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Diagnose COVID-19 by using hybrid CNN-RNN for Chest X-ray
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<p>Combating the COVID-19 epidemic has emerged as one of the most promising healthcare the world's challenges have ever seen. COVID-19 cases must be accurately and quickly diagnosed to receive proper medical treatment and limit the pandemic. Imaging approaches for chest radiography have been proven in order to be more successful in detecting coronavirus than the (RT-PCR) approach. Transfer knowledge is more suited to categorize patterns in medical pictures since the number of available medical images is limited. This paper illustrates a convolutional neural network (CNN) and recurrent neural network (RNN) hybrid architecture for the diagnosis of COVID-19 from chest X-rays. The deep transfer methods used were VGG19, DenseNet121, InceptionV3, and Inception-ResNetV2. RNN was used to classify data after extracting complicated characteristics from them using CNN. The VGG19-RNN design had the greatest accuracy of all of the networks with 97.8% accuracy. Gradient-weighted the class activation mapping (Grad-CAM) method was then used to show the decision-making areas of pictures that are distinctive to each class. In comparison to other current systems, the system produced promising findings, and it may be confirmed as additional samples become available in the future. For medical personnel, the examination revealed an excellent alternative way of diagnosing COVID-19.</p>

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Publication Date
Thu Jul 07 2022
Journal Name
Public Works Management &amp; Policy
Impact of Pandemic SARS COVID-19 on Different Construction Project Management: Problems and Solutions
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This study discusses risk management strategies caused by pandemic-related (Covid-19) suspensions in thirty-six engineering projects of different types and sizes selected from countries in the middle east and especially Iraq. The primary data collection method was a survey and questionnaire completed by selected project crew and laborers. Data were processed using Microsoft Excel to construct models to help decision-makers find solutions to the scheduling problems that may be expected to occur during a pandemic. A theoretical and practical concept for project risk management that addresses a range of global and local issues that affect schedule and cost is presented and results indicate that the most significant delays are due to a

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Publication Date
Tue Dec 30 2008
Journal Name
Al-kindy College Medical Journal
Chest Radiographic Finding in Neonatal Dyspnea
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Background: Respiratory distress is one of the
commonest disorders within the firs 48 - 72 hours of live
and any sign of postnatal respiratory distress is an
indication for roentgenogram of the chest.
Objectives: Is to show the range of chest radiographic
findings in full term newborn babies suffering from
respiratory distress, at or soon after birth.
Method: This is a prospective study that was conducted
in the special care baby units in Baghdad teaching
hospital and Children welfare teaching hospital during
2002. Anteroposterior chest radiograph in supine
position of (129) full term newborn babies, presented
with a chief complaint of respiratory distress were
examined.
Results: The commonest ca

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Publication Date
Sat Jan 31 2026
Journal Name
International Journal Of Intelligent Engineering And Systems
Low-complexity Deep Learning for Joint Channel-type Identification and SNR Estimation in MIMO-OFDM Using CNN–BRNN with LUT Labels
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Channel estimation (CE) is essential for wireless links but becomes progressively onerous as Fifth Generation (5G) Multi-Input Multi-Output (MIMO) systems and extensive fading expand the search space and increase latency. This study redefines CE support as the process of learning to deduce channel type and signal-tonoise ratio (SNR) directly from per-tone Orthogonal Frequency-Division Multiplexing (OFDM) observations,with blind channel state information (CSI). We trained a dual deep model that combined Convolutional Neural Networks (CNNs) with Bidirectional Recurrent Neural Networks (BRNNs). We used a lookup table (LUT) label for channel type (class indices instead of per-tap values) and ordinal supervision for SNR (0–20 dB,5-dB steps). T

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Publication Date
Mon Mar 08 2021
Journal Name
Baghdad Science Journal
Shield Calculation Design for gamma-ray sterilzer plant
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The present calculation covers the building shield during irradiation process and under water storage of three milion curries Cobalt-60 radiation source the calculation results in design requirement of 8m depth of water in the source stoeage pool

