{"id":"W4220674692","doi":"10.1109/icais53314.2022.9742830","title":"An Analysis on Fraud Detection in Credit Card Transactions using Machine Learning Techniques","year":2022,"lang":"en","type":"article","venue":"2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS)","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Credit card; Decision tree; Random forest; Computer science; Credit card fraud; Payment; Digitization; Feature selection; Classifier (UML); Machine learning; Artificial intelligence; Computer security; World Wide Web","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005602019,0.0002218435,0.0002610093,0.001244307,0.0004726907,0.0002965971,0.000912395,0.00008758,0.001800463],"category_scores_gemma":[0.00003554309,0.0002479054,0.0001081422,0.00103511,0.00008085208,0.0006753822,0.0001131898,0.0006130281,0.000006505877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002816253,"about_ca_system_score_gemma":0.00007826228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00125907,"about_ca_topic_score_gemma":0.002897507,"domain_scores_codex":[0.9976006,0.0003142482,0.0005270372,0.0007564962,0.0005468496,0.000254728],"domain_scores_gemma":[0.998979,0.00009294362,0.0002067911,0.0004667314,0.0001577959,0.00009669137],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001784353,0.0006000441,0.000511781,0.000005074171,0.0001647942,0.00002919251,0.0008212644,0.02273455,0.0839383,0.5317621,0.00001824248,0.3592362],"study_design_scores_gemma":[0.00002678738,0.0003942963,0.0002030489,0.000007788494,0.00001538822,0.000008216728,0.0003560596,0.795222,0.1913379,0.007243419,0.004943762,0.0002413107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0355643,0.00001804463,0.9610826,0.0005406148,0.0004412677,0.0001255052,0.0001129522,0.0002627838,0.001851881],"genre_scores_gemma":[0.9956931,0.0001020382,0.003103333,0.0004709908,0.00006353479,0.0001531585,0.0001520243,0.00001462892,0.0002472081],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9601288,"threshold_uncertainty_score":0.9999973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05931907610652261,"score_gpt":0.3084561019291918,"score_spread":0.2491370258226692,"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."}}