{"id":"https://openalex.org/W4415400191","doi":"https://doi.org/10.1109/etfa65518.2025.11205626","title":"Automatic Coin Grading: Model Based on Siamese Neural Network with EfficientNet Encoders","display_name":"Automatic Coin Grading: Model Based on Siamese Neural Network with EfficientNet Encoders","publication_year":2025,"publication_date":"2025-09-09","ids":{"openalex":"https://openalex.org/W4415400191","doi":"https://doi.org/10.1109/etfa65518.2025.11205626"},"language":null,"primary_location":{"id":"doi:10.1109/etfa65518.2025.11205626","is_oa":false,"landing_page_url":"https://doi.org/10.1109/etfa65518.2025.11205626","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 30th International Conference on Emerging Technologies and Factory Automation (ETFA)","raw_type":"proceedings-article"},"type":"conference-paper","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":null,"display_name":"Makar Korchagin","orcid":null},"institutions":[{"id":"https://openalex.org/I4210110242","display_name":"Digital Science (United States)","ror":"https://ror.org/020h4b682","country_code":"US","type":"company","lineage":["https://openalex.org/I4210110242","https://openalex.org/I4210112888","https://openalex.org/I4210118830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Makar Korchagin","raw_affiliation_strings":["Research Center for Digital Engineering and Innovation,Skoltech,Russia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research Center for Digital Engineering and Innovation,Skoltech,Russia","institution_ids":["https://openalex.org/I4210110242"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093271771","display_name":"Melaku N. Getahun","orcid":"https://orcid.org/0009-0004-8100-4472"},"institutions":[{"id":"https://openalex.org/I4210110242","display_name":"Digital Science (United States)","ror":"https://ror.org/020h4b682","country_code":"US","type":"company","lineage":["https://openalex.org/I4210110242","https://openalex.org/I4210112888","https://openalex.org/I4210118830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Melaku N. Getahun","raw_affiliation_strings":["Research Center for Digital Engineering and Innovation,Skoltech,Russia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research Center for Digital Engineering and Innovation,Skoltech,Russia","institution_ids":["https://openalex.org/I4210110242"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032481215","display_name":"Abdelrahman Metwally","orcid":"https://orcid.org/0000-0003-0558-6330"},"institutions":[{"id":"https://openalex.org/I4210110242","display_name":"Digital Science (United States)","ror":"https://ror.org/020h4b682","country_code":"US","type":"company","lineage":["https://openalex.org/I4210110242","https://openalex.org/I4210112888","https://openalex.org/I4210118830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Abdelrahman Metwally","raw_affiliation_strings":["Research Center for Digital Engineering and Innovation,Skoltech,Russia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research Center for Digital Engineering and Innovation,Skoltech,Russia","institution_ids":["https://openalex.org/I4210110242"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058667550","display_name":"Anna Baldycheva","orcid":"https://orcid.org/0000-0002-2616-595X"},"institutions":[{"id":"https://openalex.org/I23923803","display_name":"University of Exeter","ror":"https://ror.org/03yghzc09","country_code":"GB","type":"education","lineage":["https://openalex.org/I23923803"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Anna Baldycheva","raw_affiliation_strings":["University of Exeter,UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Exeter,UK","institution_ids":["https://openalex.org/I23923803"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084140816","display_name":"Andrey Somov","orcid":"https://orcid.org/0000-0002-4615-3008"},"institutions":[{"id":"https://openalex.org/I4210110242","display_name":"Digital Science (United States)","ror":"https://ror.org/020h4b682","country_code":"US","type":"company","lineage":["https://openalex.org/I4210110242","https://openalex.org/I4210112888","https://openalex.org/I4210118830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Andrey Somov","raw_affiliation_strings":["Research Center for Digital Engineering and Innovation,Skoltech,Russia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research Center for Digital Engineering and