{"id":"https://openalex.org/W4385235740","doi":"https://doi.org/10.1109/med59994.2023.10185791","title":"Unsupervised Anomaly Detection for Multivariate Incomplete Data using GAN-based Data Imputation: A Comparative Study","display_name":"Unsupervised Anomaly Detection for Multivariate Incomplete Data using GAN-based Data Imputation: A Comparative Study","publication_year":2023,"publication_date":"2023-06-26","ids":{"openalex":"https://openalex.org/W4385235740","doi":"https://doi.org/10.1109/med59994.2023.10185791"},"language":"en","primary_location":{"id":"doi:10.1109/med59994.2023.10185791","is_oa":false,"landing_page_url":"https://doi.org/10.1109/med59994.2023.10185791","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 31st Mediterranean Conference on Control and Automation (MED)","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/A5054712460","display_name":"Kisan Sarda","orcid":"https://orcid.org/0000-0002-0560-452X"},"institutions":[{"id":"https://openalex.org/I16337185","display_name":"University of Sannio","ror":"https://ror.org/04vc81p87","country_code":"IT","type":"education","lineage":["https://openalex.org/I16337185"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Kisan Sarda","raw_affiliation_strings":["University of Sannio,Department of Engineering,Benevento,Italy,82100"],"affiliations":[{"raw_affiliation_string":"University of Sannio,Department of Engineering,Benevento,Italy,82100","institution_ids":["https://openalex.org/I16337185"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088577145","display_name":"Amol Yerudkar","orcid":"https://orcid.org/0000-0003-3994-3842"},"institutions":[{"id":"https://openalex.org/I135237710","display_name":"Zhejiang Normal University","ror":"https://ror.org/01vevwk45","country_code":"CN","type":"education","lineage":["https://openalex.org/I135237710"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Amol Yerudkar","raw_affiliation_strings":["Zhejiang Normal University,School of Mathematical Sciences,Jinhua,P. R. China,321004"],"affiliations":[{"raw_affiliation_string":"Zhejiang Normal University,School of Mathematical Sciences,Jinhua,P. R. China,321004","institution_ids":["https://openalex.org/I135237710"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054886494","display_name":"Carmen Del Vecchio","orcid":"https://orcid.org/0000-0001-6937-9678"},"institutions":[{"id":"https://openalex.org/I16337185","display_name":"University of Sannio","ror":"https://ror.org/04vc81p87","country_code":"IT","type":"education","lineage":["https://openalex.org/I16337185"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Carmen Del Vecchio","raw_affiliation_strings":["University of Sannio,Department of Engineering,Benevento,Italy,82100"],"affiliations":[{"raw_affiliation_string":"University of Sannio,Department of Engineering,Benevento,Italy,82100","institution_ids":["https://openalex.org/I16337185"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5054712460"],"corresponding_institution_ids":["https://openalex.org/I16337185"],"apc_list":null,"apc_paid":null,"fwci":0.5254,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.70701345,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"55","last_page":"60"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998999834060669,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9886000156402588,"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/T11443","display_name":"Advanced Statistical Process Monitoring","score":0.9819999933242798,"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/missing-data","display_name":"Missing data","score":0.739676296710968},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.732499361038208},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.6737465858459473},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.660382866859436},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.6468210220336914},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6115133762359619},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.5142752528190613},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.49676233530044556},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.448669970035553},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3939962387084961},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3572133779525757},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.22025418281555176},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2108537256717682},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15905609726905823}],"concepts":[{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.739676296710968},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.732499361038208},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.6737465858459473},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.660382866859436},