{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00342215,0.002057994,0.001506299,0.002947715,0.0006859969,0.001090814,0.001470624,0.00195089,0.001422226],"category_scores_gemma":[0.005088059,0.0003376087,0.0007839712,0.001422315,0.0004114857,0.001593319,0.0009987958,0.001170009,0.001107969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001828228,"about_ca_system_score_gemma":0.001152784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01555005,"about_ca_topic_score_gemma":0.0114418,"domain_scores_codex":[0.9982319,0.0004568763,0.000232454,0.0003549507,0.0004199678,0.0003039328],"domain_scores_gemma":[0.9972796,0.001087015,0.0002332528,0.0001878181,0.001019708,0.0001925367],"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":[0.004499434,0.002557191,0.03303213,0.001181828,0.0007277789,0.0004853605,0.0002306776,0.2199632,0.01237323,0.0009905518,0.02975554,0.694203],"study_design_scores_gemma":[0.00004804971,0.0003210065,0.003005244,0.00004096499,0.00007197861,0.0000587965,0.00007922209,0.98729,0.007639144,0.0003198428,0.001107719,0.00001800023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9143609,0.01408301,0.04643369,0.001537525,0.001096187,0.0003468123,0.002733862,0.008996751,0.0104113],"genre_scores_gemma":[0.9547224,0.001855978,0.02906017,0.0004558371,0.0002176603,0.0001521713,0.00687839,0.00009606606,0.006561319],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01555005,"threshold_uncertainty_score":0.03091908,"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."}}