{"id":"W7117450253","doi":"10.3390/fi18010015","title":"Wangiri Fraud Detection: A Comprehensive Approach to Unlabeled Telecom Data","year":2025,"lang":"en","type":"article","venue":"Future Internet","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Exploit; Pipeline (software); Scalability; Random forest; Feature (linguistics); Class (philosophy); Multilayer perceptron; Perceptron; Feature engineering","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":[0.003116741,0.0009899447,0.0009603863,0.004125786,0.00100314,0.001663412,0.001584629,0.001320124,0.0006757784],"category_scores_gemma":[0.00878742,0.0003062194,0.0006754276,0.002682683,0.0005170045,0.001786649,0.001491415,0.001562032,0.0006879767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008128514,"about_ca_system_score_gemma":0.001407701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004779199,"about_ca_topic_score_gemma":0.007516277,"domain_scores_codex":[0.9979441,0.0005361158,0.0001704436,0.0005464354,0.00063685,0.0001660502],"domain_scores_gemma":[0.9956982,0.001294391,0.0007434494,0.00103764,0.001041493,0.0001848854],"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":[0.000663016,0.0017968,0.1064514,0.000330013,0.0002664543,0.0005706,0.0006587508,0.1451859,0.01785778,0.008986649,0.0238615,0.6933711],"study_design_scores_gemma":[0.00002564517,0.0001344685,0.01729246,0.0000656485,0.00003819567,0.0002039337,0.0002927805,0.9517776,0.008903762,0.009100526,0.0121279,0.00003710216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.28308,0.00109362,0.6893931,0.001589096,0.0001798999,0.0008856617,0.01199704,0.006046667,0.005734968],"genre_scores_gemma":[0.587527,0.0003312688,0.3883365,0.0003705512,0.0001619408,0.0004408878,0.02007742,0.0001493969,0.002604949],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004779199,"threshold_uncertainty_score":0.01648313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02906814930040017,"score_gpt":0.2891008305894757,"score_spread":0.2600326812890755,"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."}}