{"id":"W4414749338","doi":"10.3390/s25196082","title":"Personalized Smart Home Automation Using Machine Learning: Predicting User Activities","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Home automation; Adaptability; Software deployment; Activity recognition; Automation; Boosting (machine learning); Enhanced Data Rates for GSM Evolution; Software; Field (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":[],"consensus_categories":[],"category_scores_codex":[0.0001319117,0.0001516295,0.0001546537,0.0002146318,0.0001185581,0.00005163211,0.00007467914,0.00005816033,0.00005390979],"category_scores_gemma":[0.00003708131,0.0001666625,0.0000567154,0.0002491048,0.00002743042,0.0001202233,0.00004479641,0.0001698435,0.00001374601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001616976,"about_ca_system_score_gemma":0.000009929913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009928371,"about_ca_topic_score_gemma":0.00002706628,"domain_scores_codex":[0.9992555,0.00004836439,0.0001618509,0.0001533083,0.0001517293,0.0002292773],"domain_scores_gemma":[0.9997206,0.00006314426,0.00002774011,0.0001424119,0.00001545612,0.00003067134],"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":[0.000007529455,0.000007191119,0.0214892,0.00009790072,0.0001268922,0.000005118211,0.0003949228,0.9736742,0.002882247,0.0006551962,0.000229343,0.0004302885],"study_design_scores_gemma":[0.0003086157,0.000007260746,0.006436618,0.00006034887,0.00004111252,0.000002466831,0.0003770401,0.9680179,0.002541659,0.00002459859,0.02202782,0.0001545963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9862731,0.0001462334,0.004108106,0.0000572412,0.0008606808,0.0000991427,0.000002654358,0.00106782,0.007385006],"genre_scores_gemma":[0.9932052,0.00003016525,0.001406234,0.00003011902,0.00009822927,0.000008377829,0.00001422183,0.00003746339,0.005170007],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02179848,"threshold_uncertainty_score":0.6796299,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009135004001628253,"score_gpt":0.215718626525094,"score_spread":0.2065836225234657,"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."}}