{"id":"https://openalex.org/W2554061044","doi":"https://doi.org/10.1109/ijcnn.2016.7727444","title":"Anomaly detection in aviation data using extreme learning machines","display_name":"Anomaly detection in aviation data using extreme learning machines","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2554061044","doi":"https://doi.org/10.1109/ijcnn.2016.7727444","mag":"2554061044"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2016.7727444","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2016.7727444","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5103784711","display_name":"Vijay Manikandan Janakiraman","orcid":null},"institutions":[{"id":"https://openalex.org/I1280536761","display_name":"Ames Research Center","ror":"https://ror.org/02acart68","country_code":"US","type":"facility","lineage":["https://openalex.org/I1280536761","https://openalex.org/I4210124779"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vijay Manikandan Janakiraman","raw_affiliation_strings":["UARC/NASA Ames Research Center Moffett Field, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UARC/NASA Ames Research Center Moffett Field, CA, USA","institution_ids":["https://openalex.org/I1280536761"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081120272","display_name":"David Nielsen","orcid":null},"institutions":[{"id":"https://openalex.org/I1280536761","display_name":"Ames Research Center","ror":"https://ror.org/02acart68","country_code":"US","type":"facility","lineage":["https://openalex.org/I1280536761","https://openalex.org/I4210124779"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David Nielsen","raw_affiliation_strings":["MORI Associates/NASA Ames Research Center Moffett Field, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MORI Associates/NASA Ames Research Center Moffett Field, CA, USA","institution_ids":["https://openalex.org/I1280536761"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I1280536761"],"apc_list":null,"apc_paid":null,"fwci":3.1061,"has_fulltext":false,"cited_by_count":50,"citation_normalized_percentile":{"value":0.94109748,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1993","last_page":"2000"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9952999949455261,"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/T12676","display_name":"Machine Learning and ELM","score":0.9952999949455261,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9914000034332275,"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":0.9810000061988831,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.8588660359382629},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.8174054622650146},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6705250144004822},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5126854181289673},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.46040189266204834},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4572027623653412},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4569445550441742},{"id":"https://openalex.org/keywords/air-traffic-management","display_name":"Air traffic management","score":0.4271068871021271},{"id":"https://openalex.org/keywords/aviation-safety","display_name":"Aviation safety","score":0.42562738060951233},{"id":"https://openalex.org/keywords/one-class-classification","display_name":"One-class classification","score":0.41689935326576233},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.341702401638031},{"id":"https://openalex.org/keywords/aviation","display_name":"Aviation","score":0.31168133020401},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.2900186777114868},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.2607501745223999},{"id":"https://openalex.org/keywords/air-traffic-control","display_name":"Air traffic control","score":0.19615286588668823},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.134145587682724}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.8588660359382629},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8174054622650146},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6705250144004822},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5126854181289673},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46040189266204834},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4572027623653412},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4569445550441742},{"id":"https://openalex.org/C2776777543","wikidata":"https://www.wikidata.org/wiki/Q1361182","display_name":"Air traffic management","level":3,"score":0.4271068871021271},{"id":"https://openalex.org/C538199239","wikidata":"https://www.wikidata.org/wiki/Q640853","display_name":"Aviation safety","level":3,"score":0.42562738060951233},{"id":"https://openalex.org/C34872919","wikidata":"https://www.wikidata.org/wiki/Q7092302","display_name":"One-class