{"id":"W4226247552","doi":"10.1109/access.2022.3157726","title":"Activities Recognition, Anomaly Detection and Next Activity Prediction Based on Neural Networks in Smart Homes","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Universiti Kebangsaan Malaysia","keywords":"Autoencoder; Computer science; Activity recognition; Anomaly detection; Artificial intelligence; Deep learning; Recurrent neural network; Artificial neural network; Machine learning; Long short term memory; Anomaly (physics); Pattern recognition (psychology)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000361736,0.0005122679,0.0005713195,0.0004798156,0.0001426779,0.0003761564,0.0005801332,0.0005513369,0.0005147978],"category_scores_gemma":[0.0006112474,0.0002186607,0.0003547428,0.0004614602,0.0002342443,0.000733596,0.0004517059,0.0005675345,0.0001662806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005188821,"about_ca_system_score_gemma":0.000420237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009090752,"about_ca_topic_score_gemma":0.01064782,"domain_scores_codex":[0.9997401,0.00004547474,0.00002101633,0.00009349096,0.00005295833,0.00004708568],"domain_scores_gemma":[0.9998386,0.00005423402,0.00003115456,0.00001685762,0.00004460608,0.00001460356],"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":[0.0003065275,0.0003613352,0.01116523,0.0001317621,0.0001315691,0.0003276856,0.0001171188,0.557475,0.009423391,0.002653877,0.002288554,0.415618],"study_design_scores_gemma":[0.000002672549,0.00002372335,0.001373969,0.000005249442,0.0000104146,0.00002079167,0.000009529655,0.9966182,0.0009043158,0.0007966807,0.0002301203,0.000004414255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1820718,0.002433075,0.810392,0.0004337258,0.0001317778,0.00005495971,0.0003181695,0.001596246,0.002568174],"genre_scores_gemma":[0.941329,0.0007020983,0.05498305,0.0001323118,0.00005720201,0.00005235378,0.0002951976,0.000018071,0.002430772],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009090752,"threshold_uncertainty_score":0.01807564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04288334898084416,"score_gpt":0.2593795181084371,"score_spread":0.2164961691275929,"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."}}