{"id":"W3002382881","doi":"10.36548/jscp.2019.2.001","title":"PERFORMANCE ENHANCEMENTS OF COGNITIVE RADIO NETWORKS USING THE IMPROVED FUZZY LOGIC","year":2019,"lang":"en","type":"article","venue":"Journal of Soft Computing Paradigm","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carbon Engineering (Canada)","funders":"","keywords":"Cognitive radio; Computer network; Computer science; Fuzzy logic; Throughput; Channel (broadcasting); Transmission (telecommunications); Wireless; Service (business); Spectrum management; Genetic algorithm; Channel allocation schemes; Telecommunications; Artificial intelligence; Machine learning","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":[0.0006933946,0.0004171507,0.0003424574,0.0003072371,0.0002964047,0.0007363745,0.0004476108,0.0004034963,0.0007457077],"category_scores_gemma":[0.001347927,0.0001066462,0.000351601,0.0002095967,0.0002425774,0.0003524865,0.0002891487,0.0004280159,0.00008810249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007280619,"about_ca_system_score_gemma":0.0005344643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004995594,"about_ca_topic_score_gemma":0.003606433,"domain_scores_codex":[0.9995963,0.0001194425,0.00001641805,0.0000494987,0.0001567103,0.00006166431],"domain_scores_gemma":[0.9995258,0.000258755,0.00004450647,0.00002147866,0.0001355341,0.00001393823],"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":[0.0003251357,0.0001412877,0.001323104,0.00009105686,0.00005725734,0.0001400834,0.000100791,0.8822236,0.01443272,0.00830058,0.0005615398,0.09230285],"study_design_scores_gemma":[0.000007813606,0.0001009747,0.0002142399,0.00000463289,0.00001115452,0.00001742337,0.000008288686,0.9967682,0.001624042,0.001079575,0.0001581969,0.000005454121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2779843,0.001098745,0.7016998,0.0003620902,0.0001281281,0.00006996156,0.00005916047,0.0004372203,0.01816061],"genre_scores_gemma":[0.9849635,0.0001264989,0.01434801,0.00002191034,0.00001030769,0.00001404973,0.00001202494,0.000004499599,0.0004991048],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004995594,"threshold_uncertainty_score":0.009932995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01701483151726205,"score_gpt":0.2558906280732207,"score_spread":0.2388757965559586,"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."}}