{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002942519,0.0003715359,0.0003809451,0.0002429923,0.0001205558,0.0004705588,0.0002870535,0.0005651171,0.001687277],"category_scores_gemma":[0.0007439219,0.0001419784,0.0003465199,0.0003611355,0.0001761343,0.0004472327,0.0002716038,0.0007464404,0.0003350692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003065159,"about_ca_system_score_gemma":0.0001712161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002886951,"about_ca_topic_score_gemma":0.003080432,"domain_scores_codex":[0.9999161,0.00001976318,0.000002877889,0.00001963695,0.00002044071,0.00002126476],"domain_scores_gemma":[0.9997725,0.0001148095,0.00002048648,0.00001325883,0.00006469584,0.00001420301],"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":[0.00169412,0.001051639,0.02188972,0.0002576961,0.0002672038,0.0003292439,0.0003505448,0.09075955,0.07843435,0.004003187,0.01424217,0.7867206],"study_design_scores_gemma":[0.00001577387,0.00006909318,0.007082477,0.00000547386,0.00003077463,0.00003711757,0.0000527451,0.9828856,0.007291156,0.002032364,0.0004838519,0.00001357236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7659461,0.002094062,0.2229519,0.001504017,0.0002400372,0.00005053631,0.0004767685,0.0009084091,0.005828191],"genre_scores_gemma":[0.9665404,0.0005243961,0.02831008,0.0001746662,0.00009573526,0.00002156577,0.0002546506,0.00004458864,0.004033957],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002886951,"threshold_uncertainty_score":0.005740285,"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."}}