{"id":"W4391407085","doi":"10.1109/jiot.2024.3360882","title":"Neural Architecture Search for Anomaly Detection in Time-Series Data of Smart Buildings: A Reinforcement Learning Approach for Optimal Autoencoder Design","year":2024,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Autoencoder; Anomaly detection; Computer science; Reinforcement learning; Artificial intelligence; Machine learning; Recurrent neural network; Time series; Architecture; Artificial neural network; Deep learning; Data mining; Anomaly (physics); Pattern recognition (psychology)","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":[0.001635893,0.0007748451,0.0009207312,0.000511417,0.0002816995,0.0005325102,0.001033261,0.001075689,0.001003373],"category_scores_gemma":[0.004370679,0.0005047373,0.0005243891,0.0003219997,0.0007044566,0.0006513452,0.0007186469,0.00140602,0.0001865803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007521703,"about_ca_system_score_gemma":0.001010281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005252427,"about_ca_topic_score_gemma":0.005366461,"domain_scores_codex":[0.9996114,0.0001564137,0.00002513865,0.00008424686,0.00007814272,0.00004466996],"domain_scores_gemma":[0.9981021,0.001332933,0.0001364886,0.00007383021,0.0003033231,0.00005134938],"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":[0.00003440915,0.00004053666,0.0005753034,0.00002936708,0.00002995295,0.00003755204,0.00004036253,0.9664148,0.001414176,0.001987138,0.0002337928,0.0291627],"study_design_scores_gemma":[0.000001673808,0.000008925714,0.00002526709,0.000001586932,0.000001729688,0.000002894565,0.000001427037,0.9994512,0.0001297402,0.000350865,0.00002372051,9.693174e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03574319,0.0002622136,0.962638,0.000220043,0.0000191604,0.00004225034,0.00001888426,0.0002981156,0.0007581523],"genre_scores_gemma":[0.8240637,0.0001823981,0.1737923,0.000174628,0.00003034951,0.000180127,0.00009346888,0.00005762104,0.001425281],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005252427,"threshold_uncertainty_score":0.01044369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04071910626279603,"score_gpt":0.2959187986770702,"score_spread":0.2551996924142742,"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."}}