{"id":"https://openalex.org/W7152371425","doi":"https://doi.org/10.1109/icecet63943.2025.11472049","title":"One Class MonkeyPox Detection based on Feature Fusion Paper","display_name":"One Class MonkeyPox Detection based on Feature Fusion Paper","publication_year":2025,"publication_date":"2025-07-03","ids":{"openalex":"https://openalex.org/W7152371425","doi":"https://doi.org/10.1109/icecet63943.2025.11472049"},"language":null,"primary_location":{"id":"doi:10.1109/icecet63943.2025.11472049","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icecet63943.2025.11472049","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 5th International Conference on Electrical, Computer and Energy Technologies (ICECET)","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/A5056588114","display_name":"Ahlam R. Khekan","orcid":"https://orcid.org/0000-0001-7320-925X"},"institutions":[{"id":"https://openalex.org/I4210119257","display_name":"University of Information Technology and Communications","ror":"https://ror.org/028h0pd91","country_code":"IQ","type":"education","lineage":["https://openalex.org/I4210119257"]}],"countries":["IQ"],"is_corresponding":true,"raw_author_name":"Ahlam R. Khekan","raw_affiliation_strings":["University of Information Technology and Communications,BioMedical Informatics College,Iraq"],"affiliations":[{"raw_affiliation_string":"University of Information Technology and Communications,BioMedical Informatics College,Iraq","institution_ids":["https://openalex.org/I4210119257"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5056588114"],"corresponding_institution_ids":["https://openalex.org/I4210119257"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.7483689,"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":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.10209999978542328,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.10209999978542328,"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/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.09120000153779984,"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/T11241","display_name":"Advanced Malware Detection Techniques","score":0.04089999943971634,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/class","display_name":"Class (philosophy)","score":0.5386999845504761},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5340999960899353},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5325999855995178},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.44920000433921814},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.3294000029563904},{"id":"https://openalex.org/keywords/monkeypox","display_name":"Monkeypox","score":0.3262999951839447}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6388000249862671},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5418000221252441},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5386999845504761},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5340999960899353},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5325999855995178},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.44920000433921814},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3294000029563904},{"id":"https://openalex.org/C2778011067","wikidata":"https://www.wikidata.org/wiki/Q382370","display_name":"Monkeypox","level":5,"score":0.3262999951839447},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.30869999527931213},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2838999927043915},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.26460000872612},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.2605000138282776},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25679999589920044},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2547999918460846},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.2533999979496002},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.25290000438690186},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.251800000667572}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icecet63943.2025.11472049","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icecet63943.2025.11472049","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 5th International Conference on Electrical, Computer and Energy Technologies (ICECET)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2061079740","https://openalex.org/W2579949149","https://openalex.org/W3048749423","https://openalex.org/W3204120916","https://openalex.org/W4287881190","https://openalex.org/W4302286670","https://openalex.org/W4307875682","https://openalex.org/W4319300532","https://openalex.org/W4319915084","https://openalex.org/W4321495983","https://openalex.org/W4322770975","https://openalex.org/W4323060186","https://openalex.org/W4365152364","https://openalex.org/W4382241421","https://openalex.org/W4385342437","https://openalex.org/W4386989239","https://openalex.org/W4391837781","https://openalex.org/W4400510731"],"related_works":[],"abstract_inverted_index":{"Monkeypox":[0,221],"is":[1,217],"a":[2,12,171,240],"disease":[3,92],"induced":[4],"by":[5,113,179],"the":[6,48,74,82,97,105,123,126,146,162,180,224,229],"monkeypox":[7,23,91,141,212],"virus.":[8],"It":[9],"may":[10],"induce":[11],"severe":[13],"rash,":[14],"enlarged":[15],"lymph":[16],"nodes,":[17],"and":[18,32,62,121,148,192,207,223],"fever.":[19],"Models":[20],"for":[21,40,90],"detecting":[22],"with":[24,96],"optimum":[25],"accuracy,":[26],"minimal":[27,33],"false":[28,34],"acceptance":[29],"rate":[30,36],"(FAR),":[31],"rejection":[35],"(FRR)":[37],"are":[38,93,157],"crucial":[39],"dependable":[41],"use":[42],"in":[43,73,234],"healthcare.":[44],"This":[45,143],"balance":[46],"enables":[47],"model":[49,119,147,182],"to":[50,57,109,117,150,167,210],"accurately":[51,138],"detect":[52],"diseases":[53],"while":[54],"being":[55],"sensitive":[56],"errors,":[58],"facilitating":[59],"precise":[60],"diagnoses":[61],"effective":[63],"public":[64],"health":[65],"interventions.":[66],"Deep":[67],"learning":[68,194],"techniques":[69,156],"have":[70,107],"shown":[71],"success":[72],"automated":[75],"identification":[76],"of":[77,84,99,125],"skin":[78],"lesions,":[79],"contingent":[80],"on":[81],"availability":[83],"sufficient":[85],"training":[86,127],"instances.":[87],"Currently,":[88],"datasets":[89],"unavailable,":[94],"along":[95],"issue":[98],"class":[100,163],"imbalance.":[101],"In":[102,153],"recent":[103],"studies,":[104],"researchers":[106],"tried":[108],"address":[110],"these":[111,177],"issues":[112],"Using":[114],"data":[115],"augmentation":[116,155],"improve":[118,211],"performance":[120],"increase":[122],"diversity":[124],"data.":[128],"However,":[129],"augmentations":[130],"can":[131,144],"generate":[132],"unrealistic":[133],"images":[134],"that":[135,228],"do":[136],"not":[137,159],"represent":[139],"true":[140],"lesions.":[142],"confuse":[145],"lead":[149],"poor":[151],"generalization.":[152],"addition,":[154],"still":[158],"sufficiently":[160],"addressing":[161],"imbalance":[164],"which":[165],"leads":[166],"biased":[168],"predictions.":[169],"As":[170],"result,":[172],"this":[173],"paper":[174],"figures":[175],"out":[176],"problems":[178],"proposed":[181,215,237],"includes":[183],"4":[184],"phases:":[185],"preprocessing,":[186],"feature":[187,190],"extraction,":[188],"hybrid":[189],"fusion,":[191],"one-class":[193],"classification.":[195],"The":[196,214,236],"multi-feature":[197],"fusion":[198],"approach":[199,238],"employs":[200],"edge":[201],"strength,":[202],"curvature":[203],"information,":[204],"texture":[205],"homogeneity,":[206],"lesion":[208],"contrast":[209],"detection.":[213],"methodology":[216],"evaluated":[218],"using":[219],"two":[220],"databases,":[222],"experimental":[225],"findings":[226],"indicate":[227],"system":[230],"outperforms":[231],"current":[232],"methods":[233],"accuracy.":[235],"attained":[239],"5%":[241],"improvement.)":[242]},"counts_by_year":[],"updated_date":"2026-04-11T06:13:24.991567","created_date":"2026-04-10T00:00:00"}