Wencong Liu¹, Ahmed Mostafa Khalil^2,*, Rehab Basheer³, Yong Lin⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2715-2730, 2023, DOI:10.32604/cmes.2023.024755 - 23 November 2022

Abstract In early December 2019, a new virus named “2019 novel coronavirus (2019-nCoV)” appeared in Wuhan, China. The disease quickly spread worldwide, resulting in the COVID-19 pandemic. In the current work, we will propose a novel fuzzy soft modal (i.e., fuzzy-soft expert system) for early detection of COVID-19. The main construction of the fuzzy-soft expert system consists of five portions. The exploratory study includes sixty patients (i.e., forty males and twenty females) with symptoms similar to COVID-19 in (Nanjing Chest Hospital, Department of Respiratory, China). The proposed fuzzy-soft expert system depended on five symptoms of COVID-19 More > Graphic Abstract

P. Prabu¹, K. Venkatachalam², Ala Saleh Alluhaidan^3,*, Radwa Marzouk⁴, Myriam Hadjouni⁵, Sahar A. El_Rahman^5,6

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1133-1152, 2022, DOI:10.32604/cmc.2022.020919 - 03 November 2021

Abstract COVID’19 has caused the entire universe to be in existential health crisis by spreading globally in the year 2020. The lungs infection is detected in Computed Tomography (CT) images which provide the best way to increase the existing healthcare schemes in preventing the deadly virus. Nevertheless, separating the infected areas in CT images faces various issues such as low-intensity difference among normal and infectious tissue and high changes in the characteristics of the infection. To resolve these issues, a new inf-Net (Lung Infection Segmentation Deep Network) is designed for detecting the affected areas from the… More >

Anas Basalamah¹, Shadikur Rahman^2,*

Computer Systems Science and Engineering, Vol.40, No.1, pp. 375-388, 2022, DOI:10.32604/csse.2022.016949 - 26 August 2021

Abstract This paper demonstrates empirical research on using convolutional neural networks (CNN) of deep learning techniques to classify X-rays of COVID-19 patients versus normal patients by feature extraction. Feature extraction is one of the most significant phases for classifying medical X-rays radiography that requires inclusive domain knowledge. In this study, CNN architectures such as VGG-16, VGG-19, RestNet50, RestNet18 are compared, and an optimized model for feature extraction in X-ray images from various domains involving several classes is proposed. An X-ray radiography classifier with TensorFlow GPU is created executing CNN architectures and our proposed optimized model for More >

Muhammed Binsawad^1,*, Marwan Albahar², Abdullah Bin Sawad¹

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2791-2806, 2021, DOI:10.32604/cmc.2021.016141 - 13 April 2021

Abstract The coronavirus disease 2019 (COVID-19) pandemic has had a devastating impact on the health and welfare of the global population. A key measure to combat COVID-19 has been the effective screening of infected patients. A vital screening process is the chest radiograph. Initial studies have shown irregularities in the chest radiographs of COVID-19 patients. The use of the chest X-ray (CXR), a leading diagnostic technique, has been encouraged and driven by several ongoing projects to combat this disease because of its historical effectiveness in providing clinical insights on lung diseases. This study introduces a dilated… More >

Muhammad Saad¹, Maaz Bin Ahmad¹, Muhammad Asif^2,*, Khalid Masood², Mohammad A. Al Ghamdi³

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3545-3562, 2021, DOI:10.32604/cmc.2021.015720 - 01 March 2021

Abstract Social distancing and self-isolation management are crucial preventive measures that can save millions of lives during challenging pandemics of diseases such as the Spanish flu, swine flu, and coronavirus disease 2019 (COVID-19). This study describes the comprehensive and effective implementation of the Industrial Internet of Things and machine-to-machine technologies for social distancing and smart self-isolation management. These technologies can help prevent outbreaks of any disease that can disperse widely and develop into a pandemic. Initially, a smart wristband is proposed that incorporates Bluetooth beacon technology to facilitate the tracing and tracking of Bluetooth Low Energy… More >

