Identity resilience in the digital health ecosystem: A key recovery-enabled framework.

In response to the evolving landscape of digital technology in healthcare, this study addresses the multifaceted challenges pertaining to identity and data privacy. The core of our key recovery-enabled framework revolves around the establishment of a robust identity verification system, leveraging the World Wide Web Consortium(W3C) standard for verifiable credentials(VC) and a test blockchain network. The approach leverages cryptographic proofs embedded within credentials issued by various entities to securely validate the legitimacy of identities.

Cloud Enterprise Dynamic Risk Assessment (CEDRA): a dynamic risk assessment using dynamic Bayesian networks for cloud environment.

Cloud computing adoption has been increasing rapidly amid COVID-19 as organisations accelerate the implementation of their digital strategies. Most models adopt traditional dynamic risk assessment, which does not adequately quantify or monetise risks to enable business-appropriate decision-making. In view of this challenge, a new model is proposed in this paper for assignment of monetary losses terms to the consequences nodes, thereby enabling experts to understand better the financial risks of any consequence.

Challenges in management of microbial keratitis during COVID-19 pandemic related lockdown: a comparative analysis with pre pandemic data.

Purpose: To study the challenges of managing microbial keratitis(MK) during the COVID19 pandemic related lockdown and assess the outcomes of treatment at a tertiary cornea service.

Methods: Retrospective, non comparative study of electronic medical records of MK presenting to a network of four tertiary care cornea services. The medical history, presenting clinical features, microbiology work up and treatment outcomes were analyzed.

Ransomware detection using deep learning based unsupervised feature extraction and a cost sensitive Pareto Ensemble classifier.

Ransomware attacks pose a serious threat to Internet resources due to their far-reaching effects. It's Zero-day variants are even more hazardous, as less is known about them. In this regard, when used for ransomware attack detection, conventional machine learning approaches may become data-dependent, insensitive to error cost, and thus may not tackle zero-day ransomware attacks.

Enterobacter cloacae Keratitis: Clinicomicrobiological Profiles, Risk Factors, and Outcomes.

Purpose: The purpose of the study was to report the clinical features, risk factors, antibiotic susceptibility, and treatment outcomes in a series of Enterobacter cloacae keratitis.

Methods: A retrospective analysis was performed of the electronic medical records of microbial keratitis caused by E. cloacae identified by the Vitek 2 system (BioMerieux, Craponne, France).