{"id":"W2966001425","doi":"10.1109/ccece.2019.8861806","title":"Deep Learning: Edge-Cloud Data Analytics for IoT","year":2019,"lang":"en","type":"article","venue":"","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Autoencoder; Cloud computing; Computer science; Edge computing; Enhanced Data Rates for GSM Evolution; Edge device; Deep learning; Analytics; Big data; Artificial intelligence; Reduction (mathematics); Machine learning; Data analysis; Wearable computer; Data science; Data mining; Embedded system; Operating 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":[0.0004655407,0.0007034315,0.0003986295,0.0004431424,0.0002437073,0.0007504195,0.001085874,0.0005595717,0.002603606],"category_scores_gemma":[0.001280753,0.0002817452,0.0003874549,0.0007365415,0.0003329095,0.001350155,0.001435782,0.001327462,0.00106236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005711635,"about_ca_system_score_gemma":0.0006872404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004519537,"about_ca_topic_score_gemma":0.00642238,"domain_scores_codex":[0.9997378,0.00003553765,0.00001680804,0.00005724275,0.0001040562,0.00004856026],"domain_scores_gemma":[0.9997005,0.00007733828,0.00002400839,0.00006602677,0.0001068927,0.00002529596],"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":[0.0006617009,0.0005769997,0.00506885,0.0002168742,0.0001514046,0.0002844555,0.0001070544,0.2360759,0.02009657,0.0104643,0.03197891,0.694317],"study_design_scores_gemma":[0.000009993032,0.0000276606,0.0004905821,0.000009576703,0.000006014372,0.00002240273,0.00001618772,0.9863778,0.005061627,0.006027381,0.001943885,0.000006849691],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04563309,0.0008341696,0.9394346,0.0008370692,0.0001597405,0.000100257,0.001149214,0.007266098,0.004585827],"genre_scores_gemma":[0.6188546,0.0008195011,0.3696119,0.0005786932,0.00007990689,0.0001516793,0.003291139,0.0003759484,0.006236571],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004519537,"threshold_uncertainty_score":0.008986413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05753967754524434,"score_gpt":0.2837458262508334,"score_spread":0.2262061487055891,"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."}}