{"id":"https://openalex.org/W4302974417","doi":"https://doi.org/10.1109/icc45855.2022.9838636","title":"Competitive Learning for Unsupervised Anomaly Detection in Intelligent Transportation Systems","display_name":"Competitive Learning for Unsupervised Anomaly Detection in Intelligent Transportation Systems","publication_year":2022,"publication_date":"2022-05-16","ids":{"openalex":"https://openalex.org/W4302974417","doi":"https://doi.org/10.1109/icc45855.2022.9838636"},"language":"en","primary_location":{"id":"doi:10.1109/icc45855.2022.9838636","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc45855.2022.9838636","pdf_url":null,"source":{"id":"https://openalex.org/S4363607711","display_name":"ICC 2022 - IEEE International Conference on Communications","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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2022 - IEEE International Conference on Communications","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/A5075164096","display_name":"Umuralp Kaytaz","orcid":"https://orcid.org/0000-0001-6162-7390"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Umuralp Kaytaz","raw_affiliation_strings":["GT-ARC gGmbH,Berlin,Germany","GT-ARC gGmbH, Berlin, Germany"],"affiliations":[{"raw_affiliation_string":"GT-ARC gGmbH,Berlin,Germany","institution_ids":[]},{"raw_affiliation_string":"GT-ARC gGmbH, Berlin, Germany","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070946785","display_name":"Fikret Sivrikaya","orcid":"https://orcid.org/0000-0003-0067-4761"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fikret Sivrikaya","raw_affiliation_strings":["GT-ARC gGmbH,Berlin,Germany","GT-ARC gGmbH, Berlin, Germany"],"affiliations":[{"raw_affiliation_string":"GT-ARC gGmbH,Berlin,Germany","institution_ids":[]},{"raw_affiliation_string":"GT-ARC gGmbH, Berlin, Germany","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089847337","display_name":"\u015eahin Albayrak","orcid":"https://orcid.org/0000-0001-5092-4584"},"institutions":[{"id":"https://openalex.org/I4577782","display_name":"Technische Universit\u00e4t Berlin","ror":"https://ror.org/03v4gjf40","country_code":"DE","type":"education","lineage":["https://openalex.org/I4577782"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Sahin Albayrak","raw_affiliation_strings":["GT-ARC gGmbH,Berlin,Germany","DAI-Labor, Technical University of Berlin, Berlin, Germany","GT-ARC gGmbH, Berlin, Germany"],"affiliations":[{"raw_affiliation_string":"GT-ARC gGmbH,Berlin,Germany","institution_ids":[]},{"raw_affiliation_string":"DAI-Labor, Technical University of Berlin, Berlin, Germany","institution_ids":["https://openalex.org/I4577782"]},{"raw_affiliation_string":"GT-ARC gGmbH, Berlin, Germany","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5075164096"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6233,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.67722474,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":"2","issue":null,"first_page":"5433","last_page":"5438"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"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":1.0,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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":0.9930999875068665,"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/anomaly-detection","display_name":"Anomaly detection","score":0.9133071899414062},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7357041239738464},{"id":"https://openalex.org/keywords/autoregressive-integrated-moving-average","display_name":"Autoregressive integrated moving average","score":0.7181268930435181},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.6547843813896179},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.62407386302948},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5876734256744385},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5639770030975342},{"id":"https://openalex.org/keywords/centroid","display_name":"Centroid","score":0.5210853815078735},{"id":"https://openalex.org/keywords/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.4841914474964142},{"id":"https://openalex.org/keywords/competitive-learning","display_name":"Competitive