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fMRI and Artifact computation and Analysis
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Abstract<p>Over the years, the field of Medical Imagology has gained considerable importance. The number of neuroimaging studies conducted using functional magnetic resonance imaging (fMRI) has been exploding in recent years. fMRI survey gives to rise to large amounts of noisy data with a complex spatiotemporal correlation structure. Statistics play great role in clarifying the features of the data and gain results that can be used and explain by neuroscientists. Several types of artifacts can happen through a functional magnetic resonance imaging (fMRI) scanner Because of software or hardware problems, physical limitation or human physiologic phenomenon. Several of them can negatively affect diagnostic image goodness, and confused with various pathology. Artificial Characteristics is show in an image not found in the real actual object it is the necessary to recognize these artifacts according to a basic perception of their origin, especially those simulating pathology, as they can be sing to wrong diagnostic medical. As a result, it causes dangerous effects on the patient’s health. We discuss the study of fMRI data in this paper; we highlight important and significant problems where statistics already play a major role. It is include a sequence of programs for processing, analysing, and offer fMRI data. Of special regard to statisticians might be its use of functions from the statistical software package. The conventional methods are FSL and SPM. The most generally applied software is SPM (Statistical Parametric Mapping), which include groups of MATLAB functions for pre-processing, analysing, and display fMRI data.</p>
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Publication Date
Fri Jan 01 2021
Journal Name
International Journal Of Agricultural And Statistical Sciences
STATISTICAL COMPUTATION AND APPLICATION WITH GENERALIZED POISSON DISTRIBUTION
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Publication Date
Sun Sep 11 2022
Journal Name
Concurrency And Computation: Practice And Experience
Fast and accurate computation of high‐order Tchebichef polynomials
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Publication Date
Tue Feb 28 2023
Journal Name
Iraqi Journal Of Science
Hybrid Techniques with Support Vector Machine for Improving Artifact Ultrasound Images
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     The most common artifacts in ultrasound (US) imaging are reverberation and comet-tail. These are multiple reflection echoing the interface that causing them, and result in ghost echoes in the ultrasound image. A method to reduce these unwanted artifacts using a Otsu thresholding to find region of interest (reflection echoes) and output applied to median filter to remove noise. The developed method significantly reduced the magnitude of the reverberation and comet-tail artifacts. Support Vector Machine (SVM) algorithm is most suitable for hyperplane differentiate. For that, we use image enhancement, extraction of feature, region of interest, Otsu thresholding, and finally classification image datasets to normal or abnormal image.

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Publication Date
Tue Jun 20 2023
Journal Name
Baghdad Science Journal
Computation of Several Banhatti and Reven Invariants of Silicon Carbides
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Expressions for the molecular topological features of silicon carbide compounds are essential for quantitative structure-property and structure-activity interactions. Chemical Graph Theory is a subfield of computational chemistry that investigates topological indices of molecular networks that correlate well with the chemical characteristics of chemical compounds. In the modern age, topological indices are extremely important in the study of graph theory. Topological indices are critical tools for understanding the core topology of chemical structures while examining chemical substances. In this article, compute the first and second k-Banhatti index, modified first and second k-Banhatti index, first and second k-hyper Banhatti index, fir

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Publication Date
Mon Sep 11 2017
Journal Name
Journal Of Mathematical Imaging And Vision
Fast Recursive Computation of Krawtchouk Polynomials
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Publication Date
Thu Dec 01 2011
Journal Name
2011 Developments In E-systems Engineering
Enhanced Computation Time for Fast Block Matching Algorithm
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Publication Date
Sat Jan 01 2022
Journal Name
Ieee Access
Fast Computation of Hahn Polynomials for High Order Moments
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Publication Date
Mon Mar 08 2021
Journal Name
Baghdad Science Journal
Computation of The Efficiency ofHarmonic Generation Using Cascading Configuration.
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In the present work theoretical relations are derived for the efficiency evaluation for the generation of the third and the fourth harmonics u$ing crystal cascading configuration. These relations can be applied to a wide class of nonlinear optical materials. Calculations are made for beta barium borate (BBO) crystal with ruby laser /.=694.3 nm . The case study involves producing the third harmonics at X. =231.4 nm of the fundamental beam. The formula of efficiency involves many parameters, which can be changed to enhance the efficiency. The results showed that the behavior of the efficiency is not linear with the crystal length. It is found that the efficiency increases when the input power increases. 'I'he walk-off length is calculated for

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Publication Date
Wed Apr 25 2018
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Using Approximation Non-Bayesian Computation with Fuzzy Data to Estimation Inverse Weibull Parameters and Reliability Function
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        In real situations all observations and measurements are not exact numbers but more or less non-exact, also called fuzzy. So, in this paper, we use approximate non-Bayesian computational methods to estimate inverse Weibull parameters and reliability function with fuzzy data. The maximum likelihood and moment estimations are obtained as non-Bayesian estimation. The maximum likelihood estimators have been derived numerically based on two iterative techniques namely “Newton-Raphson” and the “Expectation-Maximization” techniques. In addition, we provide compared numerically through Monte-Carlo simulation study to obtained estimates of the parameters and reliability function i

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Publication Date
Mon Aug 01 2022
Journal Name
Mathematics
Face Recognition Algorithm Based on Fast Computation of Orthogonal Moments
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Face recognition is required in various applications, and major progress has been witnessed in this area. Many face recognition algorithms have been proposed thus far; however, achieving high recognition accuracy and low execution time remains a challenge. In this work, a new scheme for face recognition is presented using hybrid orthogonal polynomials to extract features. The embedded image kernel technique is used to decrease the complexity of feature extraction, then a support vector machine is adopted to classify these features. Moreover, a fast-overlapping block processing algorithm for feature extraction is used to reduce the computation time. Extensive evaluation of the proposed method was carried out on two different face ima

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