{"id":"W4413918613","doi":"10.1109/compsystech65493.2025.11137007","title":"Detecting Human Emotions Using Machine Learning Techniques: A Comprehensive Approach","year":2025,"lang":"en","type":"article","venue":"","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University of Edmonton","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Human–computer interaction","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":[0.0001329014,0.0001157832,0.0001417081,0.0002816159,0.0004202659,0.00002918243,0.00007418617,0.0001209009,0.0007193265],"category_scores_gemma":[0.00002216677,0.0001134924,0.00007573532,0.0003126683,0.00004227965,0.00004769722,0.0000478032,0.0003504123,0.00004209832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004150171,"about_ca_system_score_gemma":0.00001142577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004088065,"about_ca_topic_score_gemma":0.00003767008,"domain_scores_codex":[0.9990882,0.0001814913,0.0002143908,0.0002604705,0.00006264397,0.0001927966],"domain_scores_gemma":[0.9996063,0.00004616398,0.00006804906,0.0001527679,0.00009205172,0.00003466508],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001072044,0.002319932,0.02339387,0.0004648236,0.0009947403,0.0000372152,0.007454407,0.0004570741,0.5281928,0.1694131,0.002464069,0.2647008],"study_design_scores_gemma":[0.0211998,0.002791454,0.1334693,0.002729583,0.002121106,0.002008475,0.1517114,0.173591,0.2406291,0.03553354,0.2268022,0.007412979],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3163273,0.00007157178,0.2145084,0.00008176073,0.0002522077,0.0003465037,0.000002091213,0.0006744164,0.4677357],"genre_scores_gemma":[0.9710156,0.000002576239,0.01798588,0.0004267928,0.00006324943,0.00002749288,0.0000355468,0.00001692361,0.01042596],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6546883,"threshold_uncertainty_score":0.7876121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08125556154262908,"score_gpt":0.376252915498215,"score_spread":0.2949973539555859,"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."}}