{"id":"https://openalex.org/W4407004657","doi":"https://doi.org/10.1021/acs.jcim.4c02060","title":"Can Focusing on One Deep Learning Architecture Improve Fault Diagnosis Performance?","display_name":"Can Focusing on One Deep Learning Architecture Improve Fault Diagnosis Performance?","publication_year":2025,"publication_date":"2025-01-30","ids":{"openalex":"https://openalex.org/W4407004657","doi":"https://doi.org/10.1021/acs.jcim.4c02060","pmid":"https://pubmed.ncbi.nlm.nih.gov/39883649"},"language":"en","primary_location":{"id":"doi:10.1021/acs.jcim.4c02060","is_oa":true,"landing_page_url":"https://doi.org/10.1021/acs.jcim.4c02060","pdf_url":null,"source":{"id":"https://openalex.org/S167262187","display_name":"Journal of Chemical Information and Modeling","issn_l":"1549-9596","issn":["1549-9596","1549-960X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320006","host_organization_name":"American Chemical Society","host_organization_lineage":["https://openalex.org/P4310320006"],"host_organization_lineage_names":["American Chemical Society"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Chemical Information and Modeling","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1021/acs.jcim.4c02060","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5061669250","display_name":"Jo\u00e3o Gon\u00e7alves Neto","orcid":"https://orcid.org/0000-0002-8924-2238"},"institutions":[{"id":"https://openalex.org/I2699952","display_name":"Pontif\u00edcia Universidade Cat\u00f3lica do Rio de Janeiro","ror":"https://ror.org/01dg47b60","country_code":"BR","type":"education","lineage":["https://openalex.org/I2699952"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Jo\u00e3o G. Neto","raw_affiliation_strings":["Department of Chemical and Materials Engineering, Pontifical Catholic University of Rio de Janeiro, 225, Marqu\u00eas de S\u00e3o Vicente Street, G\u00e1vea, Rio de Janeiro, RJ 22451-900, Brazil"],"affiliations":[{"raw_affiliation_string":"Department of Chemical and Materials Engineering, Pontifical Catholic University of Rio de Janeiro, 225, Marqu\u00eas de S\u00e3o Vicente Street, G\u00e1vea, Rio de Janeiro, RJ 22451-900, Brazil","institution_ids":["https://openalex.org/I2699952"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071526705","display_name":"Karla Figueiredo","orcid":"https://orcid.org/0000-0001-8420-3937"},"institutions":[{"id":"https://openalex.org/I40034438","display_name":"Universidade do Estado do Rio de Janeiro","ror":"https://ror.org/0198v2949","country_code":"BR","type":"education","lineage":["https://openalex.org/I40034438"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Karla Figueiredo","raw_affiliation_strings":["Department of Computer Science, Rio de Janeiro State University, 524, Rector Jo\u00e3o Lyra Filho Pavilion, sixth floor, Maracan\u00e3, Rio de Janeiro, RJ 20550-013, Brazil"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Rio de Janeiro State University, 524, Rector Jo\u00e3o Lyra Filho Pavilion, sixth floor, Maracan\u00e3, Rio de Janeiro, RJ 20550-013, Brazil","institution_ids":["https://openalex.org/I40034438"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005700484","display_name":"Jo\u00e3o B. P. Soares","orcid":"https://orcid.org/0000-0001-8017-143X"},"institutions":[{"id":"https://openalex.org/I154425047","display_name":"University of Alberta","ror":"https://ror.org/0160cpw27","country_code":"CA","type":"education","lineage":["https://openalex.org/I154425047"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Jo\u00e3o B. P. Soares","raw_affiliation_strings":["Department of Chemical Engineering, University of Alberta, 9211, 116 Street, Edmonton, Alberta T6G 1H9, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Chemical Engineering, University of Alberta, 9211, 116 Street, Edmonton, Alberta T6G 1H9, Canada","institution_ids":["https://openalex.org/I154425047"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022194456","display_name":"Amanda L. T. Brand\u00e3o","orcid":"https://orcid.org/0000-0001-7602-8980"},"institutions":[{"id":"https://openalex.org/I2699952","display_name":"Pontif\u00edcia Universidade Cat\u00f3lica do Rio de Janeiro","ror":"https://ror.org/01dg47b60","country_code":"BR","type":"education","lineage":["https://openalex.org/I2699952"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Amanda L. T. Brand\u00e3o","raw_affiliation_strings":["Department of Chemical and Materials Engineering, Pontifical Catholic University of Rio de Janeiro, 225, Marqu\u00eas de S\u00e3o Vicente Street, G\u00e1vea, Rio de Janeiro, RJ 22451-900, Brazil"],"affiliations":[{"raw_affiliation_string":"Department of Chemical and Materials Engineering, Pontifical Catholic University of Rio de Janeiro, 225, Marqu\u00eas de S\u00e3o Vicente Street, G\u00e1vea, Rio de Janeiro, RJ 