{"id":"W1969134570","doi":"10.1088/1742-6596/256/1/012014","title":"CUDA-accelerated genetic feedforward-ANN training for data mining","year":2010,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Speedup; Computer science; CUDA; Artificial neural network; Genetic algorithm; Lift (data mining); Genetic programming; Graphics; Context (archaeology); Set (abstract data type); Artificial intelligence; Feed forward; Machine learning; Data mining; Parallel computing; Computer engineering; Computer graphics (images)","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":[0.000599657,0.0007320832,0.0004473679,0.0005846476,0.0006269899,0.000690656,0.001596453,0.0007096234,0.003574595],"category_scores_gemma":[0.00325024,0.0003267908,0.0004101858,0.001204211,0.0003956825,0.0005599102,0.0005177983,0.001138695,0.001150467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008947098,"about_ca_system_score_gemma":0.001490242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01479711,"about_ca_topic_score_gemma":0.0213148,"domain_scores_codex":[0.9996277,0.000103131,0.00002571924,0.00006567212,0.0001345628,0.00004324981],"domain_scores_gemma":[0.9990174,0.0003364613,0.00006149696,0.0001725169,0.0003646239,0.00004748059],"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":[0.0002788219,0.0002719501,0.003922379,0.0001988011,0.0001239869,0.000286897,0.0001735322,0.5449599,0.009961001,0.01068387,0.01844703,0.4106918],"study_design_scores_gemma":[0.00001961297,0.00001799602,0.0002295031,0.000005052568,0.00000540335,0.00002127018,0.000009503255,0.9935502,0.002749239,0.001489781,0.001897435,0.000005048733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05898281,0.0003664256,0.9184838,0.0004539054,0.0001910053,0.0001326405,0.0002777294,0.01304513,0.008066481],"genre_scores_gemma":[0.2126916,0.0001258924,0.7827609,0.0001348957,0.00002382548,0.0002715832,0.0004888923,0.0002552521,0.003247154],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01479711,"threshold_uncertainty_score":0.02942199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1484255488863258,"score_gpt":0.3266317413211823,"score_spread":0.1782061924348565,"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."}}