classification","level":3,"score":0.41689935326576233},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.341702401638031},{"id":"https://openalex.org/C74448152","wikidata":"https://www.wikidata.org/wiki/Q765633","display_name":"Aviation","level":2,"score":0.31168133020401},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2900186777114868},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2607501745223999},{"id":"https://openalex.org/C166961238","wikidata":"https://www.wikidata.org/wiki/Q221395","display_name":"Air traffic control","level":2,"score":0.19615286588668823},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.134145587682724},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2016.7727444","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2016.7727444","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1196375415","https://openalex.org/W1482203365","https://openalex.org/W1554119536","https://openalex.org/W1993576344","https://openalex.org/W2005490178","https://openalex.org/W2025768430","https://openalex.org/W2042184006","https://openalex.org/W2056702017","https://openalex.org/W2072412055","https://openalex.org/W2100556411","https://openalex.org/W2119276761","https://openalex.org/W2121971770","https://openalex.org/W2134490011","https://openalex.org/W2140369911","https://openalex.org/W2181347294","https://openalex.org/W2301541953","https://openalex.org/W2335488329","https://openalex.org/W2466466279","https://openalex.org/W4238722012","https://openalex.org/W6633249571","https://openalex.org/W6685777803","https://openalex.org/W6697650931"],"related_works":["https://openalex.org/W3186512740","https://openalex.org/W3017266184","https://openalex.org/W2918377632","https://openalex.org/W3202913553","https://openalex.org/W3046391934","https://openalex.org/W3194885736","https://openalex.org/W4363671829","https://openalex.org/W3171512724","https://openalex.org/W4221138397","https://openalex.org/W2169365377"],"abstract_inverted_index":{"We":[0,127],"develop":[1,117],"fast":[2,110],"anomaly":[3,65,119,141,171],"detection":[4,22,66,120,158,234],"algorithms":[5,67,121,131,210,228],"using":[6],"extreme":[7],"learning":[8],"machines":[9],"(ELM)":[10],"to":[11,35,84,116,139,213,231],"discover":[12],"operationally":[13],"significant":[14],"anomalies":[15,180,195],"in":[16,39,166,204,233],"large":[17,101,124,187],"aviation":[18,216],"data":[19,42,102,125,149],"sets.":[20,103,126],"Anomaly":[21],"(aka":[23],"one-class":[24],"classification":[25],"or":[26],"outlier":[27],"detection)":[28],"is":[29,43,80,174,237],"an":[30],"active":[31],"area":[32],"of":[33,73,87,157,164,243],"research":[34],"identify":[36],"safety":[37,217],"risks":[38],"aviation.":[40],"Aviation":[41],"characterized":[44],"by":[45,152,240],"high":[46],"dimensionality,":[47],"heterogeneity":[48],"(continuous":[49],"and":[50,54,112,136,151,220],"categorical":[51],"variables),":[52],"multimodality":[53],"temporality.":[55],"To":[56],"address":[57],"these":[58],"challenges,":[59],"NASA":[60],"Ames":[61],"has":[62],"developed":[63],"several":[64],"including":[68],"MKAD,":[69],"the":[70,74,85,134,147,161,167,170,182,191,197,205,221,225],"present":[71],"state":[72],"art":[75],"[1].":[76],"MKAD's":[77],"computational":[78],"complexity":[79],"quadratic":[81],"with":[82],"respect":[83],"number":[86],"training":[88,111,168,236],"examples":[89],"which":[90],"makes":[91],"it":[92],"time":[93],"consuming":[94],"(and":[95],"sometimes":[96],"infeasible)":[97],"for":[98,122],"mining":[99],"very":[100,123],"In":[104],"this":[105],"paper,":[106],"we":[107],"utilize":[108],"ELM's":[109],"good":[113],"generalization":[114],"properties":[115],"scalable":[118],"adapt":[128],"unsupervised":[129,144],"ELM":[130,226],"such":[132],"as":[133,181,196],"autoencoder":[135,177],"embedding":[137,192],"models":[138,145],"perform":[140],"detection.":[142],"The":[143,176,208],"capture":[146],"nominal":[148],"distribution":[150],"choosing":[153],"a":[154,186,202,214],"desired":[155],"strength":[156],"that":[159,184,199,224],"defines":[160],"upper":[162],"bound":[163],"outliers":[165],"data,":[169],"decision":[172],"boundary":[173],"determined.":[175],"model":[178,193],"detects":[179,194],"ones":[183,198],"have":[185],"reconstruction":[188],"error":[189],"while":[190,235],"lie":[200],"outside":[201],"hypersphere":[203],"embedded":[206],"space.":[207],"proposed":[209],"are":[211,229],"applied":[212],"real":[215],"benchmark":[218],"problem":[219],"results":[222],"show":[223],"based":[227],"comparable":[230],"MKAD":[232],"made":[238],"faster":[239],"two":[241],"orders":[242],"magnitude.":[244]},"counts_by_year":[{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":8},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":4}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
