{"id":"https://openalex.org/W3182702464","doi":"https://doi.org/10.1109/saci51354.2021.9465604","title":"Developing Novel Activation Functions in Time Series Anomaly Detection with LSTM Autoencoder","display_name":"Developing Novel Activation Functions in Time Series Anomaly Detection with LSTM Autoencoder","publication_year":2021,"publication_date":"2021-05-19","ids":{"openalex":"https://openalex.org/W3182702464","doi":"https://doi.org/10.1109/saci51354.2021.9465604","mag":"3182702464"},"language":"en","primary_location":{"id":"doi:10.1109/saci51354.2021.9465604","is_oa":false,"landing_page_url":"https://doi.org/10.1109/saci51354.2021.9465604","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI)","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/A5076701786","display_name":"Marina Adriana Mercioni","orcid":null},"institutions":[{"id":"https://openalex.org/I3122695212","display_name":"Polytechnic University of Timi\u015foara","ror":"https://ror.org/02v91gy68","country_code":"RO","type":"education","lineage":["https://openalex.org/I3122695212"]}],"countries":["RO"],"is_corresponding":true,"raw_author_name":"Marina Adriana Mercioni","raw_affiliation_strings":["Politehnica University Timisoara, Timisoara, Romania"],"affiliations":[{"raw_affiliation_string":"Politehnica University Timisoara, Timisoara, Romania","institution_ids":["https://openalex.org/I3122695212"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042610306","display_name":"\u015etefan Holban","orcid":"https://orcid.org/0009-0001-7427-2311"},"institutions":[{"id":"https://openalex.org/I3122695212","display_name":"Polytechnic University of Timi\u015foara","ror":"https://ror.org/02v91gy68","country_code":"RO","type":"education","lineage":["https://openalex.org/I3122695212"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Stefan Holban","raw_affiliation_strings":["Politehnica University Timisoara, Timisoara, Romania"],"affiliations":[{"raw_affiliation_string":"Politehnica University Timisoara, Timisoara, Romania","institution_ids":["https://openalex.org/I3122695212"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5076701786"],"corresponding_institution_ids":["https://openalex.org/I3122695212"],"apc_list":null,"apc_paid":null,"fwci":0.8158,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.77979293,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"000073","last_page":"000078"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9998000264167786,"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"}},{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9952999949455261,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/activation-function","display_name":"Activation function","score":0.7464730143547058},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.679387092590332},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6683008074760437},{"id":"https://openalex.org/keywords/hyperbolic-function","display_name":"Hyperbolic function","score":0.5482214689254761},{"id":"https://openalex.org/keywords/novelty-detection","display_name":"Novelty detection","score":0.5151011347770691},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5103310942649841},{"id":"https://openalex.org/keywords/piecewise","display_name":"Piecewise","score":0.48933905363082886},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.4890621602535248},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4437390863895416},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.4386461675167084},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.41096797585487366},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16911235451698303},{"id":"https://openalex.org/keywords/novelty","display_name":"Novelty","score":0.14035212993621826}],"concepts":[{"id":"https://openalex.org/C38365724","wikidata":"https://www.wikidata.org/wiki/Q4677469","display_name":"Activation function","level":3,"score":0.7464730143547058},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.679387092590332},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6683008074760437},{"id":"https://openalex.org/C92047909","wikidata":"https://www.wikidata.org/wiki/Q204034","display_name":"Hyperbolic function","level":2,"score":0.5482214689254761},{"id":"https://openalex.org/C2778924833","wikidata":"https://www.wikidata.org/wiki/Q7064603","display_name":"Novelty