{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004316492,0.000200529,0.0001572197,0.0001338442,0.0004297895,0.0002870369,0.0002679749,0.00008370297,0.0001679223],"category_scores_gemma":[0.0002341816,0.0002003132,0.0001022633,0.000871638,0.00009185702,0.0002758384,0.0000247447,0.0004403981,0.001869828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000184746,"about_ca_system_score_gemma":0.00003719354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003238248,"about_ca_topic_score_gemma":0.000002084232,"domain_scores_codex":[0.9979157,0.0002605453,0.0004133125,0.0007945827,0.0002777969,0.0003380685],"domain_scores_gemma":[0.9990296,0.0003192805,0.000101754,0.0003907471,0.00005914238,0.0000994703],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002966626,0.0001224225,0.00005416286,0.0002969513,0.000003413747,0.0000515703,0.001461636,0.419414,0.518688,0.001994273,0.00001689924,0.05786702],"study_design_scores_gemma":[0.00001534561,0.00005880649,0.00001104128,0.0002240359,0.000006126321,0.00003524963,0.0009747638,0.5296617,0.4670488,0.00003824297,0.001801186,0.0001247895],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2800223,0.0004599295,0.7109526,0.0006216269,0.002422906,0.0004450375,0.00002776536,0.001611782,0.003436125],"genre_scores_gemma":[0.9983229,0.00003762688,0.0005009003,0.00006434059,0.0002200085,0.00006888106,0.00001196824,0.0000434996,0.000729894],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7183006,"threshold_uncertainty_score":0.9989073,"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."}}