{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T20:10:38Z","timestamp":1780431038261,"version":"3.54.1"},"reference-count":25,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T00:00:00Z","timestamp":1771977600000},"content-version":"vor","delay-in-days":55,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100017071","name":"Toyota Motor Engineering and Manufacturing North America","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100017071","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Procedia Computer Science"],"published-print":{"date-parts":[[2026]]},"DOI":"10.1016\/j.procs.2026.02.368","type":"journal-article","created":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T07:17:59Z","timestamp":1774250279000},"page":"3318-3330","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Interpretable Machine Learning Framework for Quality Control in Resource-Constrained Industrial Settings"],"prefix":"10.1016","volume":"277","author":[{"given":"Jos\u00e9","family":"Ca\u00e7\u00e3o","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jos\u00e9 Paulo","family":"Santos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M\u00e1rio","family":"Antunes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.procs.2026.02.368_bib1","doi-asserted-by":"crossref","first-page":"101861","DOI":"10.1016\/j.rcim.2019.101861","article-title":"Manufacturing big data ecosystem: A systematic literature review","volume":"62","author":"Cui","year":"2020","journal-title":"Robotics and Computer-Integrated Manufacturing"},{"key":"10.1016\/j.procs.2026.02.368_bib2","doi-asserted-by":"crossref","first-page":"119456","DOI":"10.1016\/j.eswa.2022.119456","article-title":"Artificial intelligence for industry 4.0: Systematic review of applications","volume":"216","author":"Jan","year":"2023","journal-title":"challenges, and opportunities, Expert Systems with Applications"},{"key":"10.1016\/j.procs.2026.02.368_bib3","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.jmsy.2020.11.017","article-title":"Cyber-physical systems architectures for industrial internet of things applications in industry 4.0: A literature review","volume":"58","author":"Pivoto","year":"2021","journal-title":"Journal of Manufacturing Systems"},{"key":"10.1016\/j.procs.2026.02.368_bib4","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1016\/j.jmsy.2021.10.006","article-title":"Industry 4.0 and industry 5.0\u2014inception","volume":"61","author":"Xu","year":"2021","journal-title":"conception and perception, Journal of Manufacturing Systems"},{"issue":"9","key":"10.1016\/j.procs.2026.02.368_bib5","doi-asserted-by":"crossref","first-page":"4888","DOI":"10.1109\/TII.2019.2916622","article-title":"A rule-based approach founded on description logics for industry 4.0 smart factories","volume":"15","author":"Kourtis","year":"2019","journal-title":"IEEE Transactions on Industrial Informatics"},{"issue":"4","key":"10.1016\/j.procs.2026.02.368_bib6","doi-asserted-by":"crossref","first-page":"1925","DOI":"10.1109\/TASE.2020.2983061","article-title":"Data-driven approach for fault detection and diagnostic in semiconductor manufacturing","volume":"17","author":"Fan","year":"2020","journal-title":"IEEE Transactions on Automation Science and Engineering"},{"issue":"3","key":"10.1016\/j.procs.2026.02.368_bib7","doi-asserted-by":"crossref","first-page":"1852","DOI":"10.1109\/TII.2020.2988208","article-title":"Fault description based attribute transfer for zero-sample industrial fault diagnosis","volume":"17","author":"Feng","year":"2021","journal-title":"IEEE Transactions on Industrial Informatics"},{"issue":"3","key":"10.1016\/j.procs.2026.02.368_bib8","doi-asserted-by":"crossref","first-page":"972","DOI":"10.3390\/s21030972","article-title":"A deep learning model for predictive maintenance in cyber-physical production systems using lstm autoencoders","volume":"21","author":"Bampoula","year":"2021","journal-title":"Sensors"},{"issue":"5","key":"10.1016\/j.procs.2026.02.368_bib9","doi-asserted-by":"crossref","first-page":"3478","DOI":"10.1109\/TII.2020.3008223","article-title":"A data-driven auto-cnn-lstm prediction model for lithium-ion battery remaining useful life","volume":"17","author":"Ren","year":"2021","journal-title":"IEEE Transactions on Industrial Informatics"},{"issue":"15","key":"10.1016\/j.procs.2026.02.368_bib10","doi-asserted-by":"crossref","first-page":"5340","DOI":"10.3390\/app10155340","article-title":"Inspection and classification of semiconductor wafer surface defects using cnn deep learning networks","volume":"10","author":"Chien","year":"2020","journal-title":"Applied Sciences"},{"key":"10.1016\/j.procs.2026.02.368_bib11","doi-asserted-by":"crossref","first-page":"102470","DOI":"10.1016\/j.rcim.2022.102470","article-title":"Deep learning based online metallic surface