{"id":"W3080933029","doi":"10.1109/access.2020.3018477","title":"Time-Series Data Classification and Analysis Associated With Machine Learning Algorithms for Cognitive Perception and Phenomenon","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute for Information and Communications Technology Promotion; Trent University; Nottingham Trent University","keywords":"Dynamic time warping; Computer science; Cognition; Electroencephalography; Artificial intelligence; Machine learning; Time series; Perception; Data analysis; Functional near-infrared spectroscopy; Pattern recognition (psychology); Data mining; Psychology; Neuroscience","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002115926,0.0008403923,0.0006893556,0.00237334,0.0004057687,0.001600183,0.000771057,0.0008197852,0.001971619],"category_scores_gemma":[0.006535468,0.0001972832,0.001031783,0.003106022,0.0006801767,0.001579151,0.0005276255,0.001418181,0.0009202593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005678009,"about_ca_system_score_gemma":0.0006737213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001279914,"about_ca_topic_score_gemma":0.0008470325,"domain_scores_codex":[0.9988161,0.0003489907,0.0001618524,0.0003044833,0.000325642,0.00004292655],"domain_scores_gemma":[0.9976526,0.001409633,0.0002312534,0.0002954847,0.0003749138,0.00003627773],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001533652,0.0002750784,0.01009797,0.001094973,0.0004232583,0.0003055196,0.0004128018,0.09632361,0.01735603,0.08294433,0.00787061,0.7827424],"study_design_scores_gemma":[0.00001176399,0.0001457496,0.006758537,0.0001403746,0.000091997,0.0002749083,0.0001793901,0.8991203,0.00877075,0.06911521,0.01533367,0.00005739537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01471904,0.002948402,0.9780232,0.0006509868,0.0002341325,0.0001228772,0.0003464393,0.0007415843,0.002213289],"genre_scores_gemma":[0.3488027,0.005591281,0.639486,0.0002490235,0.0006328,0.0005057715,0.001458462,0.0001569131,0.003116997],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00237334,"threshold_uncertainty_score":0.01119018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09380553808294963,"score_gpt":0.3029352006184366,"score_spread":0.209129662535487,"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."}}