{"id":"W4404125287","doi":"10.1109/access.2024.3493753","title":"Cycle Maximum Queue Length Estimation: An Integrated Deep Learning and Adaptive Neuro-Fuzzy Inference System Framework","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Adaptive neuro fuzzy inference system; Artificial intelligence; Inference; Deep learning; Queue; Neuro-fuzzy; Estimation; Machine learning; Fuzzy control system; Fuzzy logic; Engineering; Computer network","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":[],"consensus_categories":[],"category_scores_codex":[0.0001230006,0.0002228969,0.000193183,0.000146794,0.0001091404,0.0005787499,0.0003331743,0.0001445392,0.00000915754],"category_scores_gemma":[0.0001369516,0.0002171349,0.00001854118,0.0004423041,0.00005341627,0.00201494,0.00007566635,0.0006690182,0.00001805467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001020208,"about_ca_system_score_gemma":0.0000233779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004559166,"about_ca_topic_score_gemma":0.00001551894,"domain_scores_codex":[0.9990111,0.00004621324,0.0002234913,0.0003442714,0.0001430824,0.0002318671],"domain_scores_gemma":[0.9993406,0.0002408159,0.00003610486,0.0002326416,0.00006224896,0.00008759591],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002771037,0.00002275368,0.0003339817,0.001284603,0.00007099011,0.0001727412,0.001145598,0.3865521,0.001046429,0.01134278,0.0002948291,0.5977055],"study_design_scores_gemma":[0.00004842102,0.00005204647,0.00007886501,0.0007789356,0.00001791143,0.00003073035,0.0001373192,0.9795504,0.004452269,0.01318072,0.001383275,0.0002891469],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02924725,0.0008233227,0.9642965,0.00002450265,0.0004154279,0.0001449914,0.00001561945,0.004413281,0.0006191208],"genre_scores_gemma":[0.9152144,0.0001310303,0.08438967,0.00002438756,0.00008757145,0.00005588358,0.00002495453,0.00006641113,0.000005683869],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8859671,"threshold_uncertainty_score":0.8854505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01902714524493582,"score_gpt":0.3136609760394991,"score_spread":0.2946338307945632,"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."}}