{"id":"https://openalex.org/W4393408091","doi":"https://doi.org/10.3233/ida-230651","title":"Processing and optimized learning for improved classification of categorical plant disease datasets","display_name":"Processing and optimized learning for improved classification of categorical plant disease datasets","publication_year":2024,"publication_date":"2024-04-02","ids":{"openalex":"https://openalex.org/W4393408091","doi":"https://doi.org/10.3233/ida-230651"},"language":"en","primary_location":{"id":"doi:10.3233/ida-230651","is_oa":false,"landing_page_url":"https://doi.org/10.3233/ida-230651","pdf_url":null,"source":{"id":"https://openalex.org/S2498839158","display_name":"Intelligent Data Analysis","issn_l":"1088-467X","issn":["1088-467X","1571-4128"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligent Data Analysis","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/A5103246286","display_name":"Ayushi Gupta","orcid":"https://orcid.org/0000-0002-6909-6691"},"institutions":[{"id":"https://openalex.org/I105454292","display_name":"Guru Gobind Singh Indraprastha University","ror":"https://ror.org/034q1za58","country_code":"IN","type":"education","lineage":["https://openalex.org/I105454292"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Ayushi Gupta","raw_affiliation_strings":["University School of Information, Communication & Technology, GGSIPU, New Delhi, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University School of Information, Communication & Technology, GGSIPU, New Delhi, India","institution_ids":["https://openalex.org/I105454292"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064896366","display_name":"Anuradha Chug","orcid":"https://orcid.org/0000-0002-3139-4490"},"institutions":[{"id":"https://openalex.org/I105454292","display_name":"Guru Gobind Singh Indraprastha University","ror":"https://ror.org/034q1za58","country_code":"IN","type":"education","lineage":["https://openalex.org/I105454292"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Anuradha Chug","raw_affiliation_strings":["University School of Information, Communication & Technology, GGSIPU, New Delhi, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University School of Information, Communication & Technology, GGSIPU, New Delhi, India","institution_ids":["https://openalex.org/I105454292"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008002283","display_name":"Amit Prakash Singh","orcid":"https://orcid.org/0000-0002-8675-6903"},"institutions":[{"id":"https://openalex.org/I105454292","display_name":"Guru Gobind Singh Indraprastha University","ror":"https://ror.org/034q1za58","country_code":"IN","type":"education","lineage":["https://openalex.org/I105454292"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Amit Prakash Singh","raw_affiliation_strings":["University School of Information, Communication & Technology, GGSIPU, New Delhi, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University School of Information, Communication & Technology, GGSIPU, New Delhi, India","institution_ids":["https://openalex.org/I105454292"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5103246286"],"corresponding_institution_ids":["https://openalex.org/I105454292"],"apc_list":null,"apc_paid":null,"fwci":1.6171,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.84822759,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"28","issue":"6","first_page":"1697","last_page":"1721"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10494","display_name":"Plant Virus Research Studies","score":0.9789000153541565,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.8493160605430603},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6553978323936462},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6518681645393372},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6177793145179749},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6131250262260437},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5553907155990601},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5296970009803772},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.506529688835144},{"id":"https://openalex.org/keywords/oversampling","display_name":"Oversampling","score":0.4960244596004486},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.476146936416626},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.4640699028968811},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45290452241897583},{"id":"https://openalex.org/keywords/majority-rule","display_name":"Majority rule","score":0.43838247656822205},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.436065673828125},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.41925036907196045}],"concepts":[{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.8493160605430603},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6553978323936462},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6518681645393372},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6177793145179749},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6131250262260437},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5553907155990601},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5296970009803772},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.506529688835144},{"id":"https://openalex.org/C197323446","wikidata":"https://www.wikidata.org/wiki/Q331222","display_name":"Oversampling","level":3,"score":0.4960244596004486},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.476146936416626},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.4640699028968811},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45290452241897583},{"id":"https://openalex.org/C153668964","wikidata":"https://www.wikidata.org/wiki/Q27636","display_name":"Majority rule","level":2,"score":0.43838247656822205},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.436065673828125},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.41925036907196045},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/ida-230651","is_oa":false,"landing_page_url":"https://doi.org/10.3233/ida-230651","pdf_url":null,"source":{"id":"https://openalex.org/S2498839158","display_name":"Intelligent Data Analysis","issn_l":"1088-467X","issn":["1088-467X","1571-4128"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligent Data