{"id":"W4205770184","doi":"10.4018/ijsi.289170","title":"KDA-Based WKNN-SVM Method for Activity Recognition System From Smartphone Data","year":2022,"lang":"en","type":"article","venue":"International Journal of Software Innovation","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Support vector machine; Accelerometer; Artificial intelligence; Computer science; Pattern recognition (psychology); Gyroscope; Linear discriminant analysis; Kernel (algebra); Activity recognition; Process (computing); Machine learning; Data mining; Engineering; 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":[0.002986401,0.0001808412,0.0003286549,0.000924868,0.0002212417,0.0002962843,0.002331632,0.00007121234,0.00007198627],"category_scores_gemma":[0.001063703,0.0001964236,0.0001234439,0.0009512538,0.00001936494,0.002177572,0.0005629583,0.0003933168,0.00001352408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007483643,"about_ca_system_score_gemma":0.0005312572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001794742,"about_ca_topic_score_gemma":0.00001774033,"domain_scores_codex":[0.9966515,0.0003865994,0.0009826017,0.0004509279,0.001347752,0.0001806153],"domain_scores_gemma":[0.9927852,0.00145277,0.001956273,0.0005565208,0.003196658,0.00005259759],"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":[0.0004533984,0.0003239856,0.0009953892,0.00003271441,0.0003426122,0.00005039718,0.000180967,0.0004479006,0.007965966,0.0004889221,0.005315629,0.9834021],"study_design_scores_gemma":[0.02304536,0.002163171,0.02130469,0.001612105,0.0003573921,0.002886309,0.001345875,0.6083151,0.111996,0.02943534,0.1949036,0.002635052],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05229961,0.00003368914,0.938183,0.002337199,0.00539384,0.00030361,0.001282251,0.0001367409,0.00003010853],"genre_scores_gemma":[0.7428035,0.000001126566,0.2550929,0.0005921586,0.0006886426,0.00006439983,0.0007141184,0.00002087748,0.00002224618],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9807671,"threshold_uncertainty_score":0.8009923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1039365031944924,"score_gpt":0.3432971586099202,"score_spread":0.2393606554154277,"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."}}