{"id":"https://openalex.org/W2758708146","doi":"https://doi.org/10.3390/rs9101001","title":"One-Class Classification of Airborne LiDAR Data in Urban Areas Using a Presence and Background Learning Algorithm","display_name":"One-Class Classification of Airborne LiDAR Data in Urban Areas Using a Presence and Background Learning Algorithm","publication_year":2017,"publication_date":"2017-09-27","ids":{"openalex":"https://openalex.org/W2758708146","doi":"https://doi.org/10.3390/rs9101001","mag":"2758708146"},"language":"en","primary_location":{"id":"doi:10.3390/rs9101001","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs9101001","pdf_url":"https://www.mdpi.com/2072-4292/9/10/1001/pdf?version=1506585915","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2072-4292/9/10/1001/pdf?version=1506585915","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5070526801","display_name":"Zurui Ao","orcid":"https://orcid.org/0000-0003-0444-8139"},"institutions":[{"id":"https://openalex.org/I96852419","display_name":"Capital Normal University","ror":"https://ror.org/005edt527","country_code":"CN","type":"education","lineage":["https://openalex.org/I96852419"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zurui Ao","raw_affiliation_strings":["Key Laboratory of 3D Information Acquisition of Education Ministry, School of Resources, Environment and Tourism, Capital Normal University, Beijing 100048, China"],"raw_orcid":"https://orcid.org/0000-0003-0444-8139","affiliations":[{"raw_affiliation_string":"Key Laboratory of 3D Information Acquisition of Education Ministry, School of Resources, Environment and Tourism, Capital Normal University, Beijing 100048, China","institution_ids":["https://openalex.org/I96852419"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083755109","display_name":"Yanjun Su","orcid":"https://orcid.org/0000-0001-7931-339X"},"institutions":[{"id":"https://openalex.org/I156087764","display_name":"University of California, Merced","ror":"https://ror.org/00d9ah105","country_code":"US","type":"education","lineage":["https://openalex.org/I156087764"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210158097","display_name":"Institute of Botany","ror":"https://ror.org/05hr3ch11","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210158097"]}],"countries":["CN","US"],"is_corresponding":false,"raw_author_name":"Yanjun Su","raw_affiliation_strings":["Sierra Nevada Research Institute, School of Engineering, University of California, Merced, CA 95343, USA","State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sierra Nevada Research Institute, School of Engineering, University of California, Merced, CA 95343, USA","institution_ids":["https://openalex.org/I156087764"]},{"raw_affiliation_string":"State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210158097"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009530693","display_name":"Wenkai Li","orcid":"https://orcid.org/0000-0001-6548-7882"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenkai Li","raw_affiliation_strings":["Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014968449","display_name":"Qinghua Guo","orcid":"https://orcid.org/0000-0002-1065-0838"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210158097","display_name":"Institute of Botany","ror":"https://ror.org/05hr3ch11","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210158097"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qinghua Guo","raw_affiliation_strings":["State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210158097"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042786812","display_name":"Jing Zhang","orcid":"https://orcid.org/0000-0002-9610-5821"},"institutions":[{"id":"https://openalex.org/I96852419","display_name":"Capital Normal University","ror":"https://ror.org/005edt527","country_code":"CN","type":"education","lineage":["https://openalex.org/I96852419"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jing Zhang","raw_affiliation_strings":["Key Laboratory of 3D Information Acquisition of Education Ministry, School of Resources, Environment and Tourism, Capital Normal University, Beijing 100048, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of 3D Information Acquisition of Education Ministry, School of Resources, Environment and Tourism, Capital Normal University, Beijing 100048, China","institution_ids":["https://openalex.org/I96852419"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5014968449","https://openalex.org/A5042786812"],"corresponding_institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210158097","https://openalex.org/I96852419"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2784},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2784},"fwci":2.1845,"has_fulltext":true,"cited_by_count":32,"citation_normalized_percentile":{"value":0.86125909,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"9","issue":"10","first_page":"1001","last_page":"1001"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9937000274658203,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9937000274658203,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/lidar","display_name":"Lidar","score":0.8339942693710327},{"id":"https://openalex.org/keywords/one-class-classification","display_name":"One-class