scholarly journals Social distance monitoring framework using deep learning architecture to control infection transmission of COVID-19 pandemic

2021 ◽  
pp. 102777
Author(s):  
Imran Ahmed ◽  
Misbah Ahmad ◽  
Gwanggil Jeon
Keyword(s):  
Deep Learning ◽  
Social Distance ◽  
Infection Transmission ◽  
Monitoring Framework ◽  
Control Infection

scholarly journals A deep learning-based social distance monitoring framework for COVID-19

2020 ◽  
pp. 102571 ◽  
Author(s):  
Imran Ahmed ◽  
Misbah Ahmad ◽  
Joel J.P.C. Rodrigues ◽  
Gwanggil Jeon ◽  
Sadia Din
Keyword(s):  
Deep Learning ◽  
Social Distance ◽  
Monitoring Framework

scholarly journals A Study on the Real-time Fire Classification Algorithm Based on Stacked LSTM

2021 ◽  
Vol 21 (3) ◽  
pp. 93-104
Author(s):  
Yoseob Heo ◽  
Seongho Seo ◽  
We Shim ◽  
Jongseok Kang
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Dependence ◽  
Real Time ◽  
Sensor Data ◽  
Fire Monitoring ◽  
Monitoring Framework ◽  
Sensor Signals ◽  
Fire Detector ◽  
Using Data

Several researchers have been drawn to the development of fire detector in recent years, to protect people and property from the catastrophic disaster of fire. However, studies related to fire monitoring are affected by some unique characteristics of fire sensor signals, such as time dependence and the complexity of the signal pattern based on the variety of fire types,. In this study, a new deep learning-based approach that accurately classifies various types of fire situations in real-time using data obtained from multidimensional channel fire sensor signals was proposed. The contribution of this study is to develop a stacked-LSTM model that considers the time-series characteristics of sensor data and the complexity of multidimensional channel sensing data to develop a new fire monitoring framework for fire identification based on improving existing fire detectors.

scholarly journals Real-Time Monitoring of COVID-19 SOP in Public Gathering Using Deep Learning Technique

2021 ◽  
Vol 5 ◽  
pp. 182-196
Author(s):  
Muhammad Haris Kaka Khel ◽  
Kushsairy Kadir ◽  
Waleed Albattah ◽  
Sheroz Khan ◽  
MNMM Noor ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Social Distance ◽  
Virus Transmission ◽  
World Health ◽  
Single Shot ◽  
Accuracy Score ◽  
Computationally Efficient ◽  
Safe Distance ◽  
Crowd Management

Crowd management has attracted serious attention under the prevailing pandemic conditions of COVID-19, emphasizing that sick persons do not become a source of virus transmission. World Health Organization (WHO) guidelines include maintaining a safe distance and wearing a mask in gatherings as part of standard operating procedures (SOP), considered thus far the most effective preventive measures to protect against COVID-19. Several methods and strategies have been used to construct various face detection and social distance detection models. In this paper, a deep learning model is presented to detect people without masks and those not keeping a safe distance to contain the virus. It also counts individuals who violate the SOP. The proposed model employs the Single Shot Multi-box Detector as a feature extractor, followed by Spatial Pyramid Pooling (SPP) to integrate the extracted features to improve the model's detecting capabilities. The MobilenetV2 architecture as a framework for the classifier makes the model highly light, fast, and computationally efficient, allowing it to be employed in embedded devices to do real-time mask and social distance detection, which is the sole objective of this research. This paper's technique yields an accuracy score of 99% and reduces the loss to 0.04%. Doi: 10.28991/esj-2021-SPER-14 Full Text: PDF

scholarly journals Correction: Pham et al. Bolt-Loosening Monitoring Framework Using an Image-Based Deep Learning and Graphical Model. Sensors 2020, 20, 3382

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5280
Author(s):  
Hai Chien Pham ◽  
Quoc-Bao Ta ◽  
Jeong-Tae Kim ◽  
Duc-Duy Ho ◽  
Xuan-Linh Tran ◽  
...  
Keyword(s):  
Deep Learning ◽  
Graphical Model ◽  
Monitoring Framework ◽  
Bolt Loosening

The authors wish to make the following correction to this paper [...]

scholarly journals Automatic Social Distance Monitoring system using Deep Learning Algorithms

2021 ◽  
Vol 785 (1) ◽  
pp. 012016
Author(s):  
T. Shanthi ◽  
R. Anand ◽  
K. Hareesh ◽  
M.S. Jagan ◽  
V. Baskar ◽  
...  
Keyword(s):  
Deep Learning ◽  
Monitoring System ◽  
Social Distance ◽  
Learning Algorithms

scholarly journals How risky is it to visit a supermarket during the pandemic?

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0253835
Author(s):  
Alexey A. Tsukanov ◽  
Alexandra M. Senjkevich ◽  
Maxim V. Fedorov ◽  
Nikolai V. Brilliantov
Keyword(s):  
Social Distance ◽  
Large Scale ◽  
Social Force ◽  
Infection Transmission ◽  
The Social ◽  
Customer Purchases ◽  
Spread Of Infection ◽  
Number Of Customers ◽  
Physical Layout ◽  
Transmission Models

We performed large-scale numerical simulations using a composite model to investigate the infection spread in a supermarket during a pandemic. The model is composed of the social force, purchasing strategy and infection transmission models. Specifically, we quantified the infection risk for customers while in a supermarket that depended on the number of customers, the purchase strategies and the physical layout of the supermarket. The ratio of new infections compared to sales efficiency (earned profit for customer purchases) was computed as a factor of customer density and social distance. Our results indicate that the social distance between customers is the primary factor influencing infection rate. Supermarket layout and purchasing strategy do not impact social distance and hence the spread of infection. Moreover, we found only a weak dependence of sales efficiency and customer density. We believe that our study will help to establish scientifically-based safety rules that will reduce the social price of supermarket business.

scholarly journals Finding an optimal distance of social distancing for COVID 19

2021 ◽  
Vol 3 (3) ◽  
pp. 206-220
Author(s):  
J Samuel Manoharan
Keyword(s):  
Deep Learning ◽  
Social Distance ◽  
Disease Transmission ◽  
Research Work ◽  
Computational Method ◽  
Infection Prevention And Control ◽  
Social Distancing ◽  
Control Approach ◽  
True Detection ◽  
The Impact

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.

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