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VGU RESEARCH REPOSITORY


Please use this identifier to cite or link to this item: https://epub.vgu.edu.vn/handle/dlibvgu/996
DC FieldValueLanguage
dc.contributor.authorVu Tuan Hungen_US
dc.date.accessioned2021-03-09T03:07:06Z-
dc.date.available2021-03-09T03:07:06Z-
dc.date.issued2020-
dc.identifier.urihttp://epub.vgu.edu.vn/handle/dlibvgu/996-
dc.description.abstractThis paper introduces an automated, user- and budget-friendly, effortlessly installable and intuitively self-correcting fire detection algorithm; employed aptly are the fortitude of embedded builtin thermal and air quality (smoke) sensors and a logistic regression model that is fed on incrementally accumulated data from a particular environment, and continually analyzes the metrics from that environment in real-time to assess the chance of fire. Users are forthwith notified via a mobile application should the pattern of fire be identified. The resemblance of this fire probability estimator to human cognition derives from the ability to replicate a personals course of perception. In addition, the self-correction feature weighs up the potentialities, making it a circulating maintenance-less and future-proof plan. The modularity of the logical flow of the design and its simplistic installation are expected to cultivate community's support at the beginning of deployment, making it ideal for cross-environment applicationen_US
dc.language.isoenen_US
dc.publisherVietnamese-German University-
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectEarly fire detectionen_US
dc.subjectMachine learningen_US
dc.subjectThermal sensorsen_US
dc.subjectSmoke sensorsen_US
dc.titleEarly fire detection system with machine learning and embedded systemen_US
dc.typeThesisen_US
item.fulltextWith Fulltext-
item.languageiso639-1other-
item.grantfulltextrestricted-
Appears in Collections:Computer Science (CS)
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