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Normalized-UNet Segmentation for COVID-19 Utilizing an Encoder-Decoder Connection Layer Block
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The COVID-19 pandemic has had a huge influence on human lives all around the world. The virus spread quickly and impacted millions of individuals, resulting in a large number of hospitalizations and fatalities. The pandemic has also impacted economics, education, and social connections, among other aspects of life. Coronavirus-generated Computed Tomography (CT) scans have Regions of Interest (ROIs). The use of a modified U-Net model structure to categorize the region of interest at the pixel level is a promising strategy that may increase the accuracy of detecting COVID-19-associated anomalies in CT images. The suggested method seeks to detect and isolate ROIs in CT scans that show the existence of ground-glass opacity, which is frequent in COVID-19 patients. This can assist healthcare practitioners in identifying and monitoring illness development, as well as making treatment decisions. Scale U-Net is a strong U-Net design modification that can increase the performance of semantic segmentation tasks. Our model, Normalized-UNet, uses batch normalization after each convolutional layer to decrease the internal covariate shift, which dramatically improves the network's learning efficiency.

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Publication Date
Sun Jan 30 2022
Journal Name
Gsc Advanced Research And Reviews
Review of Coronavirus disease- 2019 (COVID-19) in Iraq
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The severe acute respiratory syndrome coronavirus 2 (SARS-CoV 2) or 2019 novel coronavirus (2019-nCoV) is quickly spreading to the rest of the world, from its origin in Wuhan, Hubei Province, China. And becoming a global pandemic that affects the world's most powerful countries. The goal of this review is to assist scientists, researchers, and others in responding to the current Coronavirus disease (covid-19) is a worldwide public health contingency state. This review discusses current evidence based on recently published studies which is related to the origin of the virus, epidemiology, transmission, diagnosis, treatment, and all studies in Iraq for the effect of covid-19 diseases, as well as provide a reference for future research

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Publication Date
Sat Dec 31 2022
Journal Name
Al-kindy College Medical Journal
Changing the Trends in Surgery during the COVID-19 Times: An Experience from the Eastern Uttar Pradesh State, India: CHANGING TRENDS IN SURGERY DURING COVID TIMES
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Background: The COVID-19 pandemic has an immense effect not only on the social and economic lives of people but also on the surgical lives of surgeons, residents, nursing staff, and patients as well as ground level staff. Amidst this COVID pandemic, emergency surgeries were being done but at a decreased rate, whereas elective cases depended on the will of hospitals, surgeons, and patients. Study aims to promulgate a "Neo–Surgical Check Box" by amalgamating the WHO surgical checklist and the results obtained from the questionnaires.

Subjects and Methods: After receiving ethical clearance from the Institute Ethical Committee, an online questionnaire with 50 questions divided into

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Publication Date
Fri Feb 10 2023
Journal Name
Dentistry Journal
The Role of Social Media in Communication and Learning at the Time of COVID-19 Lockdown—An Online Survey
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This study aimed to assess orthodontic postgraduate students’ use of social media during the COVID-19 lockdown. Ninety-four postgraduate students (67 master’s students and 27 doctoral students) were enrolled in the study and asked to fill in an online questionnaire by answering questions regarding their use of social media during the COVID-19 lockdown. The frequency distributions and percentages were calculated using SPSS software. The results showed that 99% of the students used social media. The most frequently used type of social media was Facebook, 94%, followed by YouTube, 78%, and Instagram, 65%, while Twitter and Linkedin were used less, and no one used Blogger. About 63% of the students used elements of social media to l

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Publication Date
Wed Aug 30 2023
Journal Name
Baghdad Science Journal
Deep Learning-based Predictive Model of mRNA Vaccine Deterioration: An Analysis of the Stanford COVID-19 mRNA Vaccine Dataset
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The emergence of SARS-CoV-2, the virus responsible for the COVID-19 pandemic, has resulted in a global health crisis leading to widespread illness, death, and daily life disruptions. Having a vaccine for COVID-19 is crucial to controlling the spread of the virus which will help to end the pandemic and restore normalcy to society. Messenger RNA (mRNA) molecules vaccine has led the way as the swift vaccine candidate for COVID-19, but it faces key probable restrictions including spontaneous deterioration. To address mRNA degradation issues, Stanford University academics and the Eterna community sponsored a Kaggle competition.This study aims to build a deep learning (DL) model which will predict deterioration rates at each base of the mRNA

