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dc.contributor.authorKang, Byungseok
dc.contributor.authorChoo, Hyunseung
dc.date.accessioned2019-05-23T10:54:23Z
dc.date.available2019-05-23T10:54:23Z
dc.date.issued2016-05-21
dc.identifier.citationKang, B. and Choo, H., (2016). 'A deep-learning-based emergency alert system'. ICT Express, 2(2), pp.67-70. DOI: 10.1016/j.icte.2016.05.001en_US
dc.identifier.doi10.1016/j.icte.2016.05.001
dc.identifier.urihttp://hdl.handle.net/10545/623768
dc.description.abstractEmergency alert systems serve as a critical link in the chain of crisis communication, and they are essential to minimize loss during emergencies. Acts of terrorism and violence, chemical spills, amber alerts, nuclear facility problems, weather-related emergencies, flu pandemics, and other emergencies all require those responsible such as government officials, building managers, and university administrators to be able to quickly and reliably distribute emergency information to the public. This paper presents our design of a deep-learning-based emergency warning system. The proposed system is considered suitable for application in existing infrastructure such as closed-circuit television and other monitoring devices. The experimental results show that in most cases, our system immediately detects emergencies such as car accidents and natural disasters.en_US
dc.description.sponsorshipN/Aen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.urlhttps://www.sciencedirect.com/science/article/pii/S2405959516300169en_US
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectMachine learningen_US
dc.subjectDeep learningen_US
dc.subjectEmergency service selectionen_US
dc.titleA deep-learning-based emergency alert systemen_US
dc.typeArticleen_US
dc.identifier.eissn24059595
dc.contributor.departmentSungkyunkwan Universityen_US
dc.identifier.journalICT Expressen_US
dcterms.dateAccepted2016-05-04
dc.author.detail786679en_US


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CC0 1.0 Universal
Except where otherwise noted, this item's license is described as CC0 1.0 Universal