COVID-19 or novel coronavirus disease, which has already been declared as a worldwide pandemic, at first had an outbreak in a small town of China, named Wuhan. More than two hundred countries around the world have already been affected by this severe virus as it spreads by human interaction. Moreover, the symptoms of novel coronavirus are quite similar to the general flu. Screening of infected patients is considered as a critical step in the fight against COVID-19. Therefore, it is highly relevant to recognize positive cases as early as possible to avoid further spreading of this epidemic. However, there are several methods to detect COVID-19 positive patients, which are typically performed based on respiratory samples and among them one of the critical approach which is treated as radiology imaging or X-Ray imaging. Recent findings from X-Ray imaging techniques suggest that such images contain relevant information about the SARS-CoV-2 virus. In this article, we have introduced a Deep Neural Network (DNN) based Faster Regions with Convolutional Neural Networks (Faster R-CNN) framework to detect COVID-19 patients from chest X-Ray images using available open-source dataset. Our proposed approach provides a classification accuracy of 97.36%, 97.65% of sensitivity, and a precision of 99.28%. Therefore, we believe this proposed method might be of assistance for health professionals to validate their initial assessment towards COVID-19 patients.

Big data typically coins with large volume of data and various enterprises are involving day to day in cloud environment. Nowadays, Cloud facility adoption has enlarged. With the acceptance of cloud facility, numerous of the enterprises are expending to store and process Large Data in cloud. Safety methods provided by the facility providers might not be sufficient to safe the data in the cloud. Enterprise as well as users are suffering with proper security aspect to store, retrieve and process big data in cloud environment. An access control model with honeypot is presented in this paper. Access control model deals with various parameters of authentication, log etc. Various link and areas are included as honeypot to catch hackers or unauthorized users.