{"id":"https://openalex.org/W3204540942","doi":"https://doi.org/10.1109/ijcnn52387.2021.9534394","title":"Density-Sorting-Based Convolutional Fuzzy Min-Max Neural Network for Image Classification","display_name":"Density-Sorting-Based Convolutional Fuzzy Min-Max Neural Network for Image Classification","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3204540942","doi":"https://doi.org/10.1109/ijcnn52387.2021.9534394","mag":"3204540942"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn52387.2021.9534394","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9534394","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","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/A5078118482","display_name":"Mingxi Sun","orcid":null},"institutions":[{"id":"https://openalex.org/I136765683","display_name":"Tianjin University of Technology","ror":"https://ror.org/00zbe0w13","country_code":"CN","type":"education","lineage":["https://openalex.org/I136765683"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Mingxi Sun","raw_affiliation_strings":["School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, China","institution_ids":["https://openalex.org/I136765683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101748860","display_name":"Wei Huang","orcid":"https://orcid.org/0009-0007-9885-0028"},"institutions":[{"id":"https://openalex.org/I136765683","display_name":"Tianjin University of Technology","ror":"https://ror.org/00zbe0w13","country_code":"CN","type":"education","lineage":["https://openalex.org/I136765683"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Huang","raw_affiliation_strings":["School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, China","institution_ids":["https://openalex.org/I136765683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100639872","display_name":"Jinsong Wang","orcid":"https://orcid.org/0000-0002-6484-7142"},"institutions":[{"id":"https://openalex.org/I136765683","display_name":"Tianjin University of Technology","ror":"https://ror.org/00zbe0w13","country_code":"CN","type":"education","lineage":["https://openalex.org/I136765683"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinsong Wang","raw_affiliation_strings":["School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, China","institution_ids":["https://openalex.org/I136765683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5078118482"],"corresponding_institution_ids":["https://openalex.org/I136765683"],"apc_list":null,"apc_paid":null,"fwci":0.1921,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.49678105,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"15","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9973999857902527,"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"}},"topics":[{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9973999857902527,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9972000122070312,"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/T10320","display_name":"Neural Networks and Applications","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7719405889511108},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6992740631103516},{"id":"https://openalex.org/keywords/sorting","display_name":"Sorting","score":0.6925765872001648},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6908860802650452},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6651707887649536},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5554013252258301},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5326537489891052},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5194001197814941},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5121355056762695},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.5000581741333008},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.42800647020339966},{"id":"https://openalex.org/keywords/fuzzy-set","display_name":"Fuzzy set","score":0.4220907390117645},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34695327281951904},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.1502256989479065}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7719405889511108},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6992740631103516},{"id":"https://openalex.org/C111696304","wikidata":"https://www.wikidata.org/wiki/Q2303697","display_name":"Sorting","level":2,"score":0.6925765872001648},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6908860802650452},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6651707887649536},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5554013252258301},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5326537489891052},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5194001197814941},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5121355056762695},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.5000581741333008},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.42800647020339966},{"id":"https://openalex.org/C42011625","wikidata":"https://www.wikidata.org/wiki/Q1055058","display_name":"Fuzzy