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jeasiq-751
Building discriminant function for repeated measurements data under compound symmetry (CS) covariance structure and applied in the health field
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Discriminant analysis is a technique used to distinguish and classification an individual to a group among a number of  groups based on a linear combination of a set of relevant variables know discriminant function. In this research  discriminant analysis used to analysis data from repeated measurements design. We  will  deal  with the problem of  discrimination  and  classification in the case of  two  groups by assuming the Compound Symmetry covariance structure  under  the  assumption  of  normality for  univariate  repeated measures data.

 

The importance of this research represented to find the best model  to classify  a  group of  patients who  suffer  from diabetes.  For  the purpose of studying the effects of  the number of correlations, variances, and umber of  repeated  measurements  on the performance of classification rules for this  type of  data  based on monthly measurements  of  glycosylated  hemoglobin (HbA1C) in the blood was taken in three stages, which  is  the beginning  of  the experiment, and after three months, and  then  six  months for two groups of patients, the first group consists of  (38)  patients  was  suffered  from  diabetes  type (I)  and  the second group includes (33) patients suffered from diabetes type (II).

 

And through this research, concluded that when the number of parameters began to increase. Thus, the apparent error rate  begin to increasing, and this is what reduces the efficiency of classification rules for this type of data. And  we  recommend  by  using  the linear discriminant function when you focus on the least number of parameters to build the classification rule. And quadratic discriminant procedure Represented by equal the variance and different correlation parameters  under compound symmetry covariance structures

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Publication Date
Wed Mar 01 2017
Journal Name
International Journal Of Cardiology
The role of myocardial performance index in assessment of left ventricular function in patients with valvular mitral regurgitation
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Background: Mitral regurgitation (MR) is the most commonly encountered valve lesion in modern clinical practice. Severe mitral regurgitation may cause systolic dysfunction. Left ventricular ejection fraction may not be an accurate measurement of LV function in patients with mitral insufficiency. Myocardial performance index (MPI) is a simple non invasive measure of myocardial function. Methods: The study involved 50 patients with valvular mitral regurgitation and 50 healthy subjects as a control group. Transthoracic echocardiography was carried out for all patients and control group. The echocardiographic measurements included left ventricular end diastolic and end systolic dimensions, left atrial diameter, ejection fraction (EF), and myoca

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Publication Date
Sat Jul 01 2023
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
Judicial Accounting and its impact on the outcome of the activity of the Iraqi General Insurance Company: applied research
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The research aims to shed light on the importance of forensic accounting and its impact on the outcome of the activity of the Iraqi general insurance company by winning the lawsuits filed against the company because the forensic accountant (the judicial expert) possesses the ability to interpret and analyze the data. The research community represents the insurance companies in Iraq. Iraqi insurance, and the researchers adopted the descriptive approach in covering the theoretical aspect and the deductive approach in covering the practical side, depending on the financial statements of the Iraqi insurance company for the years of research.

One of the most important conclusions reached by the researchers was that forensic accounting

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Publication Date
Wed Mar 29 2023
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Improving the Recovery of Hydrocarbons in a Well in the Gullfaks Field by Injecting Sequestrated CO2
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The Gullfaks field was discovered in 1978 in the Tampen area of the North Sea and it is one of the largest Norwegian oil fields located in Block 34/10 along the western flank of the Viking Graben in the northern North Sea. The Gullfaks field came on stream in 1986 and reached a peak of production in 2001. After some years, a decrease in production was noticed due to the decrease in pressure in the well. The goal of this paper is to improve the production of a well located in Gullfaks field by injecting CO2 through coiled tubing. The use of the CO2 injection method is due to the fact that it is a greenhouse gas, and its production in the atmosphere contributes to global warming. It is important to reduce its emission

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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 proposed LAD-Atan estimator

