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Diagnosing COVID-19 Infection in Chest X-Ray Images Using Neural Network
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With its rapid spread, the coronavirus infection shocked the world and had a huge effect on billions of peoples' lives. The problem is to find a safe method to diagnose the infections with fewer casualties. It has been shown that X-Ray images are an important method for the identification, quantification, and monitoring of diseases. Deep learning algorithms can be utilized to help analyze potentially huge numbers of X-Ray examinations. This research conducted a retrospective multi-test analysis system to detect suspicious COVID-19 performance, and use of chest X-Ray features to assess the progress of the illness in each patient, resulting in a "corona score." where the results were satisfactory compared to the benchmarked techniques.  This research results showed that rapidly evolved Artificial Intelligence (AI) -based image analysis can accomplish high accuracy in detecting coronavirus infection as well as quantification and illness burden monitoring.

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Publication Date
Sun Jun 20 2021
Journal Name
Baghdad Science Journal
Immunological and Molecular Diagnosis of Cytomegalovirus Infection between Aborted & Pregnant Women in Babylon City
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Human cytomegalovirus (CMV) is the globally highly prevalent herpesvirus worldwide. CMV infects populations of all ages according to the Center for Disease Control and Prevention (CDC) and World Health Organization (WHO). CMV infections remain the most common viral complication potentially multiple in humans and are a major cause of congenital normality in women, which is why they are critical for diagnosis in several times when it happens during pregnancy. Pregnant women with CMV infection can be in charge of abortion or congenital expandaedby. This study involves the collection a total of (90) samples taken from each aborted and pregnant woman (70 with abortion cases and 20 of pregnant without history of abortion as control subjects) r

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Publication Date
Wed Nov 01 2017
Journal Name
Journal Of Economics And Administrative Sciences
strong criminal capabilities، Using simulation .
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The penalized least square method is a popular method to deal with high dimensional data ,where  the number of explanatory variables is large than the sample size . The properties of  penalized least square method are given high prediction accuracy and making estimation and variables selection

 At once. The penalized least square method gives a sparse model ,that meaning a model with small variables so that can be interpreted easily .The penalized least square is not robust ,that means very sensitive to the presence of outlying observation , to deal with this problem, we can used a robust loss function to get the robust penalized least square method ,and get robust penalized estimator and

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Publication Date
Thu Sep 13 2018
Journal Name
Baghdad Science Journal
Gamma Ray Effect on the Properties of Coumarin C47 Laser Dye
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The research is concerned about studying the absorption spectrum of the solution coumarin dye C47. The chloroform solvent was used with C47 dye in three different concentrations 10-4, 10-5 and 10-6 M. The laser dye solution was prepared by dissolving the required amount of dye in chloroform alcohol, while studying absorption spectrum before and after irradiation with gamma ray by cobalt-60 source 60Co at exposure time, which are 0, 4, 6 and 18 hours with different absorbed doses 0, 136, 204 and 612 Gy. The results show that red shift in the absorption spectrum was increased by increasing the concentration of laser dye solutions , while the increase of gamma dose led to increase the red shift after irradiation, as the exposure period and irr

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Publication Date
Sat Jul 20 2024
Journal Name
Al-rafidain Journal Of Medical Sciences ( Issn 2789-3219 )
Gender Distribution of Coronary Artery Calcium Score and Degree of Stenosis Assessed by Computed Tomography Angiography in Iraqi Patients with Chest Pain
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Background: Sex variations in coronary artery disease (CAD) are well documented. However, sex differences in coronary artery calcium (CAC) and its role in the detection of coronary artery stenosis remain controversial. Objective: To assess the impact of sex variation on coronary artery calcification and its efficacy in predicting coronary artery stenosis. Methods: This is a cross-sectional observational study including 230 consecutive patients with suspected CAD (120 men and 110 women) referred for coronary computed tomography angiography (CCTA). The study analyzed sex-based differences in the sensitivity and specificity of coronary artery calcification (CAC) for detecting moderate to severe stenosis across various coronary arteries

