{"id":"https://openalex.org/W4200189027","doi":"https://doi.org/10.1142/s021926592143009x","title":"Adaptive Deep Incremental Learning \u2014 Assisted Missing Data Imputation for Streaming Data","display_name":"Adaptive Deep Incremental Learning \u2014 Assisted Missing Data Imputation for Streaming Data","publication_year":2021,"publication_date":"2021-12-06","ids":{"openalex":"https://openalex.org/W4200189027","doi":"https://doi.org/10.1142/s021926592143009x"},"language":"en","primary_location":{"id":"doi:10.1142/s021926592143009x","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s021926592143009x","pdf_url":null,"source":{"id":"https://openalex.org/S63112013","display_name":"Journal of Interconnection Networks","issn_l":"0219-2659","issn":["0219-2659","1793-6713"],"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 Interconnection Networks","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/A5025045958","display_name":"C.V.S.R. Syavasya","orcid":null},"institutions":[{"id":"https://openalex.org/I885392262","display_name":"GITAM University","ror":"https://ror.org/0440p1d37","country_code":"IN","type":"education","lineage":["https://openalex.org/I885392262"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"C. V. S. R. Syavasya","raw_affiliation_strings":["Department of Computer Science and Engineering, Gitam University, Rudraram, Telangana 502329, India"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Gitam University, Rudraram, Telangana 502329, India","institution_ids":["https://openalex.org/I885392262"]}]},{"author_position":"last","author":{"id":null,"display_name":"M. A. Lakshmi","orcid":null},"institutions":[{"id":"https://openalex.org/I885392262","display_name":"GITAM University","ror":"https://ror.org/0440p1d37","country_code":"IN","type":"education","lineage":["https://openalex.org/I885392262"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"M. A. Lakshmi","raw_affiliation_strings":["Department of Computer Science and Engineering, Gitam University, Rudraram, Telangana 502329, India"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Gitam University, Rudraram, Telangana 502329, India","institution_ids":["https://openalex.org/I885392262"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5025045958"],"corresponding_institution_ids":["https://openalex.org/I885392262"],"apc_list":null,"apc_paid":null,"fwci":0.28,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.65917323,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"22","issue":"Supp02","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9998000264167786,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9962000250816345,"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/T12676","display_name":"Machine Learning and ELM","score":0.9904000163078308,"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/imputation","display_name":"Imputation (statistics)","score":0.8976114392280579},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.8496519327163696},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7991397380828857},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.553743302822113},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5455356240272522},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5288967490196228},{"id":"https://openalex.org/keywords/data-stream-mining","display_name":"Data stream mining","score":0.5110958218574524},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4652712345123291},{"id":"https://openalex.org/keywords/stochastic-gradient-descent","display_name":"Stochastic gradient descent","score":0.435076504945755},{"id":"https://openalex.org/keywords/data-stream","display_name":"Data stream","score":0.4309914708137512},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2653887867927551}],"concepts":[{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.8976114392280579},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.8496519327163696},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7991397380828857},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.553743302822113},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5455356240272522},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5288967490196228},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.5110958218574524},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4652712345123291},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.435076504945755},{"id":"https://openalex.org/C2778484313","wikidata":"https://www.wikidata.org/wiki/Q1172540","display_name":"Data stream","level":2,"score":0.4309914708137512},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2653887867927551},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1142/s021926592143009x","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s021926592143009x","pdf_url":null,"source":{"id":"https://openalex.org/S63112013","display_name":"Journal of Interconnection Networks","issn_l":"0219-2659","issn":["0219-2659","1793-6713"],"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 Interconnection Networks","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.8199999928474426,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2164424382","https://openalex.org/W2266336423","https://openalex.org/W2588336250","https://openalex.org/W2735864302","https://openalex.org/W2758219826","https://openalex.org/W2792660917","https://openalex.org/W2885804815","https://openalex.org/W2896540728","https://openalex.org/W2897574832","https://openalex.org/W2905503927","https://openalex.org/W2911600237","https://openalex.org/W2926585089","https://openalex.org/W2932881901","https://openalex.org/W2945929948","https://openalex.org/W2952267276","https://openalex.org/W2963360736","https://openalex.org/W2963826889","https://openalex.org/W3024849061","https://openalex.org/W3082188482","https://openalex.org/W3090934961","https://openalex.org/W4200586789","https://openalex.org/W4242785220"],"related_works":["https://openalex.org/W2181530120","https://openalex.org/W4211215373","https://openalex.org/W2024529227","https://openalex.org/W1574575415","https://openalex.org/W3144172081","https://openalex.org/W3179858851","https://openalex.org/W3028371478","https://openalex.org/W2081476516","https://openalex.org/W2581984549","https://openalex.org/W3123177881"],"abstract_inverted_index":{"With":[0],"the":[1,5,9,28,33,36,44,50,54,64,72,78,95,107,113,118,132,136,140,148,159,164,175,180,188,194],"rapid":[2],"explosion":[3],"of":[4,35,56,169],"data":[6,13,23,67,74],"streams":[7],"from":[8,178],"applications,":[10],"ensuring":[11],"accurate":[12],"analysis":[14],"is":[15],"essential":[16],"for":[17,48,66,84],"effective":[18],"real-time":[19],"decision":[20],"making.":[21],"Nowadays,":[22],"stream":[24],"applications":[25],"often":[26],"confront":[27],"missing":[29,51,73,160,176],"values":[30,150,161,177],"that":[31,187],"affect":[32],"performance":[34,65],"classification":[37],"models.":[38],"Several":[39],"imputation":[40,75,155,190],"models":[41],"have":[42],"adopted":[43],"deep":[45,80,96,102,142],"learning":[46,82,98,114,144],"algorithms":[47],"estimating":[49],"values;":[52],"however,":[53],"lack":[55],"parameter":[57],"and":[58,100,130,166],"structure":[59],"tuning":[60,112,131],"in":[61,151],"classification,":[62],"degrade":[63],"imputation.":[68,105],"This":[69],"work":[70],"presents":[71],"model":[76,191],"using":[77],"adaptive":[79],"incremental":[81,97,103,143],"algorithm":[83,99,145],"streaming":[85],"applications.":[86],"The":[87,183],"proposed":[88,108,137,189],"approach":[89,109,138],"incorporates":[90],"two":[91,152],"main":[92],"processes:":[93],"enhancing":[94,101],"learning-based":[104],"Initially,":[106],"focuses":[110],"on":[111,163],"rate":[115],"with":[116,124,201],"both":[117,179],"Adaptive":[119],"Moment":[120],"Estimation":[121],"(Adam)":[122],"along":[123],"Stochastic":[125],"Gradient":[126],"Descent":[127],"(SGD)":[128],"optimizers":[129],"hidden":[133],"neurons.":[134],"Secondly,":[135],"applies":[139],"enhanced":[141],"to":[146,157],"estimate":[147],"imputed":[149],"steps:":[153],"(i)":[154],"process":[156],"predict":[158],"based":[162],"temporal-proximity":[165],"(ii)":[167],"generation":[168],"complete":[170,199],"IoT":[171],"dataset":[172,196,200],"by":[173],"imputing":[174],"predicted":[181],"values.":[182],"experimental":[184],"outcomes":[185],"illustrate":[186],"effectively":[192],"transforms":[193],"incomplete":[195],"into":[197],"a":[198],"minimal":[202],"error.":[203]},"counts_by_year":[{"year":2024,"cited_by_count":2}],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-10-10T00:00:00"}