22451-900, Brazil","institution_ids":["https://openalex.org/I2699952"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5022194456"],"corresponding_institution_ids":["https://openalex.org/I2699952"],"apc_list":null,"apc_paid":null,"fwci":1.2158,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.7491483,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"65","issue":"3","first_page":"1289","last_page":"1304"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T10876","display_name":"Fault Detection and Control Systems","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.984000027179718,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T11357","display_name":"Risk and Safety Analysis","score":0.984000027179718,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.8411926031112671},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7646799087524414},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7301529049873352},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7229686379432678},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6941603422164917},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6399887800216675},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.5460463166236877},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5334456562995911},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.5298136472702026},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.5245556831359863},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.49443697929382324},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4906884431838989},{"id":"https://openalex.org/keywords/network-architecture","display_name":"Network architecture","score":0.45758476853370667},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.44538936018943787},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.424574613571167},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3789222240447998},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10698974132537842}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.8411926031112671},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7646799087524414},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7301529049873352},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7229686379432678},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6941603422164917},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6399887800216675},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.5460463166236877},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5334456562995911},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.5298136472702026},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.5245556831359863},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.49443697929382324},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4906884431838989},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.45758476853370667},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.44538936018943787},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.424574613571167},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3789222240447998},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10698974132537842},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0}],"mesh":[{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":3,"locations":[{"id":"doi:10.1021/acs.jcim.4c02060","is_oa":true,"landing_page_url":"https://doi.org/10.1021/acs.jcim.4c02060","pdf_url":null,"source":{"id":"https://openalex.org/S167262187","display_name":"Journal of Chemical Information and Modeling","issn_l":"1549-9596","issn":["1549-9596","1549-960X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320006","host_organization_name":"American Chemical Society","host_organization_lineage":["https://openalex.org/P4310320006"],"host_organization_lineage_names":["American Chemical Society"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Chemical Information and Modeling","raw_type":"journal-article"},{"id":"pmid:39883649","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/39883649","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of chemical information and modeling","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:11815845","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11815845","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11815845/pdf/ci4c02060.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"J Chem Inf Model","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.1021/acs.jcim.4c02060","is_oa":true,"landing_page_url":"https://doi.org/10.1021/acs.jcim.4c02060","pdf_url":null,"source":{"id":"https://openalex.org/S167262187","display_name":"Journal of Chemical Information and Modeling","issn_l":"1549-9596","issn":["1549-9596","1549-960X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320006","host_organization_name":"American Chemical Society","host_organization_lineage":["https://openalex.org/P4310320006"],"host_organization_lineage_names":["American Chemical Society"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Chemical Information and