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Publication Date
Thu Mar 31 2022
Journal Name
Iraqi Geological Journal
Development of New Models to Determine the Rheological Parameters of Water-Based Drilling Fluid using Artificial Neural Networks
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It is well known that drilling fluid is a key parameter for optimizing drilling operations, cleaning the hole, and managing the rig hydraulics and margins of surge and swab pressures. Although the experimental works represent valid and reliable results, they are expensive and time consuming. In contrast, continuous and regular determination of the rheological fluid properties can perform its essential functions during good construction. The aim of this study is to develop empirical models to estimate the drilling mud rheological properties of water-based fluids with less need for lab measurements. This study provides two predictive techniques, multiple regression analysis and artificial neural networks, to determine the rheological

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Publication Date
Fri Aug 12 2022
Journal Name
Future Internet
Improved DDoS Detection Utilizing Deep Neural Networks and Feedforward Neural Networks as Autoencoder
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Software-defined networking (SDN) is an innovative network paradigm, offering substantial control of network operation through a network’s architecture. SDN is an ideal platform for implementing projects involving distributed applications, security solutions, and decentralized network administration in a multitenant data center environment due to its programmability. As its usage rapidly expands, network security threats are becoming more frequent, leading SDN security to be of significant concern. Machine-learning (ML) techniques for intrusion detection of DDoS attacks in SDN networks utilize standard datasets and fail to cover all classification aspects, resulting in under-coverage of attack diversity. This paper proposes a hybr

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Publication Date
Tue Oct 15 2019
Journal Name
International Journal Of Electrical And Computer Engineering (ijece)
Combining Convolutional Neural Networks and Slantlet Transform For An Effective Image Retrieval Scheme
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In the latest years there has been a profound evolution in computer science and technology, which incorporated several fields. Under this evolution, Content Base Image Retrieval (CBIR) is among the image processing field. There are several image retrieval methods that can easily extract feature as a result of the image retrieval methods’ progresses. To the researchers, finding resourceful image retrieval devices has therefore become an extensive area of concern. Image retrieval technique refers to a system used to search and retrieve images from digital images’ huge database. In this paper, the author focuses on recommendation of a fresh method for retrieving image. For multi presentation of image in Convolutional Neural Network (CNN),

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Publication Date
Wed May 04 2022
Journal Name
Int. J. Nonlinear Anal. Appl.
Knee Meniscus Segmentation and Tear Detection Based On Magnitic Resonacis Images: A Review of Literature
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The meniscus has a crucial function in human anatomy, and Magnetic Resonance Imaging (M.R.I.) plays an essential role in meniscus assessment. It is difficult to identify cartilage lesions using typical image processing approaches because the M.R.I. data is so diverse. An M.R.I. data sequence comprises numerous images, and the attributes area we are searching for may differ from each image in the series. Therefore, feature extraction gets more complicated, hence specifically, traditional image processing becomes very complex. In traditional image processing, a human tells a computer what should be there, but a deep learning (D.L.) algorithm extracts the features of what is already there automatically. The surface changes become valuable when

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Publication Date
Tue Jan 08 2019
Journal Name
Iraqi Journal Of Physics
First principles calculations of Al AsxP1-x ternary nanocrystal alloying composition
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III-V zinc-blende AlP, AlAs semiconductors and their alloy Aluminum Arsenide phosphide Al AsxP1-x ternary nanocrystals have been investigated using Ab- initio density functional theory (Ab-initio-DFT) at the generalized-gradient approximation (GGA) level with STO-3G basis set coupled with large unit cell method (LUC). The dimension of crystal is found around (1.56 – 2.24) nm at a function of increasing the sizes (8, 16, 54, 64) with different concentration of arsenide (x=0, 0.25, 0.5, 0.75 and 1) respectively. Gaussian 03 code program has been used throughout this study to calculate some of the physical properties such as the electronic properties energy gap, lattice constant, valence and conduction band as well as density of state. Re

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Publication Date
Thu Oct 31 2013
Journal Name
Al-khwarizmi Engineering Journal
An Investigation Study of Thinning Distribution in Single Point Incremental Forming Using FEM Analysis
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Single Point Incremental Forming (SPIF) is a forming technique of sheet material based on layered manufacturing principles. The sheet part is locally deformed through horizontal slices. The moving locus of forming tool (called as toolpath) in these slices constructed to the finished part was performed by the CNC technology. The toolpath was created directly from CAD model of final product. The forming tool is a Ball-end forming tool, which was moved along the toolpath while the edges of sheet material were clamped rigidly on fixture.

This paper presented an investigation study of thinning distribution of a conical shapes carried out by incremental forming and the validation of finite element method to evaluate the limits of the p

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