{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T15:09:24Z","timestamp":1775228964958,"version":"3.50.1"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031580529","type":"print"},{"value":"9783031580536","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-58053-6_6","type":"book-chapter","created":{"date-parts":[[2024,5,19]],"date-time":"2024-05-19T20:25:06Z","timestamp":1716150306000},"page":"82-96","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Study of Dimensionality Reduction and Clustering Machine Learning Algorithms for the Analysis of Ship Engine Data"],"prefix":"10.1007","author":[{"given":"Theodoros","family":"Dimitriou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emmanouil","family":"Skondras","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christos","family":"Hitiris","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cleopatra","family":"Gkola","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ioannis S.","family":"Papapanagiotou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dimitrios J.","family":"Vergados","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Georgia","family":"Fasoula","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stratos","family":"Koumantakis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Angelos","family":"Michalas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dimitrios D.","family":"Vergados","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,5,20]]},"reference":[{"key":"6_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.eswa.2020.114060","volume":"166","author":"A Dogan","year":"2021","unstructured":"Dogan, A., Birant, D.: Machine learning and data mining in manufacturing. Expert Syst. Appl. 166, 1\u201322 (2021)","journal-title":"Expert Syst. Appl."},{"key":"6_CR2","doi-asserted-by":"crossref","unstructured":"Zhang, G., Wang, Z., Huang, H., Li, H., Sun, T.: Comparison and evaluation of dimensionality reduction techniques for the numerical simulations of unsteady cavitation. The acoustic signature of a propeller-hydrofoil system in the far field. Phys. Fluids 35(7) (2023)","DOI":"10.1063\/5.0161471"},{"key":"6_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.engappai.2022.104743","volume":"110","author":"AE Ezugwu","year":"2022","unstructured":"Ezugwu, A.E., et al.: A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects. Eng. Appl. Artif. Intell. 110, 1\u201343 (2022)","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"11","key":"6_CR4","first-page":"1","volume":"10","author":"J Park","year":"2022","unstructured":"Park, J., Oh, J.: Analysis of collected data and establishment of an abnormal data detection algorithm using principal component analysis and K-nearest neighbors for predictive maintenance of ship propulsion engine. Processes J. 10(11), 1\u201313 (2022)","journal-title":"Processes J."},{"key":"6_CR5","first-page":"1","volume":"122","author":"A Habibi","year":"2023","unstructured":"Habibi, A., Delavar, M.R., Sadeghian, M.S., Nazari, B., Pirasteh, S.: A hybrid of ensemble machine learning models with RFE and Boruta wrapper-based algorithms for flash flood susceptibility assessment. Int. J. Appl. Earth Obs. Geoinf. 122, 1\u201318 (2023)","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"6_CR6","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.ins.2022.11.139","volume":"622","author":"AM Ikotun","year":"2023","unstructured":"Ikotun, A.M., Ezugwu, A.E., Abualigah, L., Abuhaija, B., Heming, J.: K-means clustering algorithms: a comprehensive review, variants analysis, and advances in the era of big data. Inf. Sci. 622, 178\u2013210 (2023)","journal-title":"Inf. Sci."},{"key":"6_CR7","doi-asserted-by":"crossref","unstructured":"Xu, X., Cui, D., Li, Y., Xiao, Y.: Research on ship trajectory extraction based on multiattribute DBSCAN optimisation algorithm. Pol. Marit. Res. 136\u2013148 (2021)","DOI":"10.2478\/pomr-2021-0013"},{"key":"6_CR8","unstructured":"Scikit-learn library. https:\/\/scikit-learn.org. Accessed 30 Oct 2023"},{"key":"6_CR9","unstructured":"Decomposing signals in components, Scikit learn. https:\/\/scikit-learn.org\/stable\/modules\/decomposition.html. Accessed 30 Oct 2023"},{"key":"6_CR10","unstructured":"Principal Component Analysis (PCA) class, Scikit learn. https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.decomposition.PCA.html. Accessed 30 Oct 2023"},{"key":"6_CR11","unstructured":"The Condition Based Maintenance of Naval Propulsion Plants (CBM) dataset. https:\/\/www.kaggle.com\/datasets\/elikplim\/maintenance-of-naval-propulsion-plants-data-set. Accessed 30 Oct 2023"},{"key":"6_CR12","doi-asserted-by":"crossref","unstructured":"Coraddu, A., Oneto, L., Ghio, A., Savio, S., Figari, M., Anguita, D.: Machine learning for wear forecasting of naval assets for condition-based maintenance applications. In: IEEE International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles (ESARS), pp. 1\u20135 (2015)","DOI":"10.1109\/ESARS.2015.7101499"},{"key":"6_CR13","unstructured":"Recursive Feature Elimination with Cross-Validation (RFECV) scikit-learn class. https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.feature_selection.RFECV.html. Accessed 30 Oct 2023"},{"issue":"1","key":"6_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1029\/2020JB020135","volume":"126","author":"JH Graw","year":"2021","unstructured":"Graw, J.H., Wood, W.T., Phrampus, B.J.: Predicting global marine sediment density using the random forest regressor machine learning algorithm. J. Geophys. Res. Solid Earth 126(1), 1\u201314 (2021)","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"6_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1007\/978-3-319-29451-3_57","volume-title":"Image and Video Technology","author":"V John","year":"2016","unstructured":"John, V., Liu, Z., Guo, C., Mita, S., Kidono, K.: Real-time lane estimation using deep features and extra trees regression. In: Br\u00e4unl, T., McCane, B., Rivera, M., Yu, X. (eds.) PSIVT 2015. LNCS, vol. 9431, pp. 721\u2013733. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-29451-3_57"},{"issue":"8","key":"6_CR16","doi-asserted-by":"publisher","first-page":"9655","DOI":"10.3390\/rs70809655","volume":"7","author":"RR Colditz","year":"2015","unstructured":"Colditz, R.R.: An evaluation of different training sample allocation schemes for discrete and continuous land cover classification using decision tree-based algorithms. Remote Sens. 7(8), 9655\u20139681 (2015)","journal-title":"Remote Sens."}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Wireless Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-58053-6_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,19]],"date-time":"2024-05-19T20:25:51Z","timestamp":1716150351000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-58053-6_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031580529","9783031580536"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-58053-6_6","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"value":"1867-8211","type":"print"},{"value":"1867-822X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"20 May 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"WiCON","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Wireless Internet Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Athens","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":"15 December 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"wicon2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/wicon2023.eai-conferences.org\/","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":"EAI Confy +","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"35","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":"14","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":"40% - 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":"3","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","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)"}}]}}