IRO Journals

Journal of IoT in Social, Mobile, Analytics, and Cloud

Big Data Analytics for Improved Risk Management and Customer Segregation in Banking Applications
Volume-3 | Issue-3

Design of Deep Learning Algorithm for IoT Application by Image based Recognition
Volume-3 | Issue-3

Analysis of Serverless Computing Techniques in Cloud Software Framework
Volume-3 | Issue-3

Health Record Management System – A Web-based Application
Volume-3 | Issue-4

A Novel Signal Processing Based Driver Drowsiness Detection System
Volume-3 | Issue-3

IoT Based Monitoring and Control System using Sensors
Volume-3 | Issue-2

Secure Data Sharing Platform for Portable Social Networks with Power Saving Operation
Volume-3 | Issue-3

Review of Internet of Wearable Things and Healthcare based Computational Devices
Volume-3 | Issue-3

Stock Index Prediction with Financial News Sentiments and Technical Indicators
Volume-4 | Issue-3

Hybrid Framework on Automatic Detection and Recognition of Traffic Display board Signs
Volume-3 | Issue-3

Suspicious Human Activity Detection System
Volume-2 | Issue-4

ROBOT ASSISTED SENSING, CONTROL AND MANUFACTURE IN AUTOMOBILE INDUSTRY
Volume-1 | Issue-3

EFFICIENT RESOURCE ALLOCATION AND QOS ENHANCEMENTS OF IOT WITH FOG NETWORK
Volume-1 | Issue-2

Live Streaming Architectures for Video Data - A Review
Volume-2 | Issue-4

IoT Based Monitoring and Control System using Sensors
Volume-3 | Issue-2

Big Data Analytics for Improved Risk Management and Customer Segregation in Banking Applications
Volume-3 | Issue-3

A Novel Signal Processing Based Driver Drowsiness Detection System
Volume-3 | Issue-3

IoT BASED AIR AND SOUND POLLUTION MONITIORING SYSTEM USING MACHINE LEARNING ALGORITHMS
Volume-2 | Issue-1

Analysis of Serverless Computing Techniques in Cloud Software Framework
Volume-3 | Issue-3

Hybrid Intrusion Detection System for Internet of Things (IoT)
Volume-2 | Issue-4

Home / Archives / Volume-3 / Issue-3 / Article-3

Volume - 3 | Issue - 3 | september 2021

Finding an optimal distance of social distancing for COVID 19
Pages: 206-220
Published
17 August, 2021
Abstract

Social distancing is a non-pharmaceutical infection prevention and control approach that is now being utilized in the COVID-19 scenario to avoid or restrict the transmission of illness in a community. As a consequence, the disease transmission, as well as the morbidity and mortality associated with it are reduced. The deadly coronavirus will circulate if the distance between the two persons in each site is used. However, coronavirus exposure must be avoided at all costs. The distance varies due to different nations' political rules and the conditions of their medical embassy. The WHO established a social distance of 1 to 2 metres as the standard. This research work has developed a computational method for estimating the impact of coronavirus based on various social distancing metrics. Generally, in COVID – 19 situations, social distance ranging from long to extremely long can be a good strategy. The adoption of extremely small social distance is a harmful approach to the pandemic. This calculation can be done by using deep learning based on crowd image identification. The proposed work has been utilized to find the optimal social distancing for COVID – 19 and it is identified as 1.89 meter. The purpose of the proposed experiment is to compare the different types of deep learning based image recognition algorithms in a crowded environment. The performance can be measured with various metrics such as accuracy, precision, recall, and true detection rate.

Keywords

deep learning social distancing COVID 19

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