{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T17:49:26Z","timestamp":1780595366598,"version":"3.54.1"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031442032","type":"print"},{"value":"9783031442049","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-44204-9_25","type":"book-chapter","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T04:02:11Z","timestamp":1695268931000},"page":"295-307","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Maintenance Automation Using Deep Learning Methods: A Case Study from\u00a0the\u00a0Aerospace Industry"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6002-2496","authenticated-orcid":false,"given":"P. J.","family":"Mayhew","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5530-4894","authenticated-orcid":false,"given":"H.","family":"Ihshaish","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9551-9718","authenticated-orcid":false,"given":"I.","family":"Deza","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6918-4206","authenticated-orcid":false,"given":"A.","family":"Del Amo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"key":"25_CR1","unstructured":"Agovic, A., Shan, H., Banerjee, A.: Analyzing aviation safety reports: from topic modeling to scalable multi-label classification. In: Proceedings of the 2010 Conference on Intelligent Data Understanding, CIDU 2010, pp. 83\u201397. Citeseer (2010)"},{"key":"25_CR2","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1162\/tacl\\_a_00051","volume":"5","author":"P Bojanowski","year":"2017","unstructured":"Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. Trans. Assoc. Comput. Linguist. 5, 135\u2013146 (2017). https:\/\/doi.org\/10.1162\/tacl_a_00051","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"25_CR3","doi-asserted-by":"publisher","unstructured":"Candell, O., Karim, R., S\u00f6derholm, P.: eMaintenance-Information logistics for maintenance support (2009). https:\/\/doi.org\/10.1016\/j.rcim.2009.04.005","DOI":"10.1016\/j.rcim.2009.04.005"},{"key":"25_CR4","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1016\/j.knosys.2015.07.019","volume":"89","author":"F Charte","year":"2015","unstructured":"Charte, F., Rivera, A.J., Del Jesus, M.J., Herrera, F.: MLSMOTE: approaching imbalanced multilabel learning through synthetic instance generation. Knowl. Based Syst. 89, 385\u2013397 (2015). https:\/\/doi.org\/10.1016\/j.knosys.2015.07.019","journal-title":"Knowl. Based Syst."},{"key":"25_CR5","doi-asserted-by":"publisher","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002). https:\/\/doi.org\/10.1613\/jair.953, http:\/\/arxiv.org\/abs\/1106.1813","DOI":"10.1613\/jair.953"},{"key":"25_CR6","unstructured":"Devaney, M., Ram, A., Qiu, H., Lee, J.: Preventing failures by mining maintenance logs with case-based reasoning (2005)"},{"issue":"4","key":"25_CR7","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1007\/s10278-012-9531-1","volume":"26","author":"BH Do","year":"2013","unstructured":"Do, B.H., Wu, A.S., Maley, J., Biswal, S.: Automatic retrieval of bone fracture knowledge using natural language processing. J. Digit. Imaging 26(4), 709\u2013713 (2013). https:\/\/doi.org\/10.1007\/s10278-012-9531-1","journal-title":"J. Digit. Imaging"},{"key":"25_CR8","unstructured":"Elhadad, N., Zhang, S., Driscoll, P., Brody, S.: Characterizing the sublanguage of online breast cancer forums for medications, symptoms, and emotions. In: AMIA ... Annual Symposium Proceedings \/ AMIA Symposium. AMIA Symposium 2014, pp. 516\u2013525 (2014). https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC4419934\/"},{"issue":"5","key":"25_CR9","doi-asserted-by":"publisher","first-page":"1007","DOI":"10.1093\/jamia\/ocv180","volume":"23","author":"E Ford","year":"2016","unstructured":"Ford, E., Carroll, J.A., Smith, H.E., Scott, D., Cassell, J.A.: Extracting information from the text of electronic medical records to improve case detection: a systematic review. J. Am. Med. Inform. Assoc. 23(5), 1007\u20131015 (2016). https:\/\/doi.org\/10.1093\/jamia\/ocv180","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"25_CR10","doi-asserted-by":"publisher","unstructured":"Grivel, L.: Customer feedbacks and opinion surveys analysis in the automotive industry. text mining and its applications to intelligence. CRM Knowl. Manage. 