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Multiple and Coherent Noise Removal from X-Profile 2D Seismic Data of Southern Iraq Using Normal Move Out-Frequency Wavenumber Technique
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Multiple eliminations (de-multiple) are one of seismic processing steps to remove their effects and delineate the correct primary refractors. Using normal move out to flatten primaries is the way to eliminate multiples through transforming these data to frequency-wavenumber domain. The flatten primaries are aligned with zero axis of the frequency-wavenumber domain and any other reflection types (multiples and random noise) are distributed elsewhere. Dip-filter is applied to pass the aligned data and reject others will separate primaries from multiple after transforming the data back from frequency-wavenumber domain to time-distance domain. For that, a suggested name for this technique as normal move out- frequency-wavenumber domain method for multiple eliminations. The method is tested on a fake reflection event to authorize their validity, and applied to a real field X-profile 2D seismic data from southern Iraq. The results ensure the possibility of internal multiple types existing in the deep reflection data in Iraq and have to remove. So that the interpretation for the true reflectors be valid. The final processed stacked seismic data using normal move out- frequency-wavenumber domain technique shows good, clear, and sharp reflectors in comparison with the conventional normal move out stack data. Open-source Madagascar reproducible package is used for processing all steps of this study and the package is very efficient, accurate, and easy to implement normal move out, frequency-wavenumber domain, Dip-filter programs. The aim of the current study is to separate internal multiples and noise from the real 2D seismic data.

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Publication Date
Mon Jan 01 2024
Journal Name
Lecture Notes On Data Engineering And Communications Technologies
Utilizing Deep Learning Technique for Arabic Image Captioning
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Publication Date
Sun Sep 30 2007
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Biotreatment Technique to Treat Oil Wells Drilling Waste
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The minimization, treatment and disposal of drilling wastes especially oily wastes are important environmental issues.

In this research two fungal isolates named Pleurotus ostreatus and Trichoderma harzianum were chosen carefully f or the purpose of biotreatment of oily drilled cuttings which resulting from  drilling oil wells using oil based muds (OBMs).

A relationship of total petroleum hydrocarbon degradation in oily drilled cuttings with time has been obtained. The results showed that Pleurotus ostreatus and Trichoderma harzianum can be considered hydrocarbon degrading microorganisms and the used biotreatment is cost effective process since most of the materials used in the cultivation and growth of the present f

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Publication Date
Thu Oct 31 2019
Journal Name
Journal Of Engineering And Applied Sciences
Comparison of Estimate Methods of Multiple Linear Regression Model with Auto-Correlated Errors when the Error Distributed with General Logistic
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In this research, we studied the multiple linear regression models for two variables in the presence of the autocorrelation problem for the error term observations and when the error is distributed with general logistic distribution. The auto regression model is involved in the studying and analyzing of the relationship between the variables, and through this relationship, the forecasting is completed with the variables as values. A simulation technique is used for comparison methods depending on the mean square error criteria in where the estimation methods that were used are (Generalized Least Squares, M Robust, and Laplace), and for different sizes of samples (20, 40, 60, 80, 100, 120). The M robust method is demonstrated the best metho

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Publication Date
Thu Oct 31 2019
Journal Name
Journal Of Engineering And Applied Sciences
Comparison of Estimate Methods of Multiple Linear Regression Model with Auto-Correlated Errors when the Error Distributed with General Logistic
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In this research, we studied the multiple linear regression models for two variables in the presence of the autocorrelation problem for the error term observations and when the error is distributed with general logistic distribution. The auto regression model is involved in the studying and analyzing of the relationship between the variables, and through this relationship, the forecasting is completed with the variables as values. A simulation technique is used for comparison methods depending

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Publication Date
Fri Mar 06 2026
Journal Name
Al–bahith Al–a'alami
IRAQ YOUTH TRENDS TOWARDS CELEBRITY ADVERTISEMENTS ON SOCIAL MEDIA : (A research taken from a Master Degree thesis)
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In the light of what is witnessing in the advertising arena of new ways and methods in delivering advertising message to consumers by finding new outlets to communicate with them especially through social networking sites, which became the first choice of advertising companies in order to spread its goods and services. These companies now are relying gradually on celebrities to appear with their products and goods to drive the audience's  attention towards them. The thesis aims to find out the attitudes of young people towards the the advertisements that show famous celebrities on social networking sites. The researcher used survey method which aims to record, analyze and interpret the phenomenon after collecting the necessa

