{"id":"https://openalex.org/W7214012155","doi":"https://doi.org/10.1109/tii.2026.3730745","title":"A Novel Attention-Based Hybrid Deep Learning Framework for Interpretable Subclass Identification in Paint Manufacturing","display_name":"A Novel Attention-Based Hybrid Deep Learning Framework for Interpretable Subclass Identification in Paint Manufacturing","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7214012155","doi":"https://doi.org/10.1109/tii.2026.3730745"},"language":"en","primary_location":{"id":"doi:10.1109/tii.2026.3730745","doi":"https://doi.org/10.1109/tii.2026.3730745","is_oa":true,"landing_page_url":"https://doi.org/10.1109/tii.2026.3730745","pdf_url":null,"source":{"id":"https://openalex.org/S184777250","display_name":"IEEE Transactions on Industrial Informatics","issn_l":"1551-3203","issn":["1551-3203","1941-0050"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","ccf-c","cwts-core","dongbi-b","jufo-3","norway-2","poland-200","russia-white-list-1","vabb-shw"],"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 Transactions on Industrial Informatics","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1109/tii.2026.3730745","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5065429503","display_name":"Aziz Kubilay Ovac\u0131kl\u0131","orcid":"https://orcid.org/0000-0001-6687-7794","observed_orcids":["https://orcid.org/0000-0001-6687-7794"]},"institutions":[{"id":"https://openalex.org/I50627504","display_name":"Istanbul Arel University","ror":"https://ror.org/03natay60","country_code":"TR","type":"education","lineage":["https://openalex.org/I50627504"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Aziz Kubilay Ovacikli","raw_affiliation_strings":["Istanbul Arel University"],"raw_orcid":"https://orcid.org/0000-0001-6687-7794","affiliations":[{"raw_affiliation_string":"Istanbul Arel University","institution_ids":["https://openalex.org/I50627504"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032167789","display_name":"Mert Ya\u011fc\u0131o\u011flu","orcid":"https://orcid.org/0000-0001-6493-6447","observed_orcids":["https://orcid.org/0000-0001-6493-6447"]},"institutions":[{"id":"https://openalex.org/I50627504","display_name":"Istanbul Arel University","ror":"https://ror.org/03natay60","country_code":"TR","type":"education","lineage":["https://openalex.org/I50627504"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Mert Yagcioglu","raw_affiliation_strings":["Istanbul Arel University"],"raw_orcid":"https://orcid.org/0000-0001-6493-6447","affiliations":[{"raw_affiliation_string":"Istanbul Arel University","institution_ids":["https://openalex.org/I50627504"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I50627504"],"apc_list":{"value":2645,"currency":"USD","value_usd":2645},"apc_paid":{"value":2645,"currency":"USD","value_usd":2645},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.60141394,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"12"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9897000193595886,"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":0.9897000193595886,"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":0.007499999832361937,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.0006000000284984708,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/paint-production","display_name":"paint manufacturing","score":0.9160000085830688},{"id":"https://openalex.org/keywords/fault-diagnosis","display_name":"fault diagnosis","score":0.6949999928474426},{"id":"https://openalex.org/keywords/multisensor-data","display_name":"multi-sensor data","score":0.8259999752044678},{"id":"https://openalex.org/keywords/multi-head-self-attention","display_name":"multi-head self-attention","score":0.9300000071525574},{"id":"https://openalex.org/keywords/class-imbalance","display_name":"class imbalance","score":0.9359999895095825},{"id":"https://openalex.org/keywords/smote-oversampling","display_name":"SMOTE oversampling","score":0.7580000162124634},{"id":"https://openalex.org/keywords/interpretable-classification","display_name":"interpretable classification","score":0.5550000071525574},{"id":"https://openalex.org/keywords/feature-attribution","display_name":"feature attribution","score":0.7570000290870667},{"id":"https://openalex.org/keywords/industrial-process-monitoring","display_name":"industrial process monitoring","score":0.7480000257492065}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7904000282287598},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7440999746322632},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.6520000100135803},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5945000052452087},{"id":"https://openalex.org/C2776194381","wikidata":"https://www.wikidata.org/wiki/Q3965271","display_name":"Subclass","level":3,"score":0.5041999816894531},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4625000059604645},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39010000228881836},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3767000138759613},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.33219999074935913},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.3176000118255615},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.30550000071525574},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.296999990940094},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.26499998569488525},{"id":"https://openalex.org/C119247159","wikidata":"https://www.wikidata.org/wiki/Q1366192","display_name":"System