{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T12:55:30Z","timestamp":1763643330635,"version":"3.40.3"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030916077"},{"type":"electronic","value":"9783030916084"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-91608-4_1","type":"book-chapter","created":{"date-parts":[[2021,11,23]],"date-time":"2021-11-23T20:05:55Z","timestamp":1637697955000},"page":"3-11","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Comparison of Machine Learning Approaches for Predicting In-Car Display Production Quality"],"prefix":"10.1007","author":[{"given":"Lu\u00eds Miguel","family":"Matos","sequence":"first","affiliation":[]},{"given":"Andr\u00e9","family":"Domingues","sequence":"additional","affiliation":[]},{"given":"Guilherme","family":"Moreira","sequence":"additional","affiliation":[]},{"given":"Paulo","family":"Cortez","sequence":"additional","affiliation":[]},{"given":"Andr\u00e9","family":"Pilastri","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,11,23]]},"reference":[{"issue":"1","key":"1_CR1","doi-asserted-by":"publisher","first-page":"109","DOI":"10.3390\/s20010109","volume":"20","author":"A Angelopoulos","year":"2020","unstructured":"Angelopoulos, A., et al.: Tackling faults in the industry 4.0 era-a survey of machine-learning solutions and key aspects. Sensors 20(1), 109 (2020)","journal-title":"Sensors"},{"key":"1_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2012.10.039","volume":"225","author":"P Cortez","year":"2013","unstructured":"Cortez, P., Embrechts, M.J.: Using sensitivity analysis and visualization techniques to open black box data mining models. Inf. Sci. 225, 1\u201317 (2013)","journal-title":"Inf. Sci."},{"key":"1_CR3","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"T Fawcett","year":"2006","unstructured":"Fawcett, T.: An introduction to ROC analysis. Patt. Recogn. Lett 27, 861\u2013874 (2006)","journal-title":"Patt. Recogn. Lett"},{"key":"1_CR4","doi-asserted-by":"crossref","unstructured":"Ferreira, L., Pires, P.M., Pilastri, A., Martins, C.M., Cortez, P.: A comparison of AutoML tools for machine learning, deep learning and XGBoost. In: International Joint Conference on Neural Networks, IJCNN 2021. IEEE (2021)","DOI":"10.1109\/IJCNN52387.2021.9534091"},{"issue":"5786","key":"1_CR5","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"GE Hinton","year":"2006","unstructured":"Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504\u2013507 (2006)","journal-title":"Science"},{"key":"1_CR6","volume-title":"Nonparametric Statistical Methods","author":"M Hollander","year":"2013","unstructured":"Hollander, M., Wolfe, D.A., Chicken, E.: Nonparametric Statistical Methods. John Wiley & Sons, Hoboken (2013)"},{"key":"1_CR7","doi-asserted-by":"crossref","unstructured":"Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: Proceedings of the 8th IEEE International Conference on Data Mining (ICDM), Pisa, Italy, pp. 413\u2013422. IEEE (2008)","DOI":"10.1109\/ICDM.2008.17"},{"key":"1_CR8","doi-asserted-by":"crossref","unstructured":"Matos, L.M., Cortez, P., Mendes, R., Moreau, A.: Using deep learning for mobile marketing user conversion prediction. In: International Joint Conference on Neural Networks, IJCNN 2019 Budapest, Hungary, 14\u201319 July 2019, pp. 1\u20138. IEEE (2019)","DOI":"10.1109\/IJCNN.2019.8851888"},{"key":"1_CR9","doi-asserted-by":"crossref","unstructured":"Pandya, D., et al.: Increasing production efficiency via compressor failure predictive analytics using machine learning. In: Offshore Technology Conference, OTC-28990-MS (2018)","DOI":"10.4043\/28990-MS"},{"key":"1_CR10","doi-asserted-by":"publisher","first-page":"114287","DOI":"10.1016\/j.eswa.2020.114287","volume":"168","author":"PJ Pereira","year":"2021","unstructured":"Pereira, P.J., Cortez, P., Mendes, R.: Multi-objective grammatical evolution of decision trees for mobile marketing user conversion prediction. Expert Syst. Appl. 168, 114287 (2021)","journal-title":"Expert Syst. Appl."},{"key":"1_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1007\/978-3-030-86960-1_34","volume-title":"Computational Science and Its Applications \u2013 ICCSA 2021","author":"D Ribeiro","year":"2021","unstructured":"Ribeiro, D., Matos, L.M., Cortez, P., Moreira, G., Pilastri, A., et al.: A comparison of anomaly detection methods for industrial screw tightening. In: Gervasi, O. (ed.) ICCSA 2021. LNCS, vol. 12950, pp. 485\u2013500. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-86960-1_34"},{"key":"1_CR12","first-page":"12741","volume":"38","author":"AJ Silva","year":"2021","unstructured":"Silva, A.J., Cortez, P., Pereira, C., Pilastri, A.: Business analytics in industry 4.0: a systematic review. Exp. Syst. J. Knowl. Eng. 38, 12741 (2021)","journal-title":"Exp. Syst. J. Knowl. Eng."},{"issue":"4","key":"1_CR13","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1016\/S0169-2070(00)00065-0","volume":"16","author":"LJ Tashman","year":"2000","unstructured":"Tashman, L.J.: Out-of-sample tests of forecasting accuracy: an analysis and review. Int. Forecast. J. 16(4), 437\u2013450 (2000)","journal-title":"Int. Forecast. J."}],"container-title":["Lecture Notes in Computer Science","Intelligent Data Engineering and Automated Learning \u2013 IDEAL 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-91608-4_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T15:44:46Z","timestamp":1710258286000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-91608-4_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030916077","9783030916084"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-91608-4_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"23 November 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IDEAL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Data Engineering and Automated Learning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Manchester","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ideal2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ideal-conf.com\/ideal2021","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"85","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":"61","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":"0","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":"72% - 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":"2.8","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":"2.3","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference took place virtually due to the COVID-19 pandemic","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)"}}]}}