{"id":"W2389549257","doi":"","title":"Image Restoration Algorithm Base on BP Neural Network Optimized by Genetic and LM Algorithm","year":2010,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Optical Systems and Laser Technology","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Algorithm; Computer science; Genetic algorithm; Population-based incremental learning; Artificial neural network; Convergence (economics); Image (mathematics); Cultural algorithm; Value (mathematics); Base (topology); Artificial intelligence; Mathematics; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007365867,0.0001674225,0.0001642014,0.0000511155,0.0001212874,0.0000823003,0.0001654249,0.0001533518,0.00002511904],"category_scores_gemma":[4.369544e-7,0.0001688675,0.00003418229,0.0001720683,0.00006417211,0.00006169283,0.00004718583,0.0003058873,0.00006743333],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001890255,"about_ca_system_score_gemma":0.000004961191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000176485,"about_ca_topic_score_gemma":0.000005071779,"domain_scores_codex":[0.9991629,0.00001632325,0.0002259999,0.0002755444,0.0000666284,0.0002525905],"domain_scores_gemma":[0.9994853,0.00004958191,0.00003006328,0.0003003274,0.00003343341,0.0001012594],"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":[0.000001340842,0.00004432765,0.00001168447,0.00001518387,0.0000180542,0.000002313352,0.00001967155,0.00239465,0.02496422,0.0002449265,0.02130876,0.9509749],"study_design_scores_gemma":[0.0003485954,0.00002633739,0.0002812557,0.00000399702,0.00001164845,0.0000258818,0.000002990265,0.7294511,0.001295517,0.0002072642,0.2681539,0.0001915805],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009813641,0.0002342866,0.9882527,0.0002736905,0.00009039873,0.0005950114,0.00004797467,0.0004843026,0.0002080231],"genre_scores_gemma":[0.007334764,0.00002680835,0.991415,0.0001707791,0.0004510299,0.0004221358,0.00007539085,0.00004026761,0.00006386926],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9507833,"threshold_uncertainty_score":0.6886218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003081873257843279,"score_gpt":0.192437205091387,"score_spread":0.1893553318335437,"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."}}