{"id":"https://openalex.org/W2923018925","doi":"https://doi.org/10.1109/icaiic.2019.8669042","title":"Predictive Models of Fire via Deep learning Exploiting Colorific Variation","display_name":"Predictive Models of Fire via Deep learning Exploiting Colorific Variation","publication_year":2019,"publication_date":"2019-02-01","ids":{"openalex":"https://openalex.org/W2923018925","doi":"https://doi.org/10.1109/icaiic.2019.8669042","mag":"2923018925"},"language":"en","primary_location":{"id":"doi:10.1109/icaiic.2019.8669042","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icaiic.2019.8669042","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5072750635","display_name":"JiSeong Han","orcid":"https://orcid.org/0000-0001-7251-5175"},"institutions":[{"id":"https://openalex.org/I24062138","display_name":"Konkuk University","ror":"https://ror.org/025h1m602","country_code":"KR","type":"education","lineage":["https://openalex.org/I24062138"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"JiSeong Han","raw_affiliation_strings":["Department of Applied Statistics, Konkuk University, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"Department of Applied Statistics, Konkuk University, Seoul, South Korea","institution_ids":["https://openalex.org/I24062138"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058671894","display_name":"GwangSu Kim","orcid":"https://orcid.org/0000-0003-2299-0140"},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"GwangSu Kim","raw_affiliation_strings":["School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea"],"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea","institution_ids":["https://openalex.org/I157485424"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061999797","display_name":"ChanSeo Lee","orcid":null},"institutions":[{"id":"https://openalex.org/I52010207","display_name":"Keimyung University","ror":"https://ror.org/00tjv0s33","country_code":"KR","type":"education","lineage":["https://openalex.org/I52010207"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"ChanSeo Lee","raw_affiliation_strings":["Department of Statistics, Keimyung University, Daegu, South Korea"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, Keimyung University, Daegu, South Korea","institution_ids":["https://openalex.org/I52010207"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028613374","display_name":"YeongKwang Han","orcid":null},"institutions":[{"id":"https://openalex.org/I4575257","display_name":"Hanyang University","ror":"https://ror.org/046865y68","country_code":"KR","type":"education","lineage":["https://openalex.org/I4575257"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"YeongKwang Han","raw_affiliation_strings":["Department of Electronics and Computer Engineering, Hanyang University, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"Department of Electronics and Computer Engineering, Hanyang University, Seoul, South Korea","institution_ids":["https://openalex.org/I4575257"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049005597","display_name":"Ung Hwang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ung Hwang","raw_affiliation_strings":["R&D team Deep Visions, Daegu, South Korea"],"affiliations":[{"raw_affiliation_string":"R&D team Deep Visions, Daegu, South Korea","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100430150","display_name":"Sunghwan Kim","orcid":"https://orcid.org/0000-0002-0442-7795"},"institutions":[{"id":"https://openalex.org/I24062138","display_name":"Konkuk University","ror":"https://ror.org/025h1m602","country_code":"KR","type":"education","lineage":["https://openalex.org/I24062138"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"SungHwan Kim","raw_affiliation_strings":["Department of Applied Statistics, Konkuk University, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"Department of Applied Statistics, Konkuk University, Seoul, South Korea","institution_ids":["https://openalex.org/I24062138"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5072750635"],"corresponding_institution_ids":["https://openalex.org/I24062138"],"apc_list":null,"apc_paid":null,"fwci":1.3277,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.78954699,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"579","last_page":"581"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12597","display_name":"Fire Detection and Safety Systems","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12597","display_name":"Fire Detection and Safety Systems","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11317","display_name":"Fire dynamics and safety research","score":0.9918000102043152,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9915000200271606,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7934773564338684},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.6490263938903809},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.6467770338058472},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6456805467605591},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6099156141281128},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5964916944503784},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5826065540313721},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5160219669342041},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.48938149213790894},{"id":"https://openalex.org/keywords/sequence-learning","display_name":"Sequence learning","score":0.46764373779296875},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44433048367500305},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07975980639457703}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7934773564338684},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.6490263938903809},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.6467770338058472},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6456805467605591},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6099156141281128},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5964916944503784},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5826065540313721},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5160219669342041},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.48938149213790894},{"id":"https://openalex.org/C40506919","wikidata":"https://www.wikidata.org/wiki/Q7452469","display_name":"Sequence learning","level":2,"score":0.46764373779296875},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44433048367500305},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07975980639457703},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icaiic.2019.8669042","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icaiic.2019.8669042","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4099999964237213,"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W592963477","https://openalex.org/W1598796236","https://openalex.org/W1972058229","https://openalex.org/W2148461049","https://openalex.org/W2571300184","https://openalex.org/W2793947836","https://openalex.org/W2807860345","https://openalex.org/W2896556344","https://openalex.org/W2903844025"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W3133861977","https://openalex.org/W3008584592","https://openalex.org/W2951211570","https://openalex.org/W2964954556","https://openalex.org/W3103566983","https://openalex.org/W2597948870","https://openalex.org/W4298043863","https://openalex.org/W2428505524","https://openalex.org/W2962722950"],"abstract_inverted_index":{"Predictive":[0],"models":[1],"on":[2],"fire":[3,115],"have":[4],"been":[5],"increasingly":[6],"popular":[7],"in":[8,74,102,145],"computer":[9],"image":[10],"analysis.":[11],"Due":[12],"to":[13,38,95,98,107],"late":[14],"strides":[15],"of":[16,64,129],"deep":[17],"learning":[18],"techniques,":[19],"we":[20,56],"are":[21,36,131,142],"now":[22],"unprecedently":[23],"benefited":[24],"from":[25,126],"its":[26],"flexible":[27],"applicability.":[28],"In":[29,53,110],"most":[30],"cases,":[31],"however,":[32],"the":[33,62,86],"conventional":[34],"algorithms":[35,101],"limited":[37],"only":[39],"single-framed":[40],"images":[41],"unlike":[42],"sequence":[43,80,108],"data":[44,81],"that":[45,79,138],"inevitably":[46],"entails":[47],"heavy":[48],"computational":[49],"time":[50],"and":[51,69,122],"memory.":[52],"this":[54],"paper,":[55],"propose":[57,140],"an":[58],"effective":[59],"algorithm":[60],"exploiting":[61],"combination":[63],"CNNs":[65],"(convolution":[66],"neural":[67,72],"networks)":[68,73],"RNNs":[70],"(recurrent":[71],"a":[75,127],"consecutive":[76],"way":[77],"so":[78],"can":[82],"be":[83,96],"allowed":[84],"for":[85],"model.":[87],"The":[88],"LSTM":[89],"(long":[90],"short-term":[91],"memory)":[92],"is":[93,136],"well-known":[94],"superior":[97],"other":[99],"RNNtype":[100],"accuracy,":[103],"especially":[104],"when":[105],"applying":[106],"data.":[109],"our":[111,139],"extensive":[112],"experiments,":[113],"where":[114],"videos":[116,124],"(e.g.":[117],"indoor":[118],"fire,":[119],"forest":[120],"fire)":[121],"non-fire":[123],"collected":[125],"range":[128],"scenarios":[130],"taken":[132],"into":[133],"accounts,":[134],"it":[135],"confirmed":[137],"methods":[141],"found":[143],"outstanding":[144],"predictive":[146],"power.":[147]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":4},{"year":2020,"cited_by_count":2}],"updated_date":"2026-03-18T14:38:29.013473","created_date":"2025-10-10T00:00:00"}
