Disaster and Pandemic Management Using Machine Learning: A Survey

This article provides a literature review of state-of-the-art machine learning (ML) algorithms for disaster and pandemic management. Most nations are concerned about disasters and pandemics, which, in general, are highly unlikely events. To date, various technologies, such as IoT, object sensing, UAV, 5G, and cellular networks, smartphone-based system, and satellite-based systems have been used for disaster and pandemic management. ML algorithms can handle multidimensional, large volumes of data that occur naturally in environments related to disaster and pandemic management and are particularly well suited for important related tasks, such as recognition and classification. ML algorithms are useful for predicting disasters and assisting in disaster management tasks, such as determining crowd evacuation routes, analyzing social media posts, and handling the post-disaster situation. ML algorithms also find great application in pandemic management scenarios, such as predicting pandemics, monitoring pandemic spread, disease diagnosis, etc. This article first presents a tutorial on ML algorithms. It then presents a detailed review of several ML algorithms and how we can combine these algorithms with other technologies to address disaster and pandemic management. It also discusses various challenges, open issues and, directions for future research

Fast, Reliable, and Secure Drone Communication: A Comprehensive Survey

Drone security is currently a major topic of discussion among researchers and industrialists. Although there are multiple applications of drones, if the security challenges are not anticipated and required architectural changes are not made, the upcoming drone applications will not be able to serve their actual purpose. Therefore, in this paper, we present a detailed review of the security-critical drone applications, and security-related challenges in drone communication such as DoS attacks, Man-in-the-middle attacks, De-Authentication attacks, and so on. Furthermore, as part of solution architectures, the use of Blockchain, Software Defined Networks (SDN), Machine Learning, and Fog/Edge computing are discussed as these are the most emerging technologies. Drones are highly resource-constrained devices and therefore it is not possible to deploy heavy security algorithms on board. Blockchain can be used to cryptographically store all the data that is sent to/from the drones, thereby saving it from tampering and eavesdropping. Various ML algorithms can be used to detect malicious drones in the network and to detect safe routes. Additionally, the SDN technology can be used to make the drone network reliable by allowing the controller to keep a close check on data traffic, and fog computing can be used to keep the computation capabilities closer to the drones without overloading them.The work of Vinay Chamola and Fei Richard Yu was supported in part by the SICI SICRG Grant through the Project Artificial Intelligence Enabled Security Provisioning and Vehicular Vision Innovations for Autonomous Vehicles, and in part by the Government of Canada's National Crime Prevention Strategy and Natural Sciences and Engineering Research Council of Canada (NSERC) CREATE Program for Building Trust in Connected and Autonomous Vehicles (TrustCAV)

The sensitivity of qSOFA calculated at triage and during emergency department treatment to rapidly identify sepsis patients.

The quick sequential organ failure assessment (qSOFA) score has been proposed as a means to rapidly identify adult patients with suspected infection, in pre-hospital, Emergency Department (ED), or general hospital ward locations, who are in a high-risk category with increased likelihood of poor outcomes: a greater than 10% chance of dying or an increased likelihood of spending 3 or more days in the ICU. This score is intended to replace the use of systemic inflammatory response syndrome (SIRS) criteria as a screening tool; however, its role in ED screening and identification has yet to be fully elucidated. In this retrospective observational study, we explored the performance of triage qSOFA (tqSOFA), maximum qSOFA, and first initial serum lactate (\u3e 3 mmol/L) at predicting in-hospital mortality and compared these results to those for the initial SIRS criteria obtained in triage. A total of 2859 sepsis cases were included and the in-hospital mortality rate was 14.4%. The sensitivity of tqSOFA ≥ 2 and maximum qSOFA ≥ 2 to predict in-hospital mortality were 33% and 69%, respectively. For comparison, the triage SIRS criteria and the initial lactate \u3e 3 mmol/L had sensitivities of 82% and 65%, respectively. These results demonstrate that in a large ED sepsis database the earliest measurement of end organ impairment, tqSOFA, performed poorly at identifying patients at increased risk of mortality and maximum qSOFA did not significantly outperform initial serum lactate levels

COVID-19 Associated Mucormycosis::A Review of an Emergent Epidemic Fungal Infection in 3 Era of COVID-19 Pandemic

At a time when the COVID-19's second wave is still picking up in countries like India, a number of reports describe the potential association with a rise in the number of cases of mucormycosis, commonly known as the black fungus. This fungal infection has been around for centuries and affects those people whose immunity has been compromised due to severe health conditions. In this article, we provide a detailed overview of mucormycosis and discuss how COVID-19 could have caused a sudden spike in an otherwise rare disease in countries like India. The article discusses the various symptoms of the disease, class of people most vulnerable to this infection, preventive measures to avoid the disease, and various treatments that exist in clinical practice and research to manage the disease

Age-Related Differences in Biomarkers of Acute Inflammation During Hospitalization for Sepsis

To evaluate age-related differences in inflammation biomarkers during the first 72 hours of hospitalization for sepsis