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.6468210220336914},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6115133762359619},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.5142752528190613},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.49676233530044556},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.448669970035553},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3939962387084961},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3572133779525757},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.22025418281555176},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2108537256717682},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15905609726905823}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/med59994.2023.10185791","is_oa":false,"landing_page_url":"https://doi.org/10.1109/med59994.2023.10185791","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 31st Mediterranean Conference on Control and Automation (MED)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.4300000071525574,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1996118086","https://openalex.org/W2593414223","https://openalex.org/W2740802195","https://openalex.org/W2891273344","https://openalex.org/W2892139656","https://openalex.org/W2942578132","https://openalex.org/W2963045681","https://openalex.org/W2978834409","https://openalex.org/W3100054152","https://openalex.org/W3120740533","https://openalex.org/W3155002928","https://openalex.org/W3176880348","https://openalex.org/W3213371454","https://openalex.org/W4212986808","https://openalex.org/W4244781008","https://openalex.org/W4320013936","https://openalex.org/W4385235740","https://openalex.org/W6754355446","https://openalex.org/W6754766240","https://openalex.org/W6779669310"],"related_works":["https://openalex.org/W2181530120","https://openalex.org/W4211215373","https://openalex.org/W2024529227","https://openalex.org/W2055961818","https://openalex.org/W1574575415","https://openalex.org/W3144172081","https://openalex.org/W3179858851","https://openalex.org/W3028371478","https://openalex.org/W2081476516","https://openalex.org/W2581984549"],"abstract_inverted_index":{"With":[0],"the":[1,45,60,70,89,129,206,215,220,228],"increasing":[2],"interconnectivity":[3],"of":[4,23,62,91,152,163],"cyber-physical":[5],"systems":[6],"(CPSs)":[7],"in":[8,69,97,191],"various":[9],"fields,":[10],"such":[11,36],"as":[12,37,52],"manufacturing":[13,189],"plants,":[14,16],"power":[15],"and":[17,30,39,87,125,161,201],"smart":[18],"networked":[19],"systems,":[20],"large":[21],"amounts":[22],"multivariate":[24],"data":[25,34,116,143,213],"are":[26,95,136,155],"generated":[27],"through":[28],"sensors":[29],"actuators,":[31],"also":[32,50],"other":[33],"sources":[35],"measurements":[38],"images.":[40],"This":[41],"paper":[42],"focuses":[43],"on":[44,59,178],"anomaly":[46,175],"detection":[47,54,176],"(AD)":[48],"problem,":[49],"known":[51],"fault":[53],"or":[55],"outlier":[56],"detection,":[57],"depending":[58],"type":[61],"dataset,":[63],"which":[64,81,112,199],"involves":[65,113],"identifying":[66],"anomalous":[67,92,148],"values":[68],"dataset":[71,185,194],"using":[72,123,172],"analytical":[73],"methods.":[74],"However,":[75],"datasets":[76,122,146],"often":[77],"contain":[78],"missing":[79,164],"values,":[80],"can":[82],"lead":[83],"to":[84,119,197],"incorrect":[85],"outcomes":[86],"affect":[88],"availability":[90],"samples":[93],"that":[94,211],"fewer":[96],"amount,":[98],"making":[99],"incomplete":[100,110],"datasets.":[101,131],"Therefore,":[102],"a":[103,183,187],"generalized":[104],"AD":[105,127,167,202,231],"method":[106,203,226],"is":[107,169,195],"proposed":[108],"for":[109,128,145],"datasets,":[111,181],"two":[114],"steps:":[115],"imputation":[117,134],"(DI)":[118],"obtain":[120],"complete":[121,130],"GAN":[124],"later":[126],"While":[132],"statistical-based":[133],"methods":[135,177],"commonly":[137],"used,":[138],"they":[139],"do":[140],"not":[141],"consider":[142],"distribution":[144],"with":[147,234],"samples.":[149],"The":[150,166,208],"capabilities":[151],"GANbased":[153],"DI":[154,200,217],"tested":[156],"under":[157],"different":[158,180],"hyperparameter":[159],"settings":[160],"percentages":[162],"values.":[165],"problem":[168],"then":[170],"addressed":[171],"seven":[173],"unsupervised":[174],"six":[179],"including":[182],"real":[184],"from":[186],"steel":[188],"plant":[190],"Italy.":[192],"Each":[193],"analyzed":[196],"determine":[198],"combination":[204],"performs":[205],"best.":[207],"results":[209,232],"show":[210],"GAN-imputed":[212],"provides":[214],"best":[216,230],"performance,":[218],"while":[219],"reweighted":[221],"minimum":[222],"covariance":[223],"determinant":[224],"(RMCD)":[225],"offers":[227],"overall":[229],"combined":[233],"GAN.":[235]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