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Publication Date
Sun Feb 05 2017
Journal Name
Iraqi Journal Of Market Research And Consumer Protection
STUDY OF OPTIMUM CONDITIONS FOR REMOVAL OF HEAVY METAL ELEMENTS BY LOCALLY ISOLATED FUNGI Aspergillus niger.: STUDY OF OPTIMUM CONDITIONS FOR REMOVAL OF HEAVY METAL ELEMENTS BY LOCALLY ISOLATED FUNGI Aspergillus niger.
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The best optimum temperature for the isolate was 30○C while the pH for the maximum mineral removal was 6. The best primary mineral removal was 100mg/L, while the maximum removal for all minerals was obtained after 8 hrs, and the maximum removal efficiency was obtained after 24 hrs. The results have proved that the best aeration for maximum removal was obtained at rotation speed of 150 rpm/ minute. Inoculums of 5ml/ 100ml which contained 106 cell/ ml showed maximum removal for the isolate.

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Publication Date
Sun Jul 01 2018
Journal Name
Agronomy Journal
Use of Rainfall Data to Improve Ground-Based Active Optical Sensors Yield Estimates
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Ground-based active optical sensors (GBAOS) have been successfully used in agriculture to predict crop yield potential (YP) early in the season and to improvise N rates for optimal crop yield. However, the models were found weak or inconsistent due to environmental variation especially rainfall. The objectives of the study were to evaluate if GBAOS could predict YP across multiple locations, soil types, cultivation systems, and rainfall differences. This study was carried from 2011 to 2013 on corn (Zea mays L.) in North Dakota, and in 2017 in potatoes in Maine. Six N rates were used on 50 sites in North Dakota and 12 N rates on two sites, one dryland and one irrigated, in Maine. Two active GBAOS used for this study were GreenSeeker and Holl

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Publication Date
Mon May 11 2020
Journal Name
Baghdad Science Journal
Proposing Robust LAD-Atan Penalty of Regression Model Estimation for High Dimensional Data
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         The issue of penalized regression model has received considerable critical attention to variable selection. It plays an essential role in dealing with high dimensional data. Arctangent denoted by the Atan penalty has been used in both estimation and variable selection as an efficient method recently. However, the Atan penalty is very sensitive to outliers in response to variables or heavy-tailed error distribution. While the least absolute deviation is a good method to get robustness in regression estimation. The specific objective of this research is to propose a robust Atan estimator from combining these two ideas at once. Simulation experiments and real data applications show that the p

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Publication Date
Tue Aug 15 2023
Journal Name
Journal Of Economics And Administrative Sciences
Machine Learning Techniques for Analyzing Survival Data of Breast Cancer Patients in Baghdad
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The Machine learning methods, which are one of the most important branches of promising artificial intelligence, have great importance in all sciences such as engineering, medical, and also recently involved widely in statistical sciences and its various branches, including analysis of survival, as it can be considered a new branch used to estimate the survival and was parallel with parametric, nonparametric and semi-parametric methods that are widely used to estimate survival in statistical research. In this paper, the estimate of survival based on medical images of patients with breast cancer who receive their treatment in Iraqi hospitals was discussed. Three algorithms for feature extraction were explained: The first principal compone

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Publication Date
Tue Dec 01 2015
Journal Name
Journal Of Engineering
Ten Years of OpenStreetMap Project: Have We Addressed Data Quality Appropriately? – Review Paper
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It has increasingly been recognised that the future developments in geospatial data handling will centre on geospatial data on the web: Volunteered Geographic Information (VGI). The evaluation of VGI data quality, including positional and shape similarity, has become a recurrent subject in the scientific literature in the last ten years. The OpenStreetMap (OSM) project is the most popular one of the leading platforms of VGI datasets. It is an online geospatial database to produce and supply free editable geospatial datasets for a worldwide. The goal of this paper is to present a comprehensive overview of the quality assurance of OSM data. In addition, the credibility of open source geospatial data is discussed, highlighting the diff

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