{"id":"W4353100313","doi":"10.18280/ts.400138","title":"Biometric User Authentication System via Fingerprints Using Novel Hybrid Optimization Tuned Deep Learning Strategy","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Biometrics; Computer science; Authentication (law); Artificial intelligence; Deep learning; Fingerprint (computing); Pattern recognition (psychology); Computer security","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":[0.000316104,0.0003872592,0.0004846231,0.0002309198,0.0002390506,0.0004314133,0.0007046104,0.0004199216,0.001636311],"category_scores_gemma":[0.0003935253,0.0001676532,0.0003524433,0.0002231183,0.000214021,0.000704608,0.0006244997,0.0004334596,0.0003354691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003852796,"about_ca_system_score_gemma":0.000484509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0029313,"about_ca_topic_score_gemma":0.002768578,"domain_scores_codex":[0.9997796,0.00003358046,0.00001265653,0.00005002323,0.00008296901,0.00004121031],"domain_scores_gemma":[0.9998797,0.00002583964,0.0000210571,0.00001689971,0.00004657168,0.000009800572],"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":[0.0004295714,0.0002200787,0.002780357,0.000114266,0.0001087459,0.0001860944,0.00007718699,0.4466758,0.06364859,0.006474673,0.002481005,0.4768037],"study_design_scores_gemma":[0.00000530143,0.00007168906,0.0002803214,0.000002968271,0.000009548983,0.00003395749,0.000003093545,0.9948013,0.004007556,0.0003869473,0.0003917096,0.00000557389],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06320055,0.0005062689,0.9308841,0.0001599865,0.00005993609,0.00004244966,0.00004130544,0.001061811,0.00404366],"genre_scores_gemma":[0.9241099,0.0002257303,0.06909507,0.0001046956,0.00002297248,0.00006158763,0.00006708081,0.00002282782,0.006290062],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0029313,"threshold_uncertainty_score":0.00582844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03930864097657536,"score_gpt":0.2583828054188267,"score_spread":0.2190741644422514,"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."}}