{"id":"W4386919553","doi":"10.1109/icaiss58487.2023.10250622","title":"Analysis Face Recognition based Systems for Employees Attendance Machine Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Face recognition and analysis","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Attendance; Facial recognition system; Computer science; Process (computing); User Friendly; Class (philosophy); Multimedia; Artificial intelligence; Face (sociological concept); Learning Management; Human–computer interaction; Machine learning; Feature extraction","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":[0.0005477209,0.0004130042,0.0004869087,0.001012826,0.0003600871,0.0006315814,0.0007419714,0.0005900878,0.006415519],"category_scores_gemma":[0.00128861,0.0001469513,0.0003785767,0.0005915338,0.0001395452,0.0004941532,0.0003607116,0.0004953811,0.002757336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005921512,"about_ca_system_score_gemma":0.0004594688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00359517,"about_ca_topic_score_gemma":0.003233864,"domain_scores_codex":[0.9993348,0.00007950312,0.00003173315,0.0001384813,0.0003374318,0.00007820301],"domain_scores_gemma":[0.9995291,0.0001155941,0.00004442834,0.00006432773,0.0002280433,0.00001852617],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004182238,0.0002513631,0.006070286,0.0001742893,0.00007203873,0.0001219242,0.0001344565,0.02494895,0.0778529,0.002728545,0.01104762,0.8761794],"study_design_scores_gemma":[0.00003829071,0.0005117243,0.02620043,0.00005399186,0.00008210163,0.0003868829,0.0001407654,0.8566532,0.09529769,0.002221579,0.0183159,0.0000973292],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.140576,0.001366806,0.8281646,0.000507568,0.0003841743,0.0002975332,0.001065294,0.01088252,0.01675538],"genre_scores_gemma":[0.8333491,0.0006866702,0.1449006,0.0001996389,0.0001230891,0.0002919589,0.001127194,0.0001230617,0.01919867],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006415519,"threshold_uncertainty_score":0.02146202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04279563677628843,"score_gpt":0.2730886879388618,"score_spread":0.2302930511625734,"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."}}