{"id":"https://openalex.org/W3002227860","doi":"https://doi.org/10.1145/3372454.3372462","title":"Improvement of Big Data Stream Mining Technique for Automatic Bone Age Assessment","display_name":"Improvement of Big Data Stream Mining Technique for Automatic Bone Age Assessment","publication_year":2019,"publication_date":"2019-11-20","ids":{"openalex":"https://openalex.org/W3002227860","doi":"https://doi.org/10.1145/3372454.3372462","mag":"3002227860"},"language":"en","primary_location":{"id":"doi:10.1145/3372454.3372462","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3372454.3372462","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 3rd International Conference on Big Data Research","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/A5063216952","display_name":"Ari Wibisono","orcid":"https://orcid.org/0000-0002-2652-3227"},"institutions":[{"id":"https://openalex.org/I29617571","display_name":"University of Indonesia","ror":"https://ror.org/0116zj450","country_code":"ID","type":"education","lineage":["https://openalex.org/I29617571"]}],"countries":["ID"],"is_corresponding":true,"raw_author_name":"Ari Wibisono","raw_affiliation_strings":["Universitas Indonesia, Depok, Indonesia"],"affiliations":[{"raw_affiliation_string":"Universitas Indonesia, Depok, Indonesia","institution_ids":["https://openalex.org/I29617571"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067896571","display_name":"Jihan Adibah","orcid":null},"institutions":[{"id":"https://openalex.org/I29617571","display_name":"University of Indonesia","ror":"https://ror.org/0116zj450","country_code":"ID","type":"education","lineage":["https://openalex.org/I29617571"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Jihan Adibah","raw_affiliation_strings":["Universitas Indonesia, Depok, Indonesia"],"affiliations":[{"raw_affiliation_string":"Universitas Indonesia, Depok, Indonesia","institution_ids":["https://openalex.org/I29617571"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004862325","display_name":"Petrus Mursanto","orcid":"https://orcid.org/0000-0002-4831-4629"},"institutions":[{"id":"https://openalex.org/I29617571","display_name":"University of Indonesia","ror":"https://ror.org/0116zj450","country_code":"ID","type":"education","lineage":["https://openalex.org/I29617571"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Petrus Mursanto","raw_affiliation_strings":["Universitas Indonesia, Depok, Indonesia"],"affiliations":[{"raw_affiliation_string":"Universitas Indonesia, Depok, Indonesia","institution_ids":["https://openalex.org/I29617571"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046110268","display_name":"Mei Silviana Saputri","orcid":null},"institutions":[{"id":"https://openalex.org/I29617571","display_name":"University of Indonesia","ror":"https://ror.org/0116zj450","country_code":"ID","type":"education","lineage":["https://openalex.org/I29617571"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Mei Silviana Saputri","raw_affiliation_strings":["Universitas Indonesia, Depok, Indonesia"],"affiliations":[{"raw_affiliation_string":"Universitas Indonesia, Depok, Indonesia","institution_ids":["https://openalex.org/I29617571"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5063216952"],"corresponding_institution_ids":["https://openalex.org/I29617571"],"apc_list":null,"apc_paid":null,"fwci":0.14,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.59805679,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"119","last_page":"123"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9868000149726868,"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":0.9868000149726868,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9711999893188477,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9703999757766724,"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/computer-science","display_name":"Computer science","score":0.7738610506057739},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5991196036338806},{"id":"https://openalex.org/keywords/data-stream-mining","display_name":"Data stream mining","score":0.5882304906845093},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5360404253005981},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.5315962433815002},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.5298179388046265},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.527547299861908},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.47685492038726807},{"id":"https://openalex.org/keywords/data-stream","display_name":"Data stream","score":0.4726102650165558},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.43309032917022705},{"id":"https://openalex.org/keywords/mean-absolute-percentage-error","display_name":"Mean absolute percentage error","score":0.4272300899028778},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38460490107536316},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35129043459892273},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.14494013786315918},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.14076334238052368},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09031343460083008}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7738610506057739},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5991196036338806},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.5882304906845093},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5360404253005981},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5315962433815002},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.5298179388046265},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.527547299861908},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.47685492038726807},{"id":"https://openalex.org/C2778484313","wikidata":"https://www.wikidata.org/wiki/Q1172540","display_name":"Data