{"id":"W2888512292","doi":"10.1049/iet-net.2018.5041","title":"Optimising the power using firework‐based evolutionary algorithms for emerging IoT applications","year":2018,"lang":"en","type":"article","venue":"IET Networks","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thompson Rivers University; Simon Fraser University","funders":"","keywords":"Computer science; Internet of Things; Evolutionary algorithm; Particle swarm optimization; Metaheuristic; Population; Cluster (spacecraft); Algorithm; Distributed computing; Mathematical optimization; Artificial intelligence; Computer network; Mathematics","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0005810058,0.0002510721,0.0001964601,0.0001053062,0.001423263,0.0003009116,0.001326162,0.0001834454,0.00001812559],"category_scores_gemma":[0.00002361301,0.0002106821,0.0001621431,0.00117938,0.0002488353,0.0002119344,0.0002920302,0.0002798402,0.00001073503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001239572,"about_ca_system_score_gemma":0.00009358167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001619554,"about_ca_topic_score_gemma":0.000005928266,"domain_scores_codex":[0.997726,0.0001002377,0.0003590423,0.000629271,0.0003598256,0.0008256396],"domain_scores_gemma":[0.9977937,0.0005173024,0.0002032596,0.00103137,0.0003286715,0.000125649],"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":[0.000008160333,0.00004494487,0.00004107074,0.00000289236,0.00002007177,0.00000130903,0.00009482897,0.9728575,0.00004636064,0.005337364,0.002466207,0.0190793],"study_design_scores_gemma":[0.000231694,0.00004889658,0.00008283137,0.0000572582,0.00001703109,0.00001185182,0.00002327857,0.9765767,0.0001296811,0.0002788347,0.02227592,0.0002659569],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001359738,0.0008794763,0.9941608,0.0009950394,0.001260504,0.0005520381,0.000002637973,0.0003100742,0.0004797478],"genre_scores_gemma":[0.5551824,0.00001087572,0.4418983,0.001009268,0.001653737,0.0001181003,0.00001082837,0.0000398102,0.00007669459],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5538226,"threshold_uncertainty_score":0.9998767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02602404534061232,"score_gpt":0.2851696115422136,"score_spread":0.2591455662016013,"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."}}