{"id":"https://openalex.org/W3040984189","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533678","title":"Fruit classification using deep feature maps in the presence of deceptive similar classes","display_name":"Fruit classification using deep feature maps in the presence of deceptive similar classes","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3040984189","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533678","mag":"3040984189"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn52387.2021.9533678","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533678","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2007.05942","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5014773761","display_name":"Mohit Dandekar","orcid":"https://orcid.org/0000-0002-8713-0945"},"institutions":[{"id":"https://openalex.org/I26072440","display_name":"Indian Institute of Information Technology Allahabad","ror":"https://ror.org/03rgjt374","country_code":"IN","type":"education","lineage":["https://openalex.org/I26072440"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Mohit Dandekar","raw_affiliation_strings":["IIIT Allahabad, Prayagraj, India"],"affiliations":[{"raw_affiliation_string":"IIIT Allahabad, Prayagraj, India","institution_ids":["https://openalex.org/I26072440"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030289462","display_name":"Narinder Singh Punn","orcid":"https://orcid.org/0000-0003-1175-1865"},"institutions":[{"id":"https://openalex.org/I26072440","display_name":"Indian Institute of Information Technology Allahabad","ror":"https://ror.org/03rgjt374","country_code":"IN","type":"education","lineage":["https://openalex.org/I26072440"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Narinder Singh Punn","raw_affiliation_strings":["IIIT Allahabad, Prayagraj, India"],"affiliations":[{"raw_affiliation_string":"IIIT Allahabad, Prayagraj, India","institution_ids":["https://openalex.org/I26072440"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003833348","display_name":"Sanjay Kumar Sonbhadra","orcid":"https://orcid.org/0000-0002-7457-9655"},"institutions":[{"id":"https://openalex.org/I26072440","display_name":"Indian Institute of Information Technology Allahabad","ror":"https://ror.org/03rgjt374","country_code":"IN","type":"education","lineage":["https://openalex.org/I26072440"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sanjay Kumar Sonbhadra","raw_affiliation_strings":["IIIT Allahabad, Prayagraj, India"],"affiliations":[{"raw_affiliation_string":"IIIT Allahabad, Prayagraj, India","institution_ids":["https://openalex.org/I26072440"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004401828","display_name":"Sonali Agarwal","orcid":"https://orcid.org/0000-0001-9083-5033"},"institutions":[{"id":"https://openalex.org/I26072440","display_name":"Indian Institute of Information Technology Allahabad","ror":"https://ror.org/03rgjt374","country_code":"IN","type":"education","lineage":["https://openalex.org/I26072440"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sonali Agarwal","raw_affiliation_strings":["IIIT Allahabad, Prayagraj, India","Indian Institute of Information Technology, Allahabad#TAB#"],"affiliations":[{"raw_affiliation_string":"IIIT Allahabad, Prayagraj, India","institution_ids":["https://openalex.org/I26072440"]},{"raw_affiliation_string":"Indian Institute of Information Technology, Allahabad#TAB#","institution_ids":["https://openalex.org/I26072440"]}]},{"author_position":"last","author":{"id":null,"display_name":"Rage Uday Kiran","orcid":null},"institutions":[{"id":"https://openalex.org/I141591182","display_name":"University of Aizu","ror":"https://ror.org/02pg0e883","country_code":"JP","type":"education","lineage":["https://openalex.org/I141591182"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Rage Uday Kiran","raw_affiliation_strings":["The University of Aizu, Japan"],"affiliations":[{"raw_affiliation_string":"The University of Aizu, Japan","institution_ids":["https://openalex.org/I141591182"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5014773761"],"corresponding_institution_ids":["https://openalex.org/I26072440"],"apc_list":null,"apc_paid":null,"fwci":0.663,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.76222379,"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":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9993000030517578,"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":0.9993000030517578,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9876000285148621,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.986299991607666,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7440173625946045},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7364974021911621},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6253777742385864},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.5660185217857361},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5622417330741882},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5603917241096497},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5366254448890686},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5364423990249634},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5362331867218018},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.5026960372924805},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.49463769793510437},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.44288355112075806},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.41136303544044495},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3914441168308258},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.15212053060531616}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7440173625946045},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7364974021911621},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6253777742385864},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