{"id":"W4313398126","doi":"10.1016/j.neucom.2022.12.027","title":"An optimized fuzzy deep learning model for data classification based on NSGA-II","year":2022,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":87,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"Engineering and Physical Sciences Research Council; International University of Korea","keywords":"Artificial intelligence; Computer science; Fuzzy logic; Deep learning; Pattern recognition (psychology); Machine learning","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":[0.0007768113,0.0007212242,0.001148673,0.000511965,0.0004995822,0.0007669824,0.001264315,0.001236345,0.002430494],"category_scores_gemma":[0.001720047,0.0004428335,0.0007374018,0.0005167307,0.0003870809,0.0006393215,0.0007380606,0.001209925,0.0003999689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001030165,"about_ca_system_score_gemma":0.002327889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02123225,"about_ca_topic_score_gemma":0.01825127,"domain_scores_codex":[0.9996909,0.00007629509,0.00002134895,0.00006701686,0.00008176857,0.00006270813],"domain_scores_gemma":[0.9995866,0.0001644151,0.00002736092,0.00002370875,0.0001720548,0.00002578963],"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":[0.00005819877,0.0000388727,0.0003725823,0.00003091887,0.00002863113,0.00002702481,0.00002125561,0.9516147,0.000899617,0.001946391,0.0008774697,0.04408433],"study_design_scores_gemma":[0.000003548459,0.000006935286,0.00003414022,0.000002704027,0.00000255601,0.000002787468,0.000001733286,0.9994241,0.000113903,0.0003356256,0.00007062269,0.000001390536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03509193,0.0003525688,0.9607412,0.0002224746,0.00009307205,0.00008913673,0.00009276756,0.0005433446,0.002773571],"genre_scores_gemma":[0.7250817,0.000232069,0.2676665,0.0002894093,0.00005105451,0.0004727585,0.0003765943,0.00009231835,0.005737506],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02123225,"threshold_uncertainty_score":0.04221731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06926954337449999,"score_gpt":0.284647580213841,"score_spread":0.215378036839341,"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."}}