Target specific mining of COVID-19 scholarly articles using one-class approach

Sonbhadra, Sanjay Kumar; Agarwal, Sonali; Nagabhushan, P.

doi:10.1016/j.chaos.2020.110155

Cited by 39 publications

(25 citation statements)

References 28 publications

(28 reference statements)

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“…Many approaches related to COVID-19 problem, k-means clustering has been used. In [16], S.K. Sonbhadra et al proposed a novel bottom-up approach for COVID-19 articles using k-means clustering along with DBSCAN and HAC.…”

Section: Related Workmentioning

confidence: 99%

An Efficient K-Means Clustering Algorithm for Analysing COVID-19

Zubair

Iqbal

Shil

et al. 2021

Hybrid Intelligent Systems

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COVID-19 hits the world like a storm by arising pandemic situations for most of the countries around the world. The whole world is trying to overcome this pandemic situation. A better health care quality may help a country to tackle the pandemic. Making clusters of countries with similar types of health care quality provides an insight into the quality of health care in different countries. In the area of machine learning and data science, the K-means clustering algorithm is typically used to create clusters based on similarity. In this paper, we propose an efficient K-means clustering method that determines the initial centroids of the clusters efficiently. Based on this proposed method, we have determined health care quality clusters of countries utilizing the COVID-19 datasets. Experimental results show that our proposed method reduces the number of iterations and execution time to analyze COVID-19 while comparing with the traditional k-means clustering algorithm.

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Section: Related Workmentioning

confidence: 99%

An Efficient K-Means Clustering Algorithm for Analysing COVID-19

Zubair

Iqbal

Shil

et al. 2021

Hybrid Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…The study aimed to derive an approximation that shows how early social distancing measures can reduce economic loss and the number of new infections significantly. At the time when coronavirus is begun spreading across the individuals and society, research and scientists are starting to find out the best solution to eliminate the spread of this pandemic [6,7]. Jennifer Berglund [8], suggested tracking a person infected with COVID-19 using GPS and built-in applications in smartphones.…”

Section: Research Background and Related Workmentioning

confidence: 99%

Implementing a real-time, AI-based, people detection and social distancing measuring system for Covid-19

Saponara

Elhanashi

Gagliardi

2021

J Real-Time Image Proc

102

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COVID-19 is a disease caused by a severe respiratory syndrome coronavirus. It was identified in December 2019 in Wuhan, China. It has resulted in an ongoing pandemic that caused infected cases including many deaths. Coronavirus is primarily spread between people during close contact. Motivating to this notion, this research proposes an artificial intelligence system for social distancing classification of persons using thermal images. By exploiting YOLOv2 (you look at once) approach, a novel deep learning detection technique is developed for detecting and tracking people in indoor and outdoor scenarios. An algorithm is also implemented for measuring and classifying the distance between persons and to automatically check if social distancing rules are respected or not. Hence, this work aims at minimizing the spread of the COVID-19 virus by evaluating if and how persons comply with social distancing rules. The proposed approach is applied to images acquired through thermal cameras, to establish a complete AI system for people tracking, social distancing classification, and body temperature monitoring. The training phase is done with two datasets captured from different thermal cameras. Ground Truth Labeler app is used for labeling the persons in the images. The proposed technique has been deployed in a low-cost embedded system (Jetson Nano) which is composed of a fixed camera. The proposed approach is implemented in a distributed surveillance video system to visualize people from several cameras in one centralized monitoring system. The achieved results show that the proposed method is suitable to set up a surveillance system in smart cities for people detection, social distancing classification, and body temperature analysis.

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“…Since the onset of coronavirus pandemic, many countries have used technology-based solutions, to inhibit the spread of the disease [12,27,28]. For example, some of the developed countries, such as South Korea and India, use GPS data to monitor the movements of infected or suspected individuals to find any possible exposure among the healthy people.…”

Section: Tracking Technologiesmentioning

confidence: 99%

DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic

2020

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Social distancing is a recommended solution by the World Health Organisation (WHO) to minimise the spread of COVID-19 in public places. The majority of governments and national health authorities have set the 2-m physical distancing as a mandatory safety measure in shopping centres, schools and other covered areas. In this research, we develop a hybrid Computer Vision and YOLOv4-based Deep Neural Network (DNN) model for automated people detection in the crowd in indoor and outdoor environments using common CCTV security cameras. The proposed DNN model in combination with an adapted inverse perspective mapping (IPM) technique and SORT tracking algorithm leads to a robust people detection and social distancing monitoring. The model has been trained against two most comprehensive datasets by the time of the research—the Microsoft Common Objects in Context (MS COCO) and Google Open Image datasets. The system has been evaluated against the Oxford Town Centre dataset (including 150,000 instances of people detection) with superior performance compared to three state-of-the-art methods. The evaluation has been conducted in challenging conditions, including occlusion, partial visibility, and under lighting variations with the mean average precision of 99.8% and the real-time speed of 24.1 fps. We also provide an online infection risk assessment scheme by statistical analysis of the spatio-temporal data from people’s moving trajectories and the rate of social distancing violations. We identify high-risk zones with the highest possibility of virus spread and infection. This may help authorities to redesign the layout of a public place or to take precaution actions to mitigate high-risk zones. The developed model is a generic and accurate people detection and tracking solution that can be applied in many other fields such as autonomous vehicles, human action recognition, anomaly detection, sports, crowd analysis, or any other research areas where the human detection is in the centre of attention.

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Target specific mining of COVID-19 scholarly articles using one-class approach

Cited by 39 publications

References 28 publications

An Efficient K-Means Clustering Algorithm for Analysing COVID-19

An Efficient K-Means Clustering Algorithm for Analysing COVID-19

Implementing a real-time, AI-based, people detection and social distancing measuring system for Covid-19

DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic

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