乐胖代购免代理版

{"id":"https://openalex.org/W4205141961","doi":"https://doi.org/10.1109/bigdata52589.2021.9671632","title":"An Ensemble of Transformer and LSTM Approach for Multivariate Time Series Data Classification","display_name":"An Ensemble of Transformer and LSTM Approach for Multivariate Time Series Data Classification","publication_year":2021,"publication_date":"2021-12-15","ids":{"openalex":"https://openalex.org/W4205141961","doi":"https://doi.org/10.1109/bigdata52589.2021.9671632"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671632","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","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":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5023775002","display_name":"Aryan Narayan","orcid":null},"institutions":[{"id":"https://openalex.org/I3132975163","display_name":"KLE Technological University","ror":"https://ror.org/04yh52k23","country_code":"IN","type":"education","lineage":["https://openalex.org/I3132975163"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Aryan Narayan","raw_affiliation_strings":["KLE Technological University, Hubballi, India"],"affiliations":[{"raw_affiliation_string":"KLE Technological University, Hubballi, India","institution_ids":["https://openalex.org/I3132975163"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021191319","display_name":"Bodhi Satwa Mishra","orcid":null},"institutions":[{"id":"https://openalex.org/I3132975163","display_name":"KLE Technological University","ror":"https://ror.org/04yh52k23","country_code":"IN","type":"education","lineage":["https://openalex.org/I3132975163"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Bodhi Satwa Mishra","raw_affiliation_strings":["KLE Technological University, Hubballi, India"],"affiliations":[{"raw_affiliation_string":"KLE Technological University, Hubballi, India","institution_ids":["https://openalex.org/I3132975163"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033446615","display_name":"Pavan Hiremath","orcid":"https://orcid.org/0000-0001-8704-2921"},"institutions":[{"id":"https://openalex.org/I3132975163","display_name":"KLE Technological University","ror":"https://ror.org/04yh52k23","country_code":"IN","type":"education","lineage":["https://openalex.org/I3132975163"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"P. G. Sunitha Hiremath","raw_affiliation_strings":["KLE Technological University, Hubballi, India"],"affiliations":[{"raw_affiliation_string":"KLE Technological University, Hubballi, India","institution_ids":["https://openalex.org/I3132975163"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049706648","display_name":"Neha Tarannum Pendari","orcid":null},"institutions":[{"id":"https://openalex.org/I3132975163","display_name":"KLE Technological University","ror":"https://ror.org/04yh52k23","country_code":"IN","type":"education","lineage":["https://openalex.org/I3132975163"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Neha Tarannum Pendari","raw_affiliation_strings":["KLE Technological University, Hubballi, India"],"affiliations":[{"raw_affiliation_string":"KLE Technological University, Hubballi, India","institution_ids":["https://openalex.org/I3132975163"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082105653","display_name":"Shankar Gangisetty","orcid":"https://orcid.org/0000-0003-4448-5794"},"institutions":[{"id":"https://openalex.org/I3132975163","display_name":"KLE Technological University","ror":"https://ror.org/04yh52k23","country_code":"IN","type":"education","lineage":["https://openalex.org/I3132975163"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Shankar Gangisetty","raw_affiliation_strings":["KLE Technological University, Hubballi, India"],"affiliations":[{"raw_affiliation_string":"KLE Technological University, Hubballi, India","institution_ids":["https://openalex.org/I3132975163"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.14,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.642376,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":72,"max":76},"biblio":{"volume":null,"issue":null,"first_page":"5774","last_page":"5779"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9939,"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":0.9939,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9927,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T11443","display_name":"Advanced Statistical Process Monitoring","score":0.9767,"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/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.46827722},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble Learning","score":0.41927522}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7291852},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.67319524},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5751975},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5331262},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.530941},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5022962},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4747263},{"id":"https://openalex.org/C122342681","wikidata":"https://www.wikidata.org/wiki/Q330828","display_name":"Skewness","level":2,"score":0.4690424},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.46827722},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4638728},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.41927522},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41597626},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4103471},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40375832},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3898482},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13090274},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.105314136},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09273633},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08461678},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671632","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","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":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.6}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":15,"referenced_works":["https://openalex.org/W2742763523","https://openalex.org/W2787225914","https://openalex.org/W2788805965","https://openalex.org/W2794239758","https://openalex.org/W2812669263","https://openalex.org/W2899663614","https://openalex.org/W2966742700","https://openalex.org/W2971407654","https://openalex.org/W2990240608","https://openalex.org/W2995015263","https://openalex.org/W3003166104","https://openalex.org/W3028207611","https://openalex.org/W3168165246","https://openalex.org/W3212873837","https://openalex.org/W4205339343"],"related_works":["https://openalex.org/W4380075502","https://openalex.org/W4364306694","https://openalex.org/W4360585206","https://openalex.org/W4312200629","https://openalex.org/W4285741730","https://openalex.org/W4231994957","https://openalex.org/W4223943233","https://openalex.org/W3211546796","https://openalex.org/W3136979370","https://openalex.org/W2810053714"],"abstract_inverted_index":{"Wafer":[0],"manufacturing":[1],"is":[2,29,61,105,113,174],"a":[3,30,62,167],"complex":[4],"and":[5,24,45,69,99,108,126,165],"time":[6],"taking":[7],"process.":[8],"The":[9],"multivariate":[10],"time-series":[11],"data":[12,68,103],"collected":[13],"from":[14],"many":[15],"soft":[16,81],"sensors":[17],"in":[18,77],"the":[19,67,88,97,143,149,162,179],"process":[20],"are":[21],"highly":[22,106],"noisy":[23,107],"imbalanced.":[25,109],"Thus,":[26],"wafer":[27,56,102],"classification":[28],"challenging":[31],"task.":[32],"To":[33],"overcome":[34],"this":[35],"challenge,":[36],"we":[37],"propose":[38],"an":[39,114,157],"effective":[40,71,153],"ensemble":[41,115,133],"approach":[42,112,164],"with":[43],"transformer":[44],"long":[46],"short":[47],"term":[48],"memory":[49],"(LSTM)":[50],"based":[51,145],"deep":[52,59],"learning":[53,60],"techniques":[54],"for":[55,80],"classification.":[57],"Though":[58],"promising":[63],"technique":[64],"to":[65,84,95,151,178],"analyze":[66],"make":[70],"predictions,":[72],"but":[73],"not":[74,92],"widely":[75],"integrated":[76],"manufacture":[78],"industries":[79],"sensing":[82],"due":[83],"insufficient":[85],"research.":[86],"Also":[87],"research":[89],"community":[90],"has":[91],"been":[93],"exposed":[94],"accessing":[96],"real":[98],"large":[100],"scale":[101],"that":[104,173],"Our":[110],"proposed":[111,163],"of":[116,135,148,161,171],"four":[117],"models,":[118],"namely,":[119],"multilayer":[120,122],"LSTM,":[121],"perceptron":[123],"classifier,":[124],"transformer,":[125],"feed":[127],"forward":[128],"neural":[129],"network.":[130],"We":[131,155],"finally":[132],"all":[134],"these":[136],"models":[137,150],"using":[138],"ROC-AUC":[139],"scores":[140],"by":[141],"adjusting":[142],"weights":[144],"on":[146],"skewness":[147],"obtain":[152,166],"performance.":[154],"perform":[156],"exhaustive":[158],"empirical":[159],"analysis":[160],"best":[168],"ROC":[169],"score":[170],"0.748":[172],"significantly":[175],"better":[176],"compared":[177],"baseline":[180],"models.":[181]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4205141961","counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2025-01-16T11:34:25.855513","created_date":"2022-01-26"}