Modeling","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.4699999988079071}],"awards":[{"id":"https://openalex.org/G100368273","display_name":null,"funder_award_id":"E-26/200.150/2023","funder_id":"https://openalex.org/F4320322749","funder_display_name":"Funda\u00e7\u00e3o Carlos Chagas Filho de Amparo \u00e0 Pesquisa do Estado do Rio de Janeiro"},{"id":"https://openalex.org/G3024046282","display_name":null,"funder_award_id":"2024/10389-7","funder_id":"https://openalex.org/F4320323909","funder_display_name":"Ag\u00eancia Nacional do Petr\u00f3leo, G\u00e1s Natural e Biocombust\u00edveis"},{"id":"https://openalex.org/G5293379385","display_name":null,"funder_award_id":"88887.716415/2022-00","funder_id":"https://openalex.org/F4320321091","funder_display_name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior"}],"funders":[{"id":"https://openalex.org/F4320320997","display_name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo","ror":"https://ror.org/02ddkpn78"},{"id":"https://openalex.org/F4320321091","display_name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","ror":"https://ror.org/00x0ma614"},{"id":"https://openalex.org/F4320322749","display_name":"Funda\u00e7\u00e3o Carlos Chagas Filho de Amparo \u00e0 Pesquisa do Estado do Rio de Janeiro","ror":"https://ror.org/03kk0s825"},{"id":"https://openalex.org/F4320323909","display_name":"Ag\u00eancia Nacional do Petr\u00f3leo, G\u00e1s Natural e Biocombust\u00edveis","ror":"https://ror.org/00phthq42"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W1840106123","https://openalex.org/W1966772166","https://openalex.org/W1982291815","https://openalex.org/W2004186751","https://openalex.org/W2053257862","https://openalex.org/W2055821334","https://openalex.org/W2064675550","https://openalex.org/W2107693731","https://openalex.org/W2132029223","https://openalex.org/W2219903032","https://openalex.org/W2340289503","https://openalex.org/W2342249984","https://openalex.org/W2593390416","https://openalex.org/W2739132213","https://openalex.org/W2756109228","https://openalex.org/W2757109865","https://openalex.org/W2789999851","https://openalex.org/W2822775433","https://openalex.org/W2883908928","https://openalex.org/W2894891763","https://openalex.org/W2898511698","https://openalex.org/W2898760173","https://openalex.org/W2981386789","https://openalex.org/W2983563481","https://openalex.org/W3110939572","https://openalex.org/W3133226615","https://openalex.org/W3138501833","https://openalex.org/W3140883839","https://openalex.org/W3153457288","https://openalex.org/W3155206335","https://openalex.org/W3158079487","https://openalex.org/W3200419242","https://openalex.org/W4211119370","https://openalex.org/W4311363755","https://openalex.org/W4383032321","https://openalex.org/W4385258580","https://openalex.org/W4386281793","https://openalex.org/W4388443721","https://openalex.org/W4390533460","https://openalex.org/W4391180397","https://openalex.org/W4394744485","https://openalex.org/W4399589632","https://openalex.org/W6600553734"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W2383111961","https://openalex.org/W2365952365","https://openalex.org/W2352448290","https://openalex.org/W2380820513","https://openalex.org/W4321353415","https://openalex.org/W2913146933","https://openalex.org/W2745001401","https://openalex.org/W3114272811","https://openalex.org/W3185156046"],"abstract_inverted_index":{"Machine":[0],"learning":[1,41],"approaches":[2],"often":[3],"involve":[4],"evaluating":[5],"a":[6,22,38,47,73,81,89,103],"wide":[7],"range":[8],"of":[9,24,93,119],"models":[10],"due":[11],"to":[12,21,44,107,131],"various":[13],"available":[14],"architectures.":[15],"This":[16],"standard":[17],"strategy":[18],"can":[19],"lead":[20],"lack":[23],"depth":[25],"in":[26,53,84,111,135],"exploring":[27],"established":[28],"methods.":[29],"In":[30],"this":[31,120],"study,":[32],"we":[33],"concentrated":[34],"our":[35,66],"efforts":[36],"on":[37,72],"single":[39],"deep":[40],"architecture":[42],"type":[43],"assess":[45],"whether":[46],"focused":[48,121],"approach":[49],"could":[50,125],"enhance":[51],"performance":[52],"fault":[54],"diagnosis.":[55],"We":[56],"selected":[57],"the":[58,85,97,112,117],"benchmark":[59],"Tennessee":[60],"Eastman":[61],"Process":[62],"data":[63,133],"set":[64],"as":[65],"case":[67],"study":[68],"and":[69,129],"investigated":[70],"modifications":[71],"reference":[74],"convolutional":[75],"neural":[76],"network-based":[77],"model.":[78],"Results":[79],"indicate":[80],"considerable":[82,104],"improvement":[83,105],"overall":[86],"classification,":[87],"reaching":[88],"maximum":[90],"average":[91],"F1-score":[92],"89.85%,":[94],"7.47%":[95],"above":[96],"baseline":[98],"model,":[99],"which":[100],"is":[101],"also":[102],"compared":[106],"other":[108,132],"performances":[109],"reported":[110],"literature.":[113],"These":[114],"results":[115],"emphasize":[116],"potential":[118],"approach,":[122],"indicating":[123],"it":[124],"be":[126],"further":[127],"explored":[128],"applied":[130],"sets":[134],"future":[136],"work.":[137]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}