Muhammad Farman¹, Aqeel Ahmad¹, Ali Akgül^2,*, Muhammad Umer Saleem³, Muhammad Naeem⁴, Dumitru Baleanu^5,6,7

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3215-3227, 2021, DOI:10.32604/cmc.2021.014006 - 01 March 2021

Abstract Today, coronavirus appears as a serious challenge to the whole world. Epidemiological data of coronavirus is collected through media and web sources for the purpose of analysis. New data on COVID-19 are available daily, yet information about the biological aspects of SARS-CoV-2 and epidemiological characteristics of COVID-19 remains limited, and uncertainty remains around nearly all its parameters’ values. This research provides the scientific and public health communities better resources, knowledge, and tools to improve their ability to control the infectious diseases. Using the publicly available data on the ongoing pandemic, the present study investigates the… More >

Guanghui Lei¹, Caihong Yang^2,#, Yan Ge^3,#, Yan Zhang^2,*, Yufei Xie^4,*, Jianwen Chen², Jinyang Wu⁵

International Journal of Mental Health Promotion, Vol.23, No.1, pp. 121-140, 2021, DOI:10.32604/IJMHP.2021.013072 - 19 February 2021

Abstract To explore the relationship between social support and sleep quality of community workers in Wuhan during the coronavirus disease 2019 (the COVID-19 infection epidemic), this research constructed a mediating effect model to explore the mediating psychological mechanism of social support influencing sleep quality of front-line community workers. A total of 500 front-line community workers in Wuhan were investigated. We used the perceived social support scale (PSSS), the Connor-Davidson Resilience Scale (CD-RISC), the perceived stress scale (PSS), and the Pittsburgh sleep quality index (PSQI) to measure social support, psychological resilience, perceived stress and sleep quality. Specifically,… More >

Sally M. Elghamrawy¹, Aboul Ella Hassnien^2,*, Vaclav Snasel³

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2353-2371, 2021, DOI:10.32604/cmc.2021.014767 - 05 February 2021

Abstract Detecting COVID-19 cases as early as possible became a critical issue that must be addressed to avoid the pandemic’s additional spread and early provide the appropriate treatment to the affected patients. This study aimed to develop a COVID-19 diagnosis and prediction (AIMDP) model that could identify patients with COVID-19 and distinguish it from other viral pneumonia signs detected in chest computed tomography (CT) scans. The proposed system uses convolutional neural networks (CNNs) as a deep learning technology to process hundreds of CT chest scan images and speeds up COVID-19 case prediction to facilitate its containment.… More >

Mazin Abed Mohammed¹, Karrar Hameed Abdulkareem², Begonya Garcia-Zapirain³, Salama A. Mostafa⁴, Mashael S. Maashi⁵, Alaa S. Al-Waisy¹, Mohammed Ahmed Subhi⁶, Ammar Awad Mutlag⁷, Dac-Nhuong Le^8,9,*

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 3289-3310, 2021, DOI:10.32604/cmc.2021.012874 - 28 December 2020

Abstract The quick spread of the Coronavirus Disease (COVID-19) infection around the world considered a real danger for global health. The biological structure and symptoms of COVID-19 are similar to other viral chest maladies, which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease. In this study, an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods (e.g., artificial neural network (ANN), support vector machine (SVM), linear kernel and radial… More >

Abdu Gumaei^1,2,*, Mabrook Al-Rakhami¹, Mohamad Mahmoud Al Rahhal³, Fahad Raddah H. Albogamy³, Eslam Al Maghayreh³, Hussain AlSalman¹

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 315-329, 2021, DOI:10.32604/cmc.2020.012045 - 30 October 2020

Abstract The fast spread of coronavirus disease (COVID-19) caused by SARSCoV-2 has become a pandemic and a serious threat to the world. As of May 30, 2020, this disease had infected more than 6 million people globally, with hundreds of thousands of deaths. Therefore, there is an urgent need to predict confirmed cases so as to analyze the impact of COVID-19 and practice readiness in healthcare systems. This study uses gradient boosting regression (GBR) to build a trained model to predict the daily total confirmed cases of COVID-19. The GBR method can minimize the loss function More >