.5660185217857361},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5622417330741882},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5603917241096497},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5366254448890686},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5364423990249634},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5362331867218018},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.5026960372924805},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49463769793510437},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.44288355112075806},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.41136303544044495},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3914441168308258},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.15212053060531616},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/ijcnn52387.2021.9533678","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533678","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2007.05942","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2007.05942","pdf_url":"https://arxiv.org/pdf/2007.05942","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:3040984189","is_oa":true,"landing_page_url":"http://arxiv.org/pdf/2007.05942.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2007.05942","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2007.05942","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2007.05942","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2007.05942","pdf_url":"https://arxiv.org/pdf/2007.05942","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W6908809","https://openalex.org/W273955616","https://openalex.org/W1534477342","https://openalex.org/W1556540526","https://openalex.org/W1605688901","https://openalex.org/W1686810756","https://openalex.org/W1849277567","https://openalex.org/W1861492603","https://openalex.org/W2023401054","https://openalex.org/W2070700571","https://openalex.org/W2092466978","https://openalex.org/W2097117768","https://openalex.org/W2102505197","https://openalex.org/W2108598243","https://openalex.org/W2108899438","https://openalex.org/W2116040950","https://openalex.org/W2117539524","https://openalex.org/W2125283600","https://openalex.org/W2150757437","https://openalex.org/W2163605009","https://openalex.org/W2168809519","https://openalex.org/W2194775991","https://openalex.org/W2407521645","https://openalex.org/W2422675628","https://openalex.org/W2501369945","https://openalex.org/W2773003563","https://openalex.org/W2915754718","https://openalex.org/W2919115771","https://openalex.org/W2962949934","https://openalex.org/W2963523428","https://openalex.org/W2963912358","https://openalex.org/W3004320640","https://openalex.org/W3010795489","https://openalex.org/W3014304846","https://openalex.org/W3029224641","https://openalex.org/W6600284362","https://openalex.org/W6610017368","https://openalex.org/W6632075054","https://openalex.org/W6637373629","https://openalex.org/W6639102338","https://openalex.org/W6639204139","https://openalex.org/W6684191040","https://openalex.org/W6714138976"],"related_works":["https://openalex.org/W3160923251","https://openalex.org/W2288158490","https://openalex.org/W2592563856","https://openalex.org/W3120990978","https://openalex.org/W2623735101","https://openalex.org/W2789534470","https://openalex.org/W3014894113","https://openalex.org/W2730987516","https://openalex.org/W2521556056","https://openalex.org/W2968294129","https://openalex.org/W2120176405","https://openalex.org/W2278000355","https://openalex.org/W2888849343","https://openalex.org/W2976784389","https://openalex.org/W3046958622","https://openalex.org/W2964291102","https://openalex.org/W2746086300","https://openalex.org/W2962828243","https://openalex.org/W3193684141","https://openalex.org/W3107123124"],"abstract_inverted_index":{"Autonomous":[0],"detection":[1],"and":[2,69],"classification":[3,108,145],"of":[4,9,48,87,98,107,109,124,146,158],"objects":[5,19,49,113,147],"are":[6,131],"admired":[7],"area":[8],"research":[10,101],"in":[11,45,85],"many":[12],"industrial":[13],"applications.":[14],"Though,":[15],"humans":[16],"can":[17],"distinguish":[18],"with":[20,81,114,148],"high":[21],"multi-granular":[22,82,112],"similarities":[23],"very":[24,33],"easily;":[25],"but":[26,93],"for":[27,50,67,144,156],"the":[28,53,59,64,99,105,159,169,174],"machines,":[29],"it":[30,72,165],"is":[31,73,102,154],"a":[32],"challenging":[34],"task.":[35],"The":[36,96,151],"convolution":[37],"neural":[38],"networks":[39],"(CNN)":[40],"have":[41],"illustrated":[42],"efficient":[43],"performance":[44],"multi-level":[46],"representations":[47],"classification.":[51],"Conventionally,":[52],"existing":[54],"deep":[55,137,176],"learning":[56,177],"models":[57],"utilize":[58],"transformed":[60],"features":[61],"generated":[62],"by":[63],"rearmost":[65],"layer":[66],"training":[68],"testing.":[70],"However,":[71],"evident":[74],"that":[75,118,168],"this":[76],"does":[77],"not":[78],"work":[79],"well":[80],"data,":[83],"especially,":[84],"presence":[86],"deceptive":[88],"similar":[89,92,111,149],"classes":[90],"(almost":[91],"different":[94],"classes).":[95],"objective":[97],"present":[100],"to":[103,134],"address":[104],"challenge":[106],"deceptively":[110],"an":[115],"ensemble":[116],"approach":[117],"utilizes":[119],"activations":[120,130],"from":[121],"multiple":[122,136],"layers":[123],"CNN":[125],"(deep":[126],"features).":[127],"These":[128],"multi-layer":[129],"further":[132],"utilized":[133,155],"build":[135],"decision":[138],"trees":[139],"(known":[140],"as":[141],"Random":[142],"forest)":[143],"appearance.":[150],"Fruits-360":[152],"dataset":[153],"evaluation":[157],"proposed":[160,170],"approach.":[161],"With":[162],"extensive":[163],"trials":[164],"was":[166],"observed":[167],"model":[171],"outperformed":[172],"over":[173],"conventional":[175],"approaches.":[178]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}
