<p>Combating the COVID-19 epidemic has emerged as one of the most promising healthcare the world's challenges have ever seen. COVID-19 cases must be accurately and quickly diagnosed to receive proper medical treatment and limit the pandemic. Imaging approaches for chest radiography have been proven in order to be more successful in detecting coronavirus than the (RT-PCR) approach. Transfer knowledge is more suited to categorize patterns in medical pictures since the number of available medical images is limited. This paper illustrates a convolutional neural network (CNN) and recurrent neural network (RNN) hybrid architecture for the diagnosis of COVID-19 from chest X-rays. The deep transfer methods used were VGG19, DenseNet121, InceptionV3, and Inception-ResNetV2. RNN was used to classify data after extracting complicated characteristics from them using CNN. The VGG19-RNN design had the greatest accuracy of all of the networks with 97.8% accuracy. Gradient-weighted the class activation mapping (Grad-CAM) method was then used to show the decision-making areas of pictures that are distinctive to each class. In comparison to other current systems, the system produced promising findings, and it may be confirmed as additional samples become available in the future. For medical personnel, the examination revealed an excellent alternative way of diagnosing COVID-19.</p>

The use of the cloud by governments throughout the world is being aggressively investigated to increase efficiency and reduce costs. The majority of cloud computing risk management programs prioritize addressing cloud security issues that government organizations may face when they choose to adopt cloud computing systems, but these programs lack evidence of security risks, and problems with using cloud computing in developing nations are uncommon, so they called for more research in this area. The objective of this paper is to use quantitative models namely Spearman's Rank correlation coefficient, simple regression, and support vector machine regression (SVMR) for estimating cloud security issues based on cloud control factors for improving the mitigation of cloud computing security issues based on control factors using intelligent models in a government organization. Identify the proper cloud control factors for every cloud security issue from estimation errors using a standard for performance measurement like mean square error (MSE) and root mean square error (RMSE), performance measurement to evaluate and validate proposed models. SVMR is an approach to enhance practices for cloud security platforms to mitigate risks and infrastructure for cloud adoption in developing countries in this paper.

Cloud computing is a popular paradigm in information technology and computing as it offers numerous advantages in terms of economical saving and minimal management effort to many organizations agencies. Although elasticity and flexibility brings tremendous benefits, it still raises many information security challenges that have created a barrier against adopting this agile Cloud computing. This paper provides a review study on the cloud computing as well identifying 25-key factors to fulfil better practice in cloud computing and way of making the environment of the cloud computing more qualified to many organizations agencies.