{"id":"https://openalex.org/W4313459156","doi":"https://doi.org/10.1109/iccr56254.2022.9996022","title":"Retracted: Shape Based Feature Extraction for Currency recognition and compare the accuracy and sensitivity with ORB algorithm","display_name":"Retracted: Shape Based Feature Extraction for Currency recognition and compare the accuracy and sensitivity with ORB algorithm","publication_year":2022,"publication_date":"2022-10-06","ids":{"openalex":"https://openalex.org/W4313459156","doi":"https://doi.org/10.1109/iccr56254.2022.9996022"},"language":"en","primary_location":{"id":"doi:10.1109/iccr56254.2022.9996022","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccr56254.2022.9996022","pdf_url":null,"source":{"id":"https://openalex.org/S4363608155","display_name":"2022 International Conference on Cyber Resilience (ICCR)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Conference on Cyber Resilience (ICCR)","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/A5052027278","display_name":"S.Koti Niteesh Reddy","orcid":null},"institutions":[{"id":"https://openalex.org/I85461943","display_name":"Saveetha University","ror":"https://ror.org/0034me914","country_code":"IN","type":"education","lineage":["https://openalex.org/I85461943"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"S.Koti Niteesh Reddy","raw_affiliation_strings":["Saveetha University,Department of computer science and Engineering,Chennai,India","Department of computer science and Engineering, Saveetha University, Chennai, India"],"affiliations":[{"raw_affiliation_string":"Saveetha University,Department of computer science and Engineering,Chennai,India","institution_ids":["https://openalex.org/I85461943"]},{"raw_affiliation_string":"Department of computer science and Engineering, Saveetha University, Chennai, India","institution_ids":["https://openalex.org/I85461943"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113235547","display_name":"S. Prem Kumar","orcid":null},"institutions":[{"id":"https://openalex.org/I85461943","display_name":"Saveetha University","ror":"https://ror.org/0034me914","country_code":"IN","type":"education","lineage":["https://openalex.org/I85461943"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"S Prem Kumar","raw_affiliation_strings":["Saveetha University,Department of computer science and Engineering,Chennai,India","Department of computer science and Engineering, Saveetha University, Chennai, India"],"affiliations":[{"raw_affiliation_string":"Saveetha University,Department of computer science and Engineering,Chennai,India","institution_ids":["https://openalex.org/I85461943"]},{"raw_affiliation_string":"Department of computer science and Engineering, Saveetha University, Chennai, India","institution_ids":["https://openalex.org/I85461943"]}]},{"author_position":"last","author":{"id":null,"display_name":"R. Karthikeyan","orcid":null},"institutions":[{"id":"https://openalex.org/I85461943","display_name":"Saveetha University","ror":"https://ror.org/0034me914","country_code":"IN","type":"education","lineage":["https://openalex.org/I85461943"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"R. Karthikeyan","raw_affiliation_strings":["Saveetha University,Department of computer science and Engineering,Chennai,India","Department of computer science and Engineering, Saveetha University, Chennai, India"],"affiliations":[{"raw_affiliation_string":"Saveetha University,Department of computer science and Engineering,Chennai,India","institution_ids":["https://openalex.org/I85461943"]},{"raw_affiliation_string":"Department of computer science and Engineering, Saveetha University, Chennai, India","institution_ids":["https://openalex.org/I85461943"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5052027278"],"corresponding_institution_ids":["https://openalex.org/I85461943"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.17675442,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":true,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14319","display_name":"Currency Recognition and Detection","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T14319","display_name":"Currency Recognition and Detection","score":0.9998999834060669,"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"}},{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9646000266075134,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12357","display_name":"Digital Media Forensic Detection","score":0.95169997215271,"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/orb","display_name":"Orb (optics)","score":0.9038245677947998},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.7003831267356873},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6306242942810059},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.5691806674003601},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5662961006164551},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5355489253997803},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4747631549835205},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4420662522315979},{"id":"https://openalex.org/keywords/scale-invariant-feature-transform","display_name":"Scale-invariant feature