classification","score":0.6927725076675415},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6915854215621948},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6835086345672607},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6167043447494507},{"id":"https://openalex.org/keywords/ranging","display_name":"Ranging","score":0.5307552814483643},{"id":"https://openalex.org/keywords/terrain","display_name":"Terrain","score":0.4968290627002716},{"id":"https://openalex.org/keywords/statistical-classification","display_name":"Statistical classification","score":0.48843780159950256},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.47623732686042786},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.46781086921691895},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4613592326641083},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.45705437660217285},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4422765374183655},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4212286174297333},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.41222673654556274},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.36423566937446594},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09815692901611328}],"concepts":[{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.8339942693710327},{"id":"https://openalex.org/C34872919","wikidata":"https://www.wikidata.org/wiki/Q7092302","display_name":"One-class classification","level":3,"score":0.6927725076675415},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6915854215621948},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6835086345672607},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6167043447494507},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.5307552814483643},{"id":"https://openalex.org/C161840515","wikidata":"https://www.wikidata.org/wiki/Q186131","display_name":"Terrain","level":2,"score":0.4968290627002716},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.48843780159950256},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.47623732686042786},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.46781086921691895},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4613592326641083},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.45705437660217285},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4422765374183655},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4212286174297333},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.41222673654556274},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.36423566937446594},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09815692901611328},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3390/rs9101001","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs9101001","pdf_url":"https://www.mdpi.com/2072-4292/9/10/1001/pdf?version=1506585915","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:8cfdf30ff26a4148a564e58e1d9528ad","is_oa":true,"landing_page_url":"https://doaj.org/article/8cfdf30ff26a4148a564e58e1d9528ad","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 9, Iss 10, p 1001 (2017)","raw_type":"article"},{"id":"pmh:oai:ir.ibcas.ac.cn:2S10CLM1/22016","is_oa":false,"landing_page_url":"http://ir.ibcas.ac.cn/handle/2S10CLM1/22016","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Editorial Material"},{"id":"pmh:oai:mdpi.com:/2072-4292/9/10/1001/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs9101001","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs9101001","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs9101001","pdf_url":"https://www.mdpi.com/2072-4292/9/10/1001/pdf?version=1506585915","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.8500000238418579,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2758708146.pdf","grobid_xml":"https://content.openalex.org/works/W2758708146.grobid-xml"},"referenced_works_count":69,"referenced_works":["https://openalex.org/W1565635109","https://openalex.org/W1576520375","https://openalex.org/W1588401315","https://openalex.org/W1966811787","https://openalex.org/W1970473112","https://openalex.org/W1973644502","https://openalex.org/W1975235721","https://openalex.org/W1976826936","https://openalex.org/W1979759959","https://openalex.org/W1980501707","https://openalex.org/W1993365566","https://openalex.org/W1994699846","https://openalex.org/W2001014393","https://openalex.org