{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001611221,0.00008015898,0.00008143975,0.00004440824,0.000100982,0.00008543513,0.0002959554,0.00005432052,0.000007061445],"category_scores_gemma":[0.00003150754,0.00005261328,0.00001824824,0.0001836126,0.00004625318,0.0006057807,0.0001813401,0.00004820309,0.00001097292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000391245,"about_ca_system_score_gemma":0.00001210941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002632408,"about_ca_topic_score_gemma":0.000008588483,"domain_scores_codex":[0.9992495,0.00004110763,0.0001260807,0.000279755,0.0001146726,0.0001889386],"domain_scores_gemma":[0.9993725,0.00005257408,0.00005999433,0.0004176455,0.00004758979,0.00004965817],"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":[0.000009952392,0.00002097128,0.004284739,0.000009711824,0.00001322788,0.00000751675,0.00005740149,0.00009530028,0.1383991,0.1032987,0.004969373,0.748834],"study_design_scores_gemma":[0.001011108,0.0004493735,0.06444393,0.00009897612,0.00002437925,0.0002749627,0.00002509812,0.5290879,0.1861465,0.1865019,0.03068649,0.00124938],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0134051,0.00004470161,0.9840927,0.0006599161,0.0002983491,0.00008738582,0.00000178616,0.0005066465,0.000903448],"genre_scores_gemma":[0.8650832,0.00001942576,0.1342671,0.0003184906,0.0001725009,0.000006922246,5.9596e-7,0.000004886184,0.0001269552],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8516781,"threshold_uncertainty_score":0.2145507,"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."}}