{"id":"W777949165","doi":"10.1007/s10489-015-0682-x","title":"Optimizing channel selection for cognitive radio networks using a distributed Bayesian learning automata-based approach","year":2015,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Learning automata; Cognitive radio; Channel (broadcasting); Key (lock); Selection (genetic algorithm); Nash equilibrium; Network packet; Collision; Computer network; Point (geometry); Automaton; Distributed computing; Theoretical computer science; Machine learning; Mathematical optimization; Telecommunications; Wireless; Computer security","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":[0.001872955,0.0009887805,0.002366832,0.001019725,0.000880469,0.001697438,0.001913227,0.002073523,0.002513285],"category_scores_gemma":[0.007624533,0.001052994,0.001048299,0.0007946425,0.001895721,0.001685917,0.001612176,0.001556726,0.0002817556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002009208,"about_ca_system_score_gemma":0.002111077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01416533,"about_ca_topic_score_gemma":0.01405154,"domain_scores_codex":[0.998877,0.0004918881,0.00005255109,0.0002192916,0.0002137317,0.0001455105],"domain_scores_gemma":[0.9938322,0.005082774,0.0002739791,0.0001542798,0.0004930344,0.000163621],"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":[0.00002356146,0.00002204174,0.0001831041,0.00001424678,0.00002124806,0.00001617323,0.00002023409,0.9913004,0.0001484097,0.00405448,0.0001565272,0.004039688],"study_design_scores_gemma":[0.000005088516,0.000005309887,0.00001864585,0.000001460446,0.000003610302,0.000002094552,0.000002435088,0.9974123,0.00003214554,0.002489121,0.00002588495,0.000001917334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03746552,0.0002547929,0.9580513,0.0004294682,0.0000500037,0.00006537366,0.00005538886,0.000222153,0.003405934],"genre_scores_gemma":[0.9178891,0.0001683778,0.0790283,0.0001576787,0.00005967431,0.0001872665,0.00007365856,0.00006732431,0.002368642],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01416533,"threshold_uncertainty_score":0.02816576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04115730657447952,"score_gpt":0.2681701940156795,"score_spread":0.2270128874412,"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."}}