{"id":"https://openalex.org/W4379646507","doi":"https://doi.org/10.2298/csis221125027l","title":"Generative adversarial network based on LSTM and convolutional block attention module for industrial smoke image recognition","display_name":"Generative adversarial network based on LSTM and convolutional block attention module for industrial smoke image recognition","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4379646507","doi":"https://doi.org/10.2298/csis221125027l"},"language":"en","primary_location":{"id":"doi:10.2298/csis221125027l","is_oa":true,"landing_page_url":"http://dx.doi.org/10.2298/csis221125027l","pdf_url":"http://www.doiserbia.nb.rs/ft.aspx?id=1820-02142300027L","source":{"id":"https://openalex.org/S206939107","display_name":"Computer Science and Information Systems","issn_l":"1820-0214","issn":["1820-0214","2406-1018"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310321031","host_organization_name":"ComSIS Consortium","host_organization_lineage":["https://openalex.org/P4310321031"],"host_organization_lineage_names":["ComSIS Consortium"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computer Science and Information Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"http://www.doiserbia.nb.rs/ft.aspx?id=1820-02142300027L","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101592508","display_name":"Dahai Li","orcid":"https://orcid.org/0000-0002-2382-9917"},"institutions":[{"id":"https://openalex.org/I4210091712","display_name":"Zhengzhou University of Science and Technology","ror":"https://ror.org/00b3j7936","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210091712"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Dahai Li","raw_affiliation_strings":["School of Electronics and Electrical Engineering, Zhengzhou University of Science and Technology, Zhengzhou, China","School of Electronics and Electrical Engineering, Zhengzhou University of Science and Technology Zhengzhou 450064, China"],"affiliations":[{"raw_affiliation_string":"School of Electronics and Electrical Engineering, Zhengzhou University of Science and Technology, Zhengzhou, China","institution_ids":["https://openalex.org/I4210091712"]},{"raw_affiliation_string":"School of Electronics and Electrical Engineering, Zhengzhou University of Science and Technology Zhengzhou 450064, China","institution_ids":["https://openalex.org/I4210091712"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100614868","display_name":"Rui Yang","orcid":"https://orcid.org/0000-0003-4552-584X"},"institutions":[{"id":"https://openalex.org/I4210091712","display_name":"Zhengzhou University of Science and Technology","ror":"https://ror.org/00b3j7936","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210091712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rui Yang","raw_affiliation_strings":["School of Electronics and Electrical Engineering, Zhengzhou University of Science and Technology, Zhengzhou, China","School of Electronics and Electrical Engineering, Zhengzhou University of Science and Technology Zhengzhou 450064, China"],"affiliations":[{"raw_affiliation_string":"School of Electronics and Electrical Engineering, Zhengzhou University of Science and Technology, Zhengzhou, China","institution_ids":["https://openalex.org/I4210091712"]},{"raw_affiliation_string":"School of Electronics and Electrical Engineering, Zhengzhou University of Science and Technology Zhengzhou 450064, China","institution_ids":["https://openalex.org/I4210091712"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045788828","display_name":"Chen Su","orcid":"https://orcid.org/0000-0002-7119-8400"},"institutions":[{"id":"https://openalex.org/I4210146584","display_name":"Henan Forestry Vocational College","ror":"https://ror.org/050g87e49","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210146584"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Su Chen","raw_affiliation_strings":["Department of Mechanical and Electrical Engineering, Henan Vocational College of Water Conservancy and Environment Zhengzhou, China","Department of Mechanical and Electrical Engineering, Henan Vocational College of Water Conservancy and Environment Zhengzhou 450002, China"],"affiliations":[{"raw_affiliation_string":"Department of Mechanical and Electrical Engineering, Henan Vocational College of Water Conservancy and Environment Zhengzhou, China","institution_ids":["https://openalex.org/I4210146584"]},{"raw_affiliation_string":"Department of Mechanical and Electrical Engineering, Henan Vocational College of Water Conservancy and Environment Zhengzhou 450002, China","institution_ids":["https://openalex.org/I4210146584"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101592508"],"corresponding_institution_ids":["https://openalex.org/I4210091712"],"apc_list":null,"apc_paid":null,"fwci":0.4151,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.57744437,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":"20","issue":"4","first_page":"1707","last_page":"1728"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12597","display_name":"Fire Detection and Safety Systems","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9868999719619751,"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"}},{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.9860000014305115,"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/discriminator","display_name":"Discriminator","score":0.8655652403831482},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8480331897735596},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.665070116519928},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.6348146200180054},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5206369757652283},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5062369704246521},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4857695400714874},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.458500474691391},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4221007525920868},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.416655957698822},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.36568957567214966},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.0579662024974823}],"concepts":[{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.8655652403831482},