{"id":"W3128179785","doi":"10.18280/rces.070403","title":"Performance Evaluation of Email Spam Text Classification Using Deep Neural Networks","year":2020,"lang":"en","type":"article","venue":"Review of Computer Engineering Studies","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Spamming; Forum spam; Machine learning; Context (archaeology); Spambot; Filter (signal processing); Artificial neural network; Bag-of-words model; Scripting language; Deep learning; Information retrieval; World Wide Web; The Internet","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000603054,0.0001221263,0.0003409204,0.00004813222,0.00003653549,0.00001341392,0.0002774179,0.00002513514,0.000001351697],"category_scores_gemma":[0.00009193463,0.0001107687,0.00007813094,0.0004766823,0.00001797938,0.0002538502,0.0001254808,0.00008576155,0.000001053423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003930068,"about_ca_system_score_gemma":0.00001398125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001193922,"about_ca_topic_score_gemma":1.172585e-7,"domain_scores_codex":[0.9989036,0.00006426606,0.000385727,0.0001937477,0.0003380405,0.0001145966],"domain_scores_gemma":[0.9990863,0.00007004777,0.000210165,0.0002302008,0.0003719783,0.0000313562],"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":[0.000001267718,0.000009378876,0.0002634747,0.003461617,0.00006427686,2.013614e-7,0.0003501301,0.7558985,0.000189433,0.0001772709,0.00007211664,0.2395123],"study_design_scores_gemma":[0.00009878079,0.00006931187,0.004450701,0.001711123,0.00006230814,0.000003459854,0.000003328173,0.9932253,0.000154698,0.000003029465,0.0001223919,0.00009560047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04443059,0.08074747,0.8736542,0.0002929029,0.0005505211,0.0002390936,2.190993e-7,0.00007296033,0.00001199597],"genre_scores_gemma":[0.9638401,0.009619894,0.02618823,0.0001289539,0.0002046472,0.000009442394,0.000001040825,0.000007454293,2.073764e-7],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9194095,"threshold_uncertainty_score":0.4517017,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08941783105988464,"score_gpt":0.3050270855882377,"score_spread":0.215609254528353,"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."}}