{"id":"W2086791809","doi":"10.4018/jcini.2008070101","title":"Language Independent Recognition of Human Emotion using Artificial Neural Networks","year":2008,"lang":"en","type":"article","venue":"International Journal of Cognitive Informatics and Natural Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence; Curse of dimensionality; Modular design; Feature selection; Selection (genetic algorithm); Consistency (knowledge bases); Feature (linguistics); Pattern recognition (psychology); Identification (biology); Machine learning; Natural language processing","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":[0.0003775451,0.0004329225,0.0003358749,0.000329142,0.000124305,0.0004175193,0.0004405112,0.0004011738,0.0007319064],"category_scores_gemma":[0.001315011,0.0001318791,0.0003403808,0.0002894019,0.0001944987,0.0005147416,0.000351724,0.0003917149,0.0003525988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001749622,"about_ca_system_score_gemma":0.0001205986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006548895,"about_ca_topic_score_gemma":0.0009875497,"domain_scores_codex":[0.9997309,0.00007920295,0.00001676543,0.00006957122,0.00008027042,0.00002322337],"domain_scores_gemma":[0.9997954,0.00009163775,0.00003076649,0.000021704,0.00005378625,0.000006820251],"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":[0.0005106415,0.0001718034,0.002751184,0.0001977062,0.0001462867,0.0003237135,0.0002550287,0.07666327,0.2960725,0.002488587,0.001916837,0.6185025],"study_design_scores_gemma":[0.0000176777,0.0001152318,0.004903295,0.00001973793,0.00004590068,0.0001297742,0.00005307204,0.9586545,0.03151932,0.002993065,0.001522141,0.0000264328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2311305,0.001425117,0.7600681,0.0002717618,0.0001768212,0.00008393575,0.0001641603,0.001733845,0.004945777],"genre_scores_gemma":[0.8336334,0.0006455469,0.1611899,0.0001649535,0.00010944,0.0001103077,0.0003127608,0.00005913758,0.003774548],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0007319064,"threshold_uncertainty_score":0.002448499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04575472513614993,"score_gpt":0.3194228397995744,"score_spread":0.2736681146634245,"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."}}