{"id":"https://openalex.org/W3110666852","doi":"https://doi.org/10.1109/icccs49678.2020.9276855","title":"Empirical Evaluation of Gated Recurrent Neural Network Architectures in Aviation Delay Prediction","display_name":"Empirical Evaluation of Gated Recurrent Neural Network Architectures in Aviation Delay Prediction","publication_year":2020,"publication_date":"2020-10-14","ids":{"openalex":"https://openalex.org/W3110666852","doi":"https://doi.org/10.1109/icccs49678.2020.9276855","mag":"3110666852"},"language":"en","primary_location":{"id":"doi:10.1109/icccs49678.2020.9276855","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icccs49678.2020.9276855","pdf_url":null,"source":{"id":"https://openalex.org/S4306498618","display_name":"2020 5th International Conference on Computing, Communication and Security (ICCCS)","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":"2020 5th International Conference on Computing, Communication and Security (ICCCS)","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/A5004584857","display_name":"Amulya Arun Ballakur","orcid":null},"institutions":[{"id":"https://openalex.org/I196608512","display_name":"PES University","ror":"https://ror.org/05m169e78","country_code":"IN","type":"education","lineage":["https://openalex.org/I196608512"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Amulya Arun Ballakur","raw_affiliation_strings":["Computer Science Engg. Department, PESIT South Campus (VTU), Bangalore, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Engg. Department, PESIT South Campus (VTU), Bangalore, India","institution_ids":["https://openalex.org/I196608512"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008810162","display_name":"Arti Arya","orcid":"https://orcid.org/0000-0002-4470-0311"},"institutions":[{"id":"https://openalex.org/I196608512","display_name":"PES University","ror":"https://ror.org/05m169e78","country_code":"IN","type":"education","lineage":["https://openalex.org/I196608512"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Arti Arya","raw_affiliation_strings":["Computer Science Engg. Department, PES University, Bangalore, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Engg. Department, PES University, Bangalore, India","institution_ids":["https://openalex.org/I196608512"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I196608512"],"apc_list":null,"apc_paid":null,"fwci":4.5047,"has_fulltext":false,"cited_by_count":32,"citation_normalized_percentile":{"value":0.97933173,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9987000226974487,"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":0.9987000226974487,"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/T11489","display_name":"Air Traffic Management and Optimization","score":0.9958000183105469,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"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/T10320","display_name":"Neural Networks and Applications","score":0.9883000254631042,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/aviation","display_name":"Aviation","score":0.7570112943649292},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7143180966377258},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.7006728649139404},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5548005104064941},{"id":"https://openalex.org/keywords/long-short-term-memory","display_name":"Long short term memory","score":0.4545317590236664},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.42976051568984985},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.41444170475006104},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.41066810488700867},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3965235948562622},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3635692298412323},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.192397803068161}],"concepts":[{"id":"https://openalex.org/C74448152","wikidata":"https://www.wikidata.org/wiki/Q765633","display_name":"Aviation","level":2,"score":0.7570112943649292},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7143180966377258},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7006728649139404},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5548005104064941},{"id":"https://openalex.org/C133488467","wikidata":"https://www.wikidata.org/wiki/Q6673524","display_name":"Long short term memory","level":4,"score":0.4545317590236664},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.42976051568984985},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.41444170475006104},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.41066810488700867},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3965235948562622},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3635692298412323},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.192397803068161},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","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},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icccs49678.2020.9276855","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icccs49678.2020.9276855","pdf_url":null,"source":{"id":"https://openalex.org/S4306498618","display_name":"2020 5th International Conference on Computing, Communication and Security (ICCCS)","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":"2020 5th International Conference on Computing, Communication and Security (ICCCS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5099999904632568,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1689711448","https://openalex.org/W2058580716","https://openalex.org/W2076914349","https://openalex.org/W2079735306","https://openalex.org/W2119615570","https://openalex.org/W2122585011","https://openalex.org/W2131774270","https://openalex.org/W2143612262","https://openalex.org/W2248852507","https://openalex.org/W2293634267","https://openalex.org/W2549590463","https://openalex.org/W2566619632","https://openalex.org/W2604861992","https://openalex.org/W2784132423","https://openalex.org/W4252637405","https://openalex.org/W6677664287","https://openalex.org/W6891930171"],"related_works":["https://openalex.org/W2912153778","https://openalex.org/W4288108708","https://openalex.org/W4387163678","https://openalex.org/W2973430807","https://openalex.org/W4385280324","https://openalex.org/W2890685186","https://openalex.org/W2984436043","https://openalex.org/W4390245176","https://openalex.org/W3173606726","https://openalex.org/W4285503423"],"abstract_inverted_index":{"The":[0,65,126],"aviation":[1,63],"industry":[2,21],"is":[3,22,52],"one":[4],"of":[5,14,25,57,120,148],"the":[6,10,15,23,34,55,62,87,102,118,142],"largest":[7],"industries":[8],"in":[9,19,61,85],"transportation":[11],"sector.":[12],"One":[13],"major":[16],"problems":[17],"faced":[18],"this":[20,43,98,112],"prevalence":[24],"delays.":[26],"These":[27],"delays":[28,59],"not":[29],"only":[30],"cause":[31],"dissatisfaction":[32],"among":[33],"passengers":[35],"but":[36],"also":[37],"huge":[38],"losses":[39],"for":[40,54,89,100],"airlines.":[41],"In":[42],"study,":[44],"an":[45,132,145],"approach":[46,67,99],"using":[47],"gated":[48],"recurrent":[49],"neural":[50],"networks":[51],"explored":[53,97],"purpose":[56],"predicting":[58,86,117],"encountered":[60],"industry.":[64],"proposed":[66],"uses":[68],"Long":[69],"Short-Term":[70],"Memory(LSTM)":[71],"architectures":[72],"such":[73],"as":[74,105],"vanilla":[75],"LSTM":[76,79],"and":[77,137],"Bi-directional":[78],"to":[80,140],"empirically":[81],"evaluate":[82],"its":[83],"efficiency":[84],"delay":[88,103,107,121,143],"a":[90,113],"future":[91],"flight.":[92],"While":[93],"previous":[94],"works":[95],"have":[96],"classifying":[101],"status":[104],"either":[106],"or":[108],"no-delay,":[109],"we":[110],"take":[111],"step":[114],"further":[115],"by":[116],"amount":[119],"that":[122],"will":[123],"be":[124],"experienced.":[125],"trained":[127],"models":[128],"were":[129,138],"tested":[130],"on":[131],"unseen":[133],"test":[134],"data":[135],"set":[136],"able":[139],"predict":[141],"with":[144],"error":[146],"value":[147],"33":[149],"minutes.":[150]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":3}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}