{"id":"W4324134948","doi":"10.1109/tvt.2022.3219885","title":"A Multi-Objective Approach Based on Differential Evolution and Deep Learning Algorithms for VANETs","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières; École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Computer science; Ant colony optimization algorithms; Vehicular ad hoc network; Joins; Differential evolution; Task (project management); Genetic algorithm; Intelligent transportation system; Particle swarm optimization; Cluster analysis; Swarm intelligence; Algorithm; Reliability (semiconductor); Wireless ad hoc network; Artificial intelligence; Machine learning; Engineering; Wireless","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001578771,0.0003442634,0.0003408934,0.0009224659,0.000318633,0.00003087619,0.0001730793,0.000589191,0.000009834375],"category_scores_gemma":[0.00002005461,0.0003684793,0.0001548552,0.0008945512,0.0001209913,0.00007267457,0.000003100163,0.0008797774,0.0000408545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002123222,"about_ca_system_score_gemma":0.0000177529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008020316,"about_ca_topic_score_gemma":0.00002840495,"domain_scores_codex":[0.9983387,0.00005603522,0.0002553299,0.0005377777,0.0002056875,0.0006064587],"domain_scores_gemma":[0.9993346,0.000109297,0.00004194353,0.0003464181,0.00007680948,0.00009098789],"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":[0.00003532935,0.0001131666,0.00001153962,0.00005755862,0.0001170855,0.00001266503,0.00005245956,0.9569899,0.006589717,0.00004709194,0.00002222378,0.03595122],"study_design_scores_gemma":[0.001593338,0.0003164897,0.0002816651,0.00004297968,0.00009106952,0.00002607153,0.000155701,0.9880639,0.008542953,0.00009422335,0.0004378123,0.0003538186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1108704,0.0001155014,0.8852077,0.0000954815,0.0003997959,0.0007518948,0.00002294654,0.002506471,0.00002979861],"genre_scores_gemma":[0.9864215,0.00006327155,0.01250451,0.00002087947,0.00005047125,0.0007092929,0.00004318892,0.0001106335,0.00007621349],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8755511,"threshold_uncertainty_score":0.9998767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01093014564682203,"score_gpt":0.2218221835090596,"score_spread":0.2108920378622376,"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."}}