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Use the robust RFCH method with a polychoric correlation matrix in structural equation modeling When you are ordinal data
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Publication Date
Sun Sep 06 2009
Journal Name
Baghdad Science Journal
Extension of the Chebyshev Method of Quassi-Linear Parabolic P.D.E.S With Mixed Boundary Conditions
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The researcher [1-10] proposed a method for computing the numerical solution to quasi-linear parabolic p.d.e.s using a Chebyshev method. The purpose of this paper is to extend the method to problems with mixed boundary conditions. An error analysis for the linear problem is given and a global element Chebyshev method is described. A comparison of various chebyshev methods is made by applying them to two-point eigenproblems. It is shown by analysis and numerical examples that the approach used to derive the generalized Chebyshev method is comparable, in terms of the accuracy obtained, with existing Chebyshev methods.

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Publication Date
Sat Jan 23 2021
Journal Name
Neural Computing And Applications
Multi-objectives TLBO hybrid method to select the related risk features with rheumatism disease
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Publication Date
Thu Jul 01 2021
Journal Name
University Of Northampton Pue
Validating a Proposed Data Mining Approach (SLDM) for Motion Wearable Sensors to Detect the Early Signs of Lameness in Sheep
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Publication Date
Wed Nov 30 2022
Journal Name
Iraqi Geological Journal
A Predictive Model for Estimating Unconfined Compressive Strength from Petrophysical Properties in the Buzurgan Oilfield, Khasib Formation, Using Log Data
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Unconfined compressive strength (UCS) of rock is the most critical geomechanical property widely used as input parameters for designing fractures, analyzing wellbore stability, drilling programming and carrying out various petroleum engineering projects. The USC regulates rock deformation by measuring its strength and load-bearing capacity. The determination of UCS in the laboratory is a time-consuming and costly process. The current study aims to develop empirical equations to predict UCS using regression analysis by JMP software for the Khasib Formation in the Buzurgan oil fields, in southeastern Iraq using well-log data. The proposed equation accuracy was tested using the coefficient of determination (R²), the average absolute

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Publication Date
Fri Jun 01 2018
Journal Name
International Journal Of Computer Science Trends And Technology
Secure Video Data Deduplication in the Cloud Storage Using Compressive Sensing
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Cloud storage provides scalable and low cost resources featuring economies of scale based on cross-user architecture. As the amount of data outsourced grows explosively, data deduplication, a technique that eliminates data redundancy, becomes essential. The most important cloud service is data storage. In order to protect the privacy of data owner, data are stored in cloud in an encrypted form. However, encrypted data introduce new challenges for cloud data deduplication, which becomes crucial for data storage. Traditional deduplication schemes cannot work on encrypted data. Existing solutions of encrypted data deduplication suffer from security weakness. This paper proposes a combined compressive sensing and video deduplication to maximize

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Publication Date
Thu Nov 21 2019
Journal Name
Journal Of Engineering
Comparative Permeability Estimation Method and Identification of Rock Types using Cluster Analysis from Well Logs and Core Analysis Data in Tertiary Carbonate Reservoir-Khabaz Oil Field
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Characterization of the heterogonous reservoir is complex representation and evaluation of petrophysical properties and application of the relationships between porosity-permeability within the framework of hydraulic flow units is used to estimate permeability in un-cored wells. Techniques of flow unit or hydraulic flow unit (HFU) divided the reservoir into zones laterally and vertically which can be managed and control fluid flow within flow unit and considerably is entirely different with other flow units through reservoir. Each flow unit can be distinguished by applying the relationships of flow zone indicator (FZI) method. Supporting the relationship between porosity and permeability by using flow zone indictor is ca

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Publication Date
Sat Aug 01 2015
Journal Name
Modern Applied Science
A New Method for Detecting Cerebral Tissues Abnormality in Magnetic Resonance Images
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We propose a new method for detecting the abnormality in cerebral tissues present within Magnetic Resonance Images (MRI). Present classifier is comprised of cerebral tissue extraction, image division into angular and distance span vectors, acquirement of four features for each portion and classification to ascertain the abnormality location. The threshold value and region of interest are discerned using operator input and Otsu algorithm. Novel brain slices image division is introduced via angular and distance span vectors of sizes 24˚ with 15 pixels. Rotation invariance of the angular span vector is determined. An automatic image categorization into normal and abnormal brain tissues is performed using Support Vector Machine (SVM). St

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Publication Date
Fri Nov 29 2019
Journal Name
Iraqi Journal Of Physics
CdSe/CdS core/shell in polyacrylamide polymer matrix for Quantumdots Luminescent solar concentrator
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Luminescent solar concentrator (LSC) are used to enhance       photoresponsivity of solar cell. The Quantumdots luminescent solar concentrator (QDLSC) consists of CdSe/CdS core/shell nanoparticles embedded in polyacrylamide polymer matrix positioned on the top surface of the silicon solar cell. This procedure improves the conversion efficiency of the bare silicon solar cell. The conversion efficiency of the solar cell has increased from 7.3% to 10.3%.  this improvement is referred to the widening of the response spectral region window  of the a- Si. Solar cell.

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Publication Date
Wed Jan 26 2022
Journal Name
Iraqi Journal Of Science
Potentiometric Determination of Mebeverine Hydrochloride Using Imprinted Molecular Polymer in PVC Matrix Membraneā€
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Publication Date
Sat Dec 31 2022
Journal Name
Journal Of Economics And Administrative Sciences
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 squa

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