{"id":"W4394833979","doi":"10.52783/jes.2049","title":"Integrating machine learning techniques with IoT sensors and connected vehicles to enable real-time traffic monitoring and Adaptive signal control systems","year":2024,"lang":"en","type":"article","venue":"Journal of Electrical Systems","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Context (archaeology); Real-time computing; SIGNAL (programming language); Adaptive control; Traffic flow (computer networking); Internet of Things; Control (management); Traffic congestion; Adaptive system; Adaptive learning; Artificial intelligence; Machine learning; Embedded system; Engineering; Computer network; Transport engineering","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":[0.0005641301,0.0001989395,0.0004219596,0.0004104245,0.00007736435,0.0002708303,0.0000764932,0.0000953956,9.822128e-7],"category_scores_gemma":[0.00003663944,0.0001458545,0.00004021251,0.0003681844,0.00001945551,0.0001438164,0.00001197752,0.0005191423,0.000001210024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001588646,"about_ca_system_score_gemma":0.00001938823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005497197,"about_ca_topic_score_gemma":0.000001316028,"domain_scores_codex":[0.9987409,0.000124745,0.0004614698,0.0001624269,0.0002607504,0.0002497127],"domain_scores_gemma":[0.9993153,0.0002659227,0.00009478017,0.00005392507,0.000103489,0.0001665523],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00075249,0.000102173,0.00161537,0.001554844,0.001851137,0.0009589932,0.001839535,0.3794006,0.5282872,0.002620634,0.006473561,0.07454346],"study_design_scores_gemma":[0.0003434288,0.001342531,0.00009276738,0.001426562,0.0001013629,0.0005641364,0.0003104916,0.9903933,0.003011314,0.000002849762,0.002198875,0.0002124144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8819675,0.0111737,0.1006516,0.00007769412,0.0003474332,0.000925289,0.000007872571,0.004053736,0.0007951092],"genre_scores_gemma":[0.9980351,0.0003896975,0.001095451,0.000002537696,0.0003289836,0.00002439715,5.063108e-7,0.00004132645,0.00008202675],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6109926,"threshold_uncertainty_score":0.5947773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005402330318705519,"score_gpt":0.1996631401457268,"score_spread":0.1942608098270213,"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."}}