{"id":"W2592144640","doi":"10.1016/j.neucom.2016.08.135","title":"A privacy-preserving high-order neuro-fuzzy c-means algorithm with cloud computing","year":2017,"lang":"en","type":"article","venue":"Neurocomputing","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":61,"is_retracted":false,"has_abstract":false,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Computer science; Cloud computing; Cluster analysis; Data mining; Tensor (intrinsic definition); Fuzzy logic; Big data; Fuzzy clustering; Scheme (mathematics); Set (abstract data type); Data set; Internet of Things; Algorithm; Artificial intelligence; Mathematics; Computer security","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":["metaepi_narrow","sts","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.0008211244,0.0006600224,0.0006438295,0.000248896,0.003758456,0.00276694,0.00647156,0.000150147,0.000003115098],"category_scores_gemma":[0.000501534,0.0006188762,0.0001626614,0.0005852077,0.0001745837,0.001255516,0.006726054,0.001000197,0.00008270576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006753069,"about_ca_system_score_gemma":0.0001730209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003251852,"about_ca_topic_score_gemma":0.000004465923,"domain_scores_codex":[0.9949967,0.0002351018,0.0007259399,0.001640633,0.0008439038,0.001557777],"domain_scores_gemma":[0.9945996,0.0006264558,0.0008916422,0.003158343,0.0003738909,0.0003500737],"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":[0.0000255433,0.0003112275,0.01454689,0.0001895873,0.0001406383,0.001383345,0.003913153,0.01220753,0.001552251,0.003927928,0.0103938,0.9514081],"study_design_scores_gemma":[0.001342484,0.0002378805,0.0283904,0.0002828253,0.00002375307,0.0003039755,0.00002122315,0.9540696,0.000677851,0.001217711,0.01252755,0.0009047073],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1792998,0.00004282476,0.7998722,0.002183592,0.01190548,0.0004529695,4.837838e-7,0.001114932,0.005127676],"genre_scores_gemma":[0.5404012,0.000004038306,0.4497148,0.001391055,0.008228073,0.000005526001,0.000004156464,0.0001180863,0.0001330759],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9505034,"threshold_uncertainty_score":0.9996263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01697804207283266,"score_gpt":0.247921084173794,"score_spread":0.2309430421009613,"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."}}