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

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 CT images automatically. For the… 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

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 bi-branched convoluted neural network (CNN)… 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

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 beacon packets for smart contact… 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

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, the higher the PSQI, the… 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

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. We employed the whale optimization… More >

Yupaporn Areepong¹, Rapin Sunthornwat^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.3, pp. 927-942, 2020, DOI:10.32604/cmes.2020.012323

Abstract Coronavirus disease 2019 outbreak has spread as a pandemic since the end of year 2019. This situation has been causing a lot of problems of human beings such as economic problems, health problems. The forecasting of the number of infectious people is required by the authorities of all countries including Southeast Asian countries to make a decision and control the outbreak. This research is to investigate the suitable forecasting model for the number of infectious people in Southeast Asian countries. A comparison of forecasting models between logistic growth curve which is symmetric and Gompertz growth curve which is asymmetric based… More >