{"id":"https://openalex.org/W4285186549","doi":"https://doi.org/10.1109/jstars.2022.3174239","title":"Spatio\u2013Temporal Attention-Based Deep Learning Framework for Mesoscale Eddy Trajectory Prediction","display_name":"Spatio\u2013Temporal Attention-Based Deep Learning Framework for Mesoscale Eddy Trajectory Prediction","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4285186549","doi":"https://doi.org/10.1109/jstars.2022.3174239"},"language":"en","primary_location":{"id":"doi:10.1109/jstars.2022.3174239","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2022.3174239","pdf_url":"https://ieeexplore.ieee.org/ielx7/4609443/4609444/09773008.pdf","source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"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 Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/4609443/4609444/09773008.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5018226716","display_name":"Xuegong Wang","orcid":"https://orcid.org/0000-0001-8812-5841"},"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":"Xuegong Wang","raw_affiliation_strings":["Ocean University of China, Qingdao, China","Automation & Measurement, Ocean University of China, 12591 Qingdao, Shandong, China, 266100"],"raw_orcid":"https://orcid.org/0000-0001-8812-5841","affiliations":[{"raw_affiliation_string":"Ocean University of China, Qingdao, China","institution_ids":["https://openalex.org/I59028903"]},{"raw_affiliation_string":"Automation & Measurement, Ocean University of China, 12591 Qingdao, Shandong, China, 266100","institution_ids":["https://openalex.org/I59028903"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100412233","display_name":"Chong Li","orcid":"https://orcid.org/0000-0002-1742-0963"},"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":"Chong Li","raw_affiliation_strings":["Ocean University of China, Qingdao, China","Automation & Measurement, Ocean University of China, 12591 Qingdao, Shandong, China, 266100"],"raw_orcid":"https://orcid.org/0000-0002-1742-0963","affiliations":[{"raw_affiliation_string":"Ocean University of China, Qingdao, China","institution_ids":["https://openalex.org/I59028903"]},{"raw_affiliation_string":"Automation & Measurement, Ocean University of China, 12591 Qingdao, Shandong, China, 266100","institution_ids":["https://openalex.org/I59028903"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100751012","display_name":"Xinning Wang","orcid":"https://orcid.org/0000-0002-6053-4464"},"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":"Xinning Wang","raw_affiliation_strings":["Ocean University of China, Qingdao, China","Automation & Measurement, Ocean University of China, 12591 Qingdao, China"],"raw_orcid":"https://orcid.org/0000-0002-6053-4464","affiliations":[{"raw_affiliation_string":"Ocean University of China, Qingdao, China","institution_ids":["https://openalex.org/I59028903"]},{"raw_affiliation_string":"Automation & Measurement, Ocean University of China, 12591 Qingdao, China","institution_ids":["https://openalex.org/I59028903"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061734311","display_name":"Lining Tan","orcid":"https://orcid.org/0000-0003-1650-2585"},"institutions":[{"id":"https://openalex.org/I4210130660","display_name":"Xi'an High Tech University","ror":"https://ror.org/03vt7za95","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210130660"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lining Tan","raw_affiliation_strings":["Xi&#x2019;an Research Institute of High Technology, Xi&#x2019;an, China","Automation, Xi'an Research Institute of High Technology, 562552 Xi'an, Shaanxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xi&#x2019;an Research Institute of High Technology, Xi&#x2019;an, China","institution_ids":[]},{"raw_affiliation_string":"Automation, Xi'an Research Institute of High Technology, 562552 Xi'an, Shaanxi, China","institution_ids":["https://openalex.org/I4210130660"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078883186","display_name":"Jin Wu","orcid":"https://orcid.org/0000-0001-5930-4170"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]},{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]}],"countries":["CN","HK"],"is_corresponding":false,"raw_author_name":"Jin Wu","raw_affiliation_strings":["Hong Kong University of Science and Technology, Hong Kong","School of Automation, University of Electronic Science and Technology of China, 12599 Chengdu, Sichuan, China, 611731"],"raw_orcid":"https://orcid.org/0000-0001-5930-4170","affiliations":[{"raw_affiliation_string":"Hong Kong University of Science and Technology, Hong Kong","institution_ids":["https://openalex.org/I200769079"]},{"raw_affiliation_string":"School of Automation, University of Electronic Science and Technology of China, 12599 Chengdu, Sichuan, China, 611731","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1800,"currency":"USD","value_usd":1800},"apc_paid":{"value":1800,"currency":"USD","value_usd":1800},"fwci":3.056,"has_fulltext":true,"cited_by_count":23,"citation_normalized_percentile":{"value":0.91528611,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"15","issue":null,"first_page":"3853","last_page":"3867"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10255","display_name":"Oceanographic and Atmospheric