{"id":"https://openalex.org/W7090166759","doi":"https://doi.org/10.1109/icmlt65785.2025.11193308","title":"Neurosymbolic Artificial Intelligence for Printed Circuit Board Fault Detection","display_name":"Neurosymbolic Artificial Intelligence for Printed Circuit Board Fault Detection","publication_year":2025,"publication_date":"2025-05-23","ids":{"openalex":"https://openalex.org/W7090166759","doi":"https://doi.org/10.1109/icmlt65785.2025.11193308"},"language":"en","primary_location":{"id":"doi:10.1109/icmlt65785.2025.11193308","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlt65785.2025.11193308","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 10th International Conference on Machine Learning Technologies (ICMLT)","raw_type":"proceedings-article"},"type":"conference-paper","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":null,"display_name":"Muhammad Nouman Khan","orcid":null},"institutions":[{"id":"https://openalex.org/I27469343","display_name":"Pforzheim University of Applied Sciences","ror":"https://ror.org/00qakfc35","country_code":"DE","type":"education","lineage":["https://openalex.org/I27469343"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Muhammad Nouman Khan","raw_affiliation_strings":["Pforzheim University,Institute for Smart Systems and Services,Pforzheim,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Pforzheim University,Institute for Smart Systems and Services,Pforzheim,Germany","institution_ids":["https://openalex.org/I27469343"]}]},{"author_position":"last","author":{"id":null,"display_name":"Thomas Greiner","orcid":null},"institutions":[{"id":"https://openalex.org/I27469343","display_name":"Pforzheim University of Applied Sciences","ror":"https://ror.org/00qakfc35","country_code":"DE","type":"education","lineage":["https://openalex.org/I27469343"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Thomas Greiner","raw_affiliation_strings":["Pforzheim University,Institute for Smart Systems and Services,Pforzheim,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Pforzheim University,Institute for Smart Systems and Services,Pforzheim,Germany","institution_ids":["https://openalex.org/I27469343"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I27469343"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.60329055,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"116","last_page":"124"},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.39800000190734863,"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":0.39800000190734863,"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":0.12389999628067017,"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/T11032","display_name":"VLSI and Analog Circuit Testing","score":0.11630000174045563,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/interpretability","display_name":"Interpretability","score":0.850600004196167},{"id":"https://openalex.org/keywords/printed-circuit-board","display_name":"Printed circuit board","score":0.5857999920845032},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5852000117301941},{"id":"https://openalex.org/keywords/fault-detection-and-isolation","display_name":"Fault detection and isolation","score":0.5350000262260437},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.49880000948905945},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4672999978065491},{"id":"https://openalex.org/keywords/false-positive-paradox","display_name":"False positive paradox","score":0.4560999870300293},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.44909998774528503},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39959999918937683}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.850600004196167},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6642000079154968},{"id":"https://openalex.org/C120793396","wikidata":"https://www.wikidata.org/wiki/Q173350","display_name":"Printed circuit board","level":2,"score":0.5857999920845032},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5852000117301941},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5683000087738037},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.5350000262260437},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.49880000948905945},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4672999978065491},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.4560999870300293},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.44909998774528503},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39959999918937683},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.38679999113082886},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.3831999897956848},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3785000145435333},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.37770000100135803},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3750999867916107},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.34630000591278076},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32910001277923584},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3172000050544739},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.31049999594688416},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.30399999022483826},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.299699991941452},{"id":"https://openalex.org/C58328972","wikidata":"https://www.wikidata.org/wiki/Q184609","display_name":"Expert system","level":2,"score":0.2948000133037567},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2757999897003174},{"id":"https://openalex.org/C112789634","wikidata":"https://www.wikidata.org/wiki/Q18207010","display_name":"False positives and false negatives","level":3,"score":0.2734000086784363},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2694999873638153},{"id":"https://openalex.org/C115925183","wikidata":"https://www.wikidata.org/wiki/Q1412694","display_name":"Knowledge-based systems","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.26669999957084656},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.26579999923706055},{"id":"https://openalex.org/C157170001","wikidata":"https://www.wikidata.org/wiki/Q4781507","display_name":"Applications of artificial intelligence","level":2,"score":0.2646999955177307},{"id":"https://openalex.org/C103824480","wikidata":"https://www.wikidata.org/wiki/Q185889","display_name":"Time domain","level":2,"score":0.257099986076355},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.2567000091075897},{"id":"https://openalex.org/C5339829","wikidata":"https://www.wikidata.org/wiki/Q1425977","display_name":"Machine vision","level":2,"score":0.251800000667572}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmlt65785.2025.11193308","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlt65785.2025.11193308","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 10th International Conference on Machine Learning Technologies (ICMLT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","score":0.5748841762542725,"display_name":"Decent work and economic growth"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W2004241456","https://openalex.org/W3216178672","https://openalex.org/W4392247085","https://openalex.org/W4400771000"],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,22,28,50,62],"novel":[4],"neurosymbolic":[5],"approach":[6],"for":[7,124],"resource-efficient":[8],"Printed":[9],"Circuit":[10],"Board":[11],"(PCB)":[12],"fault":[13],"detection.":[14],"The":[15],"method":[16],"combines":[17],"classical":[18],"image":[19],"processing":[20],"with":[21,58],"Convolutional":[23],"Neural":[24],"Network":[25],"(CNN)":[26],"and":[27,35,47,71,98,118],"Knowledge":[29,51],"Graph":[30],"(KG)":[31],"to":[32,92],"capture":[33],"visual":[34,70],"structural":[36],"relationships":[37],"among":[38],"PCB":[39,43],"components.":[40],"First,":[41],"key":[42],"features":[44,60],"are":[45,56],"extracted":[46],"structured":[48,112],"into":[49],"Graph;":[52],"then":[53],"graph":[54],"embeddings":[55],"fused":[57],"CNN-derived":[59],"through":[61],"hybrid":[63],"loss":[64],"function":[65],"that":[66,80,108],"enforces":[67],"alignment":[68],"between":[69],"symbolic":[72],"knowledge.":[73],"Experiments":[74],"on":[75],"the":[76,81,85,100,103,109,121],"DeepPCB":[77],"dataset":[78],"show":[79],"proposed":[82],"framework":[83,122],"improves":[84,116],"classification":[86],"accuracy":[87],"from":[88],"83.86%":[89],"(baseline":[90],"CNN)":[91],"87.85%,":[93],"while":[94],"reducing":[95],"false":[96],"positives":[97],"improving":[99],"interpretability":[101],"of":[102,111],"model.":[104],"These":[105],"results":[106],"demonstrate":[107],"integration":[110],"domain":[113],"knowledge":[114],"significantly":[115],"performance":[117],"explainability,":[119],"making":[120],"promising":[123],"deployment":[125],"in":[126],"resource-constrained":[127],"industrial":[128],"environments.":[129]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-14T00:00:00"}