{"id":"W7108601604","doi":"10.1016/j.engappai.2025.113396","title":"Enhancing multimodal emotion recognition with dynamic fuzzy membership and attention fusion","year":2025,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute","funders":"FPT University","keywords":"Robustness (evolution); Fuzzy logic; Emotion recognition; Fusion mechanism; Key (lock); Multimodal learning; Benchmark (surveying); Modality (human–computer interaction); Feature (linguistics)","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":[0.0006151678,0.0005510061,0.0005707815,0.0005354967,0.0002872956,0.0006777821,0.0005988897,0.0006402089,0.00201015],"category_scores_gemma":[0.001678159,0.0001886889,0.0006150168,0.0004298118,0.0002213311,0.0009796264,0.0009285198,0.0006856717,0.0004191394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003742697,"about_ca_system_score_gemma":0.0002258266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001673625,"about_ca_topic_score_gemma":0.002033062,"domain_scores_codex":[0.9997119,0.00004772741,0.00001346182,0.00008102826,0.00009101896,0.00005477717],"domain_scores_gemma":[0.9996886,0.0001021613,0.00002397944,0.00003110654,0.0001316425,0.00002252417],"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":[0.0005927798,0.0003306301,0.001614719,0.0001227142,0.0001274601,0.00009341592,0.0001941789,0.032322,0.2068908,0.002831577,0.001908465,0.7529713],"study_design_scores_gemma":[0.00002054709,0.0001933827,0.004787008,0.00002036378,0.00009932367,0.0001272274,0.00007591053,0.9336659,0.05551422,0.00421353,0.001245824,0.00003679907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1052649,0.0005198878,0.8898722,0.0002088237,0.0001850349,0.00006125352,0.00007240047,0.0006406193,0.003174762],"genre_scores_gemma":[0.8226734,0.0002002001,0.1742771,0.0001809768,0.00008411148,0.00006643418,0.00009874207,0.00006066429,0.002358249],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00201015,"threshold_uncertainty_score":0.006724596,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02109882805942347,"score_gpt":0.298158617903856,"score_spread":0.2770597898444325,"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."}}