stream","level":2,"score":0.4726102650165558},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.43309032917022705},{"id":"https://openalex.org/C150217764","wikidata":"https://www.wikidata.org/wiki/Q6803607","display_name":"Mean absolute percentage error","level":3,"score":0.4272300899028778},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38460490107536316},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35129043459892273},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.14494013786315918},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.14076334238052368},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09031343460083008},{"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/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"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:10.1145/3372454.3372462","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3372454.3372462","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 3rd International Conference on Big Data Research","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.550000011920929}],"awards":[{"id":"https://openalex.org/G2663051519","display_name":null,"funder_award_id":", Hibah Publikasi Internasional Terindeks 9 (PIT 9) No:NKB-0011/UN2.R3.1/HKP.05.00/2019","funder_id":"https://openalex.org/F4320323819","funder_display_name":"Universitas Indonesia"}],"funders":[{"id":"https://openalex.org/F4320323819","display_name":"Universitas Indonesia","ror":"https://ror.org/0116zj450"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W36945325","https://openalex.org/W44634981","https://openalex.org/W2008561033","https://openalex.org/W2041217001","https://openalex.org/W2099302642","https://openalex.org/W2103012681","https://openalex.org/W2145023731","https://openalex.org/W2209603847","https://openalex.org/W2291006719","https://openalex.org/W2414372621","https://openalex.org/W2553407300","https://openalex.org/W2592765733","https://openalex.org/W2617295893","https://openalex.org/W2765289330","https://openalex.org/W2808758534","https://openalex.org/W2889850233","https://openalex.org/W4233413206"],"related_works":["https://openalex.org/W8656678","https://openalex.org/W9362070","https://openalex.org/W14043209","https://openalex.org/W2769812","https://openalex.org/W9491913","https://openalex.org/W12546350","https://openalex.org/W10015831","https://openalex.org/W13523948","https://openalex.org/W13211703","https://openalex.org/W4866187"],"abstract_inverted_index":{"Rapid":[0],"technology":[1,24,71],"growth":[2],"has":[3],"stimulated":[4],"automated":[5,30],"systems":[6],"development":[7],"in":[8,25,103,128,133,164,254,266],"the":[9,20,26,36,43,63,69,134,146,167,172,181,222,287],"medical":[10,27],"field.":[11,28],"Automatic":[12],"bone":[13,156,214],"age":[14,215],"evaluation":[15,284],"is":[16,32,60,66,89,109,150,161,178,183],"an":[17],"example":[18],"of":[19,22,85,106,136,174,190,259,276],"implementation":[21],"this":[23,144,255],"The":[29,158,195,257,274],"assessment":[31,41],"done":[33,179],"based":[34],"on":[35,228],"left-hand":[37],"X-ray":[38],"images.":[39],"This":[40,130],"helps":[42],"radiologist":[44],"and":[45,57,119,154,202,245],"pediatrician":[46],"evaluate":[47],"children's":[48],"growth.":[49],"A":[50,185],"system":[51],"that":[52,72,111,171,263],"can":[53,73,100,123,131,270],"generate":[54,74],"a":[55,75,82,90,280],"precise":[56],"reliable":[58,76],"prediction":[59],"essential.":[61],"Thus":[62],"main":[64],"challenge":[65],"to":[67,139,152,286],"determine":[68],"suitable":[70],"forecast,":[77],"mainly":[78],"when":[79,166],"working":[80],"with":[81],"large":[83,127],"quantity":[84],"data.":[86],"Big":[87],"data":[88,98,112,125,147,168,177,182,223,267],"growing":[91],"trend;":[92],"practical":[93],"computing":[94],"challenges":[95],"created":[96],"by":[97,206],"streams":[99,113],"be":[101,140],"found":[102],"several":[104],"types":[105],"applications.":[107],"It":[108],"known":[110],"are":[114],"usually":[115],"obtained":[116],"from":[117,216],"sensors":[118],"monitors":[120],"which":[121],"accumulatively":[122],"make":[124],"very":[126],"volume.":[129],"result":[132],"inability":[135],"real-time":[137,165],"processing":[138],"carried":[141,162],"out.":[142],"In":[143],"paper,":[145],"stream":[148,224,230,268],"technique":[149,226],"utilized":[151],"assess":[153],"predict":[155,213],"ages.":[157],"analysis":[159],"process":[160,173],"out":[163],"arrives":[169],"so":[170],"storing":[175],"new":[176],"after":[180],"analyzed.":[184],"9":[186],"GB":[187],"sized-dataset":[188],"consisting":[189],"12,611":[191],"images":[192,196],"were":[193],"used.":[194],"have":[197],"various":[198],"resolutions.":[199],"We":[200,232],"extracted":[201,218],"analyzed":[203],"image":[204],"features":[205],"using":[207],"Canny":[208],"Edge":[209],"feature":[210],"extraction.":[211],"To":[212],"those":[217],"features,":[219],"we":[220],"enhance":[221],"mining":[225,269],"base":[227],"tree":[229],"mining.":[231],"use":[233],"MAE":[234],"or":[235,240,247],"Mean":[236,242,248],"Absolute":[237,249],"Error,":[238,244],"RMSE":[239],"Root":[241],"Squared":[243],"MAPE":[246,275],"Percentage":[250],"Error":[251],"as":[252],"metrics":[253],"measurement.":[256],"outcomes":[258],"our":[260,264,277],"experiment":[261],"show":[262],"approach":[265,278],"increase":[271],"performance":[272],"measurements.":[273],"gives":[279],"7%":[281],"lower":[282],"error":[283],"compared":[285],"standard":[288],"method.":[289]},"counts_by_year":[{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
