{"id":"https://openalex.org/W4319988699","doi":"https://doi.org/10.1109/access.2023.3239658","title":"KeepNMax: Keep N Maximum of Epoch-Channel Ensemble Method for Deep Learning Models","display_name":"KeepNMax: Keep N Maximum of Epoch-Channel Ensemble Method for Deep Learning Models","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4319988699","doi":"https://doi.org/10.1109/access.2023.3239658"},"language":"en","primary_location":{"id":"doi:10.1109/access.2023.3239658","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3239658","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/10005208/10025707.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"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 Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/10005208/10025707.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5091106383","display_name":"Javokhir Musaev","orcid":"https://orcid.org/0000-0003-4656-0479"},"institutions":[{"id":"https://openalex.org/I55240360","display_name":"Yeungnam University","ror":"https://ror.org/05yc6p159","country_code":"KR","type":"education","lineage":["https://openalex.org/I55240360"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Javokhir Musaev","raw_affiliation_strings":["Department of Computer Engineering, Yeungnam University, Gyeongsan, South Korea"],"raw_orcid":"https://orcid.org/0000-0003-4656-0479","affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Yeungnam University, Gyeongsan, South Korea","institution_ids":["https://openalex.org/I55240360"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084255344","display_name":"Abdulaziz Anorboev","orcid":"https://orcid.org/0000-0003-1416-7138"},"institutions":[{"id":"https://openalex.org/I55240360","display_name":"Yeungnam University","ror":"https://ror.org/05yc6p159","country_code":"KR","type":"education","lineage":["https://openalex.org/I55240360"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Abdulaziz Anorboev","raw_affiliation_strings":["Department of Computer Engineering, Yeungnam University, Gyeongsan, South Korea"],"raw_orcid":"https://orcid.org/0000-0003-1416-7138","affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Yeungnam University, Gyeongsan, South Korea","institution_ids":["https://openalex.org/I55240360"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047517734","display_name":"Ngoc Thanh Nguy\u00ean","orcid":"https://orcid.org/0000-0002-3247-2948"},"institutions":[{"id":"https://openalex.org/I11923345","display_name":"Wroc\u0142aw University of Science and Technology","ror":"https://ror.org/008fyn775","country_code":"PL","type":"education","lineage":["https://openalex.org/I11923345"]},{"id":"https://openalex.org/I3020192730","display_name":"Tr\u01b0\u1eddng \u0110H Nguy\u1ec5n T\u1ea5t Th\u00e0nh","ror":"https://ror.org/04r9s1v23","country_code":"VN","type":"education","lineage":["https://openalex.org/I3020192730"]},{"id":"https://openalex.org/I686019","display_name":"AGH University of Krakow","ror":"https://ror.org/00bas1c41","country_code":"PL","type":"education","lineage":["https://openalex.org/I686019"]}],"countries":["PL","VN"],"is_corresponding":false,"raw_author_name":"Ngoc Thanh Nguyen","raw_affiliation_strings":["Faculty of Information and Communication Technology, Wroclaw University of Science and Technology, Wroclaw, Poland","Faculty of Information Technology, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam"],"raw_orcid":"https://orcid.org/0000-0002-3247-2948","affiliations":[{"raw_affiliation_string":"Faculty of Information and Communication Technology, Wroclaw University of Science and Technology, Wroclaw, Poland","institution_ids":["https://openalex.org/I11923345","https://openalex.org/I686019"]},{"raw_affiliation_string":"Faculty of Information Technology, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam","institution_ids":["https://openalex.org/I3020192730"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032265881","display_name":"Dosam Hwang","orcid":"https://orcid.org/0000-0001-7851-7323"},"institutions":[{"id":"https://openalex.org/I55240360","display_name":"Yeungnam University","ror":"https://ror.org/05yc6p159","country_code":"KR","type":"education","lineage":["https://openalex.org/I55240360"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Dosam Hwang","raw_affiliation_strings":["Department of Computer Engineering, Yeungnam University, Gyeongsan, South Korea"],"raw_orcid":"https://orcid.org/0000-0001-7851-7323","affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Yeungnam University, Gyeongsan, South Korea","institution_ids":["https://openalex.org/I55240360"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.7758,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.67633357,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"11","issue":null,"first_page":"9339","last_page":"9350"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9983000159263611,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.7759523391723633},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6043840646743774},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.5796079039573669},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5476809740066528},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.5457902550697327},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.5260258316993713},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5173913240432739},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5030755400657654},{"id":"https://openalex.org/keywords/simplicity","display_name":"Simplicity","score":0.49211615324020386},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4626276195049286},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.46224403381347656},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.45112553238868713},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3764881491661072},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33480140566825867}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7759523391723633},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6043840646743774},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5796079039573669},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5476809740066528},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.5457902550697327},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.5260258316993713},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5173913240432739},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5030755400657654},{"id":"https://openalex.org/C2776372474","wikidata":"https://www.wikidata.org/wiki/Q508291","display_name":"Simplicity","level":2,"score":0.49211615324020386},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4626276195049286},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.46224403381347656},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.45112553238868713},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3764881491661072},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33480140566825867},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"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/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2023.3239658","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3239658","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/10005208/10025707.