{"id":"W2139235138","doi":"10.1142/s0219691304000408","title":"PRE-PROCESSING DESIGN FOR MULTIWAVELET FILTERS USING NEURAL NETWORKS","year":2004,"lang":"en","type":"article","venue":"International Journal of Wavelets Multiresolution and Information Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Japan Society for the Promotion of Science; Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial neural network; Computer science; Stochastic neural network; Signal processing; Time delay neural network; Artificial intelligence; Algorithm; Digital signal processing; Computer hardware","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008027228,0.00119491,0.0007093861,0.00061437,0.0005099431,0.0009318991,0.0009977478,0.001503266,0.004635179],"category_scores_gemma":[0.001828687,0.0006252722,0.0007728224,0.0005447808,0.0006042505,0.001362082,0.000555494,0.001801434,0.001719386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006361234,"about_ca_system_score_gemma":0.0009233596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001198316,"about_ca_topic_score_gemma":0.002112757,"domain_scores_codex":[0.9995377,0.00007687663,0.00003300654,0.0000893022,0.0002144697,0.00004867808],"domain_scores_gemma":[0.9990034,0.0003786829,0.0001189581,0.000102517,0.000356346,0.00004020348],"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":[0.0005539072,0.0003227448,0.0006493184,0.0005653758,0.0001124988,0.0002168554,0.0001865753,0.2575142,0.2684101,0.01912563,0.002988262,0.4493546],"study_design_scores_gemma":[0.00004248026,0.0002523187,0.000369211,0.00004147215,0.00004784207,0.0001325277,0.00003089117,0.8839272,0.1044963,0.004551536,0.006072124,0.00003606752],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003011616,0.00007492727,0.9959583,0.00005055411,0.00003622648,0.00004539734,0.00001095123,0.0002012148,0.0006108286],"genre_scores_gemma":[0.07812872,0.0002720393,0.9181315,0.0001114942,0.0000521002,0.0002379081,0.00008826299,0.0001106943,0.002867392],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004635179,"threshold_uncertainty_score":0.01550627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03656340643534595,"score_gpt":0.3136403414119525,"score_spread":0.2770769349766066,"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."}}