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jeasiq-2423
Using Some Estimation Methods for Mixed-Random Panel Data Regression Models with Serially Correlated Errors with Application
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This research includes the study of dual data models with mixed random parameters, which contain two types of parameters, the first is random and the other is fixed. For the random parameter, it is obtained as a result of differences in the marginal tendencies of the cross sections, and for the fixed parameter, it is obtained as a result of differences in fixed limits, and random errors for each section. Accidental bearing the characteristic of heterogeneity of variance in addition to the presence of serial correlation of the first degree, and the main objective in this research is the use of efficient methods commensurate with the paired data in the case of small samples, and to achieve this goal, the feasible general least squares method (FGLS) and the mean group method (MG) were used, and then the efficiency of the extracted estimators was compared in the case of mixed random parameters and the method that gives us the efficient estimator was chosen. Real data was applied that included the per capita consumption of electric energy (Y) for five countries, which represents the number of cross-sections (N = 5) over nine years (T = 9), so the number of observations is (n = 45) observations, and the explanatory variables are the consumer price index (X1) and the per capita GDP (X2). To evaluate the performance of the estimators of the (FGLS) method and the (MG) method on the general model, the mean absolute percentage error (MAPE) scale was used to compare the efficiency of the estimators. The results showed that the mean group estimation (MG) method is the best method for parameter estimation than the (FGLS) method. Also, the (MG) appeared to be the best and best method for estimating sub-parameters for each cross-section (country).

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Publication Date
Sat Nov 28 2020
Journal Name
Iraqi Journal Of Science
An Efficient Method for Solving Coupled Time Fractional Nonlinear Evolution Equations with Conformable Fractional Derivatives
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In this article, an efficient reliable method, which is the residual power series method (RPSM), is used in order to investigate the approximate solutions of conformable time fractional nonlinear evolution equations with conformable derivatives under initial conditions. In particular, two types of equations are considered, which are time coupled diffusion-reaction equations (CD-REs) and MKdv equations coupled with conformable fractional time derivative of order α. The attitude of RPSM and the influence of different values of α are shown graphically.

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Publication Date
Fri Jun 01 2018
Journal Name
Journal Of Engineering
Compensation of the Nonlinear Power Amplifier by Using SCPWL Predistorter with Genetic Algorithm in OFDM technique
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The High Power Amplifiers (HPAs), which are used in wireless communication, are distinctly characterized by nonlinear properties. The linearity of the HPA can be accomplished by retreating an HPA to put it in a linear region on account of power performance loss. Meanwhile the Orthogonal Frequency Division Multiplex signal is very rough. Therefore, it will be required a large undo to the linear action area that leads to a vital loss in power efficiency. Thereby, back-off is not a positive solution. A Simplicial Canonical Piecewise-Linear (SCPWL) model based digital predistorters are widely employed to compensating the nonlinear distortion that introduced by a HPA component in OFDM technology. In this paper, the genetic al

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Publication Date
Fri Jan 01 2021
Journal Name
Ieee Access
Keratoconus Severity Detection From Elevation, Topography and Pachymetry Raw Data Using a Machine Learning Approach
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Publication Date
Sun Feb 27 2022
Journal Name
Iraqi Journal Of Science
Adaptive Learning System of Ontology using Semantic Web to Mining Data from Distributed Heterogeneous Environment
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    Nowadays, the process of ontology learning for describing heterogeneous systems is an influential phenomenon to enhance the effectiveness of such systems using Social Network representation and Analysis (SNA). This paper presents a novel scenario for constructing adaptive architecture to develop community performance for heterogeneous communities as a case study. The crawling of the semantic webs is a new approach to create a huge data repository for classifying these communities. The architecture of the proposed system involves two cascading modules in achieving the ontology data, which is represented in Resource Description Framework (RDF) format. The proposed system improves the enhancement of these environments ach

