{"id":"https://openalex.org/W2001144051","doi":"https://doi.org/10.1142/s0218126610005986","title":"LONG-TERM PREDICTION OF ETHERNET TRAFFIC USING MULTISCALE-BILINEAR RECURRENT NEURAL NETWORK WITH ADAPTIVE LEARNING","display_name":"LONG-TERM PREDICTION OF ETHERNET TRAFFIC USING MULTISCALE-BILINEAR RECURRENT NEURAL NETWORK WITH ADAPTIVE LEARNING","publication_year":2010,"publication_date":"2010-02-01","ids":{"openalex":"https://openalex.org/W2001144051","doi":"https://doi.org/10.1142/s0218126610005986","mag":"2001144051"},"language":"en","primary_location":{"id":"doi:10.1142/s0218126610005986","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218126610005986","pdf_url":null,"source":{"id":"https://openalex.org/S167602672","display_name":"Journal of Circuits Systems and Computers","issn_l":"0218-1266","issn":["0218-1266","1793-6454"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Circuits, Systems and Computers","raw_type":"journal-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/A5101550337","display_name":"Dong-Chul Park","orcid":"https://orcid.org/0000-0001-5577-9187"},"institutions":[{"id":"https://openalex.org/I89440247","display_name":"Myongji University","ror":"https://ror.org/00s9dpb54","country_code":"KR","type":"education","lineage":["https://openalex.org/I89440247"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"DONG-CHUL PARK","raw_affiliation_strings":["Intelligent Computing Research Laboratory, Department of Information Engineering, Myong Ji University, Yong In, KyungKi-do, 449-728, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Intelligent Computing Research Laboratory, Department of Information Engineering, Myong Ji University, Yong In, KyungKi-do, 449-728, Republic of Korea","institution_ids":["https://openalex.org/I89440247"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5101550337"],"corresponding_institution_ids":["https://openalex.org/I89440247"],"apc_list":null,"apc_paid":null,"fwci":1.1916,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.82191044,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"19","issue":"01","first_page":"155","last_page":"171"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9902999997138977,"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"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9902999997138977,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.989799976348877,"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/T10138","display_name":"Network Traffic and Congestion Control","score":0.9890000224113464,"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/recurrent-neural-network","display_name":"Recurrent neural network","score":0.5674589276313782},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5433909893035889},{"id":"https://openalex.org/keywords/bilinear-interpolation","display_name":"Bilinear interpolation","score":0.47907549142837524},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4554545283317566},{"id":"https://openalex.org/keywords/ethernet","display_name":"Ethernet","score":0.43592989444732666},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.42895787954330444},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4085693955421448},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3953682780265808},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.361357718706131},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2362043261528015},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.09948712587356567},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.08152976632118225}],"concepts":[{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.5674589276313782},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5433909893035889},{"id":"https://openalex.org/C205203396","wikidata":"https://www.wikidata.org/wiki/Q612143","display_name":"Bilinear interpolation","level":2,"score":0.47907549142837524},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4554545283317566},{"id":"https://openalex.org/C172173386","wikidata":"https://www.wikidata.org/wiki/Q79984","display_name":"Ethernet","level":2,"score":0.43592989444732666},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.42895787954330444},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4085693955421448},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3953682780265808},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.361357718706131},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2362043261528015},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.09948712587356567},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.08152976632118225},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1142/s0218126610005986","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218126610005986","pdf_url":null,"source":{"id":"https://openalex.org/S167602672","display_name":"Journal of Circuits Systems and Computers","issn_l":"0218-1266","issn":["0218-1266","1793-6454"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Circuits, Systems and Computers","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6200000047683716,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1489081227","https://openalex.org/W1535224557","https://openalex.org/W2021533878","https://openalex.org/W2057654875","https://openalex.org/W2075795701","https://openalex.org/W2098969828","https://openalex.org/W2103917855","https://openalex.org/W2112228497","https://openalex.org/W2116609512","https://openalex.org/W2116988482","https://openalex.org/W2119374546","https://openalex.org/W2120446174","https://openalex.org/W2121374566","https://openalex.org/W2122923407","https://openalex.org/W2126701066","https://openalex.org/W2132384444","https://openalex.org/W2132984323","https://openalex.org/W2142872720","https://openalex.org/W2143215115","https://openalex.org/W2145720365","https://openalex.org/W2147598519","https://openalex.org/W2148360313","https://openalex.org/W2148506461","https://openalex.org/W2158442843","https://openalex.org/W2161913203","https://openalex.org/W2163556707","https://openalex.org/W2172238182","https://openalex.org/W4298352105"],"related_works":["https://openalex.org/W2165589608","https://openalex.org/W3093557575","https://openalex.org/W3182767856","https://openalex.org/W2354231916","https://openalex.org/W2162140574","https://openalex.org/W2354073393","https://openalex.org/W2361009070","https://openalex.org/W2378988399","https://openalex.org/W1983356840","https://openalex.org/W4250600009"],"abstract_inverted_index":{"A":[0,52],"prediction":[1,103],"scheme":[2,97],"for":[3],"Ethernet":[4,89],"traffic":[5,45,50,61,91],"data":[6],"using":[7],"a":[8,86],"Multiscale-Bilinear":[9],"Recurrent":[10],"Neural":[11,116],"Network":[12],"with":[13],"Adaptive":[14],"Learning":[15],"(M-BRNN-AL)":[16],"is":[17,39,54,66],"proposed":[18,25,96],"and":[19,30,83,100,119],"presented":[20],"in":[21,69,80,123],"this":[22],"paper.":[23],"The":[24],"predictor":[26],"integrates":[27],"an":[28,31],"M-BRNN":[29,122],"AL":[32,64],"algorithm.":[33],"In":[34],"M-BRNN,":[35],"the":[36,43,73,95,106,112,120,126],"wavelet":[37],"transform":[38],"employed":[40],"to":[41,57,71],"decompose":[42],"original":[44,121],"signals":[46],"into":[47],"several":[48],"simple":[49],"signals.":[51],"BRNN":[53],"then":[55],"used":[56],"predict":[58],"each":[59,77],"decomposed":[60],"signal.":[62],"An":[63],"algorithm":[65],"also":[67],"applied":[68],"order":[70],"improve":[72],"learning":[74],"process":[75],"at":[76],"resolution":[78],"level":[79],"M-BRNN-AL.":[81],"Experiments":[82],"results":[84],"on":[85],"set":[87],"of":[88,125],"network":[90],"predictions":[92],"show":[93],"that":[94],"converges":[98],"faster":[99],"archives":[101],"better":[102],"performance":[104],"than":[105],"other":[107],"conventional":[108],"models":[109],"such":[110],"as":[111],"Multi-layer":[113],"Perception":[114],"Type":[115],"Network,":[117],"BRNN,":[118],"terms":[124],"normalized":[127],"mean":[128],"square":[129],"error.":[130]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2013,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}