{"id":"W4401829019","doi":"10.18280/ria.380406","title":"Outlier Detection in Wireless Sensor Networks Using Machine Learning and Statistical Based Approaches","year":2024,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Anomaly detection; Computer science; Wireless sensor network; Outlier; Statistical learning; Machine learning; Artificial intelligence; Wireless; Data mining; Computer network; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003249342,0.0001114777,0.0001172258,0.0001507681,0.0001473791,0.0001973243,0.000140532,0.00007344963,0.00002014686],"category_scores_gemma":[0.00001965048,0.000111124,0.00003305552,0.0006008084,0.00006059282,0.0001430462,0.0000714795,0.0003044909,0.00001773392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005208282,"about_ca_system_score_gemma":0.0000192831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008003479,"about_ca_topic_score_gemma":0.00002956625,"domain_scores_codex":[0.9989871,0.0000656305,0.0002529377,0.0004151085,0.0000788041,0.0002003531],"domain_scores_gemma":[0.9995325,0.0001716645,0.00003584194,0.0001825348,0.00002025899,0.0000571729],"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":[0.000009813412,0.00008438113,0.0008556727,0.0001023301,0.00000930826,0.00003378271,0.0003977951,0.2126386,0.007879866,0.04489307,0.000006424076,0.733089],"study_design_scores_gemma":[0.00001171632,0.00004089953,0.00003190059,0.00004578843,0.000004236404,0.00002867189,0.00007065719,0.9709801,0.02692308,0.0006488651,0.001089314,0.00012474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01653275,0.0002724211,0.9823444,0.000161538,0.00008452775,0.0001739693,0.000001616574,0.0002537952,0.0001750231],"genre_scores_gemma":[0.9641411,0.00004428601,0.03555003,0.00002146612,0.0000381631,0.00003148374,0.000002438523,0.00001267077,0.0001583709],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9476084,"threshold_uncertainty_score":0.4531505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05181408790663502,"score_gpt":0.2818255460384156,"score_spread":0.2300114581317806,"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."}}