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dc.contributor.authorThanos, Konstantinos-George
dc.contributor.authorPolydouri, Andrianna
dc.contributor.authorDanelakis, Antonios
dc.contributor.authorKyriazanos, Dimitris
dc.contributor.authorC.A. Thomopoulos, Stelios
dc.date.accessioned2021-06-02T10:12:59Z
dc.date.available2021-06-02T10:12:59Z
dc.date.issued2020
dc.identifierONIX_20210602_10.5772/intechopen.85075_463
dc.identifier.urihttps://library.oapen.org/handle/20.500.12657/49349
dc.description.abstractThe current chapter introduces a procedure that aims at determining regions that are on fire, based on Twitter posts, as soon as possible. The proposed scheme utilizes a deep learning approach for analyzing the text of Twitter posts announcing fire bursts. Deep learning is becoming very popular within different text applications involving text generalization, text summarization, and extracting text information. A deep learning network is to be trained so as to distinguish valid Twitter fire-announcing posts from junk posts. Next, the posts labeled as valid by the network have undergone traditional NLP-based information extraction where the initial unstructured text is converted into a structured one, from which potential location and timestamp of the incident for further exploitation are derived. Analytic processing is then implemented in order to output aggregated reports which are used to finally detect potential geographical areas that are probably threatened by fire. So far, the part that has been implemented is the traditional NLP-based and has already derived promising results under real-world conditions’ testing. The deep learning enrichment is to be implemented and expected to build upon the performance of the existing architecture and further improve it.
dc.languageEnglish
dc.subject.classificationbic Book Industry Communication::U Computing & information technology::UT Computer networking & communications
dc.subject.otherdeep learning, NLP procedure, fire burst detection, twitter posts, valid posts
dc.titleChapter Combined Deep Learning and Traditional NLP Approaches for Fire Burst Detection Based on Twitter Posts
dc.typechapter
oapen.identifier.doi10.5772/intechopen.85075
oapen.relation.isPublishedBy09f6769d-48ed-467d-b150-4cf2680656a1
oapen.relation.isFundedByFP7-SEC-2013-1
oapen.grant.number607276
oapen.grant.acronymAF3


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