{"id":"W4411920999","doi":"10.1016/j.eswa.2025.128806","title":"Granular computing-based fuzzy deep neural network for long-tailed fault diagnosis: Design and analysis","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China; Key Research and Development Program of Liaoning Province; Key Project of Research and Development Plan of Hunan Province","keywords":"Computer science; Artificial neural network; Artificial intelligence; Fuzzy logic; Neuro-fuzzy; Fault (geology); Data mining; Granular computing; Machine learning; Fuzzy control system; Geology; Seismology; Rough set","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003345086,0.0002189819,0.0003952578,0.0001879754,0.0005772531,0.0003433761,0.0006208072,0.00008472545,8.637879e-7],"category_scores_gemma":[0.00001122221,0.0001690613,0.0001060086,0.001748181,0.00006632429,0.0001154185,0.00007252202,0.00007965056,0.000003366613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004181565,"about_ca_system_score_gemma":0.00005181897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007556055,"about_ca_topic_score_gemma":0.00003368422,"domain_scores_codex":[0.9983279,0.0001258655,0.0003589742,0.0006427881,0.0001801749,0.0003643147],"domain_scores_gemma":[0.9981993,0.0005453091,0.0001609524,0.0008163306,0.0001611115,0.0001170045],"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":[0.00003228366,0.0001843443,0.04884469,0.00009507512,0.0003578558,0.000004475226,0.0003689732,0.8929684,0.00000649277,0.03465327,0.004406202,0.01807795],"study_design_scores_gemma":[0.0005139659,0.00007293471,0.005443854,0.00003609184,0.0001249328,0.000002724764,0.00003973316,0.9879736,0.00001267146,0.0004415557,0.005118739,0.0002191838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0003370551,0.00494772,0.9908789,0.001052206,0.0001139355,0.002290523,0.000005033392,0.0002326189,0.000141991],"genre_scores_gemma":[0.7886593,0.00002325639,0.2042754,0.000689789,0.0001351914,0.006135285,0.00004067862,0.00001277104,0.00002830818],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7883222,"threshold_uncertainty_score":0.6894118,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01585866724931555,"score_gpt":0.2638451972552217,"score_spread":0.2479865300059061,"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."}}