{"id":"https://openalex.org/W4312522535","doi":"https://doi.org/10.1109/iscc55528.2022.9912956","title":"Evaluating TCP Throughput Predictability from Packet Traces using Recurrent Neural Network","display_name":"Evaluating TCP Throughput Predictability from Packet Traces using Recurrent Neural Network","publication_year":2022,"publication_date":"2022-06-30","ids":{"openalex":"https://openalex.org/W4312522535","doi":"https://doi.org/10.1109/iscc55528.2022.9912956"},"language":"en","primary_location":{"id":"doi:10.1109/iscc55528.2022.9912956","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscc55528.2022.9912956","pdf_url":null,"source":{"id":"https://openalex.org/S4363605780","display_name":"2022 IEEE Symposium on Computers and Communications (ISCC)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE Symposium on Computers and Communications (ISCC)","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/A5068470957","display_name":"Ryu Kazama","orcid":null},"institutions":[{"id":"https://openalex.org/I146399215","display_name":"University of Tsukuba","ror":"https://ror.org/02956yf07","country_code":"JP","type":"education","lineage":["https://openalex.org/I146399215"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Ryu Kazama","raw_affiliation_strings":["University of Tsukuba,Japan","University of Tsukuba, Japan"],"affiliations":[{"raw_affiliation_string":"University of Tsukuba,Japan","institution_ids":["https://openalex.org/I146399215"]},{"raw_affiliation_string":"University of Tsukuba, Japan","institution_ids":["https://openalex.org/I146399215"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077270824","display_name":"Hirotake Abe","orcid":"https://orcid.org/0000-0003-3496-8186"},"institutions":[{"id":"https://openalex.org/I146399215","display_name":"University of Tsukuba","ror":"https://ror.org/02956yf07","country_code":"JP","type":"education","lineage":["https://openalex.org/I146399215"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hirotake Abe","raw_affiliation_strings":["University of Tsukuba,Japan","University of Tsukuba, Japan"],"affiliations":[{"raw_affiliation_string":"University of Tsukuba,Japan","institution_ids":["https://openalex.org/I146399215"]},{"raw_affiliation_string":"University of Tsukuba, Japan","institution_ids":["https://openalex.org/I146399215"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009880179","display_name":"Chunghan Lee","orcid":"https://orcid.org/0000-0002-1025-2258"},"institutions":[{"id":"https://openalex.org/I4210137853","display_name":"Toyota Motor Corporation (Japan)","ror":"https://ror.org/02zqm6r10","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210125472","https://openalex.org/I4210137853"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Chunghan Lee","raw_affiliation_strings":["Toyota Motor Corporation,Japan","Toyota Motor Corporation, Japan"],"affiliations":[{"raw_affiliation_string":"Toyota Motor Corporation,Japan","institution_ids":["https://openalex.org/I4210137853"]},{"raw_affiliation_string":"Toyota Motor Corporation, Japan","institution_ids":["https://openalex.org/I4210137853"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5068470957"],"corresponding_institution_ids":["https://openalex.org/I146399215"],"apc_list":null,"apc_paid":null,"fwci":0.6479,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.60956618,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10138","display_name":"Network Traffic and Congestion Control","score":0.9986000061035156,"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"}},"topics":[{"id":"https://openalex.org/T10138","display_name":"Network Traffic and Congestion Control","score":0.9986000061035156,"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"}},{"id":"https://openalex.org/T10714","display_name":"Software-Defined Networks and 5G","score":0.9617999792098999,"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"}},{"id":"https://openalex.org/T12326","display_name":"Network Packet Processing and Optimization","score":0.9535999894142151,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/predictability","display_name":"Predictability","score":0.8672313690185547},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8242594003677368},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.6750880479812622},{"id":"https://openalex.org/keywords/network-packet","display_name":"Network packet","score":0.6263141632080078},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6024019718170166},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.5238442420959473},{"id":"https://openalex.org/keywords/bandwidth","display_name":"Bandwidth (computing)","score":0.49744752049446106},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.4890213906764984},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4813902676105499},{"id":"https://openalex.org/keywords/packet-loss","display_name":"Packet loss","score":0.460224449634552},{"id":"https://openalex.org/keywords/network-congestion","display_name":"Network congestion","score":0.4343401789665222},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4080180525779724},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36571481823921204},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.2628124952316284},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.14824360609054565},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.07518503069877625}],"concepts":[{"id":"https://openalex.org/C197640229","wikidata":"https://www.wikidata.org/wiki/Q2534066","display_name":"Predictability","level":2,"score":0.8672313690185547},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8242594003677368},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.6750880479812622},{"id":"https://openalex.org/C158379750","wikidata":"https://www.wikidata.org/wiki/Q214111","display_name":"Network packet","level":2,"score":0.6263141632080078},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6024019718170166},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.5238442420959473},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.49744752049446106},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.4890213906764984},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4813902676105499},{"id":"https://openalex.org/C54108766","wikidata":"https://www.wikidata.org/wiki/Q391064","display_name":"Packet loss","level":3,"score":0.460224449634552},{"id":"https://openalex.org/C195563490","wikidata":"https://www.wikidata.org/wiki/Q180368","display_name":"Network congestion","level":3,"score":0.4343401789665222},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4080180525779724},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36571481823921204},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.2628124952316284},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.14824360609054565},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.07518503069877625},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iscc55528.2022.9912956","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscc55528.2022.9912956","pdf_url":null,"source":{"id":"https://openalex.org/S4363605780","display_name":"2022 IEEE Symposium on Computers and Communications (ISCC)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE Symposium on Computers and Communications (ISCC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.4699999988079071,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4226218331","https://openalex.org/W2106906543","https://openalex.org/W4381301812","https://openalex.org/W2047584981","https://openalex.org/W1561385631","https://openalex.org/W2166315921","https://openalex.org/W2071614110","https://openalex.org/W2162566722","https://openalex.org/W23565719","https://openalex.org/W1542379831"],"abstract_inverted_index":{"Congestion":[0],"control":[1],"algorithms":[2],"using":[3,24,101],"recurrent":[4],"neural":[5],"network":[6],"(RNN)":[7],"for":[8],"bandwidth":[9],"prediction":[10,39,51,76],"are":[11,32],"expected":[12],"to":[13,97],"improve":[14],"throughput.":[15],"Previous":[16],"studies":[17],"involving":[18],"performance":[19],"evaluations":[20],"were":[21],"conducted":[22],"only":[23],"simulated":[25,102],"data.":[26,81,103],"However,":[27],"simulation":[28],"and":[29,35,64],"real-world":[30,55],"environments":[31],"largely":[33],"different":[34],"rarely":[36],"provide":[37],"equivalent":[38],"accuracy.":[40],"Therefore,":[41],"we":[42],"will":[43],"verify":[44],"whether":[45],"our":[46],"proposed":[47],"method":[48],"provides":[49],"better":[50],"accuracy":[52,77],"in":[53,60],"a":[54,61],"environment.":[56],"We":[57],"measured":[58],"communications":[59],"real":[62],"environment":[63],"generated":[65,80],"training":[66],"data":[67,72],"by":[68],"converting":[69],"packet":[70],"captured":[71],"with":[73],"measurement":[74],"of":[75,89],"on":[78],"the":[79,86,98],"The":[82],"results":[83,99],"showed":[84],"that":[85],"maximum":[87],"percentage":[88],"correct":[90],"responses":[91],"was":[92,95],"79.71%,":[93],"which":[94],"comparable":[96],"obtained":[100]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}