{"id":"https://openalex.org/W4399345149","doi":"https://doi.org/10.1063/5.0199373","title":"Sneakers scoring using image regression with transfer learning","display_name":"Sneakers scoring using image regression with transfer learning","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4399345149","doi":"https://doi.org/10.1063/5.0199373"},"language":"en","primary_location":{"id":"doi:10.1063/5.0199373","doi":"https://doi.org/10.1063/5.0199373","is_oa":false,"landing_page_url":"https://doi.org/10.1063/5.0199373","pdf_url":null,"source":{"id":"https://openalex.org/S2764696622","display_name":"AIP conference proceedings","issn_l":"0094-243X","issn":["0094-243X","1551-7616","1935-0465"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","jufo-1","norway-1"],"host_organization":"https://openalex.org/P4310320257","host_organization_name":"American Institute of Physics","host_organization_lineage":["https://openalex.org/P4310320257"],"host_organization_lineage_names":["American Institute of Physics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"AIP Conference Proceedings","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5006029119","display_name":"Muhammad Adrinta Abdurrazzaq","orcid":"https://orcid.org/0000-0003-0680-7799","observed_orcids":["https://orcid.org/0000-0003-0680-7799"]},"institutions":[{"id":"https://openalex.org/I4405276025","display_name":"Universitas Kalbis","ror":"https://ror.org/04q03kr42","country_code":"ID","type":"education","lineage":["https://openalex.org/I4405276025"]}],"countries":["ID"],"is_corresponding":true,"raw_author_name":"Muhammad Adrinta Abdurrazzaq","raw_affiliation_strings":["Informatics, Kalbis Institute, Jakarta, Indonesia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Informatics, Kalbis Institute, Jakarta, Indonesia","institution_ids":["https://openalex.org/I4405276025"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5006029119"],"corresponding_institution_ids":["https://openalex.org/I4405276025"],"apc_list":null,"apc_paid":null,"fwci":0.2803,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.55291099,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":"2987","issue":null,"first_page":"020028","last_page":"020028"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9937000274658203,"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":0.9937000274658203,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.0019000000320374966,"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/T10616","display_name":"Smart Agriculture and AI","score":0.0003000000142492354,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/image-regression","display_name":"image regression","score":0.7570000290870667},{"id":"https://openalex.org/keywords/transfer-learning","display_name":"transfer learning","score":0.9729999899864197},{"id":"https://openalex.org/keywords/deep-convolutional-neural-networks","display_name":"deep convolutional neural networks","score":0.8840000033378601},{"id":"https://openalex.org/keywords/model-comparison","display_name":"model comparison","score":0.42100000381469727},{"id":"https://openalex.org/keywords/oversampling","display_name":"oversampling","score":0.6779999732971191},{"id":"https://openalex.org/keywords/fashion-industry","display_name":"fashion industry","score":0.4169999957084656}],"concepts":[{"id":"https://openalex.org/C2778447006","wikidata":"https://www.wikidata.org/wiki/Q1156474","display_name":"Icon","level":2,"score":0.9503394961357117},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7502580285072327},{"id":"https://openalex.org/C2778805511","wikidata":"https://www.wikidata.org/wiki/Q1713","display_name":"Citation","level":2,"score":0.7033204436302185},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.6204980611801147},{"id":"https://openalex.org/C2780154274","wikidata":"https://www.wikidata.org/wiki/Q7126717","display_name":"Download","level":2,"score":0.5754386782646179},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4700043797492981},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.45900675654411316},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.06632381677627563},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1063/5.0199373","doi":"https://doi.org/10.1063/5.0199373","is_oa":false,"landing_page_url":"https://doi.org/10.1063/5.0199373","pdf_url":null,"source":{"id":"https://openalex.org/S2764696622","display_name":"AIP