<p>Analyzing X-rays and computed tomography-scan (CT scan) images using a convolutional neural network (CNN) method is a very interesting subject, especially after coronavirus disease 2019 (COVID-19) pandemic. In this paper, a study is made on 423 patients’ CT scan images from Al-Kadhimiya (Madenat Al Emammain Al Kadhmain) hospital in Baghdad, Iraq, to diagnose if they have COVID or not using CNN. The total data being tested has 15000 CT-scan images chosen in a specific way to give a correct diagnosis. The activation function used in this research is the wavelet function, which differs from CNN activation functions. The convolutional wavelet neural network (CWNN) model proposed in this paper is compared with regular convol

News headlines are key elements in spreading news. They are unique texts written in a special language which enables readers understand the overall nature and importance of the topic. However, this special language causes difficulty for readers in understanding the headline. To illuminate this difficulty, it is argued that a pragmatic analysis from a speech act theory perspective is a plausible tool for a headline analysis. The main objective of the study is to pragmatically analyze the most frequently employed types of speech acts in the news headlines covering COVID-19 in Aljazeera English website. To this end, Bach and Harnish's (1979) Taxonomy of Speech Acts has been adopted to analyze the data. Thirty headlines have been collected f

Vaccination is a vital cornerstone of public health, which has saved countless lives throughout history. Therefore, achieving high vaccination uptake rates is essential for successful vaccination programs. Unfortunately, vaccine uptake has been hindered by deferent factors and challenges. The objective of this study is to assess COVID-19 vaccine uptake and associated factors among the general population.

<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

Background and Purpose: Coronavirus has posed an unfamiliar threat to the world. Despite such circumstances, Malaysians continue to stay optimistic by keeping abreast with updates and mostly by seeking refuge in hopeful and consoling messages shared by fellow citizens. This study identified Facebook postings with positive messages, posted by Malaysians during the Movement Control Order (MCO) implemented by the Malaysian government as a form of prosocial behaviour.   Methodology: Through an analytic framework consisting of Positive Discourse Analysis and Critical Discourse Analysis, 15 Facebook postings related to COVID-19 were selected and identified as positive discourse, which were coded and categorised using a thematic analysi

Thrombosis is a common clinical feature associated with morbidity and mortality in coronavirus disease-2019 (COVID-19) patients. Cytokine storm in COVID-19 increases patients' systemic inflammation, which can cause multiple health consequences. In this work, we aimed to indicate the effect of Pfizer-BioNTech vaccination on the modulation of monocyte chemoattractant protein-3 (MCP-3), matrix metalloproteinase 1 (MMP-1), and tumor necrosis factor-alpha (TNF-α) levels, and other systemic inflammatory biomarkers that associates with COVID-19 severity in patients who suffers from thrombosis consequences. For this purpose, ninety people were collected from Ibn Al-Nafees Hospital and divided into three groups each of which contained 30 people, 15

Many studies dealt with the consequences of  SARS CoV-2  (which cause COVID-19 infection) on the nervous system especially sensory nerves where the virus causes loss of taste and smell as it’s known, and may affect auditory nerves and be the expected cause of some hearing problems. A case-control analytic study was performed on a connivance sample of society of university students from a medical faculty. Each participant filled out a questionnaire contains demographic data and general, auditory and respiratory health condition, in addition to vaccination status. In the other side, the audio- examinations were performed on the study sample including Pure Tone Audiometry (PTA) and tympanometry. Two statistical methods; chi-square and t

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.

Healthcare professionals routinely use audio signals, generated by the human body, to help diagnose disease or assess its progression. With new technologies, it is now possible to collect human-generated sounds, such as coughing. Audio-based machine learning technologies can be adopted for automatic analysis of collected data. Valuable and rich information can be obtained from the cough signal and extracting effective characteristics from a finite duration time interval that changes as a function of time. This article presents a proposed approach to the detection and diagnosis of COVID-19 through the processing of cough collected from patients suffering from the most common symptoms of this pandemic. The proposed method is based on adopt