{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004776176,0.0001269081,0.0002564894,0.0006482354,0.0002143707,0.0002467157,0.0003021233,0.00004848616,0.00007543871],"category_scores_gemma":[0.0001039052,0.0001121815,0.0002991599,0.00329095,0.00001447137,0.0002593552,0.00005130082,0.00008292846,0.000482975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002436024,"about_ca_system_score_gemma":0.00002173329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001213686,"about_ca_topic_score_gemma":0.00007589677,"domain_scores_codex":[0.9987282,0.000101877,0.0002431348,0.0004181121,0.0002374502,0.0002712732],"domain_scores_gemma":[0.9991414,0.0002605205,0.00009911296,0.0002469991,0.0001657066,0.00008625662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006801752,0.0005135959,0.07034378,0.0006406161,0.004910764,0.00005487962,0.001076937,0.6105919,0.006009221,0.002575869,0.01172918,0.2914852],"study_design_scores_gemma":[0.0002460603,0.00003247762,0.0007127148,0.00001433328,0.0001483576,5.35556e-7,0.00009846713,0.9950553,0.0008326583,0.00007434732,0.002607964,0.0001767864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01245374,0.00007765552,0.9848298,0.001079774,0.0001136183,0.0001590187,0.00003357354,0.0008864565,0.0003663393],"genre_scores_gemma":[0.9804054,0.0000731798,0.01322033,0.0002517398,0.00004147484,0.0001258523,0.0004679569,0.00001447151,0.00539957],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9716095,"threshold_uncertainty_score":0.6207827,"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."}}