{"id":"https://openalex.org/W4409475683","doi":"https://doi.org/10.1109/icic64337.2024.10956823","title":"Convolutional Neural Network (CNN) Algorithm Development for Quality Measurement in Tire Product Classification: A Systematic Literature Review","display_name":"Convolutional Neural Network (CNN) Algorithm Development for Quality Measurement in Tire Product Classification: A Systematic Literature Review","publication_year":2024,"publication_date":"2024-10-24","ids":{"openalex":"https://openalex.org/W4409475683","doi":"https://doi.org/10.1109/icic64337.2024.10956823"},"language":"en","primary_location":{"id":"doi:10.1109/icic64337.2024.10956823","doi":"https://doi.org/10.1109/icic64337.2024.10956823","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icic64337.2024.10956823","pdf_url":null,"source":{"id":"https://openalex.org/S4306498392","display_name":"International Conference on Informatics and Computing (ICIC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"2024 Ninth International Conference on Informatics and Computing (ICIC)","raw_type":"proceedings-article"},"volume":null,"type":"review","is_traditional":true,"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/A5052424137","display_name":"Armando Tirta Dwilaga","orcid":"https://orcid.org/0009-0004-4370-6094","observed_orcids":["https://orcid.org/0009-0004-4370-6094"]},"institutions":[{"id":"https://openalex.org/I4210087516","display_name":"Gunadarma University","ror":"https://ror.org/0028q1g19","country_code":"ID","type":"education","lineage":["https://openalex.org/I4210087516"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Armando Tirta Dwilaga","raw_affiliation_strings":["Gunadarma Information Technology University,Doctoral Program,Department of Information Technology,West Java,Indonesia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Gunadarma Information Technology University,Doctoral Program,Department of Information Technology,West Java,Indonesia","institution_ids":["https://openalex.org/I4210087516"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064812456","display_name":"Lintang Yuniar Banowosari","orcid":"https://orcid.org/0009-0004-8792-5037","observed_orcids":["https://orcid.org/0009-0004-8792-5037"]},"institutions":[{"id":"https://openalex.org/I4210087516","display_name":"Gunadarma University","ror":"https://ror.org/0028q1g19","country_code":"ID","type":"education","lineage":["https://openalex.org/I4210087516"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Lintang Yuniar Banowosari","raw_affiliation_strings":["Gunadarma University,Department of Informatics,West Java,Indonesia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Gunadarma University,Department of Informatics,West Java,Indonesia","institution_ids":["https://openalex.org/I4210087516"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059997482","display_name":"Sudaryanto Sudaryanto","orcid":"https://orcid.org/0000-0001-9281-3962","observed_orcids":["https://orcid.org/0000-0001-9281-3962","https://orcid.org/0009-0008-6203-8131"]},"institutions":[{"id":"https://openalex.org/I4210087516","display_name":"Gunadarma University","ror":"https://ror.org/0028q1g19","country_code":"ID","type":"education","lineage":["https://openalex.org/I4210087516"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Sudaryanto","raw_affiliation_strings":["Gunadarma University,Department of Industrial Engineering and Management,West Java,Indonesia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Gunadarma University,Department of Industrial Engineering and Management,West Java,Indonesia","institution_ids":["https://openalex.org/I4210087516"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077464096","display_name":"Aini Suri Talita","orcid":"https://orcid.org/0000-0003-3614-9336","observed_orcids":["https://orcid.org/0000-0003-3614-9336"]},"institutions":[{"id":"https://openalex.org/I4210087516","display_name":"Gunadarma University","ror":"https://ror.org/0028q1g19","country_code":"ID","type":"education","lineage":["https://openalex.org/I4210087516"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Aini Suri Talita","raw_affiliation_strings":["Gunadarma University,Department of Information System Management,West Java,Indonesia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Gunadarma University,Department of Information System Management,West Java,Indonesia","institution_ids":["https://openalex.org/I4210087516"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210087516"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"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":"6"},"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":0.9922000169754028,"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":0.9922000169754028,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.0071000000461936,"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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.00019999999494757503,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.9229999780654907},{"id":"https://openalex.org/keywords/image-based-defect-detection","display_name":"image-based defect detection","score":0.6710000038146973},{"id":"https://openalex.org/keywords/small-defect-detection","display_name":"small defect detection","score":0.7129999995231628},{"id":"https://openalex.org/keywords/dropout-rates","display_name":"dropout rates","score":0.9710000157356262},{"id":"https://openalex.org/keywords/learning-rate","display_name":"learning rate","score":0.9549999833106995},{"id":"https://openalex.org/keywords/pre-trained-models","display_name":"pre-trained models","score":0.8460000157356262},{"id":"https://openalex.org/keywords/alexnet","display_name":"AlexNet","score":0.8399999737739563},{"id":"https://openalex.org/keywords/vgg16","display_name":"VGG16","score":0.9959999918937683},{"id":"https://openalex.org/keywords/adam-optimizer","display_name":"Adam