) model was constructed for calibration of a multivariate model for simultaneously quantitative analysis of the quaternary mixture composed of carbamazepine, carvedilol, diazepam, and furosemide. An eighty-four mixing formula where prepared and analyzed spectrophotometrically. Each analyte was formulated in six samples at different concentrations thus twenty four samples for the four analytes were tested. A neural network of 10 hidden neurons was capable to fit data 100%. The suggested model can be applied for the quantitative chemical analysis for the proposed quaternary mixture.
A Novel artificial neural network (ANN) model was constructed for calibration of a multivariate model for simultaneously quantitative analysis of the quaternary mixture composed of carbamazepine, carvedilol, diazepam, and furosemide. An eighty-four mixing formula where prepared and analyzed spectrophotometrically. Each analyte was formulated in six samples at different concentrations thus twentyfour samples for the four analytes were tested. A neural network of 10 hidden neurons was capable to fit data 100%. The suggested model can be applied for the quantitative chemical analysis for the proposed quaternary mixture.
The research problem was to identify the impact of monetary policies on economic growth in the oil and non-oil countries. The researcher chose the Republic of Iraq as an example for the oil countries and the Arab Republic of Egypt as an example for the non-oil countries to hold a comparison on the impact of monetary policies.
The research found that the monetary policies and their tools in the Iraqi economy affect the rate of GDP growth by 73%, which shows the strong impact of monetary policies on the economic growth in the Iraqi economy as an example of an oil state. GDP growth rate of 61%, indicating the impact of monetary policies on economic growth in the
Polish Academy of Sciences
Forest cover in Mosul Province experienced significant changes following the 2014 occupation. These changes can be effectively analyzed using multitemporal remote sensing imagery. This study aims to evaluate the ability of multi-temporal Landsat 8 images and the Forest Canopy Density (FCD) model to detect changes in forest canopy density in a protected forest in Mosul Governorate during the period from 2014 to 2025. The remote sensing data used in this research are Landsat 8 images captured on March 21, 2014, and April 4, 2025. The method employed is FCD modeling, which produces pixel-level canopy density estimates. The results of the FCD model are then used to analyze changes in canopy density following the occupation. The findings of this
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