{"id":"https://openalex.org/W7140500560","doi":"https://doi.org/10.3390/info17040322","title":"Ensemble Learning Using YOLO Models for Semiconductor E-Waste Recycling","display_name":"Ensemble Learning Using YOLO Models for Semiconductor E-Waste Recycling","publication_year":2026,"publication_date":"2026-03-26","ids":{"openalex":"https://openalex.org/W7140500560","doi":"https://doi.org/10.3390/info17040322"},"language":"en","primary_location":{"id":"doi:10.3390/info17040322","is_oa":true,"landing_page_url":"https://doi.org/10.3390/info17040322","pdf_url":null,"source":{"id":"https://openalex.org/S4210219776","display_name":"Information","issn_l":"2078-2489","issn":["2078-2489"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Information","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.3390/info17040322","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130695443","display_name":"Xinglong Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I121847817","display_name":"The Graduate Center, CUNY","ror":"https://ror.org/00awd9g61","country_code":"US","type":"education","lineage":["https://openalex.org/I121847817"]},{"id":"https://openalex.org/I174216632","display_name":"City University of New York","ror":"https://ror.org/00453a208","country_code":"US","type":"education","lineage":["https://openalex.org/I174216632"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Xinglong Zhou","raw_affiliation_strings":["The Graduate Center, City University of New York, New York, NY 10016, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Graduate Center, City University of New York, New York, NY 10016, USA","institution_ids":["https://openalex.org/I121847817","https://openalex.org/I174216632"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048285657","display_name":"Sos \u0421. Agaian","orcid":"https://orcid.org/0000-0003-4601-4507"},"institutions":[{"id":"https://openalex.org/I121847817","display_name":"The Graduate Center, CUNY","ror":"https://ror.org/00awd9g61","country_code":"US","type":"education","lineage":["https://openalex.org/I121847817"]},{"id":"https://openalex.org/I142393192","display_name":"College of Staten Island","ror":"https://ror.org/02p179j44","country_code":"US","type":"education","lineage":["https://openalex.org/I142393192"]},{"id":"https://openalex.org/I174216632","display_name":"City University of New York","ror":"https://ror.org/00453a208","country_code":"US","type":"education","lineage":["https://openalex.org/I174216632"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sos Agaian","raw_affiliation_strings":["Computer Science Department, The Graduate Center, College of Staten Island (CSI), City University of New York, New York, NY 10314, USA","The Graduate Center, City University of New York, New York, NY 10016, USA"],"raw_orcid":"https://orcid.org/0000-0003-4601-4507","affiliations":[{"raw_affiliation_string":"Computer Science Department, The Graduate Center, College of Staten Island (CSI), City University of New York, New York, NY 10314, USA","institution_ids":["https://openalex.org/I121847817","https://openalex.org/I142393192"]},{"raw_affiliation_string":"The Graduate Center, City University of New York, New York, NY 10016, USA","institution_ids":["https://openalex.org/I121847817","https://openalex.org/I174216632"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5130695443"],"corresponding_institution_ids":["https://openalex.org/I121847817","https://openalex.org/I174216632"],"apc_list":{"value":1600,"currency":"CHF","value_usd":1782},"apc_paid":{"value":1600,"currency":"CHF","value_usd":1782},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.35977232,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"17","issue":"4","first_page":"322","last_page":"322"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.7994999885559082,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.7994999885559082,"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"}},{"id":"https://openalex.org/T12017","display_name":"Recycling and Waste Management Techniques","score":0.023000000044703484,"subfield":{"id":"https://openalex.org/subfields/2311","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.016599999740719795,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.7436000108718872},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7329000234603882},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.5982999801635742},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.5045999884605408},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.49219998717308044},{"id":"https://openalex.org/keywords/majority-rule","display_name":"Majority rule","score":0.4406999945640564},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.43369999527931213}],"concepts":[{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.7436000108718872},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7329000234603882},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.5982999801635742},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5533000230789185},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.5045999884605408},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.49219998717308044},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4812999963760376},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48010000586509705},{"id":"https://openalex.org/C153668964","wikidata":"https://www.wikidata.org/wiki/Q27636","display_name":"Majority rule","level":2,"score":0.4406999945640564},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.43369999527931213},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.414000004529953},{"id":"https://openalex.org/C66018809","wikidata":"https://www.wikidata.org/wiki/Q1570432","display_name":"Semiconductor device