{"id":"W4388938770","doi":"10.1111/exsy.13507","title":"An automated face mask detection system using transfer learning based neural network to preventing viral infection","year":2023,"lang":"en","type":"article","venue":"Expert Systems","topic":"Face recognition and analysis","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Artificial intelligence; Haar-like features; Coronavirus disease 2019 (COVID-19); Face detection; Machine learning; Transfer of learning; Artificial neural network; Residual; Process (computing); Deep learning; Train; Computer vision; Pattern recognition (psychology); Facial recognition system; Infectious disease (medical specialty)","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":[0.0006457244,0.0001775504,0.00024891,0.0003077222,0.0004941244,0.0004441394,0.0002078561,0.00009558081,0.000003309912],"category_scores_gemma":[0.00001722705,0.0001762623,0.0001258419,0.001589733,0.000007218019,0.0005391148,0.00003448122,0.0001335053,0.0001098772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000158764,"about_ca_system_score_gemma":0.00002387302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008664213,"about_ca_topic_score_gemma":0.00003942012,"domain_scores_codex":[0.9978788,0.0005933469,0.0003626923,0.0004459487,0.0003132123,0.0004059828],"domain_scores_gemma":[0.9993972,0.00004396536,0.00007083989,0.0002652875,0.00007569781,0.0001470634],"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":[0.000003933669,0.0000114907,0.0004539339,0.0000559468,0.00002000361,0.000008429184,0.0007606683,0.9556301,0.0399983,0.00001591681,0.00004240804,0.002998901],"study_design_scores_gemma":[0.0001822983,0.00007893446,0.0003454213,0.0002081251,0.000009795534,0.0000192949,0.0005131631,0.994742,0.003336678,4.755042e-7,0.0003513142,0.0002125136],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4103478,0.0000286072,0.58554,0.00002820503,0.0009551001,0.0002172989,5.753116e-7,0.002858672,0.00002382156],"genre_scores_gemma":[0.9986619,0.000001438714,0.0007925685,0.00005204271,0.0003114539,0.00006666446,0.00001050646,0.00002426134,0.00007920415],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5883141,"threshold_uncertainty_score":0.7187769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02495308289389219,"score_gpt":0.2878465697433188,"score_spread":0.2628934868494266,"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."}}