{"id":"W4378418333","doi":"10.18280/ria.370205","title":"EEG Based Emotion Recognition Using Long Short Term Memory Network with Improved Rat Swarm Optimization Algorithm","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"IoT-based Smart Home Systems","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Term (time); Computer science; Swarm behaviour; Long short term memory; Electroencephalography; Optimization algorithm; Pattern recognition (psychology); Algorithm; Artificial intelligence; Emotion recognition; Speech recognition; Artificial neural network; Psychology; Mathematical optimization; Mathematics; Neuroscience; Recurrent neural network","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005736166,0.0003019443,0.0002989756,0.0002652906,0.0002340992,0.0001113506,0.0001751315,0.0001684585,0.0001094832],"category_scores_gemma":[0.00002948626,0.0003158018,0.00009836686,0.001410828,0.00005698998,0.0002963862,0.00002801368,0.0002431361,0.0002596587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002148806,"about_ca_system_score_gemma":0.00004733832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000029178,"about_ca_topic_score_gemma":0.00002433914,"domain_scores_codex":[0.9981239,0.00008096949,0.0005570786,0.0004296464,0.0002244584,0.0005838794],"domain_scores_gemma":[0.9990977,0.0001080626,0.00008156622,0.0004265442,0.0001647082,0.0001214311],"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":[0.00001459509,0.00002284527,0.0001683609,0.0001605893,0.00002461983,0.00003252976,0.000230276,0.949652,0.004129756,0.000001467928,0.0001506816,0.04541228],"study_design_scores_gemma":[0.000070708,0.00007431702,0.00004170852,0.0003859488,0.00003802163,0.0000254871,0.0004012228,0.9271498,0.07138743,0.00001164636,0.00003882227,0.0003749334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1002336,0.0000876778,0.8961757,0.00002347243,0.001503885,0.0006576764,0.00001198575,0.0008653101,0.0004406126],"genre_scores_gemma":[0.9682633,0.00005061602,0.0300144,0.00003023185,0.0006511191,0.00009551418,0.0004365563,0.0001630277,0.0002951943],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8680297,"threshold_uncertainty_score":0.9999294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03158518288613775,"score_gpt":0.2380041881702344,"score_spread":0.2064190052840966,"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."}}