{"id":"W4311511319","doi":"10.18280/i2m.210503","title":"Markov Renewal Prediction and Radial Kronecker Neural Network Based Handover for Seamless Mobility","year":2022,"lang":"en","type":"article","venue":"Instrumentation Mesure Métrologie","topic":"IPv6, Mobility, Handover, Networks, Security","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Handover; Computer science; Markov chain; Computer network; Quality of service; Kronecker delta; Wireless network; Markov model; Node (physics); Markov process; Real-time computing; Wireless; Distributed computing; Engineering; Telecommunications; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001142065,0.0002815675,0.0003174939,0.0000788177,0.0004042482,0.00007271533,0.0001836536,0.0001468261,0.0002198478],"category_scores_gemma":[0.00005057137,0.0003147659,0.0001241812,0.0002466141,0.0001009022,0.0002585292,0.00009942497,0.0003738813,0.000001500057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003671739,"about_ca_system_score_gemma":0.00005331615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000277079,"about_ca_topic_score_gemma":0.00008140735,"domain_scores_codex":[0.9978397,0.000319132,0.0004631202,0.0004842997,0.0003562029,0.0005374857],"domain_scores_gemma":[0.9991282,0.0002836086,0.0001003704,0.0003261584,0.00005286521,0.0001088009],"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":[0.0015247,0.0001332836,0.1221017,0.000131914,0.0001158239,0.000005301481,0.0002731783,0.8458791,0.0005641413,0.00007658757,0.006977788,0.02221641],"study_design_scores_gemma":[0.006610726,0.0004984467,0.09436145,0.000008166435,0.0001491118,0.00001445292,0.0001435393,0.8906432,0.0005451217,0.001775824,0.004818707,0.0004313087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9835441,0.000460664,0.009777079,0.0002261506,0.003653608,0.001343418,0.0004296122,0.0003977309,0.000167711],"genre_scores_gemma":[0.9955885,0.00002561866,0.002303933,0.0002798447,0.0005165568,0.000757278,0.0004624237,0.00004459044,0.00002130292],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04476399,"threshold_uncertainty_score":0.9999304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01364072269724732,"score_gpt":0.2349478449023368,"score_spread":0.2213071222050895,"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."}}