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Network Anomaly Detection Using Federated Deep Autoencoding Gaussian Mixture Model Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Scream to Survive(S2S): Intelligent System to Life-Saving in Disasters Relief

Authors : Nardjes Bouchemal, Aissa Serrar, Yehya Bouzeraa, Naila Bouchmemal

Published in: Machine Learning for Networking

Publisher: Springer International Publishing

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Abstract

Disasters are becoming more and more common around the world, making technology important to guarantee people’s lives as much as possible.
One of the most modern advances of recent years is how AI is used in disaster relief. Researchers propose works based on new technologies (IoT, Cloud Computing, Blockchain, etc.) and AI concepts (Machine Learning, Natural Language Processing, etc.). But these concepts are difficult to exploit in low and middle socio-demographic index (SDI) countries, especially as most disasters happen in.
In this paper we propose S2S intelligent system, based on voice recognition to life-saving in disaster relief. Generally, a disaster victim is enable to access to his Smartphone and ask help, with this system, saying “help” will be enough to send automatically alerts to the nearest Emergency Operation Services (EOS).
S2S is composed of two parts: Intelligent application embedded on citizens and victims Smartphones, and S2S System for the Emergency Operation Services.

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Metadata
Title
Scream to Survive(S2S): Intelligent System to Life-Saving in Disasters Relief
Authors
Nardjes Bouchemal
Aissa Serrar
Yehya Bouzeraa
Naila Bouchmemal
Copyright Year
2020
Book
Machine Learning for Networking

Print ISBN: 978-3-030-45777-8

Electronic ISBN: 978-3-030-45778-5

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-45778-5_29

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