{"id":"W2525985184","doi":"10.22360/springsim.2016.cns.009","title":"Credit Card Fraud Detection Using Fuzzy Logic and Neural Network","year":2016,"lang":"en","type":"article","venue":"","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Credit card fraud; Credit card; Computer science; Fuzzy logic; Defuzzification; Data mining; Toolbox; Database transaction; Artificial intelligence; Neuro-fuzzy; Artificial neural network; Fuzzy electronics; Machine learning; Fuzzy set operations; Fuzzy set; Fuzzy control system; Fuzzy number; Database; Payment","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009583941,0.0004377359,0.0005578299,0.002328489,0.0004927738,0.001157724,0.0005629585,0.0007919318,0.001125606],"category_scores_gemma":[0.002705761,0.0002244977,0.0005023034,0.001473925,0.0002632484,0.001143727,0.0003430726,0.0005072617,0.0002746494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001057903,"about_ca_system_score_gemma":0.000512079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01177381,"about_ca_topic_score_gemma":0.005580812,"domain_scores_codex":[0.9993786,0.0001327629,0.00005887741,0.0001001218,0.0002674378,0.00006220367],"domain_scores_gemma":[0.9991258,0.000356267,0.0001270628,0.00004899907,0.0003147445,0.00002703739],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008906818,0.0004735534,0.01999951,0.0001854472,0.0001976151,0.0003210605,0.0001555068,0.394584,0.01340056,0.003771589,0.00250048,0.56352],"study_design_scores_gemma":[0.000005602157,0.00002941149,0.002115298,0.00001276563,0.00001114934,0.00003939338,0.00002182598,0.9944392,0.002249704,0.000719803,0.000346109,0.000009775303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4376823,0.001973931,0.5474845,0.000809322,0.0002402033,0.000184363,0.0004357203,0.001185991,0.01000379],"genre_scores_gemma":[0.90464,0.0004472558,0.09249208,0.00007137206,0.00004033125,0.00004647024,0.0002261003,0.00001261269,0.002023786],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01177381,"threshold_uncertainty_score":0.02341056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03607747549557878,"score_gpt":0.2651603145207336,"score_spread":0.2290828390251549,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}