{"id":"W2996310699","doi":"10.1109/snpd.2019.8935752","title":"Assets Predictive Maintenance Using Convolutional Neural Networks","year":2019,"lang":"en","type":"article","venue":"","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Convolutional neural network; Computer science; Support vector machine; Artificial intelligence; Perceptron; Predictive maintenance; Transformation (genetics); Pattern recognition (psychology); Random forest; Multilayer perceptron; Classifier (UML); Machine learning; Representation (politics); Data mining; Artificial neural network; Engineering","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":[0.000345718,0.0007242696,0.000399582,0.0006753361,0.0001846766,0.0005179106,0.0008467468,0.0004892367,0.0008853585],"category_scores_gemma":[0.001012907,0.0002760779,0.0004123256,0.0006075187,0.0001942609,0.0007665572,0.0003821363,0.0005797491,0.0002910153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001004027,"about_ca_system_score_gemma":0.0004885209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0195596,"about_ca_topic_score_gemma":0.01831837,"domain_scores_codex":[0.9998347,0.00001503471,0.000008728703,0.00006163539,0.00004524288,0.00003467133],"domain_scores_gemma":[0.9997084,0.0001109112,0.00004500558,0.0000359653,0.0000885558,0.00001110387],"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":[0.0001662988,0.0001173666,0.004627485,0.00005844494,0.00008500681,0.0001237002,0.00002875646,0.6303017,0.009530609,0.001115699,0.002907607,0.3509373],"study_design_scores_gemma":[0.00000150479,0.00001197801,0.0005549865,0.000002827363,0.000005404009,0.000009266449,0.000002624552,0.9974256,0.001455716,0.000326486,0.0002010743,0.000002441359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3290082,0.002164488,0.6530744,0.0006125828,0.0001961901,0.00008541699,0.001152135,0.007988718,0.005717752],"genre_scores_gemma":[0.9547276,0.0002763038,0.04156366,0.00007194911,0.00003195689,0.00002858111,0.0009545367,0.0000378169,0.002307667],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0195596,"threshold_uncertainty_score":0.03889149,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02025516740115408,"score_gpt":0.2445189863372457,"score_spread":0.2242638189360916,"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."}}