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"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 Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:cedb4c4c906b40b0ad7cfbc663b1656a","is_oa":true,"landing_page_url":"https://doaj.org/article/cedb4c4c906b40b0ad7cfbc663b1656a","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 Access, Vol 11, Pp 9339-9350 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2023.3239658","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3239658","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/10005208/10025707.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"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 Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.5600000023841858}],"awards":[],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4319988699.pdf","grobid_xml":"https://content.openalex.org/works/W4319988699.grobid-xml"},"referenced_works_count":38,"referenced_works":["https://openalex.org/W1550585437","https://openalex.org/W1968023063","https://openalex.org/W2002767255","https://openalex.org/W2262682506","https://openalex.org/W2517968414","https://openalex.org/W2605018929","https://openalex.org/W2618530766","https://openalex.org/W2622999711","https://openalex.org/W2752650105","https://openalex.org/W2755012395","https://openalex.org/W2768275492","https://openalex.org/W2787468747","https://openalex.org/W2902240328","https://openalex.org/W2945989246","https://openalex.org/W2970602317","https://openalex.org/W2980444207","https://openalex.org/W3011171922","https://openalex.org/W3027312415","https://openalex.org/W3038010422","https://openalex.org/W3049501660","https://openalex.org/W3088070507","https://openalex.org/W3109503058","https://openalex.org/W3128189270","https://openalex.org/W3149839747","https://openalex.org/W3157506437","https://openalex.org/W3169090118","https://openalex.org/W3177177354","https://openalex.org/W3202510180","https://openalex.org/W3202832686","https://openalex.org/W3204111386","https://openalex.org/W4210530275","https://openalex.org/W4285661751","https://openalex.org/W4297094638","https://openalex.org/W4308237781","https://openalex.org/W6679154154","https://openalex.org/W6795140394","https://openalex.org/W6802394538","https://openalex.org/W6811014117"],"related_works":["https://openalex.org/W4285741730","https://openalex.org/W2810053714","https://openalex.org/W4382345315","https://openalex.org/W4308112567","https://openalex.org/W4318677156","https://openalex.org/W3124943098","https://openalex.org/W3162132941","https://openalex.org/W3034006481","https://openalex.org/W4281560664","https://openalex.org/W3136979370"],"abstract_inverted_index":{"Computer":[0],"vision":[1],"(CV)":[2],"application":[3],"is":[4,72],"becoming":[5],"a":[6,26,73,300],"crucial":[7],"factor":[8],"for":[9,29,63,217,235],"the":[10,16,33,50,65,68,97,104,109,116,122,137,150,156,167,173,178,183,187,199,203,227,230,236,250,265,270,281,283,287],"growth":[11],"of":[12,21,35,52,67,91,118,121,177,202,229,264,276,286],"developed":[13],"economies":[14],"in":[15,76,186,210],"world.":[17],"The":[18,262],"widespread":[19],"use":[20],"CV":[22,36],"applications":[23],"has":[24],"created":[25],"growing":[27],"demand":[28],"accurate":[30],"models.":[31,69,220],"Therefore,":[32],"subfields":[34],"focus":[37],"on":[38,57],"improving":[39],"existing":[40],"models":[41,234],"and":[42,46,81,94,114,145,206,232,243],"developing":[43],"new":[44],"methods":[45,246],"algorithms":[47],"to":[48,87,182,197,293],"meet":[49],"demands":[51],"different":[53,143,164,192],"sectors.":[54],"Simultaneously,":[55],"research":[56,77],"ensemble":[58,90,110,123],"learning":[59],"provides":[60],"effective":[61],"tools":[62],"increasing":[64],"accuracies":[66],"Nevertheless,":[70],"there":[71],"significant":[74,256],"gap":[75],"using":[78,96,149,166,260],"data":[79,119],"representation":[80],"model":[82,152,169,193,200,288],"features.":[83],"This":[84,213],"led":[85],"us":[86],"develop":[88],"KeepNMax\u2014an":[89],"image":[92],"channels":[93,144,165],"epochs":[95,176],"top":[98],"N":[99],"maximum":[100],"prediction":[101,273,284],"probabilities":[102],"at":[103],"final":[105],"step.":[106],"Using":[107],"KeepNMax,":[108],"error":[111],"was":[112,215,267,289],"reduced":[113],"increased":[115],"amount":[117],"knowledge":[120,296],"model.":[124,278,302],"Nine":[125],"datasets":[126,157,237,298],"were":[127,139,158,180,195,247,258],"trained.":[128],"As":[129],"long":[130],"as":[131,249],"each":[132,277],"dataset":[133],"had":[134],"three":[135,142],"channels,":[136],"images":[138],"divided":[140],"into":[141,163],"trained":[146,159],"them":[147,162],"separately":[148],"same":[151,168],"architecture.":[153,170],"In":[154,189],"addition,":[155,190],"without":[160],"dividing":[161],"After":[171],"completing":[172],"training,":[174,240],"some":[175],"training":[179],"ensembled":[181],"best":[184],"epoch":[185],"training.":[188],"two":[191],"architectures":[194],"used":[196,248],"check":[198],"dependency":[201],"proposed":[204,216,224],"method":[205,214,225,266],"achieved":[207],"remarkable":[208],"results":[209,228,257],"both":[211],"cases.":[212],"deep-learning":[218],"classification":[219],"Despite":[221],"its":[222],"simplicity,":[223],"improved":[226],"CNN":[231],"ConvMixer":[233],"used.":[238],"Classic":[239],"bootstrap":[241],"aggregation,":[242],"random":[244],"split":[245],"baseline":[251],"methods.":[252],"For":[253],"most":[254],"datasets,":[255],"obtained":[259],"KeepNMax.":[261],"success":[263],"explained":[268],"by":[269],"unique":[271],"true":[272],"($UTP$)":[274],"scope":[275,285],"By":[279],"ensembling":[280],"models,":[282],"enlarged,":[290],"allowing":[291],"it":[292],"represent":[294],"broader":[295],"about":[297],"than":[299],"simple":[301]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2025-10-10T00:00:00"}
