{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001545651,0.0001869016,0.0002109472,0.0002419087,0.00006649037,0.0002449135,0.003927411,0.0001209846,0.00001576718],"category_scores_gemma":[0.00005270027,0.000172984,0.0000373416,0.0008863132,0.00003379203,0.0004810697,0.00175221,0.0002739693,0.00015387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008889263,"about_ca_system_score_gemma":0.00006056898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004055984,"about_ca_topic_score_gemma":0.00002341794,"domain_scores_codex":[0.9983559,0.00007855095,0.0002796839,0.0008182064,0.0002096985,0.0002579852],"domain_scores_gemma":[0.9970253,0.00005661737,0.00008176845,0.002591545,0.0001653161,0.00007948857],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002727695,0.0001559767,0.00009646295,0.00004874278,0.00008311507,0.000005588035,0.0004702206,0.000005729108,0.005274755,0.07775623,0.7622884,0.1537875],"study_design_scores_gemma":[0.000203723,0.0000502665,0.002071723,0.00003358103,0.000007954406,0.00001564532,0.00006836844,0.02595154,0.02147522,0.0007617727,0.9491459,0.0002142902],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005573019,0.0001656417,0.9850746,0.003066756,0.001198233,0.0003892557,0.00004864457,0.0007323754,0.008767238],"genre_scores_gemma":[0.3885143,0.00002660093,0.597106,0.008524177,0.0006689874,0.0001866044,0.0003833331,0.00002525134,0.00456469],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3879685,"threshold_uncertainty_score":0.7298172,"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."}}