Open AccessIoT Analytics-inspired Real-time Monitoring for Early Prediction of COVID-19 Symptoms
Author(s) -
Ankush Manocha,
Gulshan Kumar,
Munish Bhatia
Publication year - 2021
Publication title -
computer journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.319
H-Index - 64
eISSN - 1460-2067
pISSN - 0010-4620
DOI - 10.1093/comjnl/bxab150
Subject(s) - seriousness , computer science , cloud computing , analytics , covid-19 , event (particle physics) , real time computing , dew point , simulation , data mining , disease , medicine , meteorology , physics , pathology , quantum mechanics , political science , infectious disease (medical specialty) , law , operating system
In this pandemic, providing a quality environment is considered one of the essential objectives of smart wellbeing observation. Therefore, the prediction of irregular events has become a fundamental requirement of assistive or clinical consideration. By concentrating on this need, a dew computation-inspired irregular physical event determination solution is presented to determine the symptoms of COVID-19 by analyzing the physical activities of the individuals at their initial stage. The benefit of the proposed solution is enhanced by forwarding the predicted outcomes and their occurrence within a speculated time on a private cloud database to decide the health seriousness. Furthermore, a dynamic decisive-module is introduced to notify medical specialists about the current wellbeing status of the individuals under monitoring. The real-time prediction efficiency of the proposed solution is determined by implementing and calculating the outcomes on both the dew and cloud platforms. The calculated outcomes exhibit the improved viability of the dew platform over the cloud platform by increasing the prediction speed of 46.27% for 40 and 45.54% for 30 frames per second. Moreover, the event prediction performance is justified over the state-of-the-art monitoring arrangements by achieving accuracy (92.88%), specificity (90.87%), sensitivity (88.26%) and F1-measure (89.53%) with the least decision-making delay.