{"id":"W4410311362","doi":"10.18280/ijsse.150305","title":"Proactive MiDLAF: A Novel Mining MinHash-Deep Learning Approach for Advanced Spam Email Filtering","year":2025,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Data science","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":[0.0003991883,0.0001172291,0.000177146,0.0002523749,0.00008620905,0.000145279,0.0004022369,0.00005825239,0.000001244132],"category_scores_gemma":[0.0002401379,0.0001193967,0.00009331677,0.0001464921,0.00001281297,0.0006547901,0.0001116681,0.0002622421,1.192696e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009318956,"about_ca_system_score_gemma":0.00003079903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004285318,"about_ca_topic_score_gemma":0.00000126734,"domain_scores_codex":[0.9991301,0.00001244074,0.0003237528,0.0001722062,0.0002165907,0.0001449357],"domain_scores_gemma":[0.9992861,0.0001786007,0.0001636893,0.00007100439,0.0002484776,0.00005211919],"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":[0.001115478,0.0003160211,0.0004637013,0.000340345,0.00125517,0.00003816064,0.01675228,0.6786886,0.0535304,0.04512214,0.0000483882,0.2023293],"study_design_scores_gemma":[0.001281909,0.0001085028,0.0007216496,0.0002134917,0.00001715721,0.0001527887,0.0002408004,0.9872108,0.004398059,0.0003146894,0.005183158,0.0001570469],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03487286,0.000349695,0.9629865,0.0003414408,0.001017006,0.00008945357,0.000002424059,0.00003796641,0.0003026316],"genre_scores_gemma":[0.8573616,0.00008384674,0.1422677,0.00004176112,0.0001888616,0.000006069113,0.000002625676,0.000007164444,0.00004039006],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8224888,"threshold_uncertainty_score":0.4868858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00695032229255905,"score_gpt":0.2279943703622339,"score_spread":0.2210440480696748,"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."}}