{"id":"W2189262360","doi":"10.32920/ryerson.14644767","title":"Developing pseudo random number generator based on neural networks and neurofuzzy systems","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Chaos-based Image/Signal Encryption","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"NIST; Pseudorandom number generator; Measure (data warehouse); Artificial neural network; Generator (circuit theory); Computer science; Random number generation; Fuzzy logic; Artificial intelligence; Algorithm; Data mining; Natural language processing; Physics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0005587515,0.0002802193,0.0002521331,0.0004154978,0.0001962204,0.0003395378,0.0003916955,0.0004708134,0.0009415402],"category_scores_gemma":[0.001476362,0.0001442211,0.0003196394,0.0003104181,0.0005039311,0.0009674707,0.0003555857,0.0004256577,0.000229463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003657514,"about_ca_system_score_gemma":0.0004106369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007375621,"about_ca_topic_score_gemma":0.0007400129,"domain_scores_codex":[0.9996778,0.0001064572,0.00001877733,0.00005259307,0.0001237111,0.00002064725],"domain_scores_gemma":[0.9996421,0.0001689689,0.0000370408,0.00004331879,0.00009540487,0.00001305478],"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":[0.0001448735,0.00008368896,0.002023173,0.0003834874,0.00009011679,0.0004743815,0.0002102519,0.44918,0.07071412,0.195424,0.00226037,0.2790115],"study_design_scores_gemma":[0.00001051084,0.00006053251,0.0002683675,0.00001403968,0.000009774661,0.000207619,0.000008851522,0.9697926,0.0118848,0.01403286,0.003694784,0.00001531946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02090248,0.0006436834,0.9744077,0.0001637047,0.00006728496,0.00008855783,0.0000273187,0.0003089611,0.003390241],"genre_scores_gemma":[0.6027094,0.001054031,0.3914599,0.0001249678,0.0000748384,0.000146549,0.00009914571,0.00004797232,0.004283189],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0009415402,"threshold_uncertainty_score":0.003149807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02347513729525818,"score_gpt":0.2520653172667134,"score_spread":0.2285901799714552,"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."}}