With growing awareness and concerns regards to Cloud Computing and Information Security, there is growing awareness and usage of Security Algorithms into data systems and processes. This paper presents a brief overview and comparison of Cryptographic algorithms, with an emphasis on Symmetric algorithms which should be used for Cloud based applications and services that require data and link encryption. In this paper we review Symmetric and Asymmetric algorithms with emphasis on Symmetric Algorithms for security consideration on which one should be used for Cloud based applications and services that require data and link encryption.

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Robotic technology has been rapidly transforming world economies in terms of business productivity and profitability. The market is shifting towards optimisation and automation – not just for the warehousing and manufacturing sectors, but even non-industrial areas such as defence, farming, hospitals, offices and even schools. The availability of open source platforms, falling hardware and electronics prices, prompt prototyping and convergence of technologies are some of the major reasons for this new revolution. However, cyber security and physical threats are high-priority areas when critical applications and missions are involved. Robotic technology has been rapidly transforming world economies in terms of business productivity and profitability. However, security threats are not always top of mind. Open source platforms, falling hardware and electronics prices and fast prototyping are some of the reasons for this new revolution. Cyber security and physical threats are high-priority areas when critical applications and missions are involved. Dr Akashdeep Bhardwaj, Dr Vinay Avasthi and Dr Sam Goundar analyse the threats to robotic systems and map the CIA model to boost security resilience.

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As an epidemic, COVID-19’s core test instrument still has serious flaws. To improve the present condition, all capabilities and tools available in this field are being used to combat the pandemic. Because of the contagious characteristics of the unique coronavirus (COVID-19) infection, an overwhelming comparison with patients queues up for pulmonary X-rays, overloading physicians and radiology and significantly impacting the quality of care, diagnosis, and outbreak prevention. Given the scarcity of clinical services such as intensive care and motorized ventilation systems in the aspect of this vastly transmissible ailment, it is critical to categorize patients as per their risk categories. This research describes a novel use of the deep convolutional neural network (CNN) technique to COVID-19 illness assessment seriousness. Utilizing chest X-ray images as contribution, an unsupervised DCNN model is constructed and suggested to split COVID-19 individuals into four seriousness classrooms: low, medium, serious, and crucial with an accuracy level of 96 percent. The efficiency of the DCNN model developed with the proposed methodology is demonstrated by empirical findings on a suitably huge sum of chest X-ray scans. To the evidence relating, it is the first COVID-19 disease incidence evaluation research with four different phases, to use a reasonably high number of X-ray images dataset and a DCNN with nearly all hyperparameters dynamically adjusted by the variable selection optimization task.