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Publication Date
Tue Aug 31 2021
Journal Name
Iraqi Geological Journal
Structural Interpretation of Yamama and Naokelekan Formations in Tuba Oil Field Using 2D Seismic Data
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This research includes structure interpretation of the Yamama Formation (Lower Cretaceous) and the Naokelekan Formation (Jurassic) using 2D seismic reflection data of the Tuba oil field region, Basrah, southern Iraq. The two reflectors (Yamama and Naokelekan) were defined and picked as peak and tough depending on the 2D seismic reflection interpretation process, based on the synthetic seismogram and well log data. In order to obtain structural settings, these horizons were followed over all the regions. Two-way travel-time maps, depth maps, and velocity maps have been produced for top Yamama and top Naokelekan formations. The study concluded that certain longitudinal enclosures reflect anticlines in the east and west of the study ar

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Publication Date
Tue Jan 01 2019
Journal Name
Proceedings Of The 5th International Conference On Information Systems Security And Privacy
Identification and Extraction of Digital Forensic Evidence from Multimedia Data Sources using Multi-algorithmic Fusion
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Publication Date
Thu Oct 20 2022
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Turbidity Changes in the Tigris River Using Satellite Data During the COVID-19 Related Lockdown
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In the present study, an attempt has been made to study the change in water quality of the river in terms of turbidity during lockdown associated with COVID-19. Iraq announced the longest-ever lockdown on 25 March 2020 due to COVID-19 pandemic. 

In the absence of ground observations, remote sensing data was adopted, especially during this period. The change in the visible region's spectral reflectance of water in part of the river has been analyzed using the Landsat 8 OLI multispectral remote sensing data at Tigris River, Salah al-Din province (Bayji / near the refinery), Iraq. It was found that the green and red bands are most sensitive and can be used to estimate turbidity. Furthermore, the temporal variation in turbidity was a

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Publication Date
Tue Jan 01 2019
Journal Name
Energy Procedia
Calculation of Salinity and Soil Moisture indices in south of Iraq - Using Satellite Image Data
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A band rationing method is applied to calculate the salinity index (SI) and Normalized Multi-Band Drought Index (NMDI) as pre-processing to take Agriculture decision in these areas is presented. To separate the land from other features that exist in the scene, the classical classification method (Maximum likelihood classification) is used by classified the study area to multi classes (Healthy vegetation (HV), Grasslands (GL), Water (W), Urban (U), Bare Soil (BS)). A Landsat 8 satellite image of an area in the south of Iraq are used, where the land cover is classified according to indicator ranges for each (SI) and (NMDI).

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Publication Date
Wed Mar 27 2019
Journal Name
Iraqi Journal Of Science
Evaluation of the Tectonic Boundaries Using Potential Data at Tikrit-Kirkuk Area, North - Central Iraq
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The gravity and magnetic data of Tikrit-Kirkuk area in central Iraq were considered to study the tectonic situation in the area. The residual anomalies were separated from regional using space windows method with space of about 24, 12 and 10km to delineate the source level of the residual anomalies. The Total Horizontal Derivative (THD) is used to identify the fault trends in the basement and sedimentary rocks depending upon gravity and magnetic data. The identified faults in the study area show (NW-SE), less common (NE-SW) and rare (N-S) trends. Some of these faults extending from the basement to the upper most layer of the sedimentary rocks. It was found that the depth of some gravity and magnetic source range 12-13Km, which confirm th

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Publication Date
Wed May 25 2022
Journal Name
Iraqi Journal Of Science
Using Persistence Barcode to Show the Impact of Data Complexity on the Neural Network Architecture
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    It is so much noticeable that initialization of architectural parameters has a great impact on whole learnability stream so that knowing  mathematical properties of dataset results in providing neural network architecture a better expressivity and capacity. In this paper, five random samples of the Volve field dataset were taken. Then a training set was specified and the persistent homology of the dataset was calculated to show impact of data complexity on selection of multilayer perceptron regressor (MLPR) architecture. By using the proposed method that provides a well-rounded strategy to compute data complexity. Our method is a compound algorithm composed of the t-SNE method, alpha-complexity algorithm, and a persistence barcod

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