{"id":"https://openalex.org/W4416078207","doi":"https://doi.org/10.1109/access.2025.3630968","title":"A Hybrid CNN-SVM Algorithm for Detecting Manufacturing Defects","display_name":"A Hybrid CNN-SVM Algorithm for Detecting Manufacturing Defects","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4416078207","doi":"https://doi.org/10.1109/access.2025.3630968"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3630968","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3630968","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3630968","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5117361942","display_name":"Bet\u00fcl Karaka\u015f","orcid":"https://orcid.org/0000-0001-5524-9864"},"institutions":[{"id":"https://openalex.org/I176037994","display_name":"Erzincan Binali Y\u0131ld\u0131r\u0131m University","ror":"https://ror.org/02h1e8605","country_code":"TR","type":"education","lineage":["https://openalex.org/I176037994"]}],"countries":["TR"],"is_corresponding":true,"raw_author_name":"Betul Karakas","raw_affiliation_strings":["Department of Computer Technologies, Computer Programming, Uzumlu Vocational School, Erzincan Binali Yildirim University, Erzincan, T&#x00FC;rkiye"],"raw_orcid":"https://orcid.org/0000-0001-5524-9864","affiliations":[{"raw_affiliation_string":"Department of Computer Technologies, Computer Programming, Uzumlu Vocational School, Erzincan Binali Yildirim University, Erzincan, T&#x00FC;rkiye","institution_ids":["https://openalex.org/I176037994"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025000229","display_name":"Sinem Kulluk","orcid":"https://orcid.org/0000-0002-0675-3113"},"institutions":[{"id":"https://openalex.org/I87673952","display_name":"Erciyes University","ror":"https://ror.org/047g8vk19","country_code":"TR","type":"education","lineage":["https://openalex.org/I87673952"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Sinem Kulluk","raw_affiliation_strings":["Department of Industrial Engineering, Faculty of Engineering, Erciyes University, Kayseri, T&#x00FC;rkiye"],"raw_orcid":"https://orcid.org/0000-0002-0675-3113","affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering, Faculty of Engineering, Erciyes University, Kayseri, T&#x00FC;rkiye","institution_ids":["https://openalex.org/I87673952"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5117361942"],"corresponding_institution_ids":["https://openalex.org/I176037994"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.44089637,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":null,"first_page":"192173","last_page":"192188"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.8235999941825867,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.8235999941825867,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.011500000022351742,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.01119999960064888,"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/feature-selection","display_name":"Feature selection","score":0.5702000260353088},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5663999915122986},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.5230000019073486},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5123000144958496},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.49540001153945923},{"id":"https://openalex.org/keywords/lasso","display_name":"Lasso (programming language)","score":0.48730000853538513},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46399998664855957},{"id":"https://openalex.org/keywords/hybrid-algorithm","display_name":"Hybrid algorithm (constraint satisfaction)","score":0.4406999945640564},{"id":"https://openalex.org/keywords/hybrid-system","display_name":"Hybrid system","score":0.4381999969482422}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.794700026512146},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5702000260353088},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5663999915122986},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5238000154495239},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5230000019073486},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5123000144958496},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.49540001153945923},{"id":"https://openalex.org/C37616216","wikidata":"https://www.wikidata.org/wiki/Q3218363","display_name":"Lasso (programming language)","level":2,"score":0.48730000853538513},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4724999964237213},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46399998664855957},{"id":"https://openalex.org/C62469222","wikidata":"https://www.wikidata.org/wiki/Q17092103","display_name":"Hybrid algorithm (constraint satisfaction)","level":5,"score":0.4406999945640564},{"id":"https://openalex.org/C50897621","wikidata":"https://www.wikidata.org/wiki/Q2665508","display_name":"Hybrid system","level":2,"score":0.4381999969482422},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4336000084877014},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.424699991941452},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.39160001277923584},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.3912000060081482},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.383899986743927},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.37209999561309814},{"id":"https://openalex.org/C2778012447","wikidata":"https://www.wikidata.org/wiki/Q1034415","display_name":"Scope (computer science)","level":2,"score":0.3718999922275543},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.367900013923645},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.3492000102996826},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3449000120162964},{"id":"https://openalex.org/C2778348673","wikidata":"https://www.wikidata.org/wiki/Q739302","display_name":"Production (economics)","level":2,"score":0.3375000059604645},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3230000138282776},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.28060001134872437},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.2651999890804291},{"id":"https://openalex.org/C2778915421","wikidata":"https://www.wikidata.org/wiki/Q3643177","display_name":"Performance