{"id":"W4400771743","doi":"10.1109/eiceeai60672.2023.10590608","title":"Deep Feature Extraction Framework Based on DNN for Enhancing Mirai Attachment Classification in Machine Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Feature extraction; Artificial intelligence; Machine learning; Artificial neural network; Deep learning; Pattern recognition (psychology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003769575,0.00009851186,0.00009050839,0.0002293115,0.0001403425,0.00009151943,0.0001618493,0.0001264523,0.00003399295],"category_scores_gemma":[0.0001703148,0.00008641495,0.00004834527,0.0004918814,0.000005282264,0.0002516999,0.00002803641,0.0002754814,0.0001508213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007040622,"about_ca_system_score_gemma":0.00002042521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001104343,"about_ca_topic_score_gemma":0.00007071476,"domain_scores_codex":[0.9990486,0.00006349036,0.0001496254,0.0003303059,0.0002009048,0.0002070867],"domain_scores_gemma":[0.999199,0.0004469018,0.00007031875,0.0002012871,0.00003880999,0.00004369884],"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":[0.0002666924,0.0005686062,0.00921414,0.0002296602,0.00002212358,0.00002226385,0.003012035,0.1521223,0.1479405,0.0127561,0.01312216,0.6607234],"study_design_scores_gemma":[0.0002687653,0.00008627443,0.006203258,0.0001170972,0.000001859163,4.962609e-7,0.0001440342,0.9771035,0.01104659,0.0009992437,0.003915386,0.0001134486],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01215115,0.00001363649,0.9811116,0.005236841,0.00032036,0.00027077,8.239462e-7,0.0003234457,0.0005713542],"genre_scores_gemma":[0.9215136,0.00001558301,0.07648764,0.0007339349,0.00006287351,0.000107133,0.00007951192,0.00001091585,0.000988769],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9093625,"threshold_uncertainty_score":0.3523899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0315724810496687,"score_gpt":0.32149157876066,"score_spread":0.2899190977109913,"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."}}