{"id":"W4409640667","doi":"10.1109/access.2025.3563158","title":"Real-Time Anomaly Detection in IoMT Networks Using Stacking Model and a Healthcare- Specific Dataset","year":2025,"lang":"en","type":"article","venue":"IEEE Access","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Anomaly detection; Stacking; Data modeling; Data mining; Anomaly (physics); Artificial intelligence; Database","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":[0.001608028,0.00108961,0.000651574,0.001575311,0.0004965409,0.0006503841,0.001246471,0.001178681,0.0004512818],"category_scores_gemma":[0.003747284,0.0001642298,0.000764201,0.00139969,0.0004415138,0.0008906866,0.0007816677,0.001034186,0.0003048335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001221796,"about_ca_system_score_gemma":0.0008367745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0191443,"about_ca_topic_score_gemma":0.0178012,"domain_scores_codex":[0.9992777,0.0001455772,0.00007497444,0.0002171171,0.0001742036,0.0001105688],"domain_scores_gemma":[0.9984202,0.0005478646,0.0002001865,0.0002834853,0.0004102862,0.0001379075],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001195207,0.001280781,0.1164406,0.000372061,0.0003397632,0.00121856,0.0002109982,0.696222,0.006766774,0.001796059,0.02908625,0.1450709],"study_design_scores_gemma":[0.00002478764,0.0002286368,0.02027126,0.0000247099,0.00004255065,0.0002585739,0.0001114513,0.9730344,0.002913233,0.0008855772,0.00217617,0.00002865895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9515696,0.001424214,0.02973401,0.001339387,0.0003040304,0.0001504343,0.01190778,0.00185562,0.001715085],"genre_scores_gemma":[0.9499023,0.0004278514,0.01716896,0.0001645437,0.00009997989,0.0000830345,0.03116301,0.00002682526,0.0009634683],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0191443,"threshold_uncertainty_score":0.03806573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04342244837744393,"score_gpt":0.3415698963583335,"score_spread":0.2981474479808896,"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."}}