{"id":"W4390071945","doi":"10.1109/iccsm60247.2023.00019","title":"Hybrid Differential Evolution and Particle Swarm Optimization for Speech Emotion Classification","year":2023,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"","keywords":"Particle swarm optimization; Differential evolution; Computer science; Differential (mechanical device); Artificial intelligence; Speech recognition; Multi-swarm optimization; Metaheuristic; Machine learning; Engineering; Aerospace engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004369157,0.00009278533,0.0001014899,0.0001695413,0.0001998371,0.0002488867,0.0002352431,0.00003941713,0.00005670482],"category_scores_gemma":[0.0002827407,0.00008841197,0.00003230694,0.0005617187,0.00002965915,0.0004748044,0.0001269155,0.000052927,0.00006542839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005861351,"about_ca_system_score_gemma":0.0000436371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007264911,"about_ca_topic_score_gemma":7.285251e-7,"domain_scores_codex":[0.9987155,0.00008039594,0.0002320455,0.0003856031,0.0003248259,0.0002616823],"domain_scores_gemma":[0.9991463,0.0001346351,0.00006539284,0.0003041317,0.0002464289,0.0001030797],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006756098,0.0003527949,0.001070756,0.0001696619,0.00006867869,0.000008287671,0.0004442189,0.186297,0.01129823,0.4756436,0.01185524,0.3127239],"study_design_scores_gemma":[0.0004337411,0.00005132406,0.004355305,0.000003931129,0.000005474382,0.000003703809,0.00002318769,0.9895029,0.002833921,0.002566581,0.0001195993,0.0001003199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02394499,0.000006636466,0.9731399,0.00155174,0.0003171427,0.000462234,0.00000511306,0.0004111276,0.0001611274],"genre_scores_gemma":[0.6987853,0.00005120278,0.2995416,0.00003380373,0.00009999247,0.00008771926,0.00008374684,0.00001444371,0.001302179],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8032059,"threshold_uncertainty_score":0.3605336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04828152097501121,"score_gpt":0.3028147372842428,"score_spread":0.2545332163092316,"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."}}