249\u2013257 (2005). https:\/\/doi.org\/10.2495\/978-1-85312-995-7\/13","DOI":"10.2495\/978-1-85312-995-7\/13"},{"key":"25_CR11","doi-asserted-by":"publisher","unstructured":"Heinze, D.T., Morsch, M.L., Holbrook, J.: Mining free-text medical records. Proceedings. In: AMIA Symposium, pp. 254\u2013258 (2001). https:\/\/doi.org\/10.1016\/j.procir.2019.02.098","DOI":"10.1016\/j.procir.2019.02.098"},{"issue":"1","key":"25_CR12","doi-asserted-by":"publisher","first-page":"46226","DOI":"10.1038\/srep46226","volume":"7","author":"K Jensen","year":"2017","unstructured":"Jensen, K., et al.: Analysis of free text in electronic health records for identification of cancer patient trajectories. Sci. Rep. 7(1), 46226 (2017). https:\/\/doi.org\/10.1038\/srep46226","journal-title":"Sci. Rep."},{"issue":"5","key":"25_CR13","doi-asserted-by":"publisher","first-page":"876","DOI":"10.1136\/amiajnl-2012-001173","volume":"20","author":"N Kang","year":"2013","unstructured":"Kang, N., Singh, B., Afzal, Z., van Mulligen, E.M., Kors, J.A.: Using rule-based natural language processing to improve disease normalization in biomedical text. J. Am. Med. Inform. Assoc. 20(5), 876\u2013881 (2013). https:\/\/doi.org\/10.1136\/amiajnl-2012-001173","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"25_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ijmedinf.2017.01.001","volume":"100","author":"FR Lucini","year":"2017","unstructured":"Lucini, F.R., et al.: Text mining approach to predict hospital admissions using early medical records from the emergency department. Int. J. Med. Inform. 100, 1\u20138 (2017). https:\/\/doi.org\/10.1016\/j.ijmedinf.2017.01.001","journal-title":"Int. J. Med. Inform."},{"key":"25_CR15","doi-asserted-by":"publisher","unstructured":"Lyall-Wilson, B., Kim, N., Hohman, E.: Modeling human factors topics in aviation reports (2019). https:\/\/doi.org\/10.1177\/1071181319631095","DOI":"10.1177\/1071181319631095"},{"issue":"2","key":"25_CR16","doi-asserted-by":"publisher","first-page":"e0212454","DOI":"10.1371\/journal.pone.0212454","volume":"14","author":"FB Maguire","year":"2019","unstructured":"Maguire, F.B., et al.: A text-mining approach to obtain detailed treatment information from free-text fields in population-based cancer registries: a study of non-small cell lung cancer in California. PLoS ONE 14(2), e0212454 (2019). https:\/\/doi.org\/10.1371\/journal.pone.0212454","journal-title":"PLoS ONE"},{"issue":"5","key":"25_CR17","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1136\/amiajnl-2014-002694","volume":"21","author":"BJ Marafino","year":"2014","unstructured":"Marafino, B.J., Davies, J.M., Bardach, N.S., Dean, M.L., Dudley, R.A.: N-gram support vector machines for scalable procedure and diagnosis classification, with applications to clinical free text data from the intensive care unit. J. Am. Med. Inform. Assoc. 21(5), 871\u2013875 (2014). https:\/\/doi.org\/10.1136\/amiajnl-2014-002694","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"25_CR18","doi-asserted-by":"publisher","unstructured":"Marev, K., Georgiev, K.: Automated aviation occurrences categorization. In: ICMT 2019\u20137th International Conference on Military Technologies, Proceedings, pp. 1\u20135 (2019). https:\/\/doi.org\/10.1109\/MILTECHS.2019.8870055","DOI":"10.1109\/MILTECHS.2019.8870055"},{"key":"25_CR19","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"590","DOI":"10.1007\/978-3-642-13022-9_59","volume-title":"Trends in Applied Intelligent Systems","author":"A McKenzie","year":"2010","unstructured":"McKenzie, A., Matthews, M., Goodman, N., Bayoumi, A.: Information extraction from helicopter maintenance records as a springboard for the future of maintenance text analysis. In: Garc\u00eda-Pedrajas, N., Herrera, F., Fyfe, C., Ben\u00edtez, J.M., Ali, M. (eds.) IEA\/AIE 2010. LNCS (LNAI), vol. 6096, pp. 590\u2013600. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-13022-9_59"},{"key":"25_CR20","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: 1st International Conference on Learning Representations, ICLR 2013 - Workshop Track Proceedings (2013). https:\/\/arxiv.org\/abs\/1301.3781"},{"key":"25_CR21","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems cs.CL, pp. 1\u20139 (2013). https:\/\/arxiv.org\/abs\/1310.4546"},{"issue":"4","key":"25_CR22","doi-asserted-by":"publisher","first-page":"51","DOI":"10.9781\/ijimai.2018.04.001","volume":"5","author":"A Moreno Sandoval","year":"2019","unstructured":"Moreno Sandoval, A., D\u00edaz, J., Campillos Llanos, L., Redondo, T.: Biomedical term extraction: NLP techniques in computational medicine. Int. J. Interact. Multimedia Artif. Intell. 5(4), 51 (2019). https:\/\/doi.org\/10.9781\/ijimai.2018.04.001","journal-title":"Int. J. Interact. Multimedia Artif. Intell."