improvement","level":2,"score":0.2531999945640564}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/access.2025.3630968","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3630968","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:93a1a107-cd69-40e7-9f7d-bbc985493e1c","is_oa":false,"landing_page_url":"https://avesis.erciyes.edu.tr/publication/details/93a1a107-cd69-40e7-9f7d-bbc985493e1c/oai","pdf_url":null,"source":{"id":"https://openalex.org/S7407055139","display_name":"Erciyes University - AVESIS","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":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:doaj.org/article:f1eef41fd2b6443ca51c54f12693785e","is_oa":true,"landing_page_url":"https://doaj.org/article/f1eef41fd2b6443ca51c54f12693785e","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 13, Pp 192173-192188 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3630968","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3630968","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":64,"referenced_works":["https://openalex.org/W1981759979","https://openalex.org/W2004209188","https://openalex.org/W2037654606","https://openalex.org/W2065604341","https://openalex.org/W2072128103","https://openalex.org/W2109925328","https://openalex.org/W2116360511","https://openalex.org/W2194775991","https://openalex.org/W2337601638","https://openalex.org/W2418691539","https://openalex.org/W2618530766","https://openalex.org/W2748128648","https://openalex.org/W2749684264","https://openalex.org/W2765965642","https://openalex.org/W2774057992","https://openalex.org/W2787723838","https://openalex.org/W2802615216","https://openalex.org/W2891336752","https://openalex.org/W2909862814","https://openalex.org/W2913133337","https://openalex.org/W2920311927","https://openalex.org/W2921163360","https://openalex.org/W2962956752","https://openalex.org/W2963125010","https://openalex.org/W2963163009","https://openalex.org/W2963587345","https://openalex.org/W2996903372","https://openalex.org/W2998651184","https://openalex.org/W2999092781","https://openalex.org/W3009635072","https://openalex.org/W3017059628","https://openalex.org/W3023211159","https://openalex.org/W3043036965","https://openalex.org/W3049495163","https://openalex.org/W3100813167","https://openalex.org/W3123602972","https://openalex.org/W3134751187","https://openalex.org/W3161021613","https://openalex.org/W3173714114","https://openalex.org/W3195969653","https://openalex.org/W3208394869","https://openalex.org/W3212210620","https://openalex.org/W4205947740","https://openalex.org/W4206444987","https://openalex.org/W4210720663","https://openalex.org/W4224218882","https://openalex.org/W4226509305","https://openalex.org/W4288061123","https://openalex.org/W4319597842","https://openalex.org/W4322619914","https://openalex.org/W4361829816","https://openalex.org/W4377021614","https://openalex.org/W4385344667","https://openalex.org/W4385664627","https://openalex.org/W4387687697","https://openalex.org/W4388817142","https://openalex.org/W4391815505","https://openalex.org/W4393010170","https://openalex.org/W4394583370","https://openalex.org/W4400593765","https://openalex.org/W4402566772","https://openalex.org/W4403971321","https://openalex.org/W4404345432","https://openalex.org/W4409557230"],"related_works":[],"abstract_inverted_index":{"In":[0],"complex":[1],"industrial":[2],"manufacturing":[3],"processes,":[4],"various":[5],"defects":[6,24,59],"may":[7,25],"occur":[8],"in":[9,160,211,226],"products":[10,56],"because":[11],"of":[12,16,67,88,121,131,137,228,240],"the":[13,17,65,85,119,122,129,138,178,197,208,217,223,232,246],"poor":[14],"quality":[15],"equipment,":[18],"materials,":[19],"and":[20,43,61,106,125,153,157,164,200,203,207,243,249],"human":[21],"factors.":[22],"These":[23],"lead":[26],"to":[27,31,54,83,110,117,188],"undesirable":[28],"situations":[29],"due":[30],"reasons":[32],"such":[33,58],"as":[34],"increasing":[35],"costs,":[36],"shortening":[37],"service":[38],"life,":[39],"reducing":[40],"customer":[41],"satisfaction,":[42],"adversely":[44],"affecting":[45],"quality.":[46],"To":[47],"reduce":[48],"these":[49],"problems,":[50],"it":[51],"is":[52,81,126,192],"crucial":[53],"detect":[55],"with":[57,101,220],"quickly":[60],"effectively.":[62],"Therefore,":[63],"within":[64],"scope":[66],"this":[68],"study,":[69],"a":[70,95,102,132,193],"hybrid":[71,79,180,199,206,219,234],"Convolutional":[72],"Neural":[73],"Network":[74],"(CNN)-Support":[75],"Vector":[76],"Machine":[77],"(SVM)":[78],"model":[80,139],"introduced":[82],"improve":[84],"classification":[86,162,238],"accuracy":[87,163],"production":[89],"defects.":[90],"The":[91,135],"proposed":[92,179],"framework":[93],"incorporates":[94],"CNN":[96,202,225],"for":[97,172,245],"feature":[98,112,123],"extraction,":[99],"coupled":[100],"Least":[103],"Absolute":[104],"Shrinkage":[105],"Selection":[107],"Operator":[108],"(LASSO)":[109],"facilitate":[111],"selection.":[113],"This":[114,213],"approach":[115],"aims":[116],"mitigate":[118],"dimensionality":[120],"space":[124],"complemented":[127],"by":[128,177],"implementation":[130],"SVM":[133],"classifier.":[134],"performance":[136,170],"was":[140],"tested":[141],"on":[142],"three":[143],"widely":[144],"used":[145],"publicly":[146],"available":[147],"datasets:":[148],"metal":[149],"casting,":[150,247],"textile":[151],"fabric,":[152],"food":[154],"images.":[155],"Significant":[156],"high-performance":[158],"results":[159],"both":[161],"execution":[165,229],"time,":[166],"which":[167],"are":[168,185],"important":[169],"metrics":[171],"real-time":[173],"applications,":[174],"were":[175],"obtained":[176],"CNN-SVM":[181,198,205],"algorithm.":[182],"Statistical":[183],"analyses":[184],"also":[186],"performed":[187],"test":[189],"whether":[190],"there":[191],"significant":[194],"difference":[195],"between":[196,204],"conventional":[201,224],"previous":[209],"studies":[210],"literature.":[212],"study":[214],"demonstrates":[215],"that":[216],"AlexNet-SVM":[218,233],"LASSO":[221,236],"outperforms":[222],"terms":[227],"time.":[230],"Furthermore,":[231],"without":[235],"achieved":[237],"accuracies":[239],"98,29%,":[241],"99,82%,":[242],"97,37%":[244],"food,":[248],"fabric":[250],"datasets,":[251],"respectively.":[252]},"counts_by_year":[],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-11-10T00:00:00"}
