The novel coronavirus named COVID-19 has quickly spread among humans worldwide, and the situation remains hazardous to the health system. The existence of this virus in the human body is identified through sputum or blood samples. Furthermore, computed tomography (CT) or X-ray has become a significant tool for quick diagnoses. Thus, it is essential to develop an online and real-time computer-aided diagnosis (CAD) approach to support physicians and avoid further spreading of the disease. In this research, a convolutional neural network (CNN) -based Residual neural network (ResNet50) has been employed to detect COVID-19 through chest X-ray images and achieved 98% accuracy. The proposed CAD system will receive the X-ray images from the remote hospitals/healthcare centers and perform diagnostic processes. Furthermore, the proposed CAD system uses advanced load balancer and resilience features to achieve fault tolerance with zero delays and perceives more infected cases during this pandemic.

The novel coronavirus that causes the Coronavirus Disease 2019 (COVID-19) has spread all over the world at an unprecedented rate. With growing recognition of the distributed nature of health services, the technology of blockchain has recently reached the impetus of the healthcare domain. This article provides: 1) a panoramic overview of existing solutions and scenarios incorporating blockchain to combat COVID-19 in the healthcare domain along with their benefits and challenges; as well as 2) a framework that will facilitate new research activities on this subject.

Currently, the world faces a novel coronavirus disease 2019 (COVID-19) challenge and infected cases are increasing exponentially. COVID-19 is a disease that has been reported by the WHO in March 2020, caused by a virus called the SARS-CoV-2. As of 10 March 2021, more than 150 million people were infected and 3v million died. Researchers strive to find out about the virus and recommend effective actions. An unprecedented increase in pathogens is happening and a major attempt is being made to tackle the epidemic. This article presents deep learning-based COVID-19 detection using CT and X-ray images and data analytics on its spread worldwide. This article's research structure builds on a recent analysis of the COVID-19 data and prospective research to systematize current resources, help the researchers, practitioners by using in-depth learning methodologies to build solutions for the COVID-19 pandemic.

Security fatigue has been used to describe experiences with online security. This study identifies the affective manifestations resulting from decision fatigue and the role it plays in users' security decisions.

The National Library of Medicine's MedlinePlus Connect service extends the reach of the consumer health website MedlinePlus.gov to deliver relevant information to patients and providers via health IT systems, electronic health records, and patient portals.

This article outlines the challenge to understand how to integrate people into a new generation of cyber-physical-human systems (CPHSs) and proposes a human service capability description model to help.

Limited health literacy is a barrier to understanding health information. Simplifying text can reduce this barrier and possibly other known disparities in health. Unfortunately, few tools exist to simplify text with demonstrated impact on comprehension. By leveraging modern data sources integrated with natural language processing algorithms, we are developing the first semi-automated text simplification tool. We present two main contributions. First, we introduce our evidence-based development strategy for designing effective text simplification software and summarize initial, promising results. Second, we present a new study examining existing readability formulas, which are the most commonly used tools for text simplification in healthcare. We compare syllable count, the proxy for word difficulty used by most readability formulas, with our new metric 'term familiarity' and find that syllable count measures how difficult words 'appear' to be, but not their actual difficulty. In contrast, term familiarity can be used to measure actual difficulty.

The C programming language was invented more than 40 years ago. It is infamous for buffer overflows. We have learned a lot about computer science, language design, and software engineering since then. Because it is unlikely that we will stop using C any time soon, we present some ways to deal with BOF. Many of these techniques are also useful for other programing languages and other classes of vulnerabilities.

Six senior computer science educators answer questions about the current state of computer science education, software engineering, and licensing software engineers.