learning","score":0.48105528950691223},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46678078174591064},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4478578567504883},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.43630513548851013},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.42441314458847046},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3876054584980011},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12669533491134644}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.9133071899414062},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7357041239738464},{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.7181268930435181},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.6547843813896179},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.62407386302948},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5876734256744385},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5639770030975342},{"id":"https://openalex.org/C146599234","wikidata":"https://www.wikidata.org/wiki/Q511093","display_name":"Centroid","level":2,"score":0.5210853815078735},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.4841914474964142},{"id":"https://openalex.org/C120822770","wikidata":"https://www.wikidata.org/wiki/Q5156355","display_name":"Competitive learning","level":3,"score":0.48105528950691223},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46678078174591064},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4478578567504883},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.43630513548851013},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.42441314458847046},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3876054584980011},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12669533491134644},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icc45855.2022.9838636","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc45855.2022.9838636","pdf_url":null,"source":{"id":"https://openalex.org/S4363607711","display_name":"ICC 2022 - IEEE International Conference on Communications","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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2022 - IEEE International Conference on Communications","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.4699999988079071}],"awards":[],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W201138236","https://openalex.org/W2016287239","https://openalex.org/W2112235833","https://openalex.org/W2774357826","https://openalex.org/W2777885857","https://openalex.org/W2786852751","https://openalex.org/W2809166736","https://openalex.org/W2897333945","https://openalex.org/W2906498146","https://openalex.org/W2913427224","https://openalex.org/W2946553955","https://openalex.org/W2947330499","https://openalex.org/W3018145837","https://openalex.org/W3021462936","https://openalex.org/W3081830030","https://openalex.org/W3107476252","https://openalex.org/W3114032569","https://openalex.org/W4253020087","https://openalex.org/W6608319088","https://openalex.org/W6787505237"],"related_works":["https://openalex.org/W3123344745","https://openalex.org/W3082895349","https://openalex.org/W4309045103","https://openalex.org/W4221136938","https://openalex.org/W4285195761","https://openalex.org/W4309113015","https://openalex.org/W2911304006","https://openalex.org/W2956138382","https://openalex.org/W3044458868","https://openalex.org/W4302974417"],"abstract_inverted_index":{"Intelligent":[0],"Transportation":[1],"Systems":[2],"(ITSs)":[3],"are":[4,150],"expected":[5],"to":[6,29,142],"have":[7],"a":[8,68],"profound":[9],"impact":[10],"on":[11],"the":[12,102],"quality":[13],"of":[14,107,113,148],"experience":[15],"in":[16,56,101,135],"future":[17],"smart":[18,104],"cities.":[19],"Anomaly":[20,64],"detection":[21,42,75,126,137],"is":[22],"an":[23],"imperative":[24],"for":[25,72],"urban":[26],"ITS":[27],"applications":[28],"alleviate":[30],"vulnerabilities":[31],"that":[32,131],"may":[33],"cause":[34],"accidents":[35],"and":[36,48,87,139],"fatal":[37],"causalities.":[38],"Previously":[39],"proposed":[40,132],"anomaly":[41,74,125],"methods":[43,144],"mostly":[44],"require":[45],"prior":[46],"knowledge":[47],"domain":[49],"specific":[50],"training":[51],"and/or":[52],"optimization":[53],"procedures.":[54],"Therefore,":[55],"this":[57],"work,":[58],"we":[59,110],"propose":[60],"Competitive":[61],"Learning":[62],"based":[63,124],"Detection":[65],"(CLAD)":[66],"as":[67,119,121],"generic":[69],"end-to-end":[70],"approach":[71,133],"unsupervised":[73,116],"using":[76],"Auto":[77],"Regressive":[78],"Integrated":[79],"Moving":[80],"Average":[81],"(ARIMA)":[82],"forecasting":[83],"model,":[84],"data":[85,95],"imaging":[86],"Centroid":[88],"Neural":[89],"Networks":[90],"(CentNNs).":[91],"Utilizing":[92],"multi-dimensional":[93],"time-series":[94],"obtained":[96],"from":[97],"diverse":[98],"sensory":[99],"measurements":[100],"DIGINET-PS":[103],"city":[105],"infrastructure":[106],"TU":[108],"Berlin,":[109],"compare":[111],"performance":[112],"CLAD":[114],"with":[115],"competitive":[117],"learning":[118,123],"well":[120],"deep":[122],"techniques.":[127],"Experimental":[128],"results":[129,134],"show":[130],"higher":[136],"accuracy":[138],"precision":[140],"compared":[141],"other":[143],"when":[145],"multiple":[146],"degrees":[147],"anomalies":[149],"considered.":[151]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
