{"id":"https://openalex.org/W3120370003","doi":"https://doi.org/10.1145/3444370.3444572","title":"Indoor Localization Algorithm based on Attribute-Independent Weighted Naive Bayesian","display_name":"Indoor Localization Algorithm based on Attribute-Independent Weighted Naive Bayesian","publication_year":2020,"publication_date":"2020-12-04","ids":{"openalex":"https://openalex.org/W3120370003","doi":"https://doi.org/10.1145/3444370.3444572","mag":"3120370003"},"language":"en","primary_location":{"id":"doi:10.1145/3444370.3444572","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3444370.3444572","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 International Conference on Cyberspace Innovation of Advanced Technologies","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5067145421","display_name":"Dapeng Man","orcid":"https://orcid.org/0000-0003-1177-3693"},"institutions":[{"id":"https://openalex.org/I151727225","display_name":"Harbin Engineering University","ror":"https://ror.org/03x80pn82","country_code":"CN","type":"education","lineage":["https://openalex.org/I151727225"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Dapeng Man","raw_affiliation_strings":["Information Security Research Center, Harbin Engineering University, Harbin City, China"],"affiliations":[{"raw_affiliation_string":"Information Security Research Center, Harbin Engineering University, Harbin City, China","institution_ids":["https://openalex.org/I151727225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100451228","display_name":"Bing Liang","orcid":"https://orcid.org/0000-0001-9628-1836"},"institutions":[{"id":"https://openalex.org/I151727225","display_name":"Harbin Engineering University","ror":"https://ror.org/03x80pn82","country_code":"CN","type":"education","lineage":["https://openalex.org/I151727225"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Bing","raw_affiliation_strings":["Information Security Research Center, Harbin Engineering University, Harbin City, China"],"affiliations":[{"raw_affiliation_string":"Information Security Research Center, Harbin Engineering University, Harbin City, China","institution_ids":["https://openalex.org/I151727225"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059685245","display_name":"Jiguang Lv","orcid":"https://orcid.org/0000-0001-5502-7217"},"institutions":[{"id":"https://openalex.org/I151727225","display_name":"Harbin Engineering University","ror":"https://ror.org/03x80pn82","country_code":"CN","type":"education","lineage":["https://openalex.org/I151727225"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiguang Lv","raw_affiliation_strings":["Information Security Research Center, Harbin Engineering University, Harbin City, China"],"affiliations":[{"raw_affiliation_string":"Information Security Research Center, Harbin Engineering University, Harbin City, China","institution_ids":["https://openalex.org/I151727225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5067145421"],"corresponding_institution_ids":["https://openalex.org/I151727225"],"apc_list":null,"apc_paid":null,"fwci":0.3082,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.58634432,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10080","display_name":"Energy Efficient Wireless Sensor Networks","score":0.9567999839782715,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9517999887466431,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.8590781688690186},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6664047837257385},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6461691856384277},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6095597743988037},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.5731416344642639},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5513069033622742},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5401235222816467},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.5350858569145203},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.5328317880630493},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.46030643582344055},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.42429178953170776},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4188840091228485},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.38866403698921204}],"concepts":[{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.8590781688690186},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6664047837257385},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6461691856384277},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6095597743988037},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.5731416344642639},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5513069033622742},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5401235222816467},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.5350858569145203},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5328317880630493},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46030643582344055},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.42429178953170776},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4188840091228485},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38866403698921204},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3444370.3444572","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3444370.3444572","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 International Conference on Cyberspace Innovation of Advanced Technologies","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1715976565","display_name":null,"funder_award_id":"61771153,61971154","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1975028026","https://openalex.org/W2039506494","https://openalex.org/W2071236694","https://openalex.org/W2133951180","https://openalex.org/W2151034334","https://openalex.org/W2164692160","https://openalex.org/W2170589361","https://openalex.org/W2276725988","https://openalex.org/W2736051620","https://openalex.org/W2949737820"],"related_works":["https://openalex.org/W1975632186","https://openalex.org/W4296209631","https://openalex.org/W3027745756","https://openalex.org/W3205213561","https://openalex.org/W2531880140","https://openalex.org/W2126145365","https://openalex.org/W2036609560","https://openalex.org/W346861917","https://openalex.org/W2375795576","https://openalex.org/W4387382336"],"abstract_inverted_index":{"In":[0,60,164],"the":[1,6,12,28,45,56,64,70,91,128,131,137,148,153,161,165,168,183,189,200,206],"field":[2],"of":[3,30,93,114,130,139,152,193],"indoor":[4,71],"positioning,":[5,25],"existing":[7],"positioning":[8,72,84,98],"systems":[9,38],"usually":[10],"use":[11,39],"classification":[13],"methods":[14],"such":[15],"as":[16,144,199],"support":[17],"vector":[18],"machine":[19],"and":[20,33,100,135,147,150,158,188],"k-nearest":[21],"neighbor":[22],"to":[23,42,62,118,126,181,185,208],"complete":[24],"which":[26,89,202],"have":[27],"characteristics":[29],"high":[31,97],"complexity":[32],"complex":[34],"fingerprint":[35,95,120,162],"database.":[36,121,163],"Some":[37],"Bayesian":[40],"classifier":[41,79,174,207],"locate,":[43],"but":[44],"accuracy":[46,99],"is":[47,52,106,117],"not":[48],"high.":[49],"The":[50,103,111],"reason":[51],"that":[53],"they":[54],"ignore":[55],"correlation":[57],"between":[58],"eigenvalues.":[59],"order":[61],"solve":[63],"above":[65],"problems,":[66],"this":[67],"paper":[68],"proposes":[69],"method":[73],"combining":[74],"PCA":[75,123,177],"with":[76,86],"naive":[77,82,172],"bayes":[78,83],"(NBC),":[80],"namely":[81],"algorithm":[85,105],"independent":[87,170],"attributes,":[88],"has":[90],"advantages":[92],"simple":[94],"database,":[96],"fast":[101],"speed.":[102],"localization":[104],"divided":[107],"into":[108,205],"two":[109],"stages.":[110],"main":[112],"purpose":[113],"offline":[115],"training":[116],"establish":[119],"Firstly,":[122],"was":[124,175,178,197,203],"used":[125,180],"reduce":[127],"dimensionality":[129],"original":[132],"CSI":[133],"data,":[134],"then":[136],"eigenvalues":[138,155],"each":[140,194],"position":[141],"were":[142,156],"modeled":[143],"normal":[145],"distribution,":[146],"mean":[149],"variance":[151,190],"new":[154],"extracted":[157],"stored":[159],"in":[160],"test":[166],"stage,":[167],"attribute":[169],"weighted":[171],"bayesian":[173],"used.":[176],"also":[179],"process":[182],"data":[184],"be":[186],"classified,":[187],"contribution":[191],"rate":[192],"principal":[195],"component":[196],"recorded":[198],"weight,":[201],"brought":[204],"achieve":[209],"positioning.":[210]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