},{"key":"25_CR23","doi-asserted-by":"publisher","unstructured":"Navinchandran, M., Sharp, M.E., Brundage, M.P., Sexton, T.B.: Studies to predict maintenance time duration and important factors from maintenanceworkorder data. In: Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM, vol. 11 (2019). https:\/\/doi.org\/10.36001\/phmconf.2019.v11i1.792","DOI":"10.36001\/phmconf.2019.v11i1.792"},{"key":"25_CR24","doi-asserted-by":"crossref","unstructured":"Nguyen, A., Moore, D., McCowan, I., Courage, M.J.: Multi-class classification of cancer stages from free-text histology reports using support vector machines. In: Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, vol. 2007, pp. 5140\u20135143. IEEE, United States (2007). DOIurl10.1109\/IEMBS.2007.4353497","DOI":"10.1109\/IEMBS.2007.4353497"},{"key":"25_CR25","doi-asserted-by":"publisher","unstructured":"Paul, S.: NLP tools used in civil aviation: a survey (2018). https:\/\/doi.org\/10.26483\/ijarcs.v9i2.5559","DOI":"10.26483\/ijarcs.v9i2.5559"},{"key":"25_CR26","doi-asserted-by":"publisher","unstructured":"Pelt, M., Stamoulis, K., Apostolidis, A.: Data analytics case studies in the maintenance, repair and overhaul (MRO) industry. In: MATEC Web of Conferences, vol. 304, p. 04005 (2019). https:\/\/doi.org\/10.1051\/matecconf\/201930404005","DOI":"10.1051\/matecconf\/201930404005"},{"key":"25_CR27","doi-asserted-by":"publisher","unstructured":"Pennington, J., Socher, R., Manning, C.D.: GloVe: global vectors for word representation. In: EMNLP 2014\u20132014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, pp. 1532\u20131543. Association for Computational Linguistics, Doha, Qatar (2014). https:\/\/doi.org\/10.3115\/v1\/d14-1162","DOI":"10.3115\/v1\/d14-1162"},{"key":"25_CR28","doi-asserted-by":"publisher","unstructured":"Robinson, S.D., Irwin, W.J., Kelly, T.K., Wu, X.O.: Application of machine learning to mapping primary causal factors in self reported safety narratives (2015). https:\/\/doi.org\/10.1016\/j.ssci.2015.02.003","DOI":"10.1016\/j.ssci.2015.02.003"},{"key":"25_CR29","doi-asserted-by":"publisher","unstructured":"Sexton, T., Hodkiewicz, M., Brundage, M.P., Smoker, T.: Benchmarking for keyword extraction methodologies in maintenance work orders. In: Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM. Philadelphia, PA (2018). https:\/\/doi.org\/10.36001\/phmconf.2018.v10i1.541","DOI":"10.36001\/phmconf.2018.v10i1.541"},{"key":"25_CR30","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.compind.2015.09.005","volume":"78","author":"L Tanguy","year":"2016","unstructured":"Tanguy, L., Tulechki, N., Urieli, A., Hermann, E., Raynal, C.: Natural language processing for aviation safety reports: from classification to interactive analysis. Comput. Ind. 78, 80\u201395 (2016). https:\/\/doi.org\/10.1016\/j.compind.2015.09.005","journal-title":"Comput. Ind."},{"issue":"1","key":"25_CR31","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1147\/JRD.2017.2648298","volume":"61","author":"J Wang","year":"2017","unstructured":"Wang, J., Li, C., Han, S., Sarkar, S., Zhou, X.: Predictive maintenance based on event-log analysis: a case study. IBM J. Res. Dev. 61(1), 121\u2013132 (2017). https:\/\/doi.org\/10.1147\/JRD.2017.2648298","journal-title":"IBM J. Res. Dev."},{"key":"25_CR32","doi-asserted-by":"publisher","unstructured":"Zhang, K., Xu, J., Min, M.R., Jiang, G., Pelechrinis, K., Zhang, H.: Automated IT system failure prediction: a deep learning approach. In: Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016, pp. 1291\u20131300. IEEE (2016). https:\/\/doi.org\/10.1109\/BigData.2016.7840733","DOI":"10.1109\/BigData.2016.7840733"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44204-9_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T06:27:24Z","timestamp":1695277644000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44204-9_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031442032","9783031442049"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44204-9_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"22 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Heraklion","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"32","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"easyacademia.org","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"947","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"426","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"22","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"45% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"type of other papers accepted  : 9 Abstract","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}