English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The spread of the novel coronavirus (COVID-19) has caused trillions ofdollars in damages to the governments and health authorities by affecting theglobal economies. The purpose of this study is to introduce a connected smartparadigm that not only detects the possible spread of viruses but also helps torestart businesses/economies, and resume social life. We are proposing aconnected Internet of Things ( IoT) based paradigm that makes use of objectdetection based on convolution neural networks (CNN), smart wearable andconnected e-health to avoid current and future outbreaks. First, connectedsurveillance cameras feed continuous video stream to the server where we detectthe inter-object distance to identify any social distancing violations. Aviolation activates area-based monitoring of active smartphone users and theircurrent state of illness. In case a confirmed patient or a person with highsymptoms is present, the system tracks exposed and infected people andappropriate measures are put into actions. We evaluated the proposed scheme forsocial distancing violation detection using YOLO (you only look once) v2 andv3, and for infection spread tracing using Python simulation.

New Normal: Cooperative Paradigm for Covid-19 Timely Detection and Containment using Internet of Things and Deep Learning