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Publication Date
Tue Jun 01 2021
Journal Name
Political Sciences Journal
The Globalization and the Recruit for Achievement of Marketing Liberalization and the Humanity Ignorant (The Impacts of COVID-19 Pandemic Typical)
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   The emerge of capitalism beside appearing modern and contemporary political systems which had become hold out it is semi-domination on more vital space of human community life, it is through some vital apparatus, which the free market apparatus had make important one which depend on achieve the privileges of the capitalism elite whom standing on it, especially the finance elite. Thus the achievement of the profit had become the main podcasted of those elite which whom the really advancer of the Globalization system, this is which incarnated by the appears and extend of the (COVID-19) fatality pandemic in the end of last year, whereas reveals widespread of it in more than one states in the world, especially the developed coun

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Publication Date
Wed Oct 09 2024
Journal Name
Engineering, Technology &amp; Applied Science Research
Improving Pre-trained CNN-LSTM Models for Image Captioning with Hyper-Parameter Optimization
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The issue of image captioning, which comprises automatic text generation to understand an image’s visual information, has become feasible with the developments in object recognition and image classification. Deep learning has received much interest from the scientific community and can be very useful in real-world applications. The proposed image captioning approach involves the use of Convolution Neural Network (CNN) pre-trained models combined with Long Short Term Memory (LSTM) to generate image captions. The process includes two stages. The first stage entails training the CNN-LSTM models using baseline hyper-parameters and the second stage encompasses training CNN-LSTM models by optimizing and adjusting the hyper-parameters of

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Publication Date
Fri Apr 21 2023
Journal Name
Technologies And Materials For Renewable Energy, Environment And Sustainability: Tmrees22fr
Modified x-ray analysis size strain plot method to determine the lattice stress and strain energy density of calcium titan oxide (CaTiO3) nanoparticles
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In this research, the size strain plot method was used to estimate the particle size and lattice strain of CaTiO3 nanoparticles. The SSP method was developed to calculate new variables, namely stress, and strain energy, and the results were crystallite size (44.7181794 nm) lattice strain (0.001211), This method has been modified to calculate new variables such as stress and its value (184.3046308X10-3Mpa) and strain energy and its value (1.115833287X10-6 KJm-3).

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Publication Date
Mon Aug 15 2022
Journal Name
Revis Bionatura
ND2 Gene Sequencing of Sub fertile Patients Recovered from COVID-19 in Association with Toxoplasmosis
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A total of (90) blood samples were collected from male patients infected with Toxoplasmosis who recovered from COVID- 19 and attended Kamal Alsamiraai Hospital from 15 January to 15 September 2021. We measured anti-Toxoplasma antibodies (IgG and IgM) detected by ELISA, whereas Anti-COVID-19 antibodies (IgG and IgM) were estimated using Elisa and Afilias. The semen characteristics were also studied among fertile, healthy individuals (control group) and sub-fertile patients. Results showed that the mean sperm count was high among the control group (40.5±1.3x 106/ml) compared with that of the sub-fertile patients (10.3±1.75 and 8.8±1.9 x 106/ml for oligozoospermia, and oligoasthenozoospermia respectively), and it was the highest (44.7±1.4

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Publication Date
Wed Aug 30 2023
Journal Name
Baghdad Science Journal
The Influence of Obesity and IL-6 on Infertile Iraqi Women with COVID-19 Complications
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Infertility is one of the types of diseases that occur in the reproductive system. Obesity is a state that can be occurred due to excessive fats, the progression in obesity stage results in a change in adipose tissue and the development of chronic inflammation, endocrine glands disorders and women’s reproductive system, and also increase the infection with covid-19. The study aimed to investigate the effect of the obesity, lipid-profile, and IL-6 on hormones-dysregulation in infertile-women with COVID-19 complications. The current study included 70 samples: 50 infertility-women-with-covid-19-infected, 20 healthy-women/control, the ages of both patients and healthy subjects were selected within the range 18-34 years. Levels of FBS, LH,

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Publication Date
Tue Nov 01 2022
Journal Name
Al-adab Journal
A Pragmatic Analysis of Implicatures in Covid-19 Coronavirus English Jokes: A Neo-Gricean Approach
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