{"id":"https://openalex.org/W4407609526","doi":"https://doi.org/10.1108/dta-06-2024-0647","title":"A new hybrid fuzzy bio-inspired classifier for cancer detection using cuckoo optimization and hyper-planes","display_name":"A new hybrid fuzzy bio-inspired classifier for cancer detection using cuckoo optimization and hyper-planes","publication_year":2025,"publication_date":"2025-02-16","ids":{"openalex":"https://openalex.org/W4407609526","doi":"https://doi.org/10.1108/dta-06-2024-0647"},"language":"en","primary_location":{"id":"doi:10.1108/dta-06-2024-0647","is_oa":false,"landing_page_url":"https://doi.org/10.1108/dta-06-2024-0647","pdf_url":null,"source":{"id":"https://openalex.org/S4210171756","display_name":"Data Technologies and Applications","issn_l":"2514-9288","issn":["2514-9288","2514-9318"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319811","host_organization_name":"Emerald Publishing Limited","host_organization_lineage":["https://openalex.org/P4310319811"],"host_organization_lineage_names":["Emerald Publishing Limited"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Technologies and Applications","raw_type":"journal-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/A5041860739","display_name":"Majid Abdolrazzagh-Nezhad","orcid":"https://orcid.org/0000-0002-3509-257X"},"institutions":[{"id":"https://openalex.org/I97626857","display_name":"University of Birjand","ror":"https://ror.org/03g4hym73","country_code":"IR","type":"education","lineage":["https://openalex.org/I97626857"]}],"countries":["IR"],"is_corresponding":true,"raw_author_name":"Majid Abdolrazzagh-Nezhad","raw_affiliation_strings":["Birjand University of Technology Department of Computer Science, Faculty of Computer and Industrial Engineering, , ,"],"affiliations":[{"raw_affiliation_string":"Birjand University of Technology Department of Computer Science, Faculty of Computer and Industrial Engineering, , ,","institution_ids":["https://openalex.org/I97626857"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010987704","display_name":"Shaghayegh Izadpanah","orcid":null},"institutions":[{"id":"https://openalex.org/I86958956","display_name":"Ferdowsi University of Mashhad","ror":"https://ror.org/00g6ka752","country_code":"IR","type":"education","lineage":["https://openalex.org/I86958956"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Shaghayegh Izadpanah","raw_affiliation_strings":["Ferdowsi University of Mashhad Department of Computer Engineering, , ,"],"affiliations":[{"raw_affiliation_string":"Ferdowsi University of Mashhad Department of Computer Engineering, , ,","institution_ids":["https://openalex.org/I86958956"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5041860739"],"corresponding_institution_ids":["https://openalex.org/I97626857"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.02671131,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"59","issue":"3","first_page":"416","last_page":"451"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9775999784469604,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9775999784469604,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/cuckoo-search","display_name":"Cuckoo search","score":0.6668013334274292},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5957638025283813},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.569280743598938},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5410887002944946},{"id":"https://openalex.org/keywords/cuckoo","display_name":"Cuckoo","score":0.49779319763183594},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.496371328830719},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4159855246543884},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38237136602401733},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.20736169815063477},{"id":"https://openalex.org/keywords/particle-swarm-optimization","display_name":"Particle swarm optimization","score":0.08563035726547241}],"concepts":[{"id":"https://openalex.org/C117241572","wikidata":"https://www.wikidata.org/wiki/Q5192379","display_name":"Cuckoo