{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001573741,0.0005081347,0.0007198162,0.001072824,0.0002892166,0.0006835789,0.0008030816,0.0008069894,0.0006911522],"category_scores_gemma":[0.004447503,0.0002355379,0.0005770194,0.0006903129,0.0005575829,0.000876028,0.0006819288,0.0006350345,0.0002561932],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006192647,"about_ca_system_score_gemma":0.0005015004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002333077,"about_ca_topic_score_gemma":0.001393517,"domain_scores_codex":[0.999121,0.0003583872,0.00006815414,0.0001547362,0.0002352974,0.00006242948],"domain_scores_gemma":[0.9986653,0.0006104475,0.0002188048,0.0001813322,0.0002863665,0.00003771175],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001559206,0.00008756954,0.003038078,0.0000574615,0.0000725889,0.00007599762,0.00007242759,0.7156482,0.006647334,0.01288012,0.0006341043,0.2606302],"study_design_scores_gemma":[0.000005205756,0.00001730416,0.0002347139,0.00000298694,0.000004416576,0.00001588994,0.000004284156,0.9967277,0.0007134654,0.00210638,0.0001642734,0.000003342261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07118127,0.0002512399,0.9258622,0.0002201588,0.0000373862,0.0000410364,0.00003423289,0.0004758484,0.001896589],"genre_scores_gemma":[0.659914,0.0001883512,0.3378282,0.000116245,0.00004578515,0.0001023214,0.0001253927,0.00004150169,0.001638204],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002333077,"threshold_uncertainty_score":0.008322835,"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."}}