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A deep-learning-based emergency alert system
Author(s) -
Byungseok Kang,
Hyunseung Choo
Publication year - 2016
Publication title -
ict express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.733
H-Index - 22
ISSN - 2405-9595
DOI - 10.1016/j.icte.2016.05.001
Subject(s) - computer security , government (linguistics) , terrorism , warning system , natural disaster , emergency management , emergency response , computer science , engineering , medical emergency , business , telecommunications , geography , political science , medicine , philosophy , linguistics , archaeology , meteorology , law
Emergency 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

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