{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T13:58:23Z","timestamp":1774965503987,"version":"3.50.1"},"reference-count":19,"publisher":"Springer Science and Business Media LLC","issue":"36","license":[{"start":{"date-parts":[[2023,4,8]],"date-time":"2023-04-08T00:00:00Z","timestamp":1680912000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,4,8]],"date-time":"2023-04-08T00:00:00Z","timestamp":1680912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s00521-023-08499-9","type":"journal-article","created":{"date-parts":[[2023,4,8]],"date-time":"2023-04-08T15:02:26Z","timestamp":1680966146000},"page":"25263-25273","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["X-ray PCB defect automatic diagnosis algorithm based on deep learning and artificial intelligence"],"prefix":"10.1007","volume":"35","author":[{"given":"Yaojun","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingjing","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuanyang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,4,8]]},"reference":[{"issue":"10","key":"8499_CR1","first-page":"008","volume":"7","author":"H Zhuo","year":"2018","unstructured":"Zhuo H, Han YP, Guo J (2018) Study on automatic defect detection technology of the cylinder liner based on X-ray. Tech Autom Appl 7(10):008\u2013016","journal-title":"Tech Autom Appl"},{"issue":"3","key":"8499_CR2","first-page":"068","volume":"10","author":"J Wang","year":"2017","unstructured":"Wang J, Wang X (2017) Automatic detection of weld defects in X-ray based on butter worth filtering. Microcomput Appl 10(3):068\u2013116","journal-title":"Microcomput Appl"},{"issue":"10","key":"8499_CR3","first-page":"108","volume":"5","author":"X Li","year":"2019","unstructured":"Li X, Zhu Y (2019) Optimizing depth discrimination restricted boltzmann machine based on genetic algorithm for fault diagnosis of transformer. Int Core J Eng 5(10):108\u2013116","journal-title":"Int Core J Eng"},{"issue":"15","key":"8499_CR4","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.apenergy.2016.11.111","volume":"188","author":"HZ Wang","year":"2017","unstructured":"Wang HZ, Li GQ, Wang GB et al (2017) Deep learning based ensemble approach for probabilistic wind power forecasting. Appl Energy 188(15):56\u201370","journal-title":"Appl Energy"},{"issue":"C","key":"8499_CR5","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.nicl.2017.08.017","volume":"17","author":"AS Heinsfeld","year":"2018","unstructured":"Heinsfeld AS, Franco AR, Craddock RC et al (2018) Identification of autism spectrum disorder using deep learning and the ABIDE dataset\u2014science direct. NeuroImage Clin 17(C):16\u201323","journal-title":"NeuroImage Clin"},{"issue":"4","key":"8499_CR6","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1109\/TCCN.2017.2758370","volume":"3","author":"T Oshea","year":"2017","unstructured":"Oshea T, Hoydis J (2017) An introduction to deep learning for the physical layer. IEEE Trans Cognit Commun Netw 3(4):563\u2013575","journal-title":"IEEE Trans Cognit Commun Netw"},{"issue":"1","key":"8499_CR7","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/JBHI.2016.2636665","volume":"21","author":"D Ravi","year":"2017","unstructured":"Ravi D, Wong C, Deligianni F et al (2017) Deep learning for health informatics. IEEE J Biomed Health Inform 21(1):4\u201321","journal-title":"IEEE J Biomed Health Inform"},{"issue":"6","key":"8499_CR8","first-page":"1275","volume":"26","author":"W Hou","year":"2017","unstructured":"Hou W, Gao X, Tao D et al (2017) Blind image quality assessment via deep learning. IEEE Trans Neural Netw Learn Syst 26(6):1275\u20131286","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"3","key":"8499_CR9","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1109\/MCI.2018.2840738","volume":"13","author":"Y Tom","year":"2018","unstructured":"Tom Y, Devamanyu H, Soujanya P et al (2018) Recent trends in deep learning based natural language processing [review article]. IEEE Comput Intell Mag 13(3):55\u201375","journal-title":"IEEE Comput Intell Mag"},{"issue":"4","key":"8499_CR10","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","volume":"5","author":"XX Zhu","year":"2018","unstructured":"Zhu XX, Tuia D, Mou L et al (2018) Deep learning in remote sensing: a comprehensive review and list of