Processes","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/1910","display_name":"Oceanography"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10255","display_name":"Oceanographic and Atmospheric Processes","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/1910","display_name":"Oceanography"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11061","display_name":"Ocean Waves and Remote Sensing","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/1910","display_name":"Oceanography"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9929999709129333,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.8576860427856445},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7460358142852783},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.7334372997283936},{"id":"https://openalex.org/keywords/mesoscale-meteorology","display_name":"Mesoscale meteorology","score":0.7051100730895996},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6794396638870239},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5580040812492371},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.5442644953727722},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4339149594306946},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4239344596862793},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38847506046295166},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.31631505489349365},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.10665559768676758},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07260063290596008}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8576860427856445},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7460358142852783},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.7334372997283936},{"id":"https://openalex.org/C40382383","wikidata":"https://www.wikidata.org/wiki/Q2399824","display_name":"Mesoscale meteorology","level":2,"score":0.7051100730895996},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6794396638870239},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5580040812492371},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.5442644953727722},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4339149594306946},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4239344596862793},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38847506046295166},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.31631505489349365},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.10665559768676758},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07260063290596008},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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":3,"locations":[{"id":"doi:10.1109/jstars.2022.3174239","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2022.3174239","pdf_url":"https://ieeexplore.ieee.org/ielx7/4609443/4609444/09773008.pdf","source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"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 Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:2bbaf8d327164bd8bfc08d87664ceade","is_oa":true,"landing_page_url":"https://doaj.org/article/2bbaf8d327164bd8bfc08d87664ceade","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"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 Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 3853-3867 (2022)","raw_type":"article"},{"id":"pmh:oai:repository.hkust.edu.hk:1783.1-118024","is_oa":false,"landing_page_url":"http://lbdiscover.ust.hk/uresolver?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rfr_id=info:sid/HKUST:SPI&rft.genre=article&rft.issn=1939-1404&rft.volume=15&rft.issue=&rft.date=2022&rft.spage=3853&rft.aulast=Wang&rft.aufirst=Xuegong&rft.atitle=Spatio-Temporal+Attention-Based+Deep+Learning+Framework+for+Mesoscale+Eddy+Trajectory+Prediction&rft.title=IEEE+JOURNAL+OF+SELECTED+TOPICS+IN+APPLIED+EARTH+OBSERVATIONS+AND+REMOTE+SENSING","pdf_url":null,"source":{"id":"https://openalex.org/S4306401796","display_name":"Rare & Special e-Zone (The Hong Kong University of Science and Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I200769079","host_organization_name":"Hong Kong University of Science and Technology","host_organization_lineage":["https://openalex.org/I200769079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"}],"best_oa_location":{"id":"doi:10.1109/jstars.2022.3174239","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2022.3174239","pdf_url":"https://ieeexplore.ieee.org/ielx7/4609443/4609444/09773008.pdf","source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"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 Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Life below water","id":"https://metadata.un.org/sdg/14","score":0.8600000143051147}],"awards":[{"id":"https://openalex.org/G2151649120","display_name":null,"funder_award_id":"62171420","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2420982029","display_name":null,"funder_award_id":"ZR201910230031","funder_id":"https://openalex.org/F4320324174","funder_display_name":"Natural Science Foundation of Shandong Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320324174","display_name":"Natural Science Foundation of Shandong