{"id":"W3004867950","doi":"10.1109/pacrim47961.2019.8985068","title":"Mobile User-Activity Prediction Utilizing LSTM Recurrent Neural Network","year":2019,"lang":"en","type":"article","venue":"","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; University of Victoria","funders":"","keywords":"Computer science; Artificial neural network; Data mining; Recurrent neural network; Big data; Artificial intelligence; Bandwidth (computing); Machine learning; Analytics; Mobile telephony; Real-time computing; Computer network; Mobile radio","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":[0.0003012145,0.0005725616,0.0004117099,0.0005447021,0.000171494,0.0003669184,0.000572956,0.0004797159,0.0008570636],"category_scores_gemma":[0.000991829,0.0002063211,0.0003918929,0.0005352811,0.0001176339,0.0005810526,0.0003228973,0.0006398948,0.0004404503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004527928,"about_ca_system_score_gemma":0.000358392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01534822,"about_ca_topic_score_gemma":0.0146571,"domain_scores_codex":[0.9998192,0.00002779566,0.00001301011,0.00006279124,0.0000395786,0.00003767576],"domain_scores_gemma":[0.9997675,0.0000674935,0.00003109677,0.00002015448,0.00009889134,0.00001490351],"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":[0.0005726222,0.0005515905,0.03186076,0.0001334367,0.0002403126,0.0005225261,0.0001551757,0.5201826,0.02194207,0.001468235,0.007324341,0.4150463],"study_design_scores_gemma":[0.000001831863,0.00001578765,0.001078229,0.000002148989,0.000006188571,0.00001233876,0.000005372685,0.9977816,0.0008272561,0.0001662995,0.00009986248,0.000003029332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5281119,0.001059021,0.4580094,0.0006589966,0.000304461,0.00009309641,0.00160723,0.004933588,0.005222166],"genre_scores_gemma":[0.9770229,0.0001844111,0.02002294,0.00005128499,0.00003717414,0.00003195706,0.0007945157,0.00002098761,0.00183399],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01534822,"threshold_uncertainty_score":0.03051776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02461317499427747,"score_gpt":0.303558281929084,"score_spread":0.2789451069348066,"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."}}