{"id":"W4388339813","doi":"10.31893/multiscience.2024049","title":"Soft computing approach for feature extraction of palm biometric","year":2023,"lang":"en","type":"article","venue":"Multidisciplinary Science Journal","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nutrasource","funders":"","keywords":"Biometrics; Convolutional neural network; Computer science; Palm print; Palm; Artificial intelligence; Feature extraction; Identification (biology); Pattern recognition (psychology); Feature (linguistics); Artificial neural network; Computer vision","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.004545099,0.0001273604,0.0001963127,0.003767085,0.001307515,0.0004648876,0.001729947,0.0000805782,0.000003049089],"category_scores_gemma":[0.0004225534,0.0001066899,0.0001454853,0.01929673,0.0002992598,0.001322033,0.0004479602,0.000274628,0.00001428261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001122315,"about_ca_system_score_gemma":0.0002963394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003774475,"about_ca_topic_score_gemma":3.322267e-7,"domain_scores_codex":[0.9976497,0.00004583862,0.000412829,0.0004702348,0.000935302,0.0004860829],"domain_scores_gemma":[0.9981822,0.0002096325,0.0004342151,0.0003935586,0.0005665094,0.000213917],"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":[0.00006697232,0.001487186,0.0488954,0.0002909512,0.00006878594,0.00004551934,0.01109215,0.009859398,0.278877,0.008687911,0.01469561,0.6259331],"study_design_scores_gemma":[0.0003456382,0.00009563175,0.2312967,0.00001156888,0.000004737554,0.0001557058,0.0004075744,0.7628081,0.003647215,0.0006624091,0.0004191702,0.000145471],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1250293,0.0001135881,0.8722582,0.0007042909,0.001367687,0.0002173664,0.000008667117,0.0001143235,0.0001865982],"genre_scores_gemma":[0.7764116,0.00001655455,0.2231742,0.00001076955,0.0001258249,0.00000318156,0.000008263411,0.000005672653,0.0002439444],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7529488,"threshold_uncertainty_score":0.9999927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0532522156398122,"score_gpt":0.3464110241375755,"score_spread":0.2931588084977633,"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."}}