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Publication Date
Wed Nov 02 2022
Journal Name
Turkish Journal Of Computer And Mathematics Education (turcomat)
Mpeg-Dash System via HTTP2 Protocol with HEVC Encoder for Video Streaming Services
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MPEG-DASH is an adaptive bitrate streaming technology that divides video content into small HTTP-objects file segments with different bitrates. With live UHD video streaming latency is the most important problem. In this paper, creating a low-delay streaming system using HTTP 2.0. Based on the network condition the proposed system adaptively determine the bitrate of segments. The video is coded using a layered H.265/HEVC compression standard, then is tested to investigate the relationship between video quality and bitrate for various HEVC parameters and video motion at each layer/resolution. The system architecture includes encoder/decoder configurations and how to embedded the adaptive video streaming. The encoder includes compression besi

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Publication Date
Sun Oct 23 2022
Journal Name
Baghdad Science Journal
Comparison Between Deterministic and Stochastic Model for Interaction (COVID-19) With Host Cells in Humans
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In this paper, the deterministic and the stochastic models are proposed to study the interaction of the Coronavirus (COVID-19) with host cells inside the human body. In the deterministic model, the value of the basic reproduction number   determines the persistence or extinction of the COVID-19. If   , one infected cell will transmit the virus to less than one cell, as a result,  the person carrying the Coronavirus will get rid of the disease .If   the infected cell  will be able to infect  all  cells that contain ACE receptors. The stochastic model proves that if  are sufficiently large then maybe  give  us ultimate disease extinction although ,  and this  facts also proved by computer simulation.

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Publication Date
Sun Apr 14 2024
Journal Name
Eastern Mediterranean Health Journal
Satisfaction of women with telehealth services during COVID-19 paves the way for wider implementation
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Background: Although underdeveloped in Iraq, telehealth was one tool used to continue health service provision during the COVID-19 pandemic. Aim: To assess women’s experiences and satisfaction with gynaecological and obstetric telehealth services in Iraq during the COVID-19 pandemic. Methods: Free telehealth services were provided by 4 obstetrician-gynaecologists associated with private clinics in 2020–2021. All patients who accessed the services between June 2020 and February 2021 were invited to complete a postconsultation survey on their experience and satisfaction with services. Results were analysed using descriptive statistics and logistic regression conducted using SPSS version 25. Results: A total of 151 (30.2%) women re

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Publication Date
Tue Aug 01 2023
Journal Name
Baghdad Science Journal
A New Model Design for Combating COVID -19 Pandemic Based on SVM and CNN Approaches
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       In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from      Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial

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Publication Date
Wed Apr 01 2015
Journal Name
Journal Of Engineering
Software Implementation of Binary BCH Decoder Using Microcontroller
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In this paper a decoder of binary BCH code is implemented using a PIC microcontroller for code length n=127 bits with multiple error correction capability, the results are presented for correcting errors up to 13 errors. The Berkelam-Massey decoding algorithm was chosen for its efficiency. The microcontroller PIC18f45k22 was chosen for the implementation and programmed using assembly language to achieve highest performance. This makes the BCH decoder implementable as a low cost module that can be used as a part of larger systems. The performance evaluation is presented in terms of total number of instructions and the bit rate.

 

 

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Publication Date
Sat Dec 31 2022
Journal Name
Al-kindy College Medical Journal
Pulmonary CT findings in Patients Recovered from COVID-19 Pneumonia
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Background: The COVID-19 infection is a more recent pandemic disease all over the world and studying the pulmonary findings on survivors of this disease has lately commenced.

Objective: We aimed to estimate the cumulative percentage of whole radiological resolution after 3 months from recovery and to define the residual chest CT findings and exploring the relevant affecting factors.

Subjects and Methods: Patients who had been previously diagnosed with COVID-19 pneumonia confirmed by RT-PCR test and had radiological evidence of pulmonary involvement by Chest CT during the acute illness were included in the present study. The radiol

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