{"id":"W3199758924","doi":"10.1007/s00521-021-06396-7","title":"A smart healthcare framework for detection and monitoring of COVID-19 using IoT and cloud computing","year":2021,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":75,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"Deanship of Scientific Research, King Saud University; Alfaisal University; King Saud University","keywords":"Computer science; Cloud computing; Robustness (evolution); Benchmark (surveying); Coronavirus disease 2019 (COVID-19); Artificial intelligence; Internet of Things; Health care; Real-time computing; Machine learning; Data mining; Embedded system","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":[0.0006338095,0.0007976544,0.0008820843,0.00127079,0.0005523957,0.00148871,0.001347945,0.0008107001,0.002928924],"category_scores_gemma":[0.00103721,0.0003276202,0.0006376377,0.0006341635,0.0003116398,0.001290527,0.001668965,0.0007453326,0.001231364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007361317,"about_ca_system_score_gemma":0.001433064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008042004,"about_ca_topic_score_gemma":0.01031902,"domain_scores_codex":[0.9994708,0.0000631145,0.00005506404,0.0001385673,0.0001742071,0.00009838644],"domain_scores_gemma":[0.9996137,0.0000742844,0.00004976125,0.00005059065,0.0001187939,0.00009281779],"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":[0.002746414,0.001297834,0.04898871,0.001417449,0.0009573326,0.004000797,0.0009659494,0.06783216,0.07162797,0.05393508,0.1387723,0.6074581],"study_design_scores_gemma":[0.0001824397,0.0003022207,0.01256505,0.0001876422,0.000286341,0.001499818,0.0002947334,0.8654363,0.02998139,0.01992588,0.06916361,0.0001745394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04578364,0.002549482,0.8724961,0.001911189,0.0005546194,0.0009828975,0.003535805,0.05696732,0.01521888],"genre_scores_gemma":[0.6849089,0.001286413,0.2976568,0.001927058,0.0001967771,0.0005598307,0.004401338,0.0005594955,0.008503452],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008042004,"threshold_uncertainty_score":0.01599038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07982361886895845,"score_gpt":0.4076644980916325,"score_spread":0.327840879222674,"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."}}