search","level":3,"score":0.6668013334274292},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5957638025283813},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.569280743598938},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5410887002944946},{"id":"https://openalex.org/C2776810535","wikidata":"https://www.wikidata.org/wiki/Q26381","display_name":"Cuckoo","level":2,"score":0.49779319763183594},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.496371328830719},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4159855246543884},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38237136602401733},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.20736169815063477},{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.08563035726547241},{"id":"https://openalex.org/C90856448","wikidata":"https://www.wikidata.org/wiki/Q431","display_name":"Zoology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1108/dta-06-2024-0647","is_oa":false,"landing_page_url":"https://doi.org/10.1108/dta-06-2024-0647","pdf_url":null,"source":{"id":"https://openalex.org/S4210171756","display_name":"Data Technologies and Applications","issn_l":"2514-9288","issn":["2514-9288","2514-9318"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319811","host_organization_name":"Emerald Publishing Limited","host_organization_lineage":["https://openalex.org/P4310319811"],"host_organization_lineage_names":["Emerald Publishing Limited"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Technologies and Applications","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":68,"referenced_works":["https://openalex.org/W152325026","https://openalex.org/W269382355","https://openalex.org/W614119048","https://openalex.org/W829846369","https://openalex.org/W1130077638","https://openalex.org/W1570286060","https://openalex.org/W1584305544","https://openalex.org/W1964176984","https://openalex.org/W1969557815","https://openalex.org/W1970104171","https://openalex.org/W1973209298","https://openalex.org/W1974186662","https://openalex.org/W1983772724","https://openalex.org/W1988610730","https://openalex.org/W1990381576","https://openalex.org/W2007898191","https://openalex.org/W2013172515","https://openalex.org/W2016204385","https://openalex.org/W2033500239","https://openalex.org/W2048629639","https://openalex.org/W2065750810","https://openalex.org/W2077792286","https://openalex.org/W2096574974","https://openalex.org/W2124611117","https://openalex.org/W2139109103","https://openalex.org/W2142490822","https://openalex.org/W2162363668","https://openalex.org/W2167389327","https://openalex.org/W2183144000","https://openalex.org/W2190746225","https://openalex.org/W2214125478","https://openalex.org/W2229377627","https://openalex.org/W2232748179","https://openalex.org/W2541785405","https://openalex.org/W2571417235","https://openalex.org/W2576787043","https://openalex.org/W2582636227","https://openalex.org/W2674501427","https://openalex.org/W2767118735","https://openalex.org/W2768473193","https://openalex.org/W2770426151","https://openalex.org/W2788926006","https://openalex.org/W2793150529","https://openalex.org/W2888805505","https://openalex.org/W2897826619","https://openalex.org/W2902274556","https://openalex.org/W2905418097","https://openalex.org/W2906593256","https://openalex.org/W2915602172","https://openalex.org/W2922207172","https://openalex.org/W2953434986","https://openalex.org/W2962004477","https://openalex.org/W2967213574","https://openalex.org/W2972574169","https://openalex.org/W3008003977","https://openalex.org/W3035846893","https://openalex.org/W3096802791","https://openalex.org/W3115041630","https://openalex.org/W3134705661","https://openalex.org/W3158505476","https://openalex.org/W3187785767","https://openalex.org/W3200060041","https://openalex.org/W4206687052","https://openalex.org/W4214932054","https://openalex.org/W4285185075","https://openalex.org/W4288036832","https://openalex.org/W4292323897","https://openalex.org/W4361984560"],"related_works":["https://openalex.org/W3183935101","https