Innovation,Skoltech,Russia","institution_ids":["https://openalex.org/I4210110242"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.31733048,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"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/T14319","display_name":"Currency Recognition and Detection","score":0.9966999888420105,"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/T14319","display_name":"Currency Recognition and Detection","score":0.9966999888420105,"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/grading","display_name":"Grading (engineering)","score":0.757099986076355},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6223000288009644},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.38999998569488525},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3781999945640564},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35089999437332153}],"concepts":[{"id":"https://openalex.org/C2777286243","wikidata":"https://www.wikidata.org/wiki/Q5591926","display_name":"Grading (engineering)","level":2,"score":0.757099986076355},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6912999749183655},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6342999935150146},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6223000288009644},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.446399986743927},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.38999998569488525},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3781999945640564},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35089999437332153},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.33219999074935913},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.31859999895095825},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.29100000858306885},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.290800005197525},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2879999876022339}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/etfa65518.2025.11205626","is_oa":false,"landing_page_url":"https://doi.org/10.1109/etfa65518.2025.11205626","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 30th International Conference on Emerging Technologies and Factory Automation (ETFA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Coin":[0],"grading":[1,22,58,164,173],"is":[2,24,94],"a":[3,10,48,67,99,180],"crucial":[4],"process":[5],"in":[6],"numismatics":[7],"that":[8,155],"determines":[9],"coin\u2019s":[11],"value":[12],"on":[13,72,98],"the":[14,20,42,54,60,81,85,120,138,143,149,169,175],"basis":[15],"of":[16,83,90],"its":[17],"condition.":[18],"However,":[19],"traditional":[21],"procedure":[23],"performed":[25],"by":[26],"human":[27],"which":[28,79],"makes":[29],"it":[30],"subjective":[31],"and":[32,87,96,131,148,183],"time-consuming,":[33],"with":[34],"different":[35,39],"experts":[36],"potentially":[37],"assigning":[38],"grades":[40,111],"to":[41,115,158],"same":[43],"coin.":[44],"This":[45],"paper":[46],"introduces":[47],"Deep":[49],"Learning":[50,145],"(DL)":[51],"model":[52,93],"for":[53,75,186],"type-invariant":[55],"automatic":[56,187],"coin":[57,77,122,160,177,188],"across":[59,119,174],"full":[61,170],"70-points":[62],"Sheldon":[63,139,171],"scale.":[64],"We":[65],"propose":[66],"Siamese":[68],"neural":[69],"network":[70],"based":[71],"EfficientNet":[73],"encoders":[74],"dual-sided":[76],"evaluation,":[78],"performs":[80],"analysis":[82],"both":[84,142],"obverse":[86],"reverse":[88],"images":[89],"coins.":[91],"The":[92,124],"trained":[95],"evaluated":[97],"comprehensive":[100],"dataset":[101],"containing":[102],"36,205":[103],"unique":[104],"professionally":[105],"graded":[106],"samples":[107],"covering":[108],"all":[109],"30":[110],"from":[112],"Poor":[113],"(P)1":[114],"Mint":[116],"State":[117],"(MS)70":[118],"multiple":[121,176],"types.":[123],"proposed":[125],"DL":[126],"solution":[127,185],"achieves":[128],"0.3403":[129],"accuracy":[130],"1.52":[132],"Mean":[133],"Absolute":[134],"Error":[135],"(MAE)":[136],"along":[137],"scale,":[140],"outperforming":[141],"Machine":[144],"(ML)":[146],"baseline":[147],"state-of-the-art":[150],"approaches.":[151],"Unlike":[152],"earlier":[153],"studies":[154],"were":[156],"limited":[157],"fewer":[159],"types":[161],"or":[162],"reduced":[163],"scales,":[165],"our":[166],"work":[167],"addresses":[168],"scale":[172],"types,":[178],"providing":[179],"more":[181],"robust":[182],"generalizable":[184],"grading.":[189]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-22T00:00:00"}