Abstract:Objective: To analyze radiological spectrum of HRCT in COVID-19 patients, clinically symptomatic but initially having negative RT-PCR.
Study Design: Prospective cross sectional descriptive study.
Place and Duration of Study: Radiology and Medicine Department, DHQ Hospital Rawalpindi, from June to November 2020
Methodology: The study included 90 patients presenting with clinical symptoms of COVID-19 but with negative RT-PCR. All patients underwent chest computed tomography (CT). Patients with … Show more
“…Various researchers had worked on chest X-ray images COVID detection mechanism [21]. Different machine learning and deep learning models had applied for prediction of COVID [22].…”
Section: Comparative Analysis Of Proposed Workmentioning
<p><span>The latest human coronavirus is COVID-19. Chest radiography imaging is essential for screening, early detection, and monitoring COVID-19 infections since the virus resides in the lungs. Classical real time reverse transcriptase polymerase chain reaction (RT-PCR) data and chest X-ray pictures will become more important for COVID-19 identification as the pandemic spreads due to their affordability, wide availability, and infection control benefits, which reduce cross-contamination. This work presents multi-modal hybrid automated approaches to classify COVID-19 illness into three clinical categories: normal, pathogenic, and COVID-19 utilising RT-PCR test data and online chest X-ray datasets. The RT-PCR and chest X-ray image datasets were processed using supervised machine learning and convolutional neural networks (CNN). Together, these measures help us separate COVID-19 patients, those with similar symptoms, and healthy persons. The author improved detection times and classification accuracy with extra tree classifier’s feature selection and openCV’s image sharpening. The proposed approaches were tested using a research dataset. The proposed methods allowed reliable COVID-19 disease categorization for clinical decision-making, with random forest (RF) classifier global precision values of 91.58% on the RT-PCR dataset and CNN model accuracy of 95.46% on improved sharpened images.</span></p>
“…Various researchers had worked on chest X-ray images COVID detection mechanism [21]. Different machine learning and deep learning models had applied for prediction of COVID [22].…”
Section: Comparative Analysis Of Proposed Workmentioning
<p><span>The latest human coronavirus is COVID-19. Chest radiography imaging is essential for screening, early detection, and monitoring COVID-19 infections since the virus resides in the lungs. Classical real time reverse transcriptase polymerase chain reaction (RT-PCR) data and chest X-ray pictures will become more important for COVID-19 identification as the pandemic spreads due to their affordability, wide availability, and infection control benefits, which reduce cross-contamination. This work presents multi-modal hybrid automated approaches to classify COVID-19 illness into three clinical categories: normal, pathogenic, and COVID-19 utilising RT-PCR test data and online chest X-ray datasets. The RT-PCR and chest X-ray image datasets were processed using supervised machine learning and convolutional neural networks (CNN). Together, these measures help us separate COVID-19 patients, those with similar symptoms, and healthy persons. The author improved detection times and classification accuracy with extra tree classifier’s feature selection and openCV’s image sharpening. The proposed approaches were tested using a research dataset. The proposed methods allowed reliable COVID-19 disease categorization for clinical decision-making, with random forest (RF) classifier global precision values of 91.58% on the RT-PCR dataset and CNN model accuracy of 95.46% on improved sharpened images.</span></p>
“…According to Khatri et al (1999), job hopping definition varies from one country to another, Settersten and Ray (2010) even renamed job hopping as job shopping which refers to the action of those who frequently change jobs, not necessarily due to anxiety or change but deploy as a strategy for better progress and profits. Generally, job hopping occurs for positive purposes, such as seeking for working experience and better rewards (Saleem et al, 2016), quick financial gains or career advancement (Naresh and Rathnam, 2015). Besides that, Yuen (2016) study conducted in Hong Kong has confirmed that job hopping occurs voluntarily and not forcibly (e.g., dismissal).…”
The employment sector around the world including Malaysia has seen an increasing involvement of millennial generation workers, however, millennial is also being described as frequent job hoppers compared to the previous generation. Millennials are easily leave their organizations less than two years. There is still a lack of knowledge on the real causes of job hopping in collectivist and high power distance society. In this study, six (6) deliberately selected journalists were interviewed and data were analysed using thematic. The findings revealed four main themes: (i) career development; (ii) salary; (iii) work-life balance; and (iv) organization justice, crucial towards preventing millennial journalist from job hopping A proposed concept was presented to explain the job hopping scenario amongst millennial journalists.
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