{"id":"https://openalex.org/W4409356705","doi":"https://doi.org/10.1109/access.2025.3560079","title":"EFM-ResNet: A Feature Enhanced Network for Tobacco Strips Classification","display_name":"EFM-ResNet: A Feature Enhanced Network for Tobacco Strips Classification","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4409356705","doi":"https://doi.org/10.1109/access.2025.3560079"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3560079","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3560079","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3560079","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Bo Zhang","orcid":"https://orcid.org/0009-0008-6829-8321"},"institutions":[{"id":"https://openalex.org/I2800556661","display_name":"China Tobacco","ror":"https://ror.org/030d08e08","country_code":"CN","type":"government","lineage":["https://openalex.org/I2800556661"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Zhang","raw_affiliation_strings":["China Tobacco Zhejiang Industrial Company Ltd., Hangzhou, China","China Tobacco Zhejiang Industrial Co Ltd, Hangzhou, China"],"raw_orcid":"https://orcid.org/0009-0008-6829-8321","affiliations":[{"raw_affiliation_string":"China Tobacco Zhejiang Industrial Company Ltd., Hangzhou, China","institution_ids":["https://openalex.org/I2800556661"]},{"raw_affiliation_string":"China Tobacco Zhejiang Industrial Co Ltd, Hangzhou, China","institution_ids":["https://openalex.org/I2800556661"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102200785","display_name":"Shanshan Zheng","orcid":null},"institutions":[{"id":"https://openalex.org/I2800556661","display_name":"China Tobacco","ror":"https://ror.org/030d08e08","country_code":"CN","type":"government","lineage":["https://openalex.org/I2800556661"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shanshan Zheng","raw_affiliation_strings":["China Tobacco Zhejiang Industrial Company Ltd., Hangzhou, China","China Tobacco Zhejiang Industrial Co Ltd, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Tobacco Zhejiang Industrial Company Ltd., Hangzhou, China","institution_ids":["https://openalex.org/I2800556661"]},{"raw_affiliation_string":"China Tobacco Zhejiang Industrial Co Ltd, Hangzhou, China","institution_ids":["https://openalex.org/I2800556661"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011396311","display_name":"Leyuan Han","orcid":null},"institutions":[{"id":"https://openalex.org/I2800556661","display_name":"China Tobacco","ror":"https://ror.org/030d08e08","country_code":"CN","type":"government","lineage":["https://openalex.org/I2800556661"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Leyuan Han","raw_affiliation_strings":["China Tobacco Zhejiang Industrial Company Ltd., Hangzhou, China","China Tobacco Zhejiang Industrial Co Ltd, Hangzhou, China"],"raw_orcid":"https://orcid.org/0009-0008-5187-3289","affiliations":[{"raw_affiliation_string":"China Tobacco Zhejiang Industrial Company Ltd., Hangzhou, China","institution_ids":["https://openalex.org/I2800556661"]},{"raw_affiliation_string":"China Tobacco Zhejiang Industrial Co Ltd, Hangzhou, China","institution_ids":["https://openalex.org/I2800556661"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006927747","display_name":"Yucan Qiu","orcid":null},"institutions":[{"id":"https://openalex.org/I2800556661","display_name":"China Tobacco","ror":"https://ror.org/030d08e08","country_code":"CN","type":"government","lineage":["https://openalex.org/I2800556661"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yucan Qiu","raw_affiliation_strings":["China Tobacco Zhejiang Industrial Company Ltd., Hangzhou, China","China Tobacco Zhejiang Industrial Co Ltd, Hangzhou, China"],"raw_orcid":"https://orcid.org/0009-0000-5244-7574","affiliations":[{"raw_affiliation_string":"China Tobacco Zhejiang Industrial Company Ltd., Hangzhou, China","institution_ids":["https://openalex.org/I2800556661"]},{"raw_affiliation_string":"China Tobacco Zhejiang Industrial Co Ltd, Hangzhou, China","institution_ids":["https://openalex.org/I2800556661"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.06105771,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":null,"first_page":"67017","last_page":"67028"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.5515999794006348,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"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/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.5515999794006348,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/strips","display_name":"STRIPS","score":0.6944394707679749},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6674463748931885},{"id":"https://openalex.org/keywords/residual-neural-network","display_name":"Residual neural network","score":0.647701621055603},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.566473662853241},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5540449619293213},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4992971420288086},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4433354139328003},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2083800733089447}],"concepts":[{"id":"https://openalex.org/C200925200","wikidata":"https://www.wikidata.org/wiki/Q7624170","display_name":"STRIPS","level":2,"score":0.6944394707679749},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6674463748931885},{"id":"https://openalex.org/C2944601119","wikidata":"https://www.wikidata.org/wiki/Q43744058","display_name":"Residual