Analysis","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":67,"referenced_works":["https://openalex.org/W349770100","https://openalex.org/W1594031697","https://openalex.org/W1991530504","https://openalex.org/W1994830145","https://openalex.org/W2001979953","https://openalex.org/W2053724458","https://openalex.org/W2088252378","https://openalex.org/W2093626865","https://openalex.org/W2096863518","https://openalex.org/W2101234009","https://openalex.org/W2148143831","https://openalex.org/W2295598076","https://openalex.org/W2765449478","https://openalex.org/W2911964244","https://openalex.org/W3048659618","https://openalex.org/W3107973285","https://openalex.org/W3114862924","https://openalex.org/W3129603666","https://openalex.org/W3144823608","https://openalex.org/W3163531312","https://openalex.org/W3165142978","https://openalex.org/W3213648242","https://openalex.org/W4212883601","https://openalex.org/W4224234929","https://openalex.org/W4224923052","https://openalex.org/W4283270853","https://openalex.org/W4283746543","https://openalex.org/W4285191954","https://openalex.org/W4293718652","https://openalex.org/W4296886708","https://openalex.org/W4298826872","https://openalex.org/W4307112907","https://openalex.org/W4308620576","https://openalex.org/W4309774534","https://openalex.org/W4313406927","https://openalex.org/W4313547427","https://openalex.org/W4313893346","https://openalex.org/W4317486332","https://openalex.org/W4318412418","https://openalex.org/W4323353637","https://openalex.org/W4360613973","https://openalex.org/W4365453241","https://openalex.org/W4377229921","https://openalex.org/W4377704697","https://openalex.org/W4380367875","https://openalex.org/W4382680691","https://openalex.org/W4385349211","https://openalex.org/W4385562058","https://openalex.org/W4387266502","https://openalex.org/W6611762666","https://openalex.org/W6675354045","https://openalex.org/W6790691442","https://openalex.org/W6795552833","https://openalex.org/W6843854813","https://openalex.org/W6855786199","https://openalex.org/W6888946113","https://openalex.org/W6907708432","https://openalex.org/W6907883083","https://openalex.org/W6926090900","https://openalex.org/W6926238688","https://openalex.org/W6926279647","https://openalex.org/W6944960951","https://openalex.org/W6945111357","https://openalex.org/W6945146601","https://openalex.org/W6963918554","https://openalex.org/W6964128495","https://openalex.org/W6964138771"],"related_works":["https://openalex.org/W2967733078","https://openalex.org/W3204430031","https://openalex.org/W3137904399","https://openalex.org/W4310492845","https://openalex.org/W2885778889","https://openalex.org/W4310224730","https://openalex.org/W2766514146","https://openalex.org/W4289703016","https://openalex.org/W2885516856","https://openalex.org/W3094138326"],"abstract_inverted_index":{"PURPOSE:":[0],"Crop":[1],"diseases":[2,21],"can":[3,262],"cause":[4],"significant":[5],"reductions":[6],"in":[7,22,135,175,252,268],"yield,":[8],"subsequently":[9],"impacting":[10],"a":[11,32,60,198],"country\u2019s":[12],"economy.":[13],"The":[14,47,142,157],"current":[15,173],"research":[16,65],"is":[17,50,91,125],"concentrated":[18],"on":[19,51],"detecting":[20],"three":[23,106],"specific":[24],"crops":[25],"\u2013":[26],"tomatoes,":[27],"soybeans,":[28],"and":[29,38,111,276,283],"mushrooms,":[30],"using":[31,105,152],"real-time":[33],"dataset":[34],"collected":[35],"for":[36,43,127,211,248],"tomatoes":[37],"two":[39,144],"publicly":[40],"accessible":[41],"datasets":[42,53,76,100,261],"the":[44,64,73,75,88,98,116,119,136,161,172,187,240,245,256,271,285],"other":[45,167],"crops.":[46],"primary":[48],"emphasis":[49],"employing":[52,197],"with":[54,230],"exclusively":[55],"categorical":[56,155,183,212,253],"attributes,":[57,74,213],"which":[58,267],"poses":[59],"notable":[61],"challenge":[62],"to":[63,72,82,93,103,149,191,259,273,279],"community.":[66],"METHODS:":[67],"After":[68],"applying":[69],"label":[70],"encoding":[71],"undergo":[77],"four":[78],"distinct":[79,140],"preprocessing":[80],"techniques":[81],"address":[83],"missing":[84,194],"values.":[85],"Following":[86],"this,":[87],"SMOTE-N":[89,203],"technique":[90,209],"employed":[92],"tackle":[94],"class":[95],"imbalance.":[96],"Subsequently,":[97],"pre-processed":[99],"are":[101,146],"subjected":[102,148],"classification":[104,117,178,250],"ensemble":[107],"methods:":[108],"bagging,":[109],"boosting,":[110],"voting.":[112],"To":[113],"further":[114,150],"refine":[115],"process,":[118],"metaheuristic":[120],"Ant":[121],"Lion":[122],"Optimizer":[123],"(ALO)":[124],"utilized":[126],"hyper-parameter":[128],"tuning.":[129],"RESULTS:":[130],"This":[131],"comprehensive":[132],"approach":[133],"results":[134],"evaluation":[137],"of":[138,177,288],"twelve":[139],"models.":[141],"top":[143],"performers":[145],"then":[147],"validation":[151],"ten":[153],"standard":[154],"datasets.":[156,184,254],"findings":[158],"demonstrate":[159],"that":[160],"hybrid":[162],"model":[163,241,258],"II-SN-OXGB,":[164],"surpasses":[165],"all":[166,181],"models":[168],"as":[169,171,206,244],"well":[170],"state-of-the-art":[174],"terms":[176],"accuracy":[179],"across":[180],"thirteen":[182],"II":[185],"utilizes":[186],"Random":[188],"Forest":[189],"classifier":[190],"iteratively":[192],"impute":[193],"feature":[195],"values,":[196],"nearest":[199,216],"features":[200],"strategy.":[201],"Meanwhile,":[202],"(SN)":[204],"serves":[205],"an":[207],"oversampling":[208],"particularly":[210],"again":[214],"utilizing":[215],"neighbors.":[217],"Optimized":[218],"(using":[219],"ALO)":[220],"Xtreme":[221],"Gradient":[222],"Boosting":[223],"OXGB,":[224],"sequentially":[225],"trains":[226],"multiple":[227],"decision":[228],"trees,":[229],"each":[231],"tree":[232],"correcting":[233],"errors":[234],"from":[235],"its":[236],"predecessor.":[237],"CONCLUSION:":[238],"Consequently,":[239],"II-SN-OXGB":[242,257],"emerges":[243],"optimal":[246],"choice":[247],"addressing":[249],"challenges":[251],"Applying":[255],"crop":[260,289],"significantly":[263],"enhance":[264],"disease":[265],"detection":[266],"turn,":[269],"enables":[270],"farmers":[272],"take":[274],"timely":[275],"appropriate":[277],"measures":[278],"prevent":[280],"yield":[281],"losses":[282],"mitigate":[284],"economic":[286],"impact":[287],"diseases.":[290]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
