Security issues and challenges in cloud of things-based applications for industrial automation.

Due to the COVID-19 outbreak, industries have gained a thrust on contactless processing for computing technologies and industrial automation. Cloud of Things (CoT) is one of the emerging computing technologies for such applications. CoT combines the most emerging cloud computing and the Internet of Things. The development in industrial automation made them highly interdependent because the cloud computing works like a backbone in IoT technology. This supports the data storage, analytics, processing, commercial application development, deployment, and security compliances. Now amalgamation of cloud technologies with IoT is making utilities more useful, smart, service-oriented, and secure application for sustainable development of industrial processes. As the pandemic has increased access to computing utilities remotely, cyber-attacks have been increased exponentially. This paper reviews the CoT's contribution to industrial automation and the various security features provided by different tools and applications used for the circular economy. The in-depth analysis of security threats, availability of different features corresponding the security issues in traditional and non-traditional CoT platforms used in industrial automation have been analysed. The security issues and challenges faced by IIoT and AIoT in industrial automation have also been addressed.

Low-Dose COVID-19 CT Image Denoising Using CNN and its Method Noise Thresholding.

Noise in computed tomography (CT) images may occur due to low radiation dose. Hence, the main aim of this paper is to reduce the noise from low dose CT images so that the risk of high radiation dose can be reduced. BACKGROUND: The novel corona virus outbreak has ushered in different new areas of research in medical instrumentation and technology. Medical diagnostics and imaging are one of the ways in which the area and level of infection can be detected. OBJECTIVE: The COVID-19 attacks people who have less immunity, so infants, kids, and pregnant women are more vulnerable to the infection. So they need to undergo CT scanning to find the infection level. But the high radiation diagnostic is also fatal for them, so the intensity of radiation needs to be reduced significantly, which may generate the noise in the CT images. METHOD: In this paper, a new denoising technique for such low dose Covid-19 CT images has been introduced using a convolution neural network (CNN) and the method noise-based thresholding. The major concern of the methodology for reducing the risk associated with radiation while diagnosing. RESULTS: The results are evaluated visually and also by using standard performance metrics. From comparative analysis, it was observed that proposed works gives better outcomes. CONCLUSIONS: The proposed low-dose COVID-19 CT image denoising model is therefore concluded to have a better potential to be effective in various pragmatic medical image processing applications in terms of noise suppression and clinical edge preservation.