{"id":"W4388413877","doi":"10.18280/isi.280505","title":"Performance Enhancement in Facial Emotion Classification Through Noise-Injected FERCNN Model: A Comparative Analysis","year":2023,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Noise (video); Psychology; Facial expression; Computer science; Speech recognition; Artificial intelligence; Pattern recognition (psychology)","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":[0.0003805775,0.0001754713,0.0002615276,0.0007949233,0.0002594166,0.0002562394,0.0003175691,0.0001127351,0.00001760126],"category_scores_gemma":[0.00004000255,0.000170905,0.00008329635,0.003411669,0.00004810137,0.006607818,0.00009858613,0.0001338928,0.0004936247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002932276,"about_ca_system_score_gemma":0.00007713025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005969161,"about_ca_topic_score_gemma":0.0000202971,"domain_scores_codex":[0.9983128,0.00008193823,0.0006517305,0.0002317327,0.0004057502,0.0003160521],"domain_scores_gemma":[0.9990612,0.00003655852,0.0002864808,0.0003096632,0.0002579921,0.0000480581],"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":[0.0002769337,0.000278282,0.01637665,0.0005920347,0.0003688908,0.000004403106,0.260531,0.4271024,0.01230895,0.009621522,0.00245913,0.2700798],"study_design_scores_gemma":[0.0003006407,0.00005884073,0.07666049,0.00007405716,0.00002049547,0.000001257018,0.0007880555,0.9175383,0.002972464,0.001294642,0.0001054092,0.0001853704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6054448,0.000003664885,0.3902,0.00006123738,0.0001086871,0.0002445507,0.000006468284,0.0002302433,0.003700286],"genre_scores_gemma":[0.9926711,0.00007121822,0.006431455,0.0001001533,0.00001529395,0.0001668743,0.00048569,0.000003722227,0.00005449324],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4904358,"threshold_uncertainty_score":0.6969303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05101750661093455,"score_gpt":0.2873030949020892,"score_spread":0.2362855882911547,"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."}}