transform","score":0.43813902139663696},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.19930297136306763},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.06259840726852417}],"concepts":[{"id":"https://openalex.org/C108260229","wikidata":"https://www.wikidata.org/wiki/Q47023","display_name":"Orb (optics)","level":3,"score":0.9038245677947998},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.7003831267356873},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6306242942810059},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.5691806674003601},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5662961006164551},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5355489253997803},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4747631549835205},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4420662522315979},{"id":"https://openalex.org/C61265191","wikidata":"https://www.wikidata.org/wiki/Q767770","display_name":"Scale-invariant feature transform","level":3,"score":0.43813902139663696},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.19930297136306763},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.06259840726852417},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccr56254.2022.9996022","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccr56254.2022.9996022","pdf_url":null,"source":{"id":"https://openalex.org/S4363608155","display_name":"2022 International Conference on Cyber Resilience (ICCR)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Conference on Cyber Resilience (ICCR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","score":0.4000000059604645,"display_name":"Decent work and economic growth"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1496029004","https://openalex.org/W2019871448","https://openalex.org/W2047867433","https://openalex.org/W2140271459","https://openalex.org/W2282043611","https://openalex.org/W2320185587","https://openalex.org/W2330365780","https://openalex.org/W2471066839","https://openalex.org/W2533907642","https://openalex.org/W2552254510","https://openalex.org/W2555817969","https://openalex.org/W2694590495","https://openalex.org/W2731625804","https://openalex.org/W2787426119","https://openalex.org/W2792342425","https://openalex.org/W2902395163","https://openalex.org/W2946218017","https://openalex.org/W2952038015","https://openalex.org/W2988809393","https://openalex.org/W3003421198","https://openalex.org/W3004245667","https://openalex.org/W3010458811","https://openalex.org/W3127375604","https://openalex.org/W3134184177","https://openalex.org/W3145891154","https://openalex.org/W3157636319","https://openalex.org/W3158389626","https://openalex.org/W3158537584","https://openalex.org/W3158620289","https://openalex.org/W3163983749","https://openalex.org/W3174247307","https://openalex.org/W3176302907","https://openalex.org/W3202485599","https://openalex.org/W4297035156"],"related_works":["https://openalex.org/W3103148063","https://openalex.org/W2794556651","https://openalex.org/W4205482147","https://openalex.org/W2763213405","https://openalex.org/W59890435","https://openalex.org/W1951615866","https://openalex.org/W4312069198","https://openalex.org/W4382882667","https://openalex.org/W2553057930","https://openalex.org/W2049930962"],"abstract_inverted_index":{"Aim:":[0],"The":[1,42,62,76,116,141],"aim":[2],"of":[3,44,60,70,129,135,143,156,194],"this":[4,16,173],"research":[5,17,63],"is":[6,24,29,72,138,150,160,165,176],"to":[7],"recognize":[8],"the":[9,27,45,120,133,144,154,157,179],"currency":[10],"using":[11,51,81],"image":[12],"processing":[13],"techniques.":[14],"In":[15,172],"novel":[18,180],"shape":[19,181],"based":[20,100,122,146,182],"feature":[21,101,123,147,183],"extraction":[22,102,124,148,184],"method":[23,125,149,185],"proposed":[25,46],"and":[26,34,40,107,132,153,196],"accuracy":[28,128,134,195],"compared":[30],"with":[31,56],"(Oriented":[32],"FAST":[33],"Rotated":[35],"BRIEF)":[36],"ORB":[37,136,158,190],"algorithm.":[38],"Materials":[39],"Methods:":[41],"performance":[43],"system":[47],"has":[48,126],"been":[49],"evaluated":[50],"a":[52,57],"challenging":[53],"dataset":[54],"Github":[55],"wide":[58],"variety":[59],"conditions.":[61],"includes":[64],"two":[65],"groups.":[66],"Total":[67],"sample":[68],"size":[69,78],"40":[71],"taken":[73],"for":[74,96],"analysis.":[75],"Sample":[77],"was":[79],"calculated":[80],"clinical.com":[82],"by":[83,87],"keeping":[84],"alpha":[85],"error-threshold":[86],"0.05,":[88,169],"95":[89],"%":[90,95,131,152],"confidence":[91],"interval,":[92],"power":[93],"80":[94],"control":[97],"group":[98,109],"(Shape":[99],"<tex":[103,112],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[104,113],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$\\mathrm{N}=20$</tex>":[105,114],")":[106],"study":[108],"(ORB":[110],"algorithm":[111,137,159,191],").":[115],"analysis":[117],"shows":[118],"that":[119,178],"Shape":[121,145],"an":[127],"97.48":[130],"89.85":[139],"%.":[140,162],"sensitivity":[142,155],"97.20":[151],"88.81":[161],"Significance":[163],"value":[164],"0.000":[166],"(p":[167],"\u00a1":[168],"2-tailed).":[170],"Conclusion:":[171],"work":[174],"it":[175],"found":[177],"performs":[186],"significantly":[187],"better":[188],"than":[189],"in":[192],"terms":[193],"sensitivity.":[197]},"counts_by_year":[],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2023-01-06T00:00:00"}