{"id":"https://openalex.org/W7082977785","doi":"https://doi.org/10.32604/cmc.2025.067241","title":"Credit Card Fraud Detection Method Based on RF-WGAN-TCN","display_name":"Credit Card Fraud Detection Method Based on RF-WGAN-TCN","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W7082977785","doi":"https://doi.org/10.32604/cmc.2025.067241"},"language":"en","primary_location":{"id":"doi:10.32604/cmc.2025.067241","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.067241","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials &amp; Continua","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.32604/cmc.2025.067241","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Ao Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ao Zhang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Hongzhen Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hongzhen Xu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Ruxin Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ruxin Liu","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"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":0.711105,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"85","issue":"3","first_page":"5159","last_page":"5181"},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.6341999769210815,"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/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.6341999769210815,"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/T13067","display_name":"Geological Modeling and Analysis","score":0.026799999177455902,"subfield":{"id":"https://openalex.org/subfields/1906","display_name":"Geochemistry and Petrology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14311","display_name":"Electrical and Electromagnetic Research","score":0.02199999988079071,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/credit-card-fraud","display_name":"Credit card fraud","score":0.8091999888420105},{"id":"https://openalex.org/keywords/credit-card","display_name":"Credit card","score":0.600600004196167},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5666000247001648},{"id":"https://openalex.org/keywords/softmax-function","display_name":"Softmax function","score":0.5393999814987183},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5203999876976013},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.48069998621940613},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.44600000977516174},{"id":"https://openalex.org/keywords/oversampling","display_name":"Oversampling","score":0.39399999380111694},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.37450000643730164}],"concepts":[{"id":"https://openalex.org/C2780747020","wikidata":"https://www.wikidata.org/wiki/Q83873","display_name":"Credit card fraud","level":4,"score":0.8091999888420105},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7128999829292297},{"id":"https://openalex.org/C2983355114","wikidata":"https://www.wikidata.org/wiki/Q161380","display_name":"Credit card","level":3,"score":0.600600004196167},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5666000247001648},{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.5393999814987183},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5203999876976013},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.48069998621940613},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.44600000977516174},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4244999885559082},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3978999853134155},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39469999074935913},{"id":"https://openalex.org/C197323446","wikidata":"https://www.wikidata.org/wiki/Q331222","display_name":"Oversampling","level":3,"score":0.39399999380111694},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.37450000643730164},{"id":"https://openalex.org/C178350159","wikidata":"https://www.wikidata.org/wiki/Q162714","display_name":"Credit risk","level":2,"score":0.3504999876022339},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.33640000224113464},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.3328000009059906},{"id":"https://openalex.org/C149728462","wikidata":"https://www.wikidata.org/wiki/Q751319","display_name":"Minimax","level":2,"score":0.3287999927997589},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.320499986410141},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.31709998846054077},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.31119999289512634},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.28060001134872437},{"id":"https://openalex.org/C53811970","wikidata":"https://www.wikidata.org/wiki/Q5062194","display_name":"Centrality","level":2,"score":0.27250000834465027},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.26660001277923584},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.26589998602867126},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.2621000111103058},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.25999999046325684},{"id":"https://openalex.org/C7797323","wikidata":"https://www.wikidata.org/wiki/Q3798612","display_name":"Pointwise