Province","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4285186549.pdf","grobid_xml":"https://content.openalex.org/works/W4285186549.grobid-xml"},"referenced_works_count":65,"referenced_works":["https://openalex.org/W1570586122","https://openalex.org/W1923108254","https://openalex.org/W1977850725","https://openalex.org/W1978147039","https://openalex.org/W1981499666","https://openalex.org/W2017647804","https://openalex.org/W2017954590","https://openalex.org/W2041947020","https://openalex.org/W2045992227","https://openalex.org/W2047202740","https://openalex.org/W2055626096","https://openalex.org/W2064030455","https://openalex.org/W2064675550","https://openalex.org/W2071998560","https://openalex.org/W2072422994","https://openalex.org/W2082338085","https://openalex.org/W2106442633","https://openalex.org/W2110485445","https://openalex.org/W2112736998","https://openalex.org/W2129017707","https://openalex.org/W2157331557","https://openalex.org/W2158534059","https://openalex.org/W2193945131","https://openalex.org/W2300271414","https://openalex.org/W2341614228","https://openalex.org/W2495712006","https://openalex.org/W2498979687","https://openalex.org/W2554327922","https://openalex.org/W2604847698","https://openalex.org/W2743026384","https://openalex.org/W2778580105","https://openalex.org/W2884585870","https://openalex.org/W2897283027","https://openalex.org/W2914223643","https://openalex.org/W2930664421","https://openalex.org/W2945471725","https://openalex.org/W2948026328","https://openalex.org/W2962740470","https://openalex.org/W2963481014","https://openalex.org/W2967877666","https://openalex.org/W2969987402","https://openalex.org/W2995676548","https://openalex.org/W3013907393","https://openalex.org/W3015420071","https://openalex.org/W3022442840","https://openalex.org/W3031648794","https://openalex.org/W3035782282","https://openalex.org/W3083771119","https://openalex.org/W3091765343","https://openalex.org/W3092160117","https://openalex.org/W3092702234","https://openalex.org/W3096600794","https://openalex.org/W3111604413","https://openalex.org/W3132784030","https://openalex.org/W3135601602","https://openalex.org/W3148366207","https://openalex.org/W3158079936","https://openalex.org/W3159713352","https://openalex.org/W3187491373","https://openalex.org/W3203231592","https://openalex.org/W3210500403","https://openalex.org/W3210553524","https://openalex.org/W4206338773","https://openalex.org/W4248568124","https://openalex.org/W6781055434"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W2145836866","https://openalex.org/W2803255133","https://openalex.org/W3048236912"],"abstract_inverted_index":{"Accurate":[0],"prediction":[1,54],"of":[2,25,34,59,92,134,202],"mesoscale":[3,60],"eddy":[4,18,41,52,119,145],"trajectories":[5,146],"requires":[6],"efficient":[7],"models":[8],"with":[9,97,111,121,147],"large-size":[10],"available":[11],"data":[12,42,83,120],"instances":[13],"to":[14,55,142],"capture":[15,57],"the":[16,32,36,63,126,132,154,164,188,193],"main":[17],"characteristics.":[19],"However,":[20],"there":[21],"is":[22,140],"a":[23,73,93,105,177],"lack":[24],"salient":[26],"attention":[27,101,113,156],"mechanisms":[28,102],"that":[29,79,153,172],"can":[30,80,158],"recognize":[31],"demand":[33],"extracting":[35],"aggregated":[37],"features":[38],"over":[39,187],"multi-dimensional":[40],"sources.":[43],"Additionally,":[44],"deep":[45,76],"learning":[46,77],"techniques":[47,201],"are":[48],"very":[49],"important":[50],"for":[51],"trajectory":[53,87],"dynamically":[56],"properties":[58],"eddies":[61],"in":[62,192],"South":[64],"China":[65],"Sea":[66],"(SCS).":[67],"In":[68],"this":[69],"paper,":[70],"we":[71],"propose":[72],"spatio-temporal":[74],"attention-based":[75],"framework":[78,174],"orchestrate":[81],"heterogeneous":[82],"integration":[84],"and":[85,99,104,124,128,183,197,207,214],"propagation":[86],"forecast":[88],"together.":[89],"It":[90],"consists":[91],"novel":[94],"autoencoder":[95],"equipped":[96],"channel":[98],"spatial":[100],"(CSA-Encoder),":[103],"gated":[106],"recurrent":[107,208],"unit":[108],"(GRU)":[109],"network":[110,210],"temporal":[112,155],"layer":[114],"(TA-GRU).":[115],"CSA-Encoder":[116],"compresses":[117],"stereoscopic":[118],"convolutional":[122],"layers":[123],"generates":[125],"small-scale":[127],"high-quality":[129],"dataset":[130],"as":[131],"input":[133],"TA-GRU.":[135],"The":[136],"finer-grained":[137],"TA-GRU":[138],"method":[139],"extended":[141],"accurately":[143],"predict":[144],"more":[148],"valuable":[149],"imagery":[150],"information":[151],"so":[152],"mechanism":[157],"automatically":[159],"select":[160],"relevant":[161],"regions":[162],"within":[163],"next":[165,194],"14":[166],"days.":[167],"Our":[168],"cross-validation":[169],"results":[170],"demonstrate":[171],"our":[173],"averagely":[175],"achieves":[176],"lower":[178],"distance":[179],"error":[180],"(9":[181],"km)":[182],"54%":[184],"performance":[185],"improvement":[186],"baseline":[189],"GRU":[190],"technique":[191],"one":[195],"day,":[196],"outperforms":[198],"two":[199],"state-of-the-art":[200],"long":[203],"short-term":[204],"memory":[205],"(LSTM)":[206],"neural":[209],"(RNN)":[211],"by":[212],"54.9%":[213],"65.6%,":[215],"respectively.":[216]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1}],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2025-10-10T00:00:00"}
