{"id":"W3160911939","doi":"10.18280/isi.260211","title":"An Efficient Data Mining Technique for Structural Strength Monitoring System","year":2021,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Rough set; Set (abstract data type); Lexicon; Data mining; Support vector machine; Artificial neural network; Gaussian; Algorithm; Identification (biology); Probabilistic logic; Bayesian network; Artificial intelligence; Pattern recognition (psychology); Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000801096,0.0006608404,0.000834444,0.001974319,0.0006501875,0.0009890326,0.0008913127,0.0005369367,0.001087084],"category_scores_gemma":[0.002039289,0.0003586932,0.0007697652,0.001807556,0.0002679226,0.001215897,0.0006196552,0.0007383079,0.0007792758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003779307,"about_ca_system_score_gemma":0.0007778665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00124586,"about_ca_topic_score_gemma":0.001320744,"domain_scores_codex":[0.9990895,0.0001147616,0.0001309054,0.0002098016,0.0004059566,0.00004902988],"domain_scores_gemma":[0.9993458,0.000178515,0.00008516163,0.0001197178,0.0002490465,0.00002159716],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000261505,0.000222589,0.005334862,0.0002936581,0.000136876,0.0003991458,0.0002236896,0.07182615,0.03932654,0.00996709,0.005017464,0.8669905],"study_design_scores_gemma":[0.00002092359,0.0001737721,0.00263858,0.00003515925,0.00005882234,0.0004478065,0.00008994673,0.9583689,0.02058707,0.008075413,0.009479385,0.00002418389],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0153972,0.0003067134,0.9812374,0.0001914112,0.00003913879,0.0001237949,0.0003377221,0.001322778,0.001043962],"genre_scores_gemma":[0.2758,0.0004675949,0.7199345,0.0001157826,0.00006229158,0.0003579792,0.001191783,0.00004909273,0.002020981],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001974319,"threshold_uncertainty_score":0.004236698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01586172326651466,"score_gpt":0.2476227879000925,"score_spread":0.2317610646335779,"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."}}