{"id":"W4246924798","doi":"10.5383/swes.8.01.006","title":"Smart Home System for Energy Saving using Genetic- Fuzzy-Neural Networks Approach","year":2016,"lang":"en","type":"article","venue":"International Journal of Sustainable Water and Environmental Systems","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Home automation; Computer science; Artificial neural network; Fuzzy logic; Genetic algorithm; Fuzzy control system; Energy (signal processing); Smart grid; Control (management); Artificial intelligence; Engineering; Machine learning; Telecommunications","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":[],"consensus_categories":[],"category_scores_codex":[0.0002550565,0.0001771905,0.000222087,0.0001735544,0.00007386581,0.0001141088,0.0002368708,0.00006320718,0.000004138728],"category_scores_gemma":[0.000002000405,0.0001145106,0.0000890773,0.00002291563,0.00003937084,0.0003295352,0.0001232416,0.00005332496,6.550773e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00079749,"about_ca_system_score_gemma":0.000003759398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005508356,"about_ca_topic_score_gemma":3.042549e-7,"domain_scores_codex":[0.9986761,0.00003157246,0.0004627935,0.0001428924,0.0002993302,0.0003873105],"domain_scores_gemma":[0.9996431,0.00002607765,0.00009417641,0.00009779054,0.00003849566,0.0001003411],"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":[0.00005842776,0.00002547726,0.003386897,0.0001310319,0.0003695118,0.0001491377,0.0001045559,0.9874839,0.00611941,0.0007877829,0.0001657989,0.001218124],"study_design_scores_gemma":[0.003009605,0.0001658536,0.002471353,0.0002593075,0.0001385283,0.002016614,0.008075682,0.9575319,0.00258456,0.0001231501,0.02299375,0.0006297199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5633827,0.001075217,0.4328478,0.00002602671,0.002192676,0.0001493867,0.000009392552,0.00003321167,0.0002836065],"genre_scores_gemma":[0.9980857,0.000116191,0.0003788787,0.00001010765,0.0009116208,0.0000227063,0.00001037431,0.00004285414,0.00042161],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.434703,"threshold_uncertainty_score":0.4669607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005599015359676197,"score_gpt":0.1672273999331688,"score_spread":0.1616283845734927,"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."}}