{"id":"W4409953991","doi":"10.18280/ts.420223","title":"Emotion Recognition in Consumers Based on Deep Learning and Image Processing: Applications in Advertising","year":2025,"lang":"en","type":"article","venue":"Traitement du signal","topic":"E-commerce and Technology Innovations","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Computer science; Image (mathematics); Advertising; Deep learning; Artificial intelligence; Multimedia; Business","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":[],"consensus_categories":[],"category_scores_codex":[0.0002504189,0.0001042613,0.0001072181,0.0008518563,0.0001630266,0.00009088591,0.00006358298,0.00006527972,0.00008203607],"category_scores_gemma":[0.00003941446,0.000112654,0.0000164789,0.001006651,0.00007435049,0.000381951,0.00002480845,0.0002090884,0.00001466198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004453518,"about_ca_system_score_gemma":0.00001441898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000997321,"about_ca_topic_score_gemma":0.0001928301,"domain_scores_codex":[0.9992867,0.00001166776,0.0002531708,0.0002106019,0.00008329384,0.0001546249],"domain_scores_gemma":[0.9997396,0.00004379046,0.00009155438,0.00005442375,0.00006669222,0.000003926002],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00005496324,0.0004269423,0.4515116,0.0003271944,0.00001101632,0.000005095817,0.00008184936,0.0003139423,0.002967553,0.009082377,0.00009453521,0.535123],"study_design_scores_gemma":[0.006184479,0.00006076854,0.6530209,0.001455035,0.0001236795,0.000001054967,0.004872005,0.300502,0.001259631,0.01906889,0.01270726,0.0007443036],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9376881,0.00006154319,0.0380742,0.004401017,0.00003391195,0.0006940121,0.000001448489,0.0001984899,0.01884724],"genre_scores_gemma":[0.9981793,0.000004400655,0.0005552354,0.0009750137,0.00002552221,0.0001207607,0.0001120885,0.000008394982,0.00001930872],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5343786,"threshold_uncertainty_score":0.4593895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01188707534653305,"score_gpt":0.2441489597881672,"score_spread":0.2322618844416341,"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."}}