mutual information","level":3,"score":0.2556999921798706}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.32604/cmc.2025.067241","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.067241","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials &amp; Continua","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.32604/cmc.2025.067241","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.067241","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials &amp; Continua","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.5952972173690796}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W2067050450","https://openalex.org/W2148143831","https://openalex.org/W2338318698","https://openalex.org/W2861780491","https://openalex.org/W2911964244","https://openalex.org/W2958026736","https://openalex.org/W3048478996","https://openalex.org/W3095164600","https://openalex.org/W3138690044","https://openalex.org/W3185324814","https://openalex.org/W3195056262","https://openalex.org/W3203380512","https://openalex.org/W3208379990","https://openalex.org/W4200457345","https://openalex.org/W4200465265","https://openalex.org/W4206464561","https://openalex.org/W4210242534","https://openalex.org/W4214628066","https://openalex.org/W4285033264","https://openalex.org/W4294192778","https://openalex.org/W4307392573","https://openalex.org/W4321377565","https://openalex.org/W4362669616","https://openalex.org/W4364361380","https://openalex.org/W4387092382","https://openalex.org/W4391092624"],"related_works":[],"abstract_inverted_index":{"Credit":[0],"card":[1,20,36,88,107],"fraud":[2,37,89,173],"is":[3,22,102,109,144,157,188],"one":[4],"of":[5,9,17,78,168],"the":[6,48,60,83,105,111,121,130,136,148,154,160,164,172,177,183,192,196,202,208,214,218,234],"primary":[7],"sources":[8],"operational":[10],"risk":[11],"in":[12,34,52,217,225],"banks,":[13],"and":[14,44,59,128,162,170,195,223,231,240,245],"accurate":[15],"prediction":[16,243],"fraudulent":[18],"credit":[19,35,87,106],"transactions":[21],"essential":[23],"to":[24,76,146,190],"minimize":[25],"banks\u2019":[26],"economic":[27],"losses.":[28],"Two":[29],"key":[30],"issues":[31],"are":[32,115,126,133,180,199],"faced":[33],"detection":[38,90],"research,":[39],"i.e.,":[40],"data":[41,45,108,156],"category":[42],"imbalance":[43],"drift.":[46],"However,":[47],"oversampling":[49],"algorithm":[50],"used":[51],"current":[53],"research":[54],"suffers":[55,69],"from":[56,70],"excessive":[57],"noise,":[58],"Long":[61],"Short-Term":[62],"Memory":[63],"Network":[64,100,142,152],"(LSTM)":[65],"based":[66,92],"temporal":[67,184],"model":[68,79],"gradient":[71],"dispersion,":[72],"which":[73],"can":[74],"lead":[75],"loss":[77],"performance.":[80],"To":[81],"address":[82],"above":[84],"problems,":[85],"a":[86],"method":[91,215],"on":[93,207],"Random":[94,118],"Forest-Wasserstein":[95],"Generative":[96,140,150],"Adversarial":[97,141,151],"Network-Temporal":[98],"Convolutional":[99],"(RF-WGAN-TCN)":[101],"proposed.":[103],"First,":[104],"preprocessed,":[110],"feature":[112],"importance":[113,125],"scores":[114],"calculated":[116],"by":[117],"Forest":[119],"(RF),":[120],"features":[122,132],"with":[123,249],"lower":[124],"eliminated,":[127],"then":[129],"remaining":[131],"standardized.":[134],"Second,":[135],"Wasserstein":[137,149],"Distance":[138],"Improvement":[139],"(GAN)":[143],"introduced":[145],"construct":[147],"(WGAN),":[153],"preprocessed":[155],"input":[158],"into":[159],"WGAN,":[161],"under":[163],"mutual":[165],"game":[166],"training":[167],"generator":[169],"discriminator,":[171],"samples":[174],"that":[175,213],"meet":[176],"target":[178],"distribution":[179],"obtained.":[181],"Finally,":[182],"convolutional":[185],"network":[186],"(TCN)":[187],"utilized":[189],"extract":[191],"long-time":[193],"dependencies,":[194],"classification":[197,246],"results":[198,206],"output":[200],"through":[201],"Softmax":[203],"layer.":[204],"Experimental":[205],"European":[209],"cardholder":[210],"dataset":[211],"show":[212],"proposed":[216],"paper":[219],"achieves":[220],"91.96%,":[221],"98.22%,":[222],"81.95%":[224],"F1-Score,":[226],"Area":[227,232],"Under":[228,233],"Curve":[229,236],"(AUC),":[230],"Precision-Recall":[235],"(AUPRC)":[237],"metrics,":[238],"respectively,":[239],"has":[241],"higher":[242],"accuracy":[244],"performance":[247],"compared":[248],"existing":[250],"mainstream":[251],"methods.":[252]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
