{"id":"W4387665333","doi":"10.21203/rs.3.rs-3130718/v1","title":"Enhancing University Security: A Machine Learning and IoT Driven Face Recognition System for Surveillance and Attendance","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Internet of Things; Facial recognition system; Computer science; Attendance; Face (sociological concept); Security system; Computer security; Machine learning; Artificial intelligence; Pattern recognition (psychology); Political science; Sociology","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":[0.001345889,0.0001945565,0.0003426916,0.0003128347,0.0003282764,0.0001037346,0.0001127363,0.0002675458,0.000003490654],"category_scores_gemma":[0.0002029351,0.0002285237,0.00006457013,0.0002552177,0.00005312662,0.00004482084,0.00029719,0.001047624,0.00002232632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003270864,"about_ca_system_score_gemma":0.00003662556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005351837,"about_ca_topic_score_gemma":0.001938924,"domain_scores_codex":[0.9982774,0.0003586266,0.0002029314,0.0004634363,0.0003081617,0.0003894166],"domain_scores_gemma":[0.9988832,0.0005223783,0.00004932522,0.000179986,0.0002293919,0.0001357178],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002070935,0.0001655498,0.1406902,0.5263191,0.002906948,0.001027047,0.04505284,0.09533163,0.03698074,0.0005978353,0.004798742,0.1440585],"study_design_scores_gemma":[0.001095017,0.0001870482,0.005343034,0.005325254,0.0000187403,0.00004149697,0.009342706,0.9576362,0.001271775,0.0001214004,0.01889891,0.0007184531],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9765999,0.003186195,0.01216362,0.0003292517,0.001022762,0.002725372,0.001436544,0.001929616,0.0006067022],"genre_scores_gemma":[0.9977701,0.001077944,0.000138951,7.629836e-7,0.0001382647,0.0000475545,0.0001451793,0.00006164244,0.0006195782],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8623045,"threshold_uncertainty_score":0.9318927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03939696181343349,"score_gpt":0.293723180858352,"score_spread":0.2543262190449185,"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."}}