{"id":"W4413394506","doi":"10.1109/iaecst64597.2024.11117868","title":"Telecom Fraud Detection Based on Deep Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Telecommunications; Computer science; Deep learning; Computer security; Artificial intelligence","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":[0.001057904,0.0008553348,0.0007030054,0.002456955,0.0004685136,0.001211807,0.0009652997,0.0008768418,0.0007149858],"category_scores_gemma":[0.002954137,0.0002572187,0.0005534411,0.001807975,0.0004338678,0.001792873,0.001061722,0.001335139,0.0003626682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001087724,"about_ca_system_score_gemma":0.0008577912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005800029,"about_ca_topic_score_gemma":0.004989293,"domain_scores_codex":[0.9993783,0.0001306486,0.0000454039,0.0001290884,0.0001864063,0.0001300617],"domain_scores_gemma":[0.9989163,0.0003428378,0.00024789,0.0001361571,0.0002830312,0.00007380916],"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":[0.0005094283,0.0007771007,0.02850864,0.00008579519,0.0002174268,0.0003329949,0.0001265188,0.2707305,0.009593437,0.006757266,0.008406915,0.673954],"study_design_scores_gemma":[0.000003768641,0.00001679221,0.0008891755,0.000004295423,0.000006804612,0.00002054553,0.000008746782,0.995943,0.001125187,0.001668023,0.0003091595,0.00000450476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3775438,0.001403063,0.6109363,0.001564316,0.0001998411,0.0001325741,0.0006623336,0.002266696,0.005291111],"genre_scores_gemma":[0.9556051,0.0002988358,0.04176716,0.0001745031,0.00005722024,0.00003567233,0.0005509462,0.00002355593,0.001487107],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005800029,"threshold_uncertainty_score":0.01153255,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01005799193352516,"score_gpt":0.2477298961950716,"score_spread":0.2376719042615464,"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."}}