://openalex.org/W3004779303","https://openalex.org/W2765530022","https://openalex.org/W4309048563","https://openalex.org/W2755443890","https://openalex.org/W2980152930","https://openalex.org/W2780013245","https://openalex.org/W25690811","https://openalex.org/W164326178","https://openalex.org/W2607258344"],"abstract_inverted_index":{"Purpose":[0],"Various":[1],"methods":[2,17,54,209,220,285,307,320],"are":[3,50,131],"used":[4],"for":[5,27],"cancer":[6,52,309,383],"detection":[7,53,219,279,310],"such":[8],"as":[9],"genetic":[10],"tests,":[11],"scanning,":[12],"MRI,":[13],"mammography,":[14],"etc.":[15],"These":[16],"help":[18],"collect":[19],"data":[20,36,47],"on":[21,123,137,302,342,386],"patients,":[22],"which":[23,164],"can":[24],"be":[25],"utilized":[26],"comparing":[28],"a":[29,71,90,109,149,343],"new":[30,72,91,110,326],"patient\u2019s":[31],"information":[32],"with":[33,55,183,221,259,347,395],"the":[34,82,100,114,138,144,146,154,177,180,197,200,203,207,215,218,241,245,248,253,260,271,276,304,319,337,355,360,368,374],"aggregated":[35],"to":[37,80,98,127,175,283,308,334,366],"detect":[38],"cancer.":[39],"The":[40,105,158,188,228,267,371],"main":[41],"step":[42],"in":[43,59,65,143,169,193,238,264,291,380],"this":[44,69,88],"process":[45,243],"is":[46,78,96,141,173,281],"classification.":[48],"There":[49],"several":[51],"their":[56,322],"own":[57],"disadvantages":[58,316],"flexibility,":[60],"non-linear":[61],"complexity":[62,185],"and":[63,119,202,212,247,270,286,311,315,321,359,390,393],"sensitive":[64],"imbalance":[66,83,101],"data.":[67,85,104],"In":[68,87],"paper,":[70,89,145],"fuzzy":[73,92,111,327,344,349],"bio-inspired":[74],"based":[75,122,301,341,385],"classification":[76,94,156,306,357],"method":[77,95,106,280,358],"designed":[79,97,333,361],"classify":[81,99],"medical":[84,103],"Design/methodology/approach":[86],"bio-inspired-based":[93],"of":[102,108,113,179,199,206,217,224,231,234,244,318,329,354,373],"consists":[107],"draft":[112,328],"Cuckoo":[115,160],"Optimization":[116,161],"Algorithm":[117,162],"(COA)":[118],"separating":[120,181],"hyper-planes":[121,182,225,235,356,392],"assigning":[124],"binary":[125],"codes":[126],"separated":[128],"regions":[129],"that":[130,275],"called":[132],"Hyper-Planes":[133],"Classifier":[134],"(HPC).":[135],"Based":[136],"technical":[139,296],"review":[140,297],"done":[142,300],"HPC":[147],"has":[148,298,331,363,377],"better":[150,288],"structural":[151],"superiority":[152],"than":[153,289,397],"other":[155,284],"algorithms.":[157,324],"Fuzzy":[159],"(FCOA),":[163],"fills":[165],"up":[166],"its":[167],"challenge":[168],"proper":[170],"tuning":[171,336],"parameters,":[172],"proposed":[174,208,277,365,375],"optimize":[176,367],"weights":[178,230],"linear":[184],"time.":[186],"Findings":[187],"experimental":[189,268],"results":[190,205,256,269],"were":[191,210,226,236,250,257],"presented":[192,272],"five":[194],"steps.":[195],"Step1,":[196],"details":[198],"average":[201],"best":[204,254,261],"reported":[211,237,262],"compared.":[213,227],"Step2,":[214],"quality":[216],"different":[222,232],"numbers":[223,233],"obtained":[229,255],"Step3.":[239],"Step4,":[240],"convergence":[242],"FCOA":[246,362],"COA":[249,330],"shown.":[251],"Step5,":[252],"compared":[258,394],"one":[263],"previous":[265,399],"literature.":[266],"comparisons":[273],"show":[274],"hybrid":[278],"comparable":[282],"operates":[287],"them":[290],"most":[292],"cases.":[293],"Originality/value":[294],"A":[295,325,351],"been":[299,332,364,378],"classifying":[303],"applied":[305],"analyzing":[312],"advantages":[313],"(+)":[314],"(\u2212)":[317],"optimizer":[323],"dynamically":[335],"Egg":[338],"Laying":[339],"Radius":[340],"inference":[345],"system":[346],"four":[348,391],"rules.":[350],"novel":[352],"hybridization":[353,376],"hyper-planes'":[369],"weights.":[370],"effectiveness":[372],"examined":[379],"famous":[381],"UCI":[382],"datasets":[384],"one,":[387],"two,":[388],"three":[389],"more":[396],"30":[398],"researches.":[400]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
