Patients infected with the COVID-19 virus develop severe pneumonia, which typically results in death. Radiological data show that the disease involves interstitial lung involvement, lung opacities, bilateral ground-glass opacities, and patchy opacities. This study aimed to improve COVID-19 diagnosis via radiological chest X-ray (CXR) image analysis, making a substantial contribution to the development of a mobile application that efficiently identifies COVID-19, saving medical professionals time and resources. It also allows for timely preventative interventions by using more than 18000 CXR lung images and the MobileNetV2 convolutional neural network (CNN) architecture. The MobileNetV2 deep-learning model performances were evaluated

KE Sharquie, AA Noaimi, Glob Dermatol, 2014 - Cited by 6

Health and environmental factors as well as operational difficulties are major challenges facing the development of an anaerobic digestion process. Some of these problems relate to the use of sludge collected from primary and secondary clarifier units in wastewater treatment plants for laboratory purposes.

The present study addresses the preparation of sludge for laboratory purposes by using a mixture that consists of the digested sludge, which is less pathogenic, compared to the collected sludge from the primary or secondary clarifier, and food wastes. The sludge has been tested experimentally for 19 and 32 days under mesophilic conditions. The results show a steady methane production rate from the anaerobic dig

KE Sharquie, AA Khorsheed, AA Al-Nuaimy, Saudi Medical Journal, 2007 - Cited by 91