/W2014274114","https://openalex.org/W2024448972","https://openalex.org/W2030937046","https://openalex.org/W2031391888","https://openalex.org/W2032558547","https://openalex.org/W2034385314","https://openalex.org/W2034439306","https://openalex.org/W2039969237","https://openalex.org/W2042092732","https://openalex.org/W2043655847","https://openalex.org/W2059221395","https://openalex.org/W2076280802","https://openalex.org/W2076627662","https://openalex.org/W2082988498","https://openalex.org/W2087556827","https://openalex.org/W2092774531","https://openalex.org/W2096936329","https://openalex.org/W2107695795","https://openalex.org/W2123314391","https://openalex.org/W2123958887","https://openalex.org/W2124431629","https://openalex.org/W2129725504","https://openalex.org/W2131256165","https://openalex.org/W2131651374","https://openalex.org/W2131775048","https://openalex.org/W2132870739","https://openalex.org/W2134510195","https://openalex.org/W2139416101","https://openalex.org/W2140890695","https://openalex.org/W2148645123","https://openalex.org/W2153183512","https://openalex.org/W2153635508","https://openalex.org/W2155503567","https://openalex.org/W2168481151","https://openalex.org/W2172138805","https://openalex.org/W2239022495","https://openalex.org/W2322112410","https://openalex.org/W2417202714","https://openalex.org/W2472610039","https://openalex.org/W2500026632","https://openalex.org/W2516127106","https://openalex.org/W2552224582","https://openalex.org/W2555102119","https://openalex.org/W2564563592","https://openalex.org/W2572207425","https://openalex.org/W2590106682","https://openalex.org/W2599022140","https://openalex.org/W2603092519","https://openalex.org/W2963809831","https://openalex.org/W4285719527","https://openalex.org/W4388680028","https://openalex.org/W6635310694","https://openalex.org/W6658357288","https://openalex.org/W6682095516","https://openalex.org/W6684275732","https://openalex.org/W6879037577"],"related_works":["https://openalex.org/W2594043982","https://openalex.org/W3036493597","https://openalex.org/W2486104965","https://openalex.org/W4293094720","https://openalex.org/W2739701376","https://openalex.org/W4292814203","https://openalex.org/W2171206798","https://openalex.org/W2116045975","https://openalex.org/W1876722760","https://openalex.org/W2565151897"],"abstract_inverted_index":{"Automatic":[0],"classification":[1,32,76,93,160,208],"of":[2,13,37,68,88,154,209],"light":[3],"detection":[4],"and":[5,26,52,56,98,138,177,183],"ranging":[6],"(LiDAR)":[7],"data":[8,106],"in":[9,72,107,206],"urban":[10,109],"areas":[11],"is":[12,60,203],"great":[14],"importance":[15],"for":[16,65,152],"many":[17],"applications":[18],"such":[19],"as":[20],"generating":[21],"three-dimensional":[22],"(3D)":[23],"building":[24],"models":[25],"monitoring":[27],"power":[28,136],"lines.":[29],"Traditional":[30],"supervised":[31,75],"methods":[33,77,180],"require":[34],"training":[35,47,63],"samples":[36,48,64],"all":[38,153],"classes":[39,156],"to":[40,54,103,195],"construct":[41],"a":[42,57,66,90,129,191],"reliable":[43],"classifier.":[44],"However,":[45],"complete":[46],"are":[49,70],"normally":[50],"hard":[51],"costly":[53],"collect,":[55],"common":[58],"circumstance":[59],"that":[61,114],"only":[62],"class":[67,131],"interest":[69],"available,":[71],"which":[73,164],"traditional":[74],"may":[78],"be":[79],"inappropriate.":[80],"In":[81],"this":[82,201],"study,":[83],"we":[84],"investigated":[85],"the":[86,96,115,155,158,187,196,207,210],"possibility":[87],"using":[89],"novel":[91],"one-class":[92,170],"algorithm,":[94,102],"i.e.,":[95],"presence":[97],"background":[99],"learning":[100],"(PBL)":[101],"classify":[104,128],"LiDAR":[105,142,211],"an":[108],"scenario.":[110],"The":[111,149],"results":[112,161],"demonstrated":[113],"PBL":[116],"algorithm":[117,189],"implemented":[118],"by":[119],"back":[120],"propagation":[121],"(BP)":[122],"neural":[123],"network":[124],"(PBL-BP)":[125],"could":[126],"effectively":[127],"single":[130],"(e.g.,":[132],"building,":[133],"tree,":[134],"terrain,":[135],"line,":[137],"others)":[139],"from":[140,157,169],"airborne":[141],"point":[143,212],"cloud":[144],"with":[145],"very":[146,204],"high":[147],"accuracy.":[148],"mean":[150],"F-score":[151],"PBL-BP":[159,188],"was":[162,165],"0.94,":[163],"higher":[166],"than":[167],"those":[168],"support":[171],"vector":[172],"machine":[173],"(SVM),":[174],"biased":[175],"SVM,":[176],"maximum":[178],"entropy":[179],"(0.68,":[181],"0.82":[182],"0.93,":[184],"respectively).":[185],"Moreover,":[186],"yielded":[190],"comparable":[192],"overall":[193],"accuracy":[194],"multi-class":[197],"SVM":[198],"method.":[199],"Therefore,":[200],"method":[202],"promising":[205],"cloud.":[213]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":9},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2017-10-06T00:00:00"}