fabrication","level":3,"score":0.3675000071525574},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.36320000886917114},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3562000095844269},{"id":"https://openalex.org/C520049643","wikidata":"https://www.wikidata.org/wiki/Q189760","display_name":"Voting","level":3,"score":0.3425999879837036},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.33799999952316284},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.31839999556541443},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.2904999852180481},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2849000096321106},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27889999747276306},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.259799987077713}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/info17040322","is_oa":true,"landing_page_url":"https://doi.org/10.3390/info17040322","pdf_url":null,"source":{"id":"https://openalex.org/S4210219776","display_name":"Information","issn_l":"2078-2489","issn":["2078-2489"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Information","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:070cd46a9ff2419eac584e486748a2b1","is_oa":true,"landing_page_url":"https://doaj.org/article/070cd46a9ff2419eac584e486748a2b1","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":"Information, Vol 17, Iss 4, p 322 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/info17040322","is_oa":true,"landing_page_url":"https://doi.org/10.3390/info17040322","pdf_url":null,"source":{"id":"https://openalex.org/S4210219776","display_name":"Information","issn_l":"2078-2489","issn":["2078-2489"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Information","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.4703938066959381}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W2144506857","https://openalex.org/W2570343428","https://openalex.org/W2963037989","https://openalex.org/W2964121718","https://openalex.org/W3089790585","https://openalex.org/W3127743092","https://openalex.org/W4312726147","https://openalex.org/W4361023909","https://openalex.org/W4361275271","https://openalex.org/W4382280585","https://openalex.org/W4386076325","https://openalex.org/W4387757657","https://openalex.org/W4393252696","https://openalex.org/W4400352389","https://openalex.org/W4403770406","https://openalex.org/W4408124754","https://openalex.org/W4410952171","https://openalex.org/W4412692164","https://openalex.org/W4414099519","https://openalex.org/W7124742584","https://openalex.org/W7131104353"],"related_works":[],"abstract_inverted_index":{"The":[0,136],"global":[1],"rise":[2,229],"in":[3,8,56,170,219,230,245,271],"electronic":[4],"waste":[5],"(e-waste),":[6],"especially":[7],"semiconductor":[9,54],"components":[10,55],"such":[11],"as":[12],"circuit":[13],"boards":[14],"and":[15,41,83,101,109,208,238,268],"microchips,":[16],"underscores":[17],"a":[18,126,149,203,227,233,261],"critical":[19],"need":[20],"for":[21,52,129,221,252],"improved":[22],"recycling":[23,43],"technology.":[24],"Current":[25],"industrial":[26],"sorters":[27],"often":[28],"miss":[29],"small,":[30],"high-value":[31],"components.":[32],"This":[33,45,240,259],"leads":[34],"to":[35],"the":[36,65,130,153,180,193],"loss":[37],"of":[38,67,147,256,276],"precious":[39],"metals":[40],"inefficient":[42],"processes.":[44],"paper":[46],"introduces":[47],"an":[48,145],"automated":[49],"detection":[50,75,206],"framework":[51],"detecting":[53],"e-waste.":[57],"It":[58,162],"assesses":[59],"ensemble":[60,86,142,195],"learning":[61],"methods":[62],"that":[63,116,266],"leverage":[64],"strengths":[66],"multiple":[68,118],"YOLO":[69],"(You":[70],"Only":[71],"Look":[72],"Once)":[73],"object":[74],"models,":[76],"including":[77],"YOLOv5,":[78],"YOLOv8,":[79],"YOLOv9,":[80],"YOLOv10,":[81],"YOLOv11,":[82],"YOLOv12.":[84],"Three":[85],"fusion":[87,214],"strategies":[88,97,215],"are":[89,273],"systematically":[90],"compared:":[91],"standard":[92],"Non-Maximum":[93],"Suppression":[94],"(NMS),":[95],"voting-based":[96],"(Affirmative,":[98],"Consensus,":[99],"Unanimous),":[100],"Weighted":[102],"Box":[103],"Fusion":[104],"(WBF)":[105],"with":[106],"both":[107],"static":[108],"dynamic":[110],"weight":[111],"optimization.":[112],"Our":[113],"simulations":[114],"demonstrate":[115],"using":[117],"models":[119],"together":[120],"is":[121,184],"far":[122],"more":[123,253],"effective":[124],"than":[125],"single":[127],"model":[128,156,183],"following":[131],"reasons.":[132],"1.":[133],"Higher":[134],"Accuracy:":[135],"best":[137,154,194],"configuration,":[138],"Top-4":[139],"Consensus":[140],"Voting":[141],"strategy,":[143],"achieved":[144,226],"mAP@0.5":[146],"59.63%,":[148],"10.3%":[150],"improvement":[151],"over":[152],"individual":[155,181],"(YOLOv8s,":[157],"54.04%);":[158],"2.":[159],"Greater":[160],"Reliability:":[161],"significantly":[163],"reduced":[164],"\u201cfalse":[165],"negatives\u201d":[166],"(missed":[167],"detections),":[168],"even":[169],"cluttered":[171],"or":[172],"crowded":[173],"e-waste":[174],"scenarios;":[175],"3.":[176],"Enhanced":[177],"Detection:":[178],"While":[179],"YOLOv8":[182],"fast":[185],"(taking":[186],"only":[187],"62.6":[188],"ms),":[189],"supporting":[190],"real-time":[191],"detection,":[192],"configuration":[196],"(Consensus":[197],"Top-4)":[198],"takes":[199],"384.9":[200],"ms,":[201],"creating":[202],"trade-off":[204],"between":[205,236],"accuracy":[207],"speed;":[209],"4.":[210],"Well-Balanced":[211],"Performance:":[212],"Some":[213],"showed":[216],"slight":[217],"trade-offs":[218],"mAP":[220],"certain":[222],"parts,":[223],"but":[224],"collectively":[225],"7%":[228],"F1-score,":[231],"indicating":[232],"better":[234],"balance":[235],"precision":[237],"recall.":[239],"research":[241],"marks":[242],"significant":[243],"progress":[244],"smart":[246],"recycling.":[247],"Improved":[248],"component":[249],"identification":[250],"allows":[251],"efficient":[254],"recovery":[255],"high-purity":[257],"materials.":[258],"promotes":[260],"circular":[262],"economy":[263],"by":[264],"ensuring":[265],"rare":[267],"strategic":[269],"materials":[270],"electronics":[272],"reused":[274],"instead":[275],"discarded.":[277]},"counts_by_year":[],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2026-03-27T00:00:00"}