{"id":"W4416021517","doi":"10.36948/ijfmr.2025.v07i06.59649","title":"Securing Credit: A Hybrid multi-dimensional model using ensemble machine learning classifier with data sampling to detect and prevent credit card fraud.","year":2025,"lang":"","type":"article","venue":"International Journal For Multidisciplinary Research","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Oversampling; Credit card fraud; Ensemble learning; Credit card; Classifier (UML); k-nearest neighbors algorithm; Ensemble forecasting","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","sts","scholarly_communication","open_science","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.006814805,0.0005985949,0.0006303342,0.002157481,0.002786927,0.002053718,0.005265391,0.000227888,0.00001730604],"category_scores_gemma":[0.001808931,0.0005594777,0.0001708216,0.0008855851,0.0004017775,0.00243083,0.01030303,0.002852665,0.000009293996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001272285,"about_ca_system_score_gemma":0.002080391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001529787,"about_ca_topic_score_gemma":0.0001258917,"domain_scores_codex":[0.9917882,0.0006382019,0.001336302,0.002033378,0.002866852,0.001337075],"domain_scores_gemma":[0.9921123,0.001407721,0.0005246824,0.001508493,0.00380802,0.0006387635],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.006731334,0.001510174,0.003303826,0.0006041066,0.002340892,0.000771082,0.00419672,0.6056737,0.1562353,0.002408047,0.008486261,0.2077386],"study_design_scores_gemma":[0.002002555,0.0005198035,0.0005871535,0.001923395,0.00006116107,0.0006976373,0.0002167346,0.9743959,0.01073615,0.004689793,0.003621327,0.000548332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0602461,0.001141693,0.9294189,0.004777364,0.001597681,0.001504248,0.001145475,0.0001116832,0.00005687181],"genre_scores_gemma":[0.437755,0.0003866575,0.5590333,0.00009114945,0.0006808816,0.0001158542,0.0002581533,0.00007755297,0.001601431],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3775089,"threshold_uncertainty_score":0.9996856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2499298905329593,"score_gpt":0.4748435481249088,"score_spread":0.2249136575919495,"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."}}