New normal: cooperative paradigm for COVID-19 timely detection and containment using Internet of things and deep learning
DC Field | Value | Language |
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dc.contributor.author | Kumbhar, Farooque Hassan | - |
dc.contributor.author | Hassan, Syed Ali | - |
dc.contributor.author | Shin, Soo Young | - |
dc.date.accessioned | 2021-11-17T01:40:22Z | - |
dc.date.available | 2021-11-17T01:40:22Z | - |
dc.date.created | 2021-11-17 | - |
dc.date.issued | 2021-10 | - |
dc.identifier.issn | 1300-0632 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/20285 | - |
dc.description.abstract | The spread of the novel coronavirus (COVID-19) has caused trillions of dollars of damages to the governments and health authorities by affecting the global economies. It is essential to identify, track and trace COVID-19 spread at its earliest detection. Timely action can not only reduce further spread but also help in providing an efficient medical response. Existing schemes rely on volunteer participation, and/or mobile traceability, which leads to delays in containing the spread. There is a need for an autonomous, connected, and centralized paradigm that can identify, trace and inform connected personals. We propose a novel connected Internet of Things (IoT) based paradigm using convolution neural networks (CNN), smart wearable, and connected E-Health to help governments resume normal life again. Our autonomous scheme provides three-level detection: inter-object distance for social distancing violations using CNN, area-based monitoring of active smartphone users and their current state of illness using connected E-Health, and smart wearable. Our exhaustive performance evaluation identifies that the proposed scheme with CNN YOLOv3 achieves up to 90% mean average precision in detecting social distancing violations, as compared to existing YOLOv2 achieving 70% and faster R-CNN with 76%. Our evaluation also identifies that wearing protective gear reduces spread by 50%, and timely actions in the first hour can help avoid three times COVID-19 exposure. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEY | - |
dc.title | New normal: cooperative paradigm for COVID-19 timely detection and containment using Internet of things and deep learning | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kumbhar, Farooque Hassan | - |
dc.contributor.affiliatedAuthor | Hassan, Syed Ali | - |
dc.contributor.affiliatedAuthor | Shin, Soo Young | - |
dc.identifier.doi | 10.3906/elk-2105-96 | - |
dc.identifier.wosid | 000709712800002 | - |
dc.identifier.bibliographicCitation | TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, v.29, pp.2795 - 2806 | - |
dc.relation.isPartOf | TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES | - |
dc.citation.title | TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES | - |
dc.citation.volume | 29 | - |
dc.citation.startPage | 2795 | - |
dc.citation.endPage | 2806 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordPlus | AI | - |
dc.subject.keywordAuthor | Convolution neural network | - |
dc.subject.keywordAuthor | contagious diseases | - |
dc.subject.keywordAuthor | internet of things | - |
dc.subject.keywordAuthor | smart city | - |
dc.subject.keywordAuthor | tracking | - |
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