{"id":"https://openalex.org/W2981927672","doi":"https://doi.org/10.1109/access.2019.2949287","title":"Time Series Data Classification Based on Dual Path CNN-RNN Cascade Network","display_name":"Time Series Data Classification Based on Dual Path CNN-RNN Cascade Network","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2981927672","doi":"https://doi.org/10.1109/access.2019.2949287","mag":"2981927672"},"language":"en","primary_location":{"id":"doi:10.1109/access.2019.2949287","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2949287","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08882352.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08882352.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5072105113","display_name":"Chao Yang","orcid":"https://orcid.org/0000-0002-3763-5080"},"institutions":[{"id":"https://openalex.org/I59028903","display_name":"Ocean University of China","ror":"https://ror.org/04rdtx186","country_code":"CN","type":"education","lineage":["https://openalex.org/I59028903"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chao Yang","raw_affiliation_strings":["School of Information Science and Engineering, Ocean University of China, Qingdao, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Ocean University of China, Qingdao, China","institution_ids":["https://openalex.org/I59028903"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103280388","display_name":"Wenxiang Jiang","orcid":"https://orcid.org/0009-0000-9794-8241"},"institutions":[{"id":"https://openalex.org/I59028903","display_name":"Ocean University of China","ror":"https://ror.org/04rdtx186","country_code":"CN","type":"education","lineage":["https://openalex.org/I59028903"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenxiang Jiang","raw_affiliation_strings":["School of Information Science and Engineering, Ocean University of China, Qingdao, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Ocean University of China, Qingdao, China","institution_ids":["https://openalex.org/I59028903"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100766490","display_name":"Zhongwen Guo","orcid":"https://orcid.org/0000-0002-6890-0107"},"institutions":[{"id":"https://openalex.org/I59028903","display_name":"Ocean University of China","ror":"https://ror.org/04rdtx186","country_code":"CN","type":"education","lineage":["https://openalex.org/I59028903"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongwen Guo","raw_affiliation_strings":["School of Information Science and Engineering, Ocean University of China, Qingdao, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Ocean University of China, Qingdao, China","institution_ids":["https://openalex.org/I59028903"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5072105113"],"corresponding_institution_ids":["https://openalex.org/I59028903"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":3.8407,"has_fulltext":true,"cited_by_count":56,"citation_normalized_percentile":{"value":0.94565482,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"7","issue":null,"first_page":"155304","last_page":"155312"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"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"}},"topics":[{"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9991999864578247,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8573144674301147},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.7370215654373169},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7095412015914917},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.603678822517395},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5598595142364502},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5302231907844543},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.4757416248321533},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.46994879841804504},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.43802231550216675},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43328866362571716},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4162309169769287},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.38748231530189514}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8573144674301147},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.7370215654373169},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7095412015914917},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.603678822517395},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5598595142364502},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5302231907844543},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.4757416248321533},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.46994879841804504},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.43802231550216675},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43328866362571716},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4162309169769287},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.38748231530189514},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2019.2949287","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2949287","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08882352.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:b7ca5a57ed9e43b1b6e60b6825448f00","is_oa":true,"landing_page_url":"https://doaj.org/article/b7ca5a57ed9e43b1b6e60b6825448f00","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 7, Pp 155304-155312 (2019)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2019.2949287","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2949287","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08882352.