{"id":"https://openalex.org/W2536957381","doi":"https://doi.org/10.1109/fskd.2016.7603142","title":"Cascading model based back propagation neural network in enabling precise classification","display_name":"Cascading model based back propagation neural network in enabling precise classification","publication_year":2016,"publication_date":"2016-08-01","ids":{"openalex":"https://openalex.org/W2536957381","doi":"https://doi.org/10.1109/fskd.2016.7603142","mag":"2536957381"},"language":"en","primary_location":{"id":"doi:10.1109/fskd.2016.7603142","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fskd.2016.7603142","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","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/A5100355861","display_name":"Yang Liu","orcid":"https://orcid.org/0000-0002-3660-6464"},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]},{"id":"https://openalex.org/I24201400","display_name":"Chengdu University of Information Technology","ror":"https://ror.org/01yxwrh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I24201400"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Liu","raw_affiliation_strings":["School of Electrical Engineering and Information, Sichuan University, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering and Information, Sichuan University, Chengdu, China","institution_ids":["https://openalex.org/I24201400","https://openalex.org/I24185976"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044160052","display_name":"Weizhe Jing","orcid":"https://orcid.org/0009-0006-1496-3694"},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]},{"id":"https://openalex.org/I24201400","display_name":"Chengdu University of Information Technology","ror":"https://ror.org/01yxwrh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I24201400"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weizhe Jing","raw_affiliation_strings":["School of Electrical Engineering and Information, Sichuan University, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering and Information, Sichuan University, Chengdu, China","institution_ids":["https://openalex.org/I24201400","https://openalex.org/I24185976"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046289864","display_name":"Lixiong Xu","orcid":"https://orcid.org/0000-0002-1697-5684"},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]},{"id":"https://openalex.org/I24201400","display_name":"Chengdu University of Information Technology","ror":"https://ror.org/01yxwrh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I24201400"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lixiong Xu","raw_affiliation_strings":["School of Electrical Engineering and Information, Sichuan University, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering and Information, Sichuan University, Chengdu, China","institution_ids":["https://openalex.org/I24201400","https://openalex.org/I24185976"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.3249,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.87612851,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"7","last_page":"11"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.996399998664856,"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"}},"topics":[{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.996399998664856,"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"}},{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9876000285148621,"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"}},{"id":"https://openalex.org/T13717","display_name":"Advanced Algorithms and Applications","score":0.9824000000953674,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7695391178131104},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.7552075386047363},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.7054632902145386},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6829730272293091},{"id":"https://openalex.org/keywords/sigmoid-function","display_name":"Sigmoid function","score":0.6788105964660645},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.560793936252594},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5217291116714478},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5024802684783936},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4984748363494873},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.44268545508384705},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.40410274267196655},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2892066240310669}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7695391178131104},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7552075386047363},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.7054632902145386},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6829730272293091},{"id":"https://openalex.org/C81388566","wikidata":"https://www.wikidata.org/wiki/Q526668","display_name":"Sigmoid function","level":3,"score":0.6788105964660645},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.560793936252594},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5217291116714478},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5024802684783936},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4984748363494873},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.44268545508384705},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40410274267196655},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2892066240310669},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fskd.2016.7603142","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fskd.2016.7603142","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1825077972","https://openalex.org/W2007933982","https://openalex.org/W2025680518","https://openalex.org/W2043708219","https://openalex.org/W2086964121","https://openalex.org/W2109552880","https://openalex.org/W2137351756","https://openalex.org/W2168816827","https://openalex.org/W2176499795","https://openalex.org/W2534579575","https://openalex.org/W6728646047","https://openalex.org/W6903033436"],"related_works":["https://openalex.org/W2894173309","https://openalex.org/W3001577138","https://openalex.org/W2240896738","https://openalex.org/W2115605526","https://openalex.org/W3170224572","https://openalex.org/W4387932263","https://openalex.org/W2993645418","https://openalex.org/W2098962763","https://openalex.org/W3093883775","https://openalex.org/W2790814253"],"abstract_inverted_index":{"Artificial":[0],"Neural":[1,43],"Network":[2,44],"has":[3,46,99,114],"been":[4],"widely":[5],"used":[6],"in":[7,70,117,137,141],"types":[8],"of":[9,31,108,122,162],"tasks":[10],"which":[11,155],"are":[12,28],"related":[13],"to":[14,53,66,77,105,170],"classification,":[15],"for":[16],"example":[17],"image":[18],"annotation,":[19],"pattern":[20],"recognition,":[21],"trend":[22],"prediction":[23],"and":[24,63,72,95,110],"so":[25],"on.":[26],"There":[27],"a":[29,149],"number":[30,107,121],"neural":[32],"network":[33],"algorithm":[34],"theories":[35],"based":[36,152],"on":[37,82],"various":[38],"concepts,":[39],"however,":[40],"Back":[41],"Propagation":[42],"(BPNN)":[45],"become":[47],"the":[48,68,79,90,127,159,182],"most":[49],"famous":[50],"one":[51,101],"due":[52],"its":[54],"remarkable":[55],"function":[56],"approximation":[57],"ability.":[58],"BPNN":[59,86,98,113,153],"employs":[60,167],"feed":[61,75],"forward":[62,76],"back":[64],"propagation":[65],"tune":[67],"parameters":[69],"neurons,":[71],"uses":[73],"only":[74],"execute":[78],"classification.":[80],"Based":[81],"gradient":[83],"descent":[84],"mechanism,":[85],"can":[87,180],"effectively":[88],"find":[89],"mapping":[91],"relationships":[92],"between":[93],"input":[94],"output.":[96],"However,":[97,133],"met":[100],"critical":[102],"issue.":[103],"Due":[104],"large":[106,120],"sum":[109],"sigmoid":[111],"operations,":[112],"low":[115],"efficiency":[116],"dealing":[118],"with":[119],"instances.":[123],"To":[124],"speed":[125,171],"up":[126,172],"processing,":[128],"researchers":[129],"employed":[130],"data":[131,135],"separation.":[132],"simple":[134],"separation":[136],"training":[138],"phase":[139],"results":[140,176],"classification":[142,160,183],"accuracy":[143,161],"loss.":[144],"Therefore,":[145],"this":[146],"paper":[147,165],"presents":[148],"cascading":[150],"model":[151],"(CBPNN)":[154],"aims":[156],"at":[157],"enhancing":[158],"BPNN.":[163],"The":[164,174],"further":[166],"multi-threading":[168],"technique":[169],"CBPNN.":[173],"experimental":[175],"indicate":[177],"that":[178],"CBPNN":[179],"improve":[181],"precision":[184],"whilst":[185],"maintains":[186],"satisfied":[187],"efficiency.":[188]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}