Medical images play a crucial role in the classification of various diseases and conditions. One of the imaging modalities is X-rays which provide valuable visual information that helps in the identification and characterization of various medical conditions. Chest radiograph (CXR) images have long been used to examine and monitor numerous lung disorders, such as tuberculosis, pneumonia, atelectasis, and hernia. COVID-19 detection can be accomplished using CXR images as well. COVID-19, a virus that causes infections in the lungs and the airways of the upper respiratory tract, was first discovered in 2019 in Wuhan Province, China, and has since been thought to cause substantial airway damage, badly impacting the lungs of affected persons. The virus was swiftly gone viral around the world and a lot of fatalities and cases growing were recorded on a daily basis. CXR can be used to monitor the effects of COVID-19 on lung tissue. This study examines a comparison analysis of k-nearest neighbors (KNN), Extreme Gradient Boosting (XGboost), and Support-Vector Machine (SVM) are some classification approaches for feature selection in this domain using The Moth-Flame Optimization algorithm (MFO), The Grey Wolf Optimizer algorithm (GWO), and The Glowworm Swarm Optimization algorithm (GSO). For this study, researchers employed a data set consisting of two sets as follows: 9,544 2D X-ray images, which were classified into two sets utilizing validated tests: 5,500 images of healthy lungs and 4,044 images of lungs with COVID-19. The second set includes 800 images, 400 of healthy lungs and 400 of lungs affected with COVID-19. Each image has been resized to 200x200 pixels. Precision, recall, and the F1-score were among the quantitative evaluation criteria used in this study.
The objective of this study is to attempt to provide a quantitative analysis to the causes of unemployment in Iraq and its mechanisms of generation, as well as a review of the most important types of both visible and invisible unemployment, and an attempt to measure the disguised unemployment and analyze the causes. The problem of the research lies in the fact that the Iraqi Economy has been suffered for a long time although its characterized by abundant physical and natural resources, from the existence of the phenomenon of unemployment in the previous two types. Causing a lot of economic problems, represented by the great waste of resources and
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It highlights the importance of assessing the demand for money function in Iraq through the understanding of the relationship between him and affecting the variables by searching the stability of this function and the extent of their influence in the Iraqi dinar exchange rate in order to know the amount of their contribution to the monetary policies of the Iraqi economy fee, as well as through study behavior of the demand for money function in Iraq and analyze the determinants of the demand for money for the period 1991-2013 and the impact of these determinants in the demand for money in Iraq.
And that the problem that we face is how to estimate the total demand for money in
... Show MoreShadow removal is crucial for robot and machine vision as the accuracy of object detection is greatly influenced by the uncertainty and ambiguity of the visual scene. In this paper, we introduce a new algorithm for shadow detection and removal based on different shapes, orientations, and spatial extents of Gaussian equations. Here, the contrast information of the visual scene is utilized for shadow detection and removal through five consecutive processing stages. In the first stage, contrast filtering is performed to obtain the contrast information of the image. The second stage involves a normalization process that suppresses noise and generates a balanced intensity at a specific position compared to the neighboring intensit
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Suffering the human because of pressure normal life of exposure to several types of heart disease as a result of due to different factors. Therefore, and in order to find out the case of a death whether or not, are to be modeled using binary logistic regression model
In this research used, one of the most important models of nonlinear regression models extensive use in the modeling of applications statistical, in terms of heart disease which is the binary logistic regression model. and then estimating the parameters of this model using the statistical estimation methods, another problem will be appears in estimating its parameters, as well as when the numbe
... Show MoreIn this study, a genetic algorithm (GA) is used to detect damage in curved beam model, stiffness as well as mass matrices of the curved beam elements is formulated using Hamilton's principle. Each node of the curved beam element possesses seven degrees of freedom including the warping degree of freedom. The curved beam element had been derived based on the Kang and Yoo’s thin-walled curved beam theory. The identification of damage is formulated as an optimization problem, binary and continuous genetic algorithms
(BGA, CGA) are used to detect and locate the damage using two objective functions (change in natural frequencies, Modal Assurance Criterion MAC). The results show the objective function based on change in natural frequency i
The Twofish cipher is a very powerful algorithm with a fairly complex structure that permeates most data parsing and switching and can be easily implemented. The keys of the Twofish algorithm are of variable length (128, 192, or 256 bits), and the key schedule is generated once and repeated in encrypting all message blocks, whatever their number, and this reduces the confidentiality of encryption. This article discusses the process of generating cipher keys for each block. This concept is new and unknown in all common block cipher algorithms. It is based on the permanent generation of sub keys for all blocks and the key generation process, each according to its work. The Geffe's Generator is used to generate subkeys to make eac
... Show MoreFeature selection represents one of the critical processes in machine learning (ML). The fundamental aim of the problem of feature selection is to maintain performance accuracy while reducing the dimension of feature selection. Different approaches were created for classifying the datasets. In a range of optimization problems, swarming techniques produced better outcomes. At the same time, hybrid algorithms have gotten a lot of attention recently when it comes to solving optimization problems. As a result, this study provides a thorough assessment of the literature on feature selection problems using hybrid swarm algorithms that have been developed over time (2018-2021). Lastly, when compared with current feature selection procedu
... Show MoreA new series of metal ions complexes of VO(II), Cr(III), Mn(II), Zn(II), Cd(II) and Ce(III) have been synthesized from the Schiff bases (4-chlorobenzylidene)-urea amine (L1) and (4-bromobenzylidene)-urea amine (L2). Structural features were obtained from their elemental microanalyses, magnetic susceptibility, molar conductance, FT-IR, UV–Vis, LC-Mass and 1HNMR spectral studies. The UV–Vis, magnetic susceptibility and molar conductance data of the complexes suggest a tetrahedral geometry around the central metal ion except, VOII complexes that has square pyramidal geometry, but CrIII and CeIII octahedral geometry. The biological activity for the ligand (L1) and its Vanadium and Cadmium complexes were studied. Structural geometries of com
... Show MoreThe differential cross section for the Rhodium and Tantalum has been calculated by using the Cross Section Calculations (CSC) in range of energy(1keV-1MeV) . This calculations based on the programming of the Klein-Nashina and Rayleigh Equations. Atomic form factors as well as the coherent functions in Fortran90 language Machine proved very fast an accurate results and the possibility of application of such model to obtain the total coefficient for any elements or compounds.