{"id":"W4395109657","doi":"10.18280/ria.380233","title":"An Efficient Machine Learning Based Attendance Monitoring System Through Face Recognition","year":2024,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Facial recognition system; Attendance; Computer science; Artificial intelligence; Machine learning; Face (sociological concept); Pattern recognition (psychology); Political science; Sociology","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":[0.0002589648,0.0004265857,0.0008722453,0.0008381487,0.0003807257,0.0004542239,0.001191836,0.0006375341,0.003401824],"category_scores_gemma":[0.000467341,0.0002091601,0.0003934589,0.0004477883,0.0001047732,0.0005602516,0.0005396105,0.0004727981,0.002177152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000318849,"about_ca_system_score_gemma":0.0004977707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001841409,"about_ca_topic_score_gemma":0.003102067,"domain_scores_codex":[0.9996742,0.00002861531,0.00001488359,0.00009655762,0.0001377723,0.00004793534],"domain_scores_gemma":[0.9997128,0.00004391306,0.00003119089,0.00004085102,0.0001337861,0.00003744603],"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":[0.0006504817,0.0004509445,0.00397299,0.0001234816,0.00007615107,0.0002116151,0.00003846319,0.002852057,0.2532546,0.0005280768,0.009009245,0.7288319],"study_design_scores_gemma":[0.00015924,0.001115034,0.02473545,0.00004073275,0.0003380303,0.001734912,0.00008377319,0.6609675,0.2969634,0.00116114,0.01255531,0.0001454817],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.193216,0.001069838,0.7783505,0.0004714107,0.0008877777,0.0003151907,0.0007977661,0.01756155,0.007329903],"genre_scores_gemma":[0.735287,0.0003494838,0.2508133,0.0004566756,0.0003067791,0.0002196019,0.0006706621,0.0001231623,0.01177333],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003401824,"threshold_uncertainty_score":0.0113802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09302490034075193,"score_gpt":0.3109614946603642,"score_spread":0.2179365943196122,"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."}}