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Publication Date
Sun Apr 30 2023
Journal Name
Iraqi Geological Journal
Evaluating Machine Learning Techniques for Carbonate Formation Permeability Prediction Using Well Log Data
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Machine learning has a significant advantage for many difficulties in the oil and gas industry, especially when it comes to resolving complex challenges in reservoir characterization. Permeability is one of the most difficult petrophysical parameters to predict using conventional logging techniques. Clarifications of the work flow methodology are presented alongside comprehensive models in this study. The purpose of this study is to provide a more robust technique for predicting permeability; previous studies on the Bazirgan field have attempted to do so, but their estimates have been vague, and the methods they give are obsolete and do not make any concessions to the real or rigid in order to solve the permeability computation. To

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Publication Date
Sun Mar 01 2015
Journal Name
Journal Of Engineering
Multi-Sites Multi-Variables Forecasting Model for Hydrological Data using Genetic Algorithm Modeling
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A two time step stochastic multi-variables multi-sites hydrological data forecasting model was developed and verified using a case study. The philosophy of this model is to use the cross-variables correlations, cross-sites correlations and the two steps time lag correlations simultaneously, for estimating the parameters of the model which then are modified using the mutation process of the genetic algorithm optimization model. The objective function that to be minimized is the Akiake test value. The case study is of four variables and three sites. The variables are the monthly air temperature, humidity, precipitation, and evaporation; the sites are Sulaimania, Chwarta, and Penjwin, which are located north Iraq. The model performance was

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Publication Date
Thu Jun 01 2023
Journal Name
Bulletin Of Electrical Engineering And Informatics
A missing data imputation method based on salp swarm algorithm for diabetes disease
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Most of the medical datasets suffer from missing data, due to the expense of some tests or human faults while recording these tests. This issue affects the performance of the machine learning models because the values of some features will be missing. Therefore, there is a need for a specific type of methods for imputing these missing data. In this research, the salp swarm algorithm (SSA) is used for generating and imputing the missing values in the pain in my ass (also known Pima) Indian diabetes disease (PIDD) dataset, the proposed algorithm is called (ISSA). The obtained results showed that the classification performance of three different classifiers which are support vector machine (SVM), K-nearest neighbour (KNN), and Naïve B

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Publication Date
Mon Aug 01 2022
Journal Name
Baghdad Science Journal
A Novel Technique for Secure Data Cryptosystem Based on Chaotic Key Image Generation
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The advancements in Information and Communication Technology (ICT), within the previous decades, has significantly changed people’s transmit or store their information over the Internet or networks. So, one of the main challenges is to keep these information safe against attacks. Many researchers and institutions realized the importance and benefits of cryptography in achieving the efficiency and effectiveness of various aspects of secure communication.This work adopts a novel technique for secure data cryptosystem based on chaos theory. The proposed algorithm generate 2-Dimensional key matrix having the same dimensions of the original image that includes random numbers obtained from the 1-Dimensional logistic chaotic map for given con

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Publication Date
Mon Oct 22 2018
Journal Name
Journal Of Economics And Administrative Sciences
Dimensions of binding and non-binding social responsibility and its relationship to organizational performance / Field Study in telecommunications companies in the Republic of yemen
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The study aims at measuring the dimensions of binding and non-binding social responsibility and its relation to the organizational performance in telecommunication companies in the Republic of Yemen using analytical descriptive approach and questionnaire as a main tool for data collection and comprehensive inventory method.

It has been found  that there is a positive effect and significant moral relation between social responsibility with its binding and non-binding dimensions  and the organizational performance of telecommunication companies in the Republic of Yemen at a level of significance below (0.05). It has also been found that the correlation between the non-binding social responsibilit

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Publication Date
Sat Jun 29 2013
Journal Name
Journal Of Statistics Applications & Probability
Analyzing Skewed Data with the Epsilon Skew Gamma distribution
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A new distribution, the Epsilon Skew Gamma (ESΓ ) distribution, which was first introduced by Abdulah [1], is used on a near Gamma data. We first redefine the ESΓ distribution, its properties, and characteristics, and then we estimate its parameters using the maximum likelihood and moment estimators. We finally use these estimators to fit the data with the ESΓ distribution

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