Cryptocurrency bounced back based on cryptography technology during the COVID-19 pandemic

We all realized that the Corona pandemic created a shift in the daily life style of individuals and organizations based on few rules like Touch-less, contact-less and spacing between individuals which all prevent the spread of this new virus. We also noticed that digital technologies were the valuable tool to cope with this new life style. Teaching remotely, working from home, examining patients using mobile applications, and paying the bills from your PC are all examples of mobilization of digital technologies to respond to the current crisis. Of course, the extensive use of remote applications requires as much confidentiality and security as possible due to the privacy related information, or due to the financial transactions. Cryptography and its applications seemed to be the adequate solution to the security issue. And one of the hot cryptography applications are BlockChains which were the heart of cryptocurrency appearance as an alternative for digitized money. This paper discusses the cryptocurrency and its mechanisms as ways to support the online transactions within the new corona lifestyle. The aim of this paper is to describe how cryptography reinforced the use of cryptocurrency application based on blockchain technology. We emphasize on the fact that asymmetric cryptography-based security represents a powerful tool for these applications. © 2021 IEEE.

The Use of an Incremental Learning Algorithm for Diagnosing COVID-19 from Chest X-ray Images

The new Coronavirus or simply Covid-19 causes an acute deadly disease. It has spread rapidly across the world, which has caused serious consequences for health professionals and researchers. This is due to many reasons including the lack of vaccine, shortage of testing kits and resources. Therefore, the main purpose of this study is to present an inexpensive alternative diagnostic tool for the detection of Covid-19 infection by using chest radiographs and Deep Convolutional Neural Network (DCNN) technique. In this paper, we have proposed a reliable and economical solution to detect COVID-19. This will be achieved by using X-rays of patients and an Incremental-DCNN (I-DCNN) based on ResNet-101 architec-ture. The datasets used in this study were collected from publicly available chest radiographs on medical repositories. The proposed I-DCNN method will help in diagnosing the positive Covid-19 patient by utilising three chest X-ray imagery groups, these will be: Covid-19, viral pneumonia, and healthy cases. Furthermore, the main contribution of this paper resides on the use of incremental learning in order to accommodate the detection system. This has high computational energy requirements, time consuming challenges, while working with large-scale and reg-ularly evolving images. The incremental learning process will allow the recognition system to learn new datasets, while keeping the convolutional layers learned pre-viously. The overall Covid-19 detection rate obtained using the proposed I-DCNN was of 98.70% which undeniably can contribute effectively to the detection of COVID-19 infection. © 2022, Brno University of Technology. All rights reserved.