فهرست مطالب sawsan dheyaa mahmood

SARS-CoV-2 and the consequential COVID-19 virus is one of the major concerns of the 21st century. Pertaining to the novelty of the disease, it became necessary to discover the efficacy of deep learning techniques in the quick and consistent discovery of COVID-19 based on chest X-ray and CT scan image analysis. In this related work, Prognostic tool using regression was designed for patients with COVID-19 and recognizing prediction patterns to make available important prognostic information on mortality or severity in COVID-19 patients. And reliable convolutional neural network (CNN) architecture models (DenseNet, VGG16, ResNet, Inception Net)to institute whether it would work preeminent in terms of accuracy as well as efficiency with image datasets with Transfer Learning. CNN with Transfer Learning were functional to accomplish the involuntary recognition of COVID-19 from numerary chest X-ray and CT scan images. The experimental results emphasize that selected models, which is formerly broadly tuned through suitable parameters, executes in extensive levels of COVID-19 discovery against pneumonia or normal or lung opacity through the precision of up to 87% for X-Ray and 91% intended for CT scans.

In industry-academy studies, the cloud computing model goes way above the ground. Cloud has emerged as a fantastic business model for service users and, depending on consumer requirements, can be used pay per usage base. Due to inadequate hardware or software resources, When the quantity of client requests for their high-demand service requirements is large, they prefer to wait in a server queue. As a result, in this study, Reduction in overall waiting time and server utilization factor has been focused on. Comparison has been made on average waiting time and analysis made on server utilization using the M/M/c queuing model.