neural network","level":3,"score":0.647701621055603},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.566473662853241},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5540449619293213},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4992971420288086},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4433354139328003},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2083800733089447},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3560079","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3560079","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:fb7ba748988c4edd82bfa5762668849b","is_oa":true,"landing_page_url":"https://doaj.org/article/fb7ba748988c4edd82bfa5762668849b","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 13, Pp 67017-67028 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3560079","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3560079","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.7300000190734863,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W2051247173","https://openalex.org/W2194775991","https://openalex.org/W2800276543","https://openalex.org/W2921203591","https://openalex.org/W2978305650","https://openalex.org/W3013952416","https://openalex.org/W3016324002","https://openalex.org/W3017431055","https://openalex.org/W3034679438","https://openalex.org/W3146366485","https://openalex.org/W3175484621","https://openalex.org/W3195865861","https://openalex.org/W4200006080","https://openalex.org/W4205098595","https://openalex.org/W4225015119","https://openalex.org/W4296532116","https://openalex.org/W4308478566","https://openalex.org/W4310164935","https://openalex.org/W4311496177","https://openalex.org/W4318773551","https://openalex.org/W4321020076","https://openalex.org/W4378904024","https://openalex.org/W4381925181","https://openalex.org/W4382404897","https://openalex.org/W4383200154","https://openalex.org/W4383535210","https://openalex.org/W4387247603","https://openalex.org/W4388414612","https://openalex.org/W4388505392","https://openalex.org/W4389210490","https://openalex.org/W4392667100","https://openalex.org/W4393622158","https://openalex.org/W4399308307","https://openalex.org/W4400438454","https://openalex.org/W4400620712","https://openalex.org/W4401099530","https://openalex.org/W4401332568","https://openalex.org/W4402476250","https://openalex.org/W4402660030","https://openalex.org/W6888950403"],"related_works":["https://openalex.org/W2601157893","https://openalex.org/W2373006798","https://openalex.org/W2131735617","https://openalex.org/W2056912418","https://openalex.org/W2033213769","https://openalex.org/W4312376745","https://openalex.org/W2136016640","https://openalex.org/W2049538278","https://openalex.org/W2886173746","https://openalex.org/W4200043248"],"abstract_inverted_index":{"Tobacco":[0],"strips":[1,142,166],"are":[2],"the":[3,11,49,59,75,82,98,103,119,122,131,140,155,175,179],"core":[4],"raw":[5],"material":[6],"in":[7,61,81,139,159],"tobacco":[8,29,141,165,180],"products,":[9],"and":[10,20,40,58,149,169],"accuracy":[12,136,168],"of":[13,51,121,137,178],"their":[14],"classification":[15,32,143,156,167],"directly":[16],"impacts":[17],"product":[18],"quality":[19],"production":[21,177],"efficiency.":[22],"In":[23],"this":[24,160],"paper,":[25],"we":[26,66,89],"propose":[27],"a":[28,68,91,111],"strip":[30],"image":[31],"model":[33,76,104],"based":[34],"on":[35],"multi-scale":[36,69],"fusion":[37,70],"attention":[38,71],"mechanism":[39,72],"feature":[41,56,86,92,99],"enhancement":[42,93],"improved":[43],"ResNet,":[44],"named":[45],"EFM-ResNet,":[46],"to":[47,77,95,105,117],"address":[48,118],"issues":[50],"detail":[52],"information":[53,80],"loss":[54,113,125],"during":[55],"extraction":[57],"difficulty":[60],"capturing":[62],"long-range":[63],"dependencies.":[64],"First,":[65],"introduce":[67],"that":[73,130],"enables":[74],"capture":[78,106],"key":[79,108],"image,":[83],"thereby":[84],"enriching":[85],"details.":[87],"Second,":[88],"develop":[90],"module":[94],"further":[96],"improve":[97,164],"representation":[100],"capability,":[101],"allowing":[102],"more":[107],"information.":[109],"Finally,":[110],"WFL":[112],"function":[114],"is":[115],"designed":[116],"limitations":[120],"traditional":[123,147],"cross-entropy":[124],"function.":[126],"Experimental":[127],"results":[128],"show":[129],"proposed":[132,158],"method":[133,157],"achieves":[134],"an":[135],"95.87%":[138],"task,":[144],"significantly":[145],"outperforming":[146],"methods":[148],"existing":[150],"deep":[151],"learning":[152],"models.":[153],"Therefore,":[154],"paper":[161],"can":[162],"substantially":[163],"provide":[170],"strong":[171],"technical":[172],"support":[173],"for":[174],"intelligent":[176],"industry.":[181]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