detection","level":3,"score":0.5151011347770691},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5103310942649841},{"id":"https://openalex.org/C164660894","wikidata":"https://www.wikidata.org/wiki/Q2037833","display_name":"Piecewise","level":2,"score":0.48933905363082886},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.4890621602535248},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4437390863895416},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.4386461675167084},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.41096797585487366},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16911235451698303},{"id":"https://openalex.org/C2778738651","wikidata":"https://www.wikidata.org/wiki/Q16546687","display_name":"Novelty","level":2,"score":0.14035212993621826},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C27206212","wikidata":"https://www.wikidata.org/wiki/Q34178","display_name":"Theology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/saci51354.2021.9465604","is_oa":false,"landing_page_url":"https://doi.org/10.1109/saci51354.2021.9465604","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":56,"referenced_works":["https://openalex.org/W323291900","https://openalex.org/W1415308085","https://openalex.org/W1683511521","https://openalex.org/W1921523184","https://openalex.org/W1977212008","https://openalex.org/W2049058890","https://openalex.org/W2049124016","https://openalex.org/W2057074436","https://openalex.org/W2064675550","https://openalex.org/W2116261113","https://openalex.org/W2122410182","https://openalex.org/W2122585011","https://openalex.org/W2131904035","https://openalex.org/W2140827645","https://openalex.org/W2141125852","https://openalex.org/W2156387975","https://openalex.org/W2176412452","https://openalex.org/W2399941526","https://openalex.org/W2525778437","https://openalex.org/W2599354622","https://openalex.org/W2620661538","https://openalex.org/W2660006607","https://openalex.org/W2747117577","https://openalex.org/W2747681982","https://openalex.org/W2751802138","https://openalex.org/W2786827964","https://openalex.org/W2805182254","https://openalex.org/W2902455138","https://openalex.org/W2910068345","https://openalex.org/W2919115771","https://openalex.org/W2963285578","https://openalex.org/W2963887617","https://openalex.org/W2964211630","https://openalex.org/W2964248614","https://openalex.org/W2967845527","https://openalex.org/W2970783931","https://openalex.org/W3006682208","https://openalex.org/W3015777882","https://openalex.org/W3104355964","https://openalex.org/W3106543020","https://openalex.org/W4231254085","https://openalex.org/W4239562425","https://openalex.org/W4297792526","https://openalex.org/W4301971222","https://openalex.org/W4394647109","https://openalex.org/W6637359451","https://openalex.org/W6640185926","https://openalex.org/W6679539681","https://openalex.org/W6680715401","https://openalex.org/W6685562342","https://openalex.org/W6727690538","https://openalex.org/W6746730328","https://openalex.org/W6756753118","https://openalex.org/W6758101687","https://openalex.org/W6775563663","https://openalex.org/W6864715598"],"related_works":["https://openalex.org/W2995944953","https://openalex.org/W2893539081","https://openalex.org/W3024877706","https://openalex.org/W3022392884","https://openalex.org/W4385451479","https://openalex.org/W4210854505","https://openalex.org/W3012219884","https://openalex.org/W4295036712","https://openalex.org/W4375958661","https://openalex.org/W4211198594"],"abstract_inverted_index":{"Our":[0],"proposal":[1,87,126],"consists":[2,151],"of":[3,148,152,156,159,169],"developing":[4],"two":[5,71],"novel":[6,72],"activation":[7,30,49,143,161],"functions":[8,73,132],"in":[9,32,42,50,85,100,103,127,163,173],"time":[10],"series":[11],"anomaly":[12],"detection,":[13],"they":[14],"have":[15],"the":[16,20,46,76,91,98,146,167],"capability":[17],"to":[18,44,56,74,120,129,165],"reduce":[19],"validation":[21],"loss.":[22],"The":[23,82],"approach":[24],"is":[25,88],"based":[26],"on":[27],"a":[28,35,51,111,117,170],"current":[29],"function":[31,162],"Deep":[33,174],"Learning,":[34],"very":[36],"intensive":[37],"field":[38],"studied":[39],"over":[40],"time,":[41],"order":[43,55,164],"find":[45],"most":[47],"suitable":[48],"neural":[52,171],"network.":[53],"In":[54],"achieve":[57],"this":[58,105,149],"purpose,":[59],"we":[60],"used":[61],"an":[62,160],"LSTM":[63],"(Long":[64],"Short-Term":[65],"Memory)":[66],"Autoencoder":[67],"architecture,":[68],"using":[69],"these":[70],"see":[75],"network's":[77],"behavior":[78,158],"through":[79],"introducing":[80],"them.":[81],"key":[83],"point":[84],"our":[86,125],"given":[89],"by":[90],"learnable":[92],"parameter,":[93],"assuring":[94],"more":[95,108],"flexibility":[96],"within":[97],"network":[99,172],"weights'":[101],"updates,":[102],"fact,":[104],"property":[106],"being":[107],"powerful":[109],"than":[110],"predefined":[112],"parameter":[113],"that":[114],"will":[115],"bring":[116],"constraint":[118],"due":[119],"its":[121],"limit.":[122],"We":[123],"tested":[124],"comparison":[128],"other":[130],"popular":[131],"such":[133],"as":[134],"ReLU":[135],"(Linear":[136],"Rectifier":[137],"Unit),":[138],"hyperbolic":[139],"tangent":[140],"(tanh),":[141],"Talu":[142],"function.":[144],"Also,":[145],"novelty":[147],"paper":[150],"taking":[153],"into":[154],"consideration":[155],"piecewise":[157],"increase":[166],"performance":[168],"Learning.":[175]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2}],"updated_date":"2026-01-21T23:30:37.877113","created_date":"2025-10-10T00:00:00"}
