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.  T

<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

Corona Virus Disease-2019 (COVID-19) is a novel virus belongs to the corona virus's family. It spreads very quickly and causes many deaths around the world. The early diagnosis of the disease can help in providing the proper therapy and saving the humans' life. However, it founded that the diagnosis of chest radiography can give an indicator of coronavirus. Thus, a Corner-based Weber Local Descriptor (CWLD) for COVID-19 diagnostics based on chest X-Ray image analysis is presented in this article. The histogram of Weber differential excitation and gradient orientation of the local regions surrounding points of interest are proposed to represent the patterns of the chest X-Ray image. Support Vector Machine (SVM) and Deep Belief Network (DBN)

      Today, the world is living in a time of epidemic diseases that spread unnaturally and infect and kill millions of people worldwide. The COVID-19 virus, which is one of the most well-known epidemic diseases currently spreading, has killed more than six million people as of May 2022. The World Health Organization (WHO) declared the 2019 coronavirus disease (COVID-19) after an outbreak of SARS-CoV-2 infection. COVID-19 is a severe and potentially fatal respiratory disease caused by the SARS-CoV-2 virus, which was first noticed at the end of 2019 in Wuhan city. Artificial intelligence plays a meaningful role in analyzing medical images and giving accurate results that serve healthcare workers, especially X-ray images, which are co

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.

United Arab of Emirates made efforts in developing workforce in the field of nuclear energy that it had a chance to attract specialized of this field that it reflect growth in every field of life through which it take special procedures to face casas as fokoshima .the state had started to apply the UAE peaceful nuclear program by opening (Baraka)station in 2010 as to be the first in the Arab world to aim to enhance and diversification energy sources of electricity there in addition to enhance the state’s efforts to achieve economic refreshing and environmental protect.

Because of what Iraq faced in the last quarter of the last century of wars and military conflicts and the aftermath of the economic blockade, as well as the recent occupation suffered by Iraq in the contemporary century, which culminated in lawlessness and the spread of terrorism and the most prominent on the Iraqi street manifestations not prepared before paved to the aggravation and collapse in the community system As a result of the political and social changes, all this highlighted the need for public facilities specialized in social protection for young people to provide all the new requirements, and the find a legal system commensurate with the circumstances and circumstances.

In light of the current challenges, the State h

يسلط هذا البحث الضوء على حماية حقوق الملكية الفكرية للمؤسسات الإعلامية فيما يخص الإعلام الإلكتروني على وجه التحديد، حيث إن التطورات التكنولوجية الحديثة في مجال الإعلام ومع كل جوانبها الإيجابية في سرعة نشر الأخبار والحوارات والمعلومات، وتمكين الجمهور من الحصول عليها بسهولة ويسر وبطريقة أكثر تفاعليه؛ إلا أنها جاءت ومعها العديد من الانتهاكات لقوانين الملكية الفكرية المرتبطة بحق المؤلف والحقوق المجاورة.

Background: The use of the chest x-ray measurements which includes the cardiothoracic ratio(C-T) and frontal area (FA) of the heart by the CXR are useful measures for primary assessment of the cardiac dysfunction.
Patients and Methods: A Prospective study was done from the 1st of January 2005 to the 1st of October in the same year on a 120 consecutive patients who have been admitted for coronary and L.V angiogram at IBN-AL-BITAR hospital. The C-T ratio and the frontal area were measured.
Results: The study comprised 120 subjects who were admitted for coronary and L.V angiogram for diagnostic reasons. 89subjects (74.2%) are male and 31subjects (25.8%) are female .17(14%) subjects have left ventricular d