{"id":"W4289823448","doi":"10.1109/tii.2022.3196343","title":"Privacy Preserving Ear Recognition System Using Transfer Learning in Industry 4.0","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Lenovo Group; National Institute of Technology Rourkela","keywords":"Biometrics; Computer science; Unavailability; Convolutional neural network; Encoding (memory); Feature extraction; Artificial intelligence; Deep learning; Feature (linguistics); Transfer of learning; Computation; Speech recognition; Machine learning; Pattern recognition (psychology); Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0007101538,0.0004576606,0.0005119509,0.0005884254,0.0003359853,0.000602541,0.0008578884,0.0007001337,0.002605896],"category_scores_gemma":[0.0008358928,0.0001863407,0.0004375175,0.0004397354,0.000303274,0.001408584,0.001490462,0.0006297202,0.001294016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005871248,"about_ca_system_score_gemma":0.0008698862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002215597,"about_ca_topic_score_gemma":0.001964317,"domain_scores_codex":[0.9993654,0.00007014628,0.00003138123,0.0001328416,0.0002732455,0.0001270559],"domain_scores_gemma":[0.999708,0.00004185106,0.00003456262,0.0000821334,0.0001104925,0.00002301212],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009664741,0.0005352061,0.005151503,0.0001655241,0.0001293399,0.0009069612,0.0001402062,0.08105232,0.1017746,0.007043541,0.009708269,0.792426],"study_design_scores_gemma":[0.00004193015,0.0004135725,0.00233508,0.0000168748,0.00004964136,0.0006931023,0.00004276277,0.9069213,0.07934777,0.004003451,0.006089808,0.00004473054],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1763461,0.0009144541,0.7950634,0.0005373858,0.0002999791,0.0002555286,0.000379306,0.01457871,0.01162511],"genre_scores_gemma":[0.9020572,0.0003011454,0.08849831,0.0003583431,0.00005433326,0.0001291881,0.0005286008,0.00008368793,0.007989207],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002605896,"threshold_uncertainty_score":0.008717537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1123655294327593,"score_gpt":0.2740873273461674,"score_spread":0.161721797913408,"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."}}