{"id":"W3159085127","doi":"10.18280/rces.080103","title":"Image Denoising Based on Improved Hybrid Genetic Algorithm","year":2021,"lang":"en","type":"article","venue":"Review of Computer Engineering Studies","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Direction Générale de la Recherche Scientifique et du Développement Technologique","keywords":"Crossover; Benchmark (surveying); Noise reduction; Computer science; Genetic algorithm; Artificial intelligence; Image (mathematics); Noise (video); Digital image; Population; Pattern recognition (psychology); Process (computing); Algorithm; Computer vision; Image processing; Machine learning; Geography","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":[0.0005685664,0.0005618125,0.0009730972,0.0009481641,0.0002765384,0.000706277,0.001047831,0.0009303458,0.0007632109],"category_scores_gemma":[0.001088939,0.0002536057,0.0008253137,0.0007381651,0.0003932356,0.0005149392,0.0004474464,0.0005511466,0.0002255245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005148484,"about_ca_system_score_gemma":0.0005552997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003796172,"about_ca_topic_score_gemma":0.002689678,"domain_scores_codex":[0.9996126,0.00007386364,0.00001674188,0.0000827337,0.0001807244,0.00003333867],"domain_scores_gemma":[0.9997057,0.0001256083,0.00002830878,0.00002425766,0.0001057029,0.00001034001],"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":[0.0000926733,0.00006256271,0.001085028,0.0001300237,0.0001549769,0.0001594453,0.0001305278,0.7490035,0.02373863,0.01008111,0.001354454,0.2140071],"study_design_scores_gemma":[0.00001023738,0.00003328768,0.0002081159,0.000008689111,0.00002175453,0.00006452832,0.000008056203,0.9947142,0.002469179,0.001248351,0.001205627,0.00000797945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02100359,0.0007639034,0.9746284,0.00008601375,0.00005259405,0.00003408514,0.00001499933,0.0004274669,0.002988995],"genre_scores_gemma":[0.4565008,0.001365879,0.5349534,0.0002068487,0.00007890596,0.0001831029,0.000138782,0.0001318355,0.006440424],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003796172,"threshold_uncertainty_score":0.007548153,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01501700426242396,"score_gpt":0.2823968678606595,"score_spread":0.2673798635982355,"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."}}