{"id":"W3199549508","doi":"10.17762/de.vi.4215","title":"Ensemble Learning based Age Invariant Fea-ture Recognition Using Soft Computing","year":2021,"lang":"en","type":"article","venue":"Design Engineering","topic":"Face recognition and analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Support vector machine; Artificial intelligence; Computer science; Convolutional neural network; Facial recognition system; Machine learning; Pattern recognition (psychology); Invariant (physics); Artificial neural network; Kernel (algebra); Face (sociological concept); Mathematics","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":[0.0007460023,0.0006309304,0.001015064,0.001107079,0.0002810257,0.0006318974,0.0008991914,0.000594058,0.001422697],"category_scores_gemma":[0.001524249,0.0002335461,0.0008331001,0.0007235142,0.0002340711,0.000882984,0.0007510319,0.0007113005,0.0006510873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004653104,"about_ca_system_score_gemma":0.0003984525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002882629,"about_ca_topic_score_gemma":0.003951352,"domain_scores_codex":[0.9995794,0.00005801895,0.00002522155,0.0001233925,0.0001508532,0.0000630681],"domain_scores_gemma":[0.9994259,0.0001379506,0.00006832958,0.0001160271,0.0002141498,0.00003773691],"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":[0.0001841131,0.0001611805,0.006066796,0.00005473531,0.0001450898,0.0001246073,0.0000604287,0.3351958,0.01486126,0.002308728,0.003935117,0.6369021],"study_design_scores_gemma":[0.000001176226,0.00002686565,0.0007768604,0.000002462504,0.000009630839,0.00003485081,0.00000670745,0.9959731,0.002134896,0.0006774843,0.0003509505,0.000005029391],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.109775,0.0005225954,0.8848943,0.0001600726,0.0001348627,0.00005297567,0.0002781556,0.001827034,0.002355016],"genre_scores_gemma":[0.8527043,0.0002855146,0.1420321,0.0001012347,0.00007097889,0.00006035315,0.0008879739,0.00009019604,0.003767311],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002882629,"threshold_uncertainty_score":0.005731642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04432080172560433,"score_gpt":0.2257823765218374,"score_spread":0.1814615747962331,"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."}}