{"id":"W2770146469","doi":"10.3390/jsan6040026","title":"Big Sensed Data Meets Deep Learning for Smarter Health Care in Smart Cities","year":2017,"lang":"en","type":"article","venue":"Journal of Sensor and Actuator Networks","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Deep learning; Smart city; Wearable computer; Data science; Data acquisition; Artificial intelligence; Internet of Things; Big data; Wearable technology; Machine learning; Human–computer interaction; Data mining; World Wide Web; Embedded system","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002224809,0.0006016337,0.0007799949,0.0007706581,0.0003458973,0.001870738,0.001272202,0.001310649,0.001437803],"category_scores_gemma":[0.005301943,0.000372313,0.0005355293,0.001139707,0.0009371695,0.004040694,0.00210602,0.002734933,0.0003678907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001533909,"about_ca_system_score_gemma":0.001477863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003961585,"about_ca_topic_score_gemma":0.005700661,"domain_scores_codex":[0.9992284,0.0003096774,0.00006660765,0.0001270803,0.0001756901,0.00009256857],"domain_scores_gemma":[0.9981481,0.001057352,0.0001531867,0.0002155611,0.0003206663,0.0001052337],"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":[0.0003528325,0.0002775739,0.01828064,0.0008328707,0.0002547912,0.0003247836,0.0004454916,0.339567,0.003535953,0.1408744,0.02353332,0.4717202],"study_design_scores_gemma":[0.00001076233,0.00002284757,0.00108316,0.00007662374,0.00001990092,0.00003652069,0.0001239426,0.8947927,0.001184559,0.0971956,0.005442076,0.00001139093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05835808,0.00987493,0.8921274,0.02884148,0.0004077629,0.0001080483,0.0008303348,0.00101418,0.00843766],"genre_scores_gemma":[0.8050578,0.006842464,0.1824201,0.001731628,0.0005106362,0.0001171794,0.0008894292,0.00008227794,0.002348491],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003961585,"threshold_uncertainty_score":0.01176602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.031164035700136,"score_gpt":0.2972979377633438,"score_spread":0.2661339020632079,"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."}}