{"id":"W1535586468","doi":"","title":"Iris recognition using genetic algorithms and asymmetrical SVMs","year":2010,"lang":"en","type":"article","venue":"Machine Graphics & Vision International Journal archive","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Iris recognition; Biometrics; Computer science; Support vector machine; Artificial intelligence; IRIS (biosensor); Pattern recognition (psychology); Preprocessor; Feature selection; Fitness function; Feature extraction; Machine learning; Genetic algorithm","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":[],"consensus_categories":[],"category_scores_codex":[0.0006644186,0.0001509531,0.0001380656,0.001863474,0.0003159081,0.0008059979,0.0009380872,0.00007986431,0.00006282095],"category_scores_gemma":[0.0002832417,0.0001320071,0.0001263302,0.0009201037,0.0001262065,0.0003764204,0.0003561347,0.0008916295,0.00002174214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002644989,"about_ca_system_score_gemma":0.000056687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001289486,"about_ca_topic_score_gemma":0.00001853905,"domain_scores_codex":[0.9980444,0.0001311278,0.000434592,0.0003501512,0.0008387287,0.0002009469],"domain_scores_gemma":[0.9986032,0.000223983,0.0002620218,0.0002144062,0.0004428753,0.0002535191],"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":[0.00003190556,0.0003120876,0.006241586,0.000005790308,0.0001138457,0.0001262238,0.0005210036,0.0000122118,0.00799781,0.01804457,0.0008452638,0.9657477],"study_design_scores_gemma":[0.001023964,0.0001378784,0.1294186,0.00003219995,0.00002147746,0.003002315,0.00001664754,0.6856166,0.0003136501,0.1590748,0.02098683,0.0003550975],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1634565,0.000145553,0.8315611,0.00194626,0.002508026,0.00008115111,0.00004682236,0.00003707726,0.00021749],"genre_scores_gemma":[0.619709,0.0005121083,0.3785954,0.0006734355,0.0004445707,0.000002821414,0.00002835185,0.0000131584,0.00002122376],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9653926,"threshold_uncertainty_score":0.7772256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02183363179614844,"score_gpt":0.3050060169374069,"score_spread":0.2831723851412584,"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."}}