{"id":"W4322756840","doi":"10.5281/zenodo.7691575","title":"IMPROVING SMART HOME SAFETY WITH FACE RECOGNITION USING MACHINE LEARNING","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Hermeneutics and Narrative Identity","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Facial recognition system; Computer science; Face (sociological concept); Human–computer interaction; Artificial intelligence; Psychology; Machine learning; Pattern recognition (psychology); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005740314,0.0006887114,0.0006185221,0.0009063161,0.0002838498,0.000609217,0.0006355942,0.0008516256,0.002669642],"category_scores_gemma":[0.001331863,0.0002217779,0.0007129296,0.0005127965,0.0002849379,0.001285522,0.0005559161,0.000653472,0.001494875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004652763,"about_ca_system_score_gemma":0.0004110599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002208238,"about_ca_topic_score_gemma":0.001559105,"domain_scores_codex":[0.9995424,0.00007528925,0.00001919046,0.0001059115,0.0002070045,0.00005037588],"domain_scores_gemma":[0.9995527,0.0001430257,0.00005223401,0.00005780631,0.0001805286,0.00001372779],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001526371,0.0002746492,0.003398598,0.0001312366,0.00007322816,0.000100593,0.00007959773,0.03409718,0.04181586,0.001319457,0.004135522,0.9144214],"study_design_scores_gemma":[0.00002986773,0.000304445,0.006008152,0.00006152828,0.00009070209,0.0003069835,0.0001071734,0.9143198,0.06722832,0.004778088,0.006707591,0.00005737556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05226632,0.001105581,0.9362948,0.0004811822,0.0001973979,0.0001035809,0.0001306561,0.003458867,0.00596154],"genre_scores_gemma":[0.6332126,0.001486855,0.3583389,0.0004787278,0.0001584996,0.0001610805,0.0004014044,0.0001282583,0.005633668],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002669642,"threshold_uncertainty_score":0.008930802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06617332634500868,"score_gpt":0.2347374497670619,"score_spread":0.1685641234220532,"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."}}