{"id":"https://openalex.org/W2896901616","doi":"https://doi.org/10.1109/ijcnn.2018.8489423","title":"An Ensemble of Convolutional Neural Networks for Unbalanced Datasets: A case Study with Wagon Component Inspection","display_name":"An Ensemble of Convolutional Neural Networks for Unbalanced Datasets: A case Study with Wagon Component Inspection","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2896901616","doi":"https://doi.org/10.1109/ijcnn.2018.8489423","mag":"2896901616"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2018.8489423","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2018.8489423","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5087162817","display_name":"Everl\u00e2ndio Fernandes","orcid":"https://orcid.org/0000-0002-7205-1072"},"institutions":[{"id":"https://openalex.org/I17974374","display_name":"Universidade de S\u00e3o Paulo","ror":"https://ror.org/036rp1748","country_code":"BR","type":"education","lineage":["https://openalex.org/I17974374"]},{"id":"https://openalex.org/I4210161302","display_name":"Vale Technological Institute","ror":"https://ror.org/05wnasr61","country_code":"BR","type":"education","lineage":["https://openalex.org/I4210161302"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Everlandio Fernandes","raw_affiliation_strings":["Instituto Tecnológico Vale –, Belém, PA, Brazil","Institute of Mathematical and Computer Sciences - University of S\u00e3o Paulo - S\u00e3o Paulo, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Instituto Tecnológico Vale –, Belém, PA, Brazil","institution_ids":["https://openalex.org/I4210161302"]},{"raw_affiliation_string":"Institute of Mathematical and Computer Sciences - University of S\u00e3o Paulo - S\u00e3o Paulo, Brazil","institution_ids":["https://openalex.org/I17974374"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062579246","display_name":"Rafael de Lima Rocha","orcid":"https://orcid.org/0000-0001-5298-7028"},"institutions":[{"id":"https://openalex.org/I4210161302","display_name":"Vale Technological Institute","ror":"https://ror.org/05wnasr61","country_code":"BR","type":"education","lineage":["https://openalex.org/I4210161302"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Rafael L. Rocha","raw_affiliation_strings":["Instituto Tecnológico Vale –, Belém, PA, Brazil","Instituto Tecnol\u00f3gico Vale \u2013, Bel\u00e9m, PA, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Instituto Tecnológico Vale –, Belém, PA, Brazil","institution_ids":["https://openalex.org/I4210161302"]},{"raw_affiliation_string":"Instituto Tecnol\u00f3gico Vale \u2013, Bel\u00e9m, PA, Brazil","institution_ids":["https://openalex.org/I4210161302"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090973825","display_name":"Bruno V. Ferreira","orcid":"https://orcid.org/0000-0002-7855-5378"},"institutions":[{"id":"https://openalex.org/I1329807341","display_name":"Servi\u00e7o Nacional de Aprendizagem Industrial","ror":"https://ror.org/035c3nf67","country_code":"BR","type":"nonprofit","lineage":["https://openalex.org/I1329807341"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Bruno Ferreira","raw_affiliation_strings":["SENAI Innovation Institute for Mineral Technologies –, Belém, PA, Brazil","SENAI Innovation Institute for Mineral Technologies \u2013, Bel\u00e9m, PA, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SENAI Innovation Institute for Mineral Technologies –, Belém, PA, Brazil","institution_ids":["https://openalex.org/I1329807341"]},{"raw_affiliation_string":"SENAI Innovation Institute for Mineral Technologies \u2013, Bel\u00e9m, PA, Brazil","institution_ids":["https://openalex.org/I1329807341"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050722957","display_name":"Eduardo Atem de Carvalho","orcid":"https://orcid.org/0000-0001-8141-7518"},"institutions":[{"id":"https://openalex.org/I1329807341","display_name":"Servi\u00e7o Nacional de Aprendizagem Industrial","ror":"https://ror.org/035c3nf67","country_code":"BR","type":"nonprofit","lineage":["https://openalex.org/I1329807341"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Eduardo Carvalho","raw_affiliation_strings":["SENAI Innovation Institute for Mineral Technologies –, Belém, PA, Brazil","Social Service of Industry - Regional Department of Par\u00e1, SESI, Bel\u00e9m, PA, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SENAI Innovation Institute for Mineral Technologies –, Belém, PA, Brazil","institution_ids":["https://openalex.org/I1329807341"]},{"raw_affiliation_string":"Social Service of Industry - Regional Department of Par\u00e1, SESI, Bel\u00e9m, PA, Brazil","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049786535","display_name":"Ana Carolina