set","level":3,"score":0.4220907390117645},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34695327281951904},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.1502256989479065},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn52387.2021.9534394","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9534394","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G277236360","display_name":null,"funder_award_id":"19JCJQJC61500","funder_id":"https://openalex.org/F4320323993","funder_display_name":"Natural Science Foundation of Tianjin City"},{"id":"https://openalex.org/G8489316083","display_name":null,"funder_award_id":"61673295","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320323993","display_name":"Natural Science Foundation of Tianjin City","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W1480067276","https://openalex.org/W1541193055","https://openalex.org/W1636682230","https://openalex.org/W1673310716","https://openalex.org/W1682403713","https://openalex.org/W1686810756","https://openalex.org/W1977496278","https://openalex.org/W1991426267","https://openalex.org/W2007187232","https://openalex.org/W2016883203","https://openalex.org/W2040870580","https://openalex.org/W2082381659","https://openalex.org/W2095705004","https://openalex.org/W2112796928","https://openalex.org/W2125684070","https://openalex.org/W2127436605","https://openalex.org/W2128084896","https://openalex.org/W2129021863","https://openalex.org/W2133218851","https://openalex.org/W2143287292","https://openalex.org/W2160642098","https://openalex.org/W2160815625","https://openalex.org/W2194775991","https://openalex.org/W2353489846","https://openalex.org/W2473930607","https://openalex.org/W2546487983","https://openalex.org/W2559906442","https://openalex.org/W2606202972","https://openalex.org/W2786498526","https://openalex.org/W2901312569","https://openalex.org/W2907609579","https://openalex.org/W2949068435","https://openalex.org/W2964189064","https://openalex.org/W3003734944","https://openalex.org/W3024410842","https://openalex.org/W4247105055","https://openalex.org/W6632368903","https://openalex.org/W6637131181","https://openalex.org/W6637373629","https://openalex.org/W6644413929","https://openalex.org/W6674330103","https://openalex.org/W6748453352","https://openalex.org/W6757737955","https://openalex.org/W7036188076"],"related_works":["https://openalex.org/W2952813363","https://openalex.org/W2911497689","https://openalex.org/W4360783045","https://openalex.org/W2963346891","https://openalex.org/W2770149305","https://openalex.org/W2972076240","https://openalex.org/W3167930666","https://openalex.org/W3014952856","https://openalex.org/W2964843961","https://openalex.org/W3010730661"],"abstract_inverted_index":{"Traditional":[0],"image":[1,33,58,158],"classification":[2,34,141,173],"methods":[3],"mostly":[4],"use":[5],"offline":[6],"learning":[7,182],"mode,":[8],"which":[9,70],"takes":[10],"a":[11,24],"lot":[12],"of":[13,85,118,130,136],"time":[14],"when":[15],"data":[16],"is":[17,40,55,66,113,143],"updated.":[18],"In":[19,82,133],"this":[20,37],"paper,":[21],"we":[22],"propose":[23],"density-sorting-based":[25,49],"convolutional":[26,44],"fuzzy":[27,50,62,76,139],"min-max":[28,51,63,77],"neural":[29,52,64,79],"network":[30,65,80,177],"(DCFMNN)":[31],"for":[32,57,68,109,162],"to":[35,92,114,145,154],"solve":[36],"problem.":[38],"DCFMNN":[39,170],"realized":[41],"based":[42],"on":[43,127,164],"Neural":[45],"Network":[46],"(CNN)":[47],"and":[48,75,103,175,179],"network.":[53],"CNN":[54,150],"applied":[56,153],"feature":[59],"extraction.":[60],"Density-sorting-based":[61],"used":[67,144],"classification,":[69],"includes":[71],"density-based":[72,86],"sorting":[73],"part":[74,84,135],"(FMM)":[78],"part.":[81],"the":[83,93,96,100,116,119,124,128,131,134,138,185],"sorting,":[87],"patterns":[88],"are":[89,107,152],"sorted":[90],"according":[91],"points":[94,106],"with":[95],"highest":[97],"density":[98],"in":[99,123],"same":[101],"class":[102],"two":[104],"densest":[105],"considered":[108],"selection.":[110],"The":[111,156],"purpose":[112],"overcome":[115],"influence":[117],"pattern":[120],"input":[121],"order":[122],"original":[125],"FMM":[126],"creation":[129],"hyperbox.":[132],"FMM,":[137],"set":[140],"method":[142],"enable":[146],"online":[147,181],"learning.":[148],"Diverse":[149],"architectures":[151],"DCFMNN.":[155,165],"benchmark":[157],"datasets":[159],"were":[160],"employed":[161],"evaluation":[163],"Experimental":[166],"results":[167],"show":[168],"that":[169],"has":[171],"high":[172],"accuracy":[174],"less":[176],"complexity,":[178],"its":[180],"ability":[183],"reduces":[184],"training":[186],"time.":[187]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
