{"id":"W2191460748","doi":"","title":"Automatic Number Carplate Recognition with Means Algorithm and Neural Network","year":2015,"lang":"en","type":"article","venue":"Journal of academic and applied studies","topic":"Vehicle License Plate Recognition","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Computer science; Fuzzy logic; Artificial intelligence; Sensitivity (control systems); Process (computing); Algorithm; Sample (material); Image (mathematics); Pattern recognition (psychology); Computer vision; Fuzzy control system; Time delay neural network; Engineering","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":[0.000526227,0.0006522465,0.0005395563,0.001122934,0.0004736511,0.0007148178,0.0008048755,0.0007821151,0.002496765],"category_scores_gemma":[0.0009582613,0.0003876472,0.0005808859,0.0006961875,0.0004256776,0.0009996559,0.0004437598,0.0008021011,0.0009742624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005328981,"about_ca_system_score_gemma":0.0006541201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003252516,"about_ca_topic_score_gemma":0.003203446,"domain_scores_codex":[0.9993891,0.00009561232,0.00002948761,0.0001513551,0.0002944721,0.00003991275],"domain_scores_gemma":[0.9996697,0.00008858084,0.00004290653,0.00003501321,0.0001524009,0.00001143857],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003146657,0.0001087973,0.001470136,0.0002290255,0.0001018425,0.00009723589,0.0001001814,0.04939696,0.06932091,0.00441873,0.003155278,0.8712863],"study_design_scores_gemma":[0.00002872859,0.0002165953,0.003285297,0.00003457813,0.00005542733,0.0003143684,0.0000530817,0.9250484,0.06121445,0.002209529,0.007469457,0.00007002476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02442431,0.000466995,0.9697148,0.0001077655,0.0001048702,0.00007920137,0.00004465128,0.00176381,0.003293611],"genre_scores_gemma":[0.2840227,0.0005153639,0.7043604,0.00009606002,0.00009020854,0.0002021006,0.0002370664,0.0001036042,0.01037247],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003252516,"threshold_uncertainty_score":0.008352578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03470792291996981,"score_gpt":0.2572645347065808,"score_spread":0.222556611786611,"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."}}