{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003281867,0.0005752495,0.0006324355,0.001285571,0.0003142626,0.0006432358,0.0005240963,0.0005591468,0.00264907],"category_scores_gemma":[0.0007607747,0.0002088114,0.0008040035,0.001282131,0.0003356036,0.0007183806,0.0005303265,0.0007190864,0.001079665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003182907,"about_ca_system_score_gemma":0.0006055875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001782716,"about_ca_topic_score_gemma":0.001619556,"domain_scores_codex":[0.9995715,0.00005015463,0.00003999904,0.00008340747,0.0002129785,0.00004200007],"domain_scores_gemma":[0.9998065,0.00006034968,0.00002332884,0.00002008676,0.00008023256,0.00000957453],"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":[0.0001664637,0.0001276739,0.001309434,0.000419647,0.0001212306,0.0002229634,0.0001042227,0.09464575,0.08822139,0.01632961,0.003572825,0.7947589],"study_design_scores_gemma":[0.000009514161,0.0001095183,0.001887661,0.00003365702,0.00002942939,0.0002120299,0.00005335834,0.9542693,0.02836339,0.008905274,0.006097591,0.00002938261],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01072638,0.0006828628,0.9856496,0.0001432539,0.00007484151,0.00004231563,0.00007491649,0.0003605064,0.002245243],"genre_scores_gemma":[0.4333434,0.001957844,0.5513242,0.000234098,0.0001639308,0.0002422863,0.0005729911,0.0001067941,0.01205439],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00264907,"threshold_uncertainty_score":0.008862019,"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."}}