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1231421488","display_name":null,"funder_award_id":"under","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1454226941","display_name":null,"funder_award_id":"6182781","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2087396116","display_name":null,"funder_award_id":"China","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5848258319","display_name":null,"funder_award_id":"0 and","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5994120800","display_name":null,"funder_award_id":"Natural","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6020922088","display_name":null,"funder_award_id":"61827810","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2981927672.pdf","grobid_xml":"https://content.openalex.org/works/W2981927672.grobid-xml"},"referenced_works_count":54,"referenced_works":["https://openalex.org/W177922316","https://openalex.org/W639708223","https://openalex.org/W1836465849","https://openalex.org/W2002016471","https://openalex.org/W2016589492","https://openalex.org/W2061359421","https://openalex.org/W2064675550","https://openalex.org/W2091921805","https://openalex.org/W2095705004","https://openalex.org/W2097117768","https://openalex.org/W2112796928","https://openalex.org/W2133564696","https://openalex.org/W2143612262","https://openalex.org/W2157331557","https://openalex.org/W2163605009","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2301358467","https://openalex.org/W2306394264","https://openalex.org/W2311161367","https://openalex.org/W2342724584","https://openalex.org/W2557908743","https://openalex.org/W2565639579","https://openalex.org/W2584424117","https://openalex.org/W2604352584","https://openalex.org/W2613904329","https://openalex.org/W2888477829","https://openalex.org/W2900615125","https://openalex.org/W2905724129","https://openalex.org/W2911964244","https://openalex.org/W2919115771","https://openalex.org/W2963247196","https://openalex.org/W2963840672","https://openalex.org/W2964350391","https://openalex.org/W3106250896","https://openalex.org/W4239510810","https://openalex.org/W4247241850","https://openalex.org/W4297779039","https://openalex.org/W4399647672","https://openalex.org/W6620707391","https://openalex.org/W6638667902","https://openalex.org/W6674330103","https://openalex.org/W6674914833","https://openalex.org/W6679434410","https://openalex.org/W6684191040","https://openalex.org/W6694260854","https://openalex.org/W6696085341","https://openalex.org/W6698169118","https://openalex.org/W6733093781","https://openalex.org/W6735265152","https://openalex.org/W6737778391","https://openalex.org/W6755310813","https://openalex.org/W6785652829","https://openalex.org/W6869608176"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W2626256601","https://openalex.org/W147410782","https://openalex.org/W2900413183","https://openalex.org/W4401096132","https://openalex.org/W4226493464","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3008584592"],"abstract_inverted_index":{"Time":[0],"series":[1,98,110],"data":[2,32,99,111,208],"classification":[3],"is":[4,29,63,105,121],"a":[5,15,64,91,129,145,151,192],"significant":[6],"topic":[7],"as":[8,81,112],"its":[9,82],"application":[10],"can":[11,114],"be":[12,48],"found":[13],"in":[14,74,96],"various":[16],"domain.":[17],"Recent":[18],"studies":[19],"have":[20],"shown":[21],"that":[22,69,136,232],"data-driven":[23],"approach":[24],"based":[25,205],"on":[26,201,206],"deep":[27,37],"learning":[28,38,140],"powerful":[30],"for":[31,50,84,93,107,141,155,191],"mining":[33],"tasks.":[34],"A":[35],"typical":[36],"method,":[39],"Artificial":[40],"Neural":[41,102],"Network":[42,103,134],"(ANN),":[43],"has":[44,70],"been":[45,71],"proven":[46],"to":[47,56,123,149,167,186],"capable":[49,190],"match":[51],"complicated":[52],"functions":[53],"thus":[54],"leading":[55],"the":[57,75,163,169,172,176,236,242],"popularity.":[58],"Convolutional":[59],"neural":[60],"network":[61],"(CNN)":[62],"special":[65],"kind":[66],"of":[67,77,182,195,227,244],"ANN":[68],"widely":[72],"used":[73],"area":[76],"image":[78],"processing":[79],"tasks":[80],"ability":[83],"extracting":[85],"spatial":[86],"features.":[87],"However,":[88],"it":[89,113,120],"remains":[90],"challenge":[92],"implementing":[94],"CNN":[95,148],"time":[97,109],"classification.":[100,142],"Recurrent":[101],"(RNN)":[104],"popular":[106,217],"tackling":[108],"effectively":[115],"utilize":[116],"temporal":[117],"information.":[118],"But":[119],"time-consuming":[122],"train":[124],"RNN.":[125],"This":[126],"paper":[127],"proposes":[128],"Dual":[130],"Path":[131],"CNN-RNN":[132],"Cascade":[133],"(DPCRCN)":[135],"achieves":[137],"an":[138],"end-to-end":[139],"We":[143,178,197,219],"use":[144,180],"dual":[146],"path":[147],"achieve":[150],"multi-size":[152],"receptive":[153],"field":[154],"better":[156],"feature":[157],"extraction,":[158],"then":[159],"using":[160,224],"RNN":[161],"and":[162,175,212],"following":[164],"fully-connected":[165],"layers":[166],"learn":[168],"map":[170],"between":[171],"given":[173],"features":[174],"output.":[177],"also":[179,220],"Region":[181],"Interest":[183],"(RoI)":[184],"pooling":[185],"make":[187],"our":[188,199,222,233,246],"model":[189,200,223,234],"flexible":[193],"shape":[194,226],"data.":[196,228],"evaluate":[198,221],"Activity":[202],"Recognition":[203],"system":[204],"Multisensor":[207],"fusion":[209],"(AReM)":[210],"dataset":[211],"we":[213,240],"compare":[214],"with":[215],"many":[216],"algorithms.":[218],"different":[225],"The":[229],"results":[230],"show":[231],"outperforms":[235],"alternatives.":[237],"In":[238],"addition,":[239],"provide":[241],"details":[243],"training":[245],"model.":[247]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":14},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":2}],"updated_date":"2026-04-13T07:58:08.660418","created_date":"2025-10-10T00:00:00"}