resources. IEEE Geosci Remote Sens Mag 5(4):8\u201336","journal-title":"IEEE Geosci Remote Sens Mag"},{"key":"8499_CR11","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1016\/j.future.2021.09.026","volume":"127","author":"TT Wang","year":"2022","unstructured":"Wang TT, Yu HL, Wang KC (2022) Fault localization based on wide and deep learning model by mining software behavior. Future Gener Comput Syst 127:309\u2013319","journal-title":"Future Gener Comput Syst"},{"issue":"6","key":"8499_CR12","doi-asserted-by":"publisher","first-page":"1113","DOI":"10.1109\/JIOT.2016.2558659","volume":"3","author":"X Wang","year":"2017","unstructured":"Wang X, Gao L, Mao S (2017) CSI phase fingerprinting for indoor localization with a deep learning approach. IEEE Internet Things J 3(6):1113\u20131123","journal-title":"IEEE Internet Things J"},{"issue":"16","key":"8499_CR13","doi-asserted-by":"publisher","first-page":"1291","DOI":"10.1002\/jcc.24764","volume":"38","author":"GB Goh","year":"2017","unstructured":"Goh GB, Hodas NO, Vishnu A (2017) Deep learning for computational chemistry. J Comput Chem 38(16):1291\u20131307","journal-title":"J Comput Chem"},{"issue":"4","key":"8499_CR14","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1147\/JRD.2017.2708299","volume":"61","author":"N Codella","year":"2017","unstructured":"Codella N, Nguyen QB, Pankanti S et al (2017) Deep learning ensembles for melanoma recognition in dermoscopy images. Ibm J Res Dev 61(4):51\u2013515","journal-title":"Ibm J Res Dev"},{"key":"8499_CR15","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1007\/s10044-017-0640-9","volume":"21","author":"VH Gaidhane","year":"2018","unstructured":"Gaidhane VH, Hote YV, Singh V (2018) An efficient similarity measure approach for PCB surface defect detection. Pattern Anal Appl 21:277\u2013289","journal-title":"Pattern Anal Appl"},{"key":"8499_CR16","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1007\/s00138-021-01192-8","volume":"32","author":"TC Tsan","year":"2021","unstructured":"Tsan TC, Shih TF, Fuh CS (2021) TsanKit: artificial intelligence for solder ball head-in-pillow defect inspection. Mach Vis Appl 32:66","journal-title":"Mach Vis Appl"},{"key":"8499_CR17","doi-asserted-by":"publisher","first-page":"13697","DOI":"10.1007\/s00521-022-07192-7","volume":"34","author":"D Kang","year":"2022","unstructured":"Kang D, Han Y, Zhu J et al (2022) An axially decomposed self-attention network for the precise segmentation of surface defects on printed circuit boards. Neural Comput Appl 34:13697\u201313712","journal-title":"Neural Comput Appl"},{"key":"8499_CR18","doi-asserted-by":"publisher","first-page":"18887","DOI":"10.1007\/s11227-022-04610-4","volume":"78","author":"L Gao","year":"2022","unstructured":"Gao L, Zheng F, Bian JY (2022) Using computer theory to detect PCB defects in an IoT environment. J Supercomput 78:18887\u201318914","journal-title":"J Supercomput"},{"key":"8499_CR19","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1007\/s00521-017-3123-4","volume":"29","author":"A Kumar","year":"2018","unstructured":"Kumar A, Kumar R (2018) Adaptive artificial intelligence for automatic identification of defect in the angular contact bearing. Neural Comput Appl 29:277\u2013287","journal-title":"Neural Comput Appl"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08499-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08499-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08499-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T15:07:05Z","timestamp":1701270425000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08499-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,8]]},"references-count":19,"journal-issue":{"issue":"36","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["8499"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08499-9","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,8]]},"assertion":[{"value":"18 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 March 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 April 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The datasets generated during and\/or analysed during the current study are available from the corresponding author on reasonable request.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Data availability"}}]}}