{"id":"W3119750220","doi":"10.5539/nct.v5n2p34","title":"Indoor Localization Based on Optimized KNN","year":2020,"lang":"en","type":"article","venue":"Network and Communication Technologies","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Fingerprint (computing); Hybrid positioning system; Hotspot (geology); Real-time computing; Positioning technology; Indoor positioning system; Wireless; Signal strength; Received signal strength indication; RSS; Positioning system; Artificial intelligence; Accelerometer; Telecommunications; Engineering; Node (physics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0004370762,0.0008982589,0.001115074,0.00104181,0.0006740055,0.0007639494,0.001213923,0.0009123972,0.002289873],"category_scores_gemma":[0.001692612,0.0004287,0.0006126822,0.001421282,0.0005431258,0.001336918,0.0009809266,0.0006108732,0.001083305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007908575,"about_ca_system_score_gemma":0.0009693044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01536964,"about_ca_topic_score_gemma":0.01291454,"domain_scores_codex":[0.9989911,0.000132463,0.00006204022,0.0003452392,0.0003065518,0.0001626163],"domain_scores_gemma":[0.9995334,0.0001012369,0.00005747467,0.00004733489,0.0002373551,0.00002326223],"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":[0.000209856,0.00004286101,0.001728494,0.0001307866,0.00006290098,0.00009368153,0.0001026307,0.6286975,0.00688757,0.004511871,0.002922884,0.354609],"study_design_scores_gemma":[0.00001165874,0.00004094228,0.0004261144,0.00001223164,0.00001764886,0.00008279303,0.00003454274,0.9937336,0.002055944,0.001972452,0.001594721,0.0000173295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01215025,0.0004346287,0.9836057,0.00007870743,0.00009160922,0.00003356567,0.00006017811,0.0007872459,0.002758126],"genre_scores_gemma":[0.6192763,0.0007872744,0.3691906,0.000213697,0.0001331979,0.0001502969,0.0005274446,0.0001980866,0.009523161],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01536964,"threshold_uncertainty_score":0.03056031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01157829333716283,"score_gpt":0.19910371742433,"score_spread":0.1875254240871672,"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."}}