optimizer","score":0.753000020980835},{"id":"https://openalex.org/keywords/lighting-techniques","display_name":"lighting techniques","score":0.5590000152587891},{"id":"https://openalex.org/keywords/tire-manufacturing","display_name":"tire manufacturing","score":0.6909999847412109}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8521502017974854},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7041648626327515},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.5375956892967224},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5200397968292236},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.48981401324272156},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4201587736606598},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34597766399383545},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33967316150665283},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1033954918384552},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icic64337.2024.10956823","doi":"https://doi.org/10.1109/icic64337.2024.10956823","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icic64337.2024.10956823","pdf_url":null,"source":{"id":"https://openalex.org/S4306498392","display_name":"International Conference on Informatics and Computing (ICIC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"2024 Ninth International Conference on Informatics and Computing (ICIC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[{"id":"https://openalex.org/study-designs/systematic-review","display_name":"Systematic Review"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W2741224071","https://openalex.org/W2807234793","https://openalex.org/W2886037842","https://openalex.org/W2926743806","https://openalex.org/W2998234644","https://openalex.org/W3009886659","https://openalex.org/W3035372121","https://openalex.org/W3035841695","https://openalex.org/W3084475696","https://openalex.org/W3089334509","https://openalex.org/W3113879609","https://openalex.org/W3118321684","https://openalex.org/W3124186538","https://openalex.org/W3138890122","https://openalex.org/W3165670597","https://openalex.org/W3177301635","https://openalex.org/W3182843231","https://openalex.org/W3206555527","https://openalex.org/W3209971249","https://openalex.org/W3213055223","https://openalex.org/W3213693699","https://openalex.org/W4200156383","https://openalex.org/W4206712501","https://openalex.org/W4221079468","https://openalex.org/W4281261257","https://openalex.org/W4288693944","https://openalex.org/W4310191216","https://openalex.org/W4318480143","https://openalex.org/W4320920894","https://openalex.org/W4376873434","https://openalex.org/W4391300512","https://openalex.org/W4392482980","https://openalex.org/W4395013054","https://openalex.org/W4397002046"],"related_works":["https://openalex.org/W4309524231","https://openalex.org/W4401432196","https://openalex.org/W2741224071","https://openalex.org/W3124186538","https://openalex.org/W3094848758","https://openalex.org/W4224660134","https://openalex.org/W4402204278","https://openalex.org/W4406336790","https://openalex.org/W2807234793","https://openalex.org/W4413619164"],"abstract_inverted_index":{"Convolutional":[0],"Neural":[1],"Networks":[2],"(CNN)":[3],"have":[4],"shown":[5],"potential":[6],"in":[7,65,102,122,161,167,190],"tire":[8,191],"defect":[9,103,179],"classification,":[10],"but":[11,158],"challenges":[12,101,159],"like":[13],"image":[14],"quality":[15,192],"variations":[16],"and":[17,34,38,49,63,93,99,125,132,141,172,186],"model":[18],"limitations":[19],"persist.":[20],"This":[21,181],"Systematic":[22,32],"Literature":[23],"Review":[24],"(SLR)":[25],"employs":[26],"the":[27,44,76,111,116,128],"Preferred":[28],"Reporting":[29],"Items":[30],"for":[31],"Reviews":[33],"Meta-Analyses":[35],"(PRISMA)":[36],"methodology":[37],"a":[39,142],"qualitative":[40],"approach.":[41],"It":[42],"uses":[43],"Population,":[45],"Intervention,":[46],"Comparison,":[47],"Outcome,":[48],"Context":[50],"(PICOC)":[51],"framework":[52],"to":[53,177],"analyze":[54],"30":[55],"articles":[56],"from":[57],"Google":[58],"Scholar,":[59],"published":[60],"between":[61],"2018":[62],"2024":[64],"Q1-Q4":[66],"journals.":[67],"The":[68,149],"review":[69,182],"examines":[70],"article":[71],"distribution":[72],"by":[73],"publisher,":[74,114],"evaluates":[75],"impact":[77],"of":[78,119],"key":[79],"pre-training":[80],"parameters":[81],"(such":[82],"as":[83],"data":[84,135],"split":[85,136],"ratio,":[86,137],"dropout":[87,139],"rate,":[88,92,140],"optimization":[89,153],"algorithm,":[90],"learning":[91,144],"pre-trained":[94],"models":[95],"on":[96],"CNN":[97,168],"performance),":[98],"identifies":[100],"classification.":[104],"MDPI":[105],"(Multidisciplinary":[106],"Digital":[107],"Publishing":[108],"Institute)":[109],"is":[110,155],"most":[112,129],"frequent":[113],"with":[115],"highest":[117],"number":[118],"publications":[120],"occurring":[121],"2021.":[123],"AlexNet":[124],"VGG16":[126],"are":[127,146,175],"common":[130],"models,":[131,169],"an":[133],"80:20":[134],"0.5":[138],"0.001":[143],"rate":[145],"frequently":[147],"used.":[148],"Adam":[150],"(Adaptive":[151],"Momentum)":[152],"algorithm":[154],"widely":[156],"adopted,":[157],"remain":[160],"detecting":[162],"small":[163],"defects.":[164],"Further":[165],"improvements":[166],"lighting":[170],"techniques,":[171],"larger":[173],"datasets":[174],"needed":[176],"enhance":[178],"detection.":[180],"highlights":[183],"current":[184],"trends":[185],"future":[187],"research":[188],"opportunities":[189],"measurement.":[193]},"counts_by_year":[],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2025-04-16T00:00:00"}
