{"id":"W4410310416","doi":"10.18280/ijsse.150307","title":"SIOPA-DLMUC: A Self-Improved Orca Predation Algorithm with Deep Learning for Enhancing 5G Enabled Cognitive Radio Network Security","year":2025,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cognitive radio; Cognition; Computer science; Artificial intelligence; Algorithm; Computer network; Psychology; Telecommunications; Neuroscience; Wireless","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":[0.001372827,0.001230445,0.001226851,0.0008477847,0.000739369,0.0009988177,0.003046402,0.002039154,0.001740232],"category_scores_gemma":[0.004454817,0.0004022131,0.0007380993,0.0004707759,0.001047893,0.001411507,0.002143945,0.002083669,0.0004490934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001314728,"about_ca_system_score_gemma":0.002071273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00819085,"about_ca_topic_score_gemma":0.01075757,"domain_scores_codex":[0.9992892,0.0001488987,0.00003046119,0.0001665597,0.0001950769,0.0001697335],"domain_scores_gemma":[0.998805,0.000512076,0.0001351875,0.0001223932,0.0003134475,0.0001118696],"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":[0.0003483452,0.0004180657,0.008172419,0.0001352171,0.0002541046,0.0002855003,0.0001699406,0.5450705,0.008011217,0.007423887,0.01331174,0.4163992],"study_design_scores_gemma":[0.00001007598,0.00005222209,0.0001875813,0.00000723396,0.00000892248,0.00002901149,0.000007933503,0.9971635,0.0008570161,0.001155991,0.0005140934,0.000006440685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1509285,0.002027924,0.8317069,0.001785838,0.000481464,0.0002694424,0.0002794833,0.004102217,0.008418182],"genre_scores_gemma":[0.828953,0.0003520787,0.1592187,0.001720598,0.0001305302,0.000248365,0.0006767577,0.0001895079,0.008510337],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00819085,"threshold_uncertainty_score":0.01628631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002545042861739545,"score_gpt":0.2013365556424139,"score_spread":0.1987915127806743,"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."}}