identification","level":3,"score":0.262800008058548}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tii.2026.3730745","doi":"https://doi.org/10.1109/tii.2026.3730745","is_oa":true,"landing_page_url":"https://doi.org/10.1109/tii.2026.3730745","pdf_url":null,"source":{"id":"https://openalex.org/S184777250","display_name":"IEEE Transactions on Industrial Informatics","issn_l":"1551-3203","issn":["1551-3203","1941-0050"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","ccf-c","cwts-core","dongbi-b","jufo-3","norway-2","poland-200","russia-white-list-1","vabb-shw"],"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 Transactions on Industrial Informatics","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1109/tii.2026.3730745","doi":"https://doi.org/10.1109/tii.2026.3730745","is_oa":true,"landing_page_url":"https://doi.org/10.1109/tii.2026.3730745","pdf_url":null,"source":{"id":"https://openalex.org/S184777250","display_name":"IEEE Transactions on Industrial Informatics","issn_l":"1551-3203","issn":["1551-3203","1941-0050"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","ccf-c","cwts-core","dongbi-b","jufo-3","norway-2","poland-200","russia-white-list-1","vabb-shw"],"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 Transactions on Industrial Informatics","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.651199996471405,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7139139601","https://openalex.org/W6960168281","https://openalex.org/W4251160751","https://openalex.org/W6922623276","https://openalex.org/W2530314134","https://openalex.org/W1532218853","https://openalex.org/W1995660627","https://openalex.org/W7133204197","https://openalex.org/W3205299945","https://openalex.org/W6925690709"],"abstract_inverted_index":{"Industrial":[0],"paint":[1,148],"manufacturing":[2,51,149,201],"processes":[3],"involve":[4],"complex":[5],"interactions":[6,113],"among":[7],"heterogeneous":[8,115],"sensor":[9],"measurements,":[10],"making":[11],"fine-grained":[12,141],"subclass":[13,142,192],"identification":[14,193],"challenging.":[15],"Existing":[16],"studies":[17],"in":[18,42,50,98,161,189],"fault":[19],"diagnosis":[20],"and":[21,83,110,158,168,194],"attention-based":[22,70,174],"learning":[23,60,131],"primarily":[24],"focus":[25],"on":[26,77,146],"homogeneous":[27],"data":[28,79,150],"representations,":[29],"image-transformed":[30],"signals,":[31],"or":[32],"single-variable":[33],"optimization,":[34],"limiting":[35],"their":[36],"ability":[37],"to":[38,139,165],"model":[39,72],"cross-domain":[40],"dependencies":[41,109],"real-world":[43,147],"industrial":[44,190],"systems.":[45,202],"Moreover,":[46],"severe":[47],"class":[48,166],"imbalance":[49],"datasets":[52],"further":[53],"reduces":[54],"the":[55,95,129,181],"reliability":[56],"of":[57],"conventional":[58],"deep":[59,157],"approaches.":[61],"To":[62],"address":[63],"these":[64],"limitations,":[65],"this":[66],"study":[67],"proposes":[68],"an":[69],"hybrid":[71],"(ABHM)":[73],"that":[74,103,152,180],"directly":[75],"operates":[76],"multisensor":[78,191],"via":[80,133],"sensorwise":[81],"tokenization":[82],"dual":[84],"multihead":[85],"self-attention.":[86],"Rather":[87],"than":[88],"introducing":[89],"a":[90,99,196],"fundamentally":[91],"new":[92],"attention":[93],"mechanism,":[94],"contribution":[96],"lies":[97],"domain-specific":[100],"architectural":[101],"adaptation":[102],"explicitly":[104],"captures":[105],"both":[106],"intradomain":[107],"temporal":[108],"interdomain":[111],"physical":[112],"across":[114],"process":[116],"variables":[117],"without":[118],"requiring":[119],"signal-to-image":[120],"transformation.":[121],"An":[122],"imbalance-aware":[123],"training":[124],"strategy":[125],"is":[126],"incorporated":[127],"into":[128],"end-to-end":[130],"framework":[132,183],"synthetic":[134],"minority":[135],"oversampling":[136,138],"technique-based":[137],"enhance":[140],"discrimination.":[143],"Experimental":[144],"evaluations":[145],"demonstrate":[151],"ABHM":[153],"consistently":[154],"outperforms":[155],"state-of-the-art":[156],"attention-driven":[159],"baselines":[160],"classification":[162],"accuracy,":[163],"robustness":[164],"imbalance,":[167],"provides":[169,195],"enhanced":[170],"diagnostic":[171],"transparency":[172],"through":[173],"feature":[175],"attribution.":[176],"These":[177],"findings":[178],"indicate":[179],"proposed":[182],"effectively":[184],"bridges":[185],"existing":[186],"research":[187],"gaps":[188],"scalable":[197],"solution":[198],"for":[199],"intelligent":[200]},"counts_by_year":[],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2026-09-23T00:00:00"}