conference proceedings","issn_l":"0094-243X","issn":["0094-243X","1551-7616","1935-0465"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","jufo-1","norway-1"],"host_organization":"https://openalex.org/P4310320257","host_organization_name":"American Institute of Physics","host_organization_lineage":["https://openalex.org/P4310320257"],"host_organization_lineage_names":["American Institute of Physics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"AIP Conference Proceedings","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W2097117768","https://openalex.org/W2108598243","https://openalex.org/W2194775991","https://openalex.org/W2328737873","https://openalex.org/W2887280559","https://openalex.org/W2963163009","https://openalex.org/W2963446712","https://openalex.org/W2964350391","https://openalex.org/W3027320938","https://openalex.org/W3132197188","https://openalex.org/W3171828692","https://openalex.org/W3182706339","https://openalex.org/W3211442754","https://openalex.org/W4225100132","https://openalex.org/W4232721352","https://openalex.org/W6793164127"],"related_works":["https://openalex.org/W3006891127","https://openalex.org/W4324134695","https://openalex.org/W4379875293","https://openalex.org/W2893877690","https://openalex.org/W4399357234","https://openalex.org/W3027022769","https://openalex.org/W6885108398","https://openalex.org/W6922733623","https://openalex.org/W4294405869","https://openalex.org/W2902324157"],"abstract_inverted_index":{"Sneakers":[0],"are":[1,128],"a":[2,15,86,101],"type":[3],"of":[4,54,67,156,177],"shoe":[5],"seen":[6],"not":[7],"only":[8],"as":[9,14,100,120],"ordinary":[10],"footwear":[11],"but":[12],"also":[13],"culture":[16],"and":[17,135],"fashion":[18,28],"style.":[19],"During":[20],"the":[21,27,36,39,52,65,75,108,121,125,161,165,170,174,182],"pandemic,":[22],"all":[23],"industrial":[24],"sectors,":[25],"including":[26],"industry,":[29],"move":[30],"faster":[31],"in":[32,94],"digital":[33],"transformation.":[34],"On":[35],"other":[37,152],"hand,":[38],"sneaker":[40],"resale":[41],"industry":[42],"continues":[43],"to":[44,50,84,90],"proliferate.":[45],"Several":[46],"studies":[47,62],"have":[48,63],"tried":[49,64],"predict":[51],"price":[53],"sneakers":[55,96],"based":[56,73],"on":[57,74,160],"historical":[58],"sales":[59],"data.":[60,172,188],"Other":[61],"problem":[66],"image":[68,99],"regression,":[69],"like":[70],"age":[71],"prediction":[72],"face":[76],"image.":[77],"For":[78],"this":[79,81,142],"reason,":[80],"study":[82],"aims":[83],"create":[85],"deep":[87,113],"learning":[88,110,127],"model":[89,104,123,167,184],"imitate":[91],"expert":[92],"judgment":[93],"assessing":[95],"using":[97,107,185],"its":[98],"feature.":[102],"The":[103,112,137],"is":[105,148],"built":[106],"transfer":[109,126],"method.":[111],"convolutional":[114],"neural":[115],"network":[116],"(DCNN)":[117],"architectures":[118],"used":[119],"base":[122],"for":[124],"VGG19,":[129],"ResNet152V2,":[130],"InceptionV3,":[131],"InceptionResNetV2,":[132],"MobileNetV2,":[133],"DenseNetV2,":[134],"EfficientNetV2L.":[136],"test":[138,162],"results":[139],"show":[140],"that":[141],"study\u2019s":[143],"most":[144],"effective":[145],"DCNN":[146],"architecture":[147],"EfficienNetV2L.":[149],"It":[150],"outperforms":[151],"models.":[153],"RMSE":[154,175],"score":[155,176],"5.255":[157],"was":[158,179],"obtained":[159,180],"data":[163],"with":[164],"trained":[166,183],"without":[168],"oversampling":[169],"training":[171,187],"While":[173],"9.588":[178],"by":[181],"oversampled":[186]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2024-06-05T00:00:00"}