Siravenha","orcid":"https://orcid.org/0000-0001-6664-9847"},"institutions":[{"id":"https://openalex.org/I1329807341","display_name":"Servi\u00e7o Nacional de Aprendizagem Industrial","ror":"https://ror.org/035c3nf67","country_code":"BR","type":"nonprofit","lineage":["https://openalex.org/I1329807341"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Ana Carolina Siravenha","raw_affiliation_strings":["SENAI Innovation Institute for Mineral Technologies –, Belém, PA, Brazil","Social Service of Industry - Regional Department of Par\u00e1, SESI, Bel\u00e9m, PA, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SENAI Innovation Institute for Mineral Technologies –, Belém, PA, Brazil","institution_ids":["https://openalex.org/I1329807341"]},{"raw_affiliation_string":"Social Service of Industry - Regional Department of Par\u00e1, SESI, Bel\u00e9m, PA, Brazil","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014594358","display_name":"Ana Claudia Gomes","orcid":"https://orcid.org/0000-0002-3961-892X"},"institutions":[{"id":"https://openalex.org/I1329807341","display_name":"Servi\u00e7o Nacional de Aprendizagem Industrial","ror":"https://ror.org/035c3nf67","country_code":"BR","type":"nonprofit","lineage":["https://openalex.org/I1329807341"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Ana Claudia S. Gomes","raw_affiliation_strings":["SENAI Innovation Institute for Mineral Technologies –, Belém, PA, Brazil","SENAI Innovation Institute for Mineral Technologies \u2013, Bel\u00e9m, PA, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SENAI Innovation Institute for Mineral Technologies –, Belém, PA, Brazil","institution_ids":["https://openalex.org/I1329807341"]},{"raw_affiliation_string":"SENAI Innovation Institute for Mineral Technologies \u2013, Bel\u00e9m, PA, Brazil","institution_ids":["https://openalex.org/I1329807341"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003216180","display_name":"Schubert R. Carvalho","orcid":null},"institutions":[{"id":"https://openalex.org/I4210161302","display_name":"Vale Technological Institute","ror":"https://ror.org/05wnasr61","country_code":"BR","type":"education","lineage":["https://openalex.org/I4210161302"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Schubert Carvalho","raw_affiliation_strings":["Instituto Tecnológico Vale –, Belém, PA, Brazil","Instituto Tecnol\u00f3gico Vale \u2013, Bel\u00e9m, PA, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Instituto Tecnológico Vale –, Belém, PA, Brazil","institution_ids":["https://openalex.org/I4210161302"]},{"raw_affiliation_string":"Instituto Tecnol\u00f3gico Vale \u2013, Bel\u00e9m, PA, Brazil","institution_ids":["https://openalex.org/I4210161302"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064508238","display_name":"Cleidson R. B. de Souza","orcid":"https://orcid.org/0000-0003-3240-3122"},"institutions":[{"id":"https://openalex.org/I59606676","display_name":"Universidade Federal do Par\u00e1","ror":"https://ror.org/03q9sr818","country_code":"BR","type":"education","lineage":["https://openalex.org/I59606676"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Cleidson R. B. de Souza","raw_affiliation_strings":["Universidade Federal do Pará, Belém, PA, Brazil","Universidade Federal do Par\u00e1, Bel\u00e9m, PA, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidade Federal do Pará, Belém, PA, Brazil","institution_ids":["https://openalex.org/I59606676"]},{"raw_affiliation_string":"Universidade Federal do Par\u00e1, Bel\u00e9m, PA, Brazil","institution_ids":["https://openalex.org/I59606676"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.994700014591217,"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"}},{"id":"https://openalex.org/T12707","display_name":"Vehicle License Plate Recognition","score":0.9876000285148621,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/computer-science","display_name":"Computer science","score":0.7950255274772644},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7938312888145447},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7401202321052551},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.7122957110404968},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6576614379882812},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5808216333389282},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.5611850023269653},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.534213662147522},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5291417241096497},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5239013433456421},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.44683367013931274},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42152345180511475},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3719291687011719},