{"id":"https://openalex.org/W4381389710","doi":"https://doi.org/10.1145/3587828.3587860","title":"A Fraud Detection System Using Decision Trees Classification in An Online Transactions","display_name":"A Fraud Detection System Using Decision Trees Classification in An Online Transactions","publication_year":2023,"publication_date":"2023-02-23","ids":{"openalex":"https://openalex.org/W4381389710","doi":"https://doi.org/10.1145/3587828.3587860"},"language":"en","primary_location":{"id":"doi:10.1145/3587828.3587860","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3587828.3587860","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 12th International Conference on Software and Computer Applications","raw_type":"proceedings-article"},"type":"article","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/A5022832812","display_name":"Yousef Qawqzeh","orcid":"https://orcid.org/0000-0001-7774-062X"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yousef K Qawqzeh","raw_affiliation_strings":["Information Technology College, University of Fujairah, UAE"],"raw_orcid":"https://orcid.org/0000-0001-7774-062X","affiliations":[{"raw_affiliation_string":"Information Technology College, University of Fujairah, UAE","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101775774","display_name":"Mahwish Ashraf","orcid":"https://orcid.org/0009-0005-4045-4378"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mahwish Ashraf","raw_affiliation_strings":["Information Technology College,, University of Fujairah, UAE"],"raw_orcid":"https://orcid.org/0009-0005-4045-4378","affiliations":[{"raw_affiliation_string":"Information Technology College,, University of Fujairah, UAE","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5022832812"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.852,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.781978,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"212","last_page":"217"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9994000196456909,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9994000196456909,"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9679999947547913,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12519","display_name":"Cybercrime and Law Enforcement Studies","score":0.9534000158309937,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.7622777223587036},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.7562573552131653},{"id":"https://openalex.org/keywords/credit-card","display_name":"Credit card","score":0.6753252148628235},{"id":"https://openalex.org/keywords/database-transaction","display_name":"Database transaction","score":0.6498518586158752},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6194000244140625},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6144452691078186},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5938770174980164},{"id":"https://openalex.org/keywords/credit-card-fraud","display_name":"Credit card fraud","score":0.5849136114120483},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5093942284584045},{"id":"https://openalex.org/keywords/decision-tree-learning","display_name":"Decision tree learning","score":0.4634869396686554},{"id":"https://openalex.org/keywords/f1-score","display_name":"F1 score","score":0.4574197828769684},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.4482090473175049},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.4377286732196808},{"id":"https://openalex.org/keywords/statistical-classification","display_name":"Statistical classification","score":0.43500617146492004},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.41760963201522827},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3427906632423401},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.12886381149291992},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.11701002717018127},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.08903607726097107}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7622777223587036},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.7562573552131653},{"id":"https://openalex.org/C2983355114","wikidata":"https://www.wikidata.org/wiki/Q161380","display_name":"Credit card","level":3,"score":0.6753252148628235},{"id":"https://openalex.org/C75949130","wikidata":"https://www.wikidata.org/wiki/Q848010","display_name":"Database transaction","level":2,"score":0.6498518586158752},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6194000244140625},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6144452691078186},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5938770174980164},{"id":"https://openalex.org/C2780747020","wikidata":"https://www.wikidata.org/wiki/Q83873","display_name":"Credit card fraud","level":4,"score":0.5849136114120483},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5093942284584045},{"id":"https://openalex.org/C5481197","wikidata":"https://www.wikidata.org/wiki/Q16766476","display_name":"Decision tree learning","level":3,"score":0.4634869396686554},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.4574197828769684},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.4482090473175049},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.4377286732196808},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.43500617146492004},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.41760963201522827},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3427906632423401},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.12886381149291992},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.11701002717018127},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.08903607726097107},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C145097563","wikidata":"https://www.wikidata.org/wiki/Q1148747","display_name":"Payment","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3587828.3587860","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3587828.3587860","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 12th International Conference on Software and Computer Applications","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7300000190734863,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1517113043","https://openalex.org/W2032435122","https://openalex.org/W2086457091","https://openalex.org/W2522604709","https://openalex.org/W2772947247","https://openalex.org/W2785726556","https://openalex.org/W2983924154","https://openalex.org/W3015795707","https://openalex.org/W3149292517","https://openalex.org/W3150250798","https://openalex.org/W3159916456","https://openalex.org/W3207285437","https://openalex.org/W4285253854","https://openalex.org/W4307549181","https://openalex.org/W4312188033"],"related_works":["https://openalex.org/W4315815629","https://openalex.org/W4388305876","https://openalex.org/W3205325062","https://openalex.org/W2385238472","https://openalex.org/W2623725391","https://openalex.org/W2795722355","https://openalex.org/W2592504384","https://openalex.org/W765607776","https://openalex.org/W2359364539","https://openalex.org/W2155850863"],"abstract_inverted_index":{"Individuals":[0],"and":[1,19,29,57,78,104,141,156],"businesses":[2],"are":[3],"frequently":[4],"seen":[5],"engaging":[6],"in":[7,13,106,128,161],"a":[8,32,40,46,75,116,125,146],"fraud":[9,66],"scheme,":[10],"which":[11],"results":[12,100],"the":[14,94,148],"loss":[15],"of":[16,31,130,150],"funds,":[17],"rights,":[18],"assets.":[20],"This":[21,122],"paper":[22],"aims":[23],"to":[24,54,73,82,115],"provide":[25],"an":[26,107,162],"empirical":[27],"analysis":[28],"study":[30],"supervised":[33,60],"learning":[34,62],"technique,":[35],"decision":[36,95],"trees":[37],"(DT),":[38],"on":[39],"credit":[41],"card":[42],"transaction":[43,109],"dataset":[44],"as":[45,134],"benchmark.":[47],"The":[48,65,98],"proposed":[49],"approach":[50],"can":[51,112,153],"be":[52,113],"employed":[53],"reduce":[55,154],"FPs":[56,103,155],"FNs":[58,105],"through":[59],"machine":[61],"(ML)":[63],"technique.":[64],"detection":[67],"system":[68],"also":[69],"uses":[70],"historical":[71],"data":[72],"construct":[74],"training":[76],"set":[77],"then":[79],"analyses":[80],"it":[81],"identify":[83],"fraudulent":[84],"activity.":[85],"In":[86],"this":[87],"study,":[88],"suspicious":[89],"activity":[90,111],"is":[91],"detected":[92],"using":[93,119],"tree":[96],"classifier.":[97],"obtained":[99],"showed":[101],"that":[102],"online":[108,163],"or":[110],"reduced":[114],"large":[117],"extent":[118],"DT":[120],"classification.":[121],"method":[123],"achieved":[124],"promising":[126],"result":[127],"terms":[129],"performance":[131],"measures":[132],"such":[133],"precision":[135],"(99.7%),":[136],"accuracy":[137],"(92.25%),":[138],"recall":[139],"(81.49%),":[140],"F1_score":[142],"(86.47%)":[143],"respectively.":[144],"As":[145],"result,":[147],"use":[149],"ML":[151],"classifiers":[152],"FNs,":[157],"increasing":[158],"customer":[159],"satisfaction":[160],"transaction.":[164]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2023,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}