{"id":"W4230977378","doi":"10.35940/ijeat.a2084.109119","title":"Visual Modeling of Data using Convolutional Neural Networks","year":2019,"lang":"en","type":"article","venue":"International Journal of Engineering and Advanced Technology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Convolutional neural network; Computer science; Normalization (sociology); Artificial intelligence; Artificial neural network; Feedforward neural network; Feed forward; Deep learning; Computer vision; Pattern recognition (psychology); Machine learning; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000826355,0.00006380033,0.0001248165,0.000191231,0.00001394761,0.00001745638,0.0008239814,0.00004433818,0.00000152935],"category_scores_gemma":[0.00002061771,0.00005950375,0.00002392501,0.0001584511,0.00001921844,0.0004455712,0.0003120663,0.0001756648,2.207336e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001583333,"about_ca_system_score_gemma":0.00001631617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001105554,"about_ca_topic_score_gemma":1.360131e-7,"domain_scores_codex":[0.9993833,0.000003576025,0.0002574407,0.0001255254,0.000134672,0.00009549455],"domain_scores_gemma":[0.9994434,0.00004060789,0.0001483833,0.0001635728,0.0001780277,0.00002606445],"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":[0.000004685841,0.00001273716,0.0002195912,0.000002134421,0.00002387045,0.000004062173,0.00000282873,0.9594164,0.008795599,0.01578836,0.000001758055,0.01572791],"study_design_scores_gemma":[0.0002430417,0.00003060562,0.00004221141,0.00004261517,0.000003247317,0.0002146118,0.000006423731,0.9981954,0.000209102,0.0007701708,0.0001889474,0.00005365504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3546576,0.0004354387,0.6442138,0.0003182693,0.0003302398,0.00002170157,0.000001649131,0.00001786993,0.000003463204],"genre_scores_gemma":[0.9280803,0.00006992709,0.07174688,0.00001610784,0.00007806635,4.128893e-7,0.000001742539,0.00000432331,0.000002197365],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5734227,"threshold_uncertainty_score":0.2426493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0170601043596542,"score_gpt":0.287120931749509,"score_spread":0.2700608273898548,"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."}}