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1294482946395874}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7950255274772644},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7938312888145447},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7401202321052551},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.7122957110404968},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6576614379882812},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5808216333389282},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5611850023269653},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.534213662147522},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5291417241096497},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5239013433456421},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.44683367013931274},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42152345180511475},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3719291687011719},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1294482946395874},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2018.8489423","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2018.8489423","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W39416996","https://openalex.org/W85350352","https://openalex.org/W169539560","https://openalex.org/W1666541006","https://openalex.org/W1977245551","https://openalex.org/W1993989674","https://openalex.org/W2001120434","https://openalex.org/W2010941383","https://openalex.org/W2015452969","https://openalex.org/W2024223694","https://openalex.org/W2029896651","https://openalex.org/W2033110225","https://openalex.org/W2042086215","https://openalex.org/W2063045209","https://openalex.org/W2078885513","https://openalex.org/W2099454382","https://openalex.org/W2103614420","https://openalex.org/W2109655672","https://openalex.org/W2112796928","https://openalex.org/W2119191234","https://openalex.org/W2126105956","https://openalex.org/W2135293965","https://openalex.org/W2140190241","https://openalex.org/W2148143831","https://openalex.org/W2155851694","https://openalex.org/W2350179566","https://openalex.org/W2561820719","https://openalex.org/W4236597936","https://openalex.org/W4237938455","https://openalex.org/W4241005218","https://openalex.org/W6603460400","https://openalex.org/W6606918967"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3167935049","https://openalex.org/W3103566983","https://openalex.org/W3029198973","https://openalex.org/W3124943098","https://openalex.org/W4308112567"],"abstract_inverted_index":{"Railway":[0],"component":[1,147],"inspection":[2,45],"is":[3,20,48,53],"a":[4,21,108,168],"technique":[5],"widely":[6],"used":[7,144],"for":[8,35,145],"maintenance":[9],"because":[10,24,71],"defective":[11,18,133],"components":[12,19],"pose":[13],"safety":[14],"issues.":[15],"Nevertheless,":[16],"finding":[17],"hard":[22,33],"task":[23],"they":[25],"are":[26,68,74],"normally":[27,49],"hidden":[28],"by":[29,46],"dusty,":[30],"which":[31],"poses":[32],"problems":[34],"the":[36,72,91,94,100,113,125,165,181,187],"image":[37],"segmentation":[38],"algorithms.":[39],"To":[40],"approach":[41],"this":[42,104],"problem,":[43],"manual":[44],"humans":[47],"used,":[50],"but":[51],"it":[52],"time":[54],"consuming,":[55],"expensive":[56],"and":[57,167],"sometimes":[58],"dangerous.":[59],"Meanwhile,":[60],"automatic":[61],"approaches":[62],"that":[63,84,111,176],"uses":[64],"machine":[65,129],"learning":[66,122,130],"algorithms":[67],"also":[69],"difficult":[70],"datasets":[73,78],"strongly":[75],"unbalanced.":[76],"Such":[77],"usually":[79],"induce":[80],"biased":[81],"classification":[82],"models":[83],"identify":[85,132],"new":[86,109],"instances":[87],"as":[88,164],"members":[89],"of":[90,97,115,127,157],"class":[92],"with":[93,120,139,154],"greatest":[95],"abundance":[96],"examples":[98],"in":[99],"training":[101],"data.":[102],"In":[103],"paper,":[105],"we":[106,150],"propose":[107],"method":[110,136,153,179],"combines":[112],"use":[114],"Convolutional":[116],"Neural":[117],"Networks":[118],"(CNN)":[119],"imbalanced":[121],"to":[123,131,186],"address":[124],"challenge":[126],"using":[128,160],"components.":[134],"Our":[135],"was":[137],"tested":[138],"realworld":[140],"data":[141],"from":[142],"images":[143],"wagon":[146],"inspection.":[148],"Moreover,":[149],"compared":[151,185],"our":[152,177],"an":[155],"ensemble":[156,172],"MLP":[158],"networks":[159],"features":[161],"extraction,":[162],"such":[163],"LeNet,":[166],"CNN":[169],"network":[170],"without":[171],"learning.":[173],"Results":[174],"indicate":[175],"proposed":[178],"produced":[180],"higher":[182],"overall":[183],"accuracy":[184],"other":[188],"methods.":[189]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}