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8480331897735596},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.665070116519928},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.6348146200180054},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5206369757652283},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5062369704246521},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4857695400714874},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.458500474691391},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4221007525920868},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.416655957698822},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.36568957567214966},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0579662024974823},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.2298/csis221125027l","is_oa":true,"landing_page_url":"http://dx.doi.org/10.2298/csis221125027l","pdf_url":"http://www.doiserbia.nb.rs/ft.aspx?id=1820-02142300027L","source":{"id":"https://openalex.org/S206939107","display_name":"Computer Science and Information Systems","issn_l":"1820-0214","issn":["1820-0214","2406-1018"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310321031","host_organization_name":"ComSIS Consortium","host_organization_lineage":["https://openalex.org/P4310321031"],"host_organization_lineage_names":["ComSIS Consortium"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computer Science and Information Systems","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.2298/csis221125027l","is_oa":true,"landing_page_url":"http://dx.doi.org/10.2298/csis221125027l","pdf_url":"http://www.doiserbia.nb.rs/ft.aspx?id=1820-02142300027L","source":{"id":"https://openalex.org/S206939107","display_name":"Computer Science and Information Systems","issn_l":"1820-0214","issn":["1820-0214","2406-1018"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310321031","host_organization_name":"ComSIS Consortium","host_organization_lineage":["https://openalex.org/P4310321031"],"host_organization_lineage_names":["ComSIS Consortium"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computer Science and Information Systems","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.7099999785423279,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4379646507.pdf"},"referenced_works_count":33,"referenced_works":["https://openalex.org/W2032490954","https://openalex.org/W2884585870","https://openalex.org/W2900883088","https://openalex.org/W2901982721","https://openalex.org/W2905297638","https://openalex.org/W2915725179","https://openalex.org/W2948892662","https://openalex.org/W2953489985","https://openalex.org/W2956265248","https://openalex.org/W2963306157","https://openalex.org/W2964117661","https://openalex.org/W2979526390","https://openalex.org/W2979650406","https://openalex.org/W3032646901","https://openalex.org/W3033924721","https://openalex.org/W3035231706","https://openalex.org/W3092552955","https://openalex.org/W3110137169","https://openalex.org/W3119569715","https://openalex.org/W3122502958","https://openalex.org/W3138834557","https://openalex.org/W3155378141","https://openalex.org/W3172200761","https://openalex.org/W3198675125","https://openalex.org/W4200301490","https://openalex.org/W4205943898","https://openalex.org/W4213188730","https://openalex.org/W4229055310","https://openalex.org/W4280612217","https://openalex.org/W4283755424","https://openalex.org/W4285345504","https://openalex.org/W4296378254","https://openalex.org/W4296378279"],"related_works":["https://openalex.org/W4293202849","https://openalex.org/W1980965563","https://openalex.org/W1489300767","https://openalex.org/W2387995142","https://openalex.org/W4380714744","https://openalex.org/W4319453655","https://openalex.org/W2089959425","https://openalex.org/W2057775761","https://openalex.org/W2795259429","https://openalex.org/W2565656575"],"abstract_inverted_index":{"The":[0,127],"industrial":[1,54,81,101,195],"smoke":[2,17,55,82,102,128,184,196],"scene":[3,38,69,197],"is":[4,19,27,113,130,148,160,224],"complex":[5],"and":[6,8,41,49,64,95,137,199,211],"diverse,":[7],"the":[9,23,46,53,118,121,125,133,152,163,167,172,179,182,200,204,216,225,229],"cost":[10],"of":[11,16,36,78,124,171,181],"labeling":[12],"a":[13,33,75,87,108,140,155],"large":[14,34,76],"number":[15,35],"data":[18,40,110],"too":[20],"high.":[21],"Under":[22],"existing":[24,37],"conditions,":[25],"it":[26],"very":[28],"challenging":[29],"to":[30,44,67,115,165,177],"efficiently":[31,65],"use":[32],"annotation":[39],"network":[42,91],"models":[43],"complete":[45],"image":[47,103,129,185,209],"classification":[48,210],"recognition":[50,186,212],"task":[51],"in":[52,80,120,135],"scene.":[56,83],"Traditional":[57],"deep":[58],"learn-based":[59],"networks":[60],"can":[61],"be":[62,74],"directly":[63],"applied":[66],"normal":[68],"classification,":[70],"but":[71],"there":[72],"will":[73],"loss":[77],"accuracy":[79],"Therefore,":[84],"we":[85],"propose":[86],"novel":[88],"generative":[89],"adversarial":[90],"based":[92],"on":[93,192],"LSTM":[94,134],"convolutional":[96,156],"block":[97,157],"attention":[98,158],"module":[99,159],"for":[100],"recognition.":[104],"In":[105,214],"this":[106],"paper,":[107],"low-cost":[109],"enhancement":[111],"method":[112,206],"used":[114],"effectively":[116],"reduce":[117],"difference":[119],"pixel":[122],"field":[123],"image.":[126],"input":[131],"into":[132,151,162],"generator":[136],"encoded":[138],"as":[139,176],"hidden":[141,145],"layer":[142,146],"vector.":[143],"This":[144],"vector":[147],"then":[149],"entered":[150],"discriminator.":[153],"Meanwhile,":[154],"integrated":[161],"discriminator":[164,173],"improve":[166,178],"feature":[168],"self-extraction":[169],"ability":[170],"model,":[174],"so":[175],"performance":[180],"whole":[183],"network.":[187],"Experiments":[188],"are":[189,219],"carried":[190],"out":[191],"real":[193],"diversified":[194],"data,":[198],"results":[201],"show":[202],"that":[203],"proposed":[205],"achieves":[207],"better":[208],"effect.":[213],"particular,":[215],"F":[217],"scores":[218],"all":[220,228],"above":[221],"89%,":[222],"which":[223],"best":[226],"among":[227],"results.":[230]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-01-20T17:24:06.736184","created_date":"2025-10-10T00:00:00"}