defect detection method for wire and arc additive manufacturing","volume":"80","author":"Li","year":"2023","journal-title":"Robotics and Computer-Integrated Manufacturing"},{"issue":"2","key":"10.1016\/j.procs.2026.02.368_bib12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3439950","article-title":"Deep learning for anomaly detection: A review","volume":"54","author":"Pang","year":"2021","journal-title":"ACM Computing Surveys"},{"key":"10.1016\/j.procs.2026.02.368_bib13","doi-asserted-by":"crossref","unstructured":"P. P. Angelov, E. A. Soares, R. Jiang, N. I. Arnold, P. M. Atkinson, Explainable artificial intelligence: an analytical review, WIREs Data Mining and Knowledge Discovery 11 (5) (Jul. 2021). doi: 10.1002\/widm.1424.","DOI":"10.1002\/widm.1424"},{"key":"10.1016\/j.procs.2026.02.368_bib14","doi-asserted-by":"crossref","first-page":"102470","DOI":"10.1016\/j.media.2022.102470","article-title":"Explainable artificial intelligence (xai) in deep learning-based medical image analysis","volume":"79","author":"van der Velden","year":"2022","journal-title":"Medical Image Analysis"},{"issue":"3","key":"10.1016\/j.procs.2026.02.368_bib15","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1038\/s42256-022-00463-x","article-title":"The transformational role of gpu computing and deep learning in drug discovery","volume":"4","author":"Pandey","year":"2022","journal-title":"Nature Machine Intelligence"},{"key":"10.1016\/j.procs.2026.02.368_bib16","unstructured":"Boston Consulting Group, Bcg-wef project: Ai-powered industrial operations (2023). URL https:\/\/www.bcg.com\/about\/partner-ecosystem\/world-economic-forum\/ai-project-survey"},{"key":"10.1016\/j.procs.2026.02.368_bib17","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.1016\/j.rser.2016.01.025","article-title":"A review on the applications of programmable logic controllers (plcs)","volume":"60","author":"Alphonsus","year":"2016","journal-title":"Renewable and Sustainable Energy Reviews"},{"key":"10.1016\/j.procs.2026.02.368_bib18","unstructured":"European Union, Eu artificial intelligence act (2024). URL https:\/\/artificialintelligenceact.eu\/"},{"key":"10.1016\/j.procs.2026.02.368_bib19","unstructured":"Pfeiffer Vacuum, Leak Test Instrument Model E2 and Model VE2 - Programming and Operation Manual (Jul. 2021). URL https:\/\/usa.pfeiffer-vacuum.com\/wp-content\/uploads\/MODEL-E2_VE2_Manual-Rev-2.3.13-1.pdf"},{"key":"10.1016\/j.procs.2026.02.368_bib20","first-page":"2579","article-title":"Laurens; Hinton","volume":"9","author":"Van Der Maaten","year":"2008","journal-title":"Visualizing data using t-sne, Journal of Machine Learning Research"},{"key":"10.1016\/j.procs.2026.02.368_bib21","unstructured":"C. Molnar, Interpretable machine learning, second edition Edition, Christoph Molnar, Munich, Germany, 2022."},{"key":"10.1016\/j.procs.2026.02.368_bib22","first-page":"82","article-title":"Explainable artificial intelligence (xai): Concepts","volume":"58","author":"Barredo Arrieta","year":"2020","journal-title":"taxonomies, opportunities and challenges toward responsible ai, Information Fusion"},{"key":"10.1016\/j.procs.2026.02.368_bib23","unstructured":"S. Watanabe, Tree-structured parzen estimator: Understanding its algorithm components and their roles for better empirical performance (2023). doi: 10.48550\/ARXIV.2304.11127."},{"key":"10.1016\/j.procs.2026.02.368_bib24","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"Smote: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"Journal of Artificial Intelligence Research"},{"issue":"1","key":"10.1016\/j.procs.2026.02.368_bib25","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1109\/LES.2017.2654160","article-title":"Accurate measurement of small execution times\u2014getting around measurement errors","volume":"9","author":"Moreno","year":"2017","journal-title":"IEEE Embedded Systems Letters"}],"container-title":["Procedia Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1877050926004898?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1877050926004898?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T19:39:26Z","timestamp":1780429166000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1877050926004898"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":25,"alternative-id":["S1877050926004898"],"URL":"https:\/\/doi.org\/10.1016\/j.procs.2026.02.368","relation":{},"ISSN":["1877-0509"],"issn-type":[{"value":"1877-0509","type":"print"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Interpretable Machine Learning Framework for Quality Control in Resource-Constrained Industrial Settings","name":"articletitle","label":"Article Title"},{"value":"Procedia Computer Science","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.procs.2026.02.368","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}]}}