{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T10:31:24Z","timestamp":1743071484760,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031417733"},{"type":"electronic","value":"9783031417740"}],"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\/978-3-031-41774-0_25","type":"book-chapter","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T03:25:20Z","timestamp":1695266720000},"page":"313-325","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Design Science Research Approach Towards Knowledge Discovery and\u00a0Predictive Maintenance of\u00a0MEMS Inertial Sensors Using Machine Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7999-5597","authenticated-orcid":false,"given":"Itilekha","family":"Podder","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6018-2411","authenticated-orcid":false,"given":"Udo","family":"Bub","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"key":"25_CR1","unstructured":"(cs)2; the 13th conference of phd students in computer science, June 2022. https:\/\/www.inf.u-szeged.hu\/cscs\/"},{"key":"25_CR2","doi-asserted-by":"crossref","unstructured":"\u00c7\u0131nar, Z.M., Abdussalam Nuhu, A., Zeeshan, Q., Korhan, O., Asmael, M., Safaei, B.: Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0. Sustainability 12(19), 8211 (2020)","DOI":"10.3390\/su12198211"},{"key":"25_CR3","doi-asserted-by":"crossref","unstructured":"Dalzochio, J., et al.: Machine learning and reasoning for predictive maintenance in industry 4.0: current status and challenges. Comput. Ind. 123, 103298 (2020)","DOI":"10.1016\/j.compind.2020.103298"},{"key":"25_CR4","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1016\/j.sna.2018.04.008","volume":"279","author":"R Fontanella","year":"2018","unstructured":"Fontanella, R., Accardo, D., Moriello, R.S.L., Angrisani, L., De Simone, D.: Mems gyros temperature calibration through artificial neural networks. Sens. Actuators A 279, 553\u2013565 (2018)","journal-title":"Sens. Actuators A"},{"key":"25_CR5","doi-asserted-by":"crossref","unstructured":"Gregor, S.: Building theory in the sciences of the artificial. In: Proceedings of the 4th International Conference on Design Science Research in Information Systems and Technology, pp. 1\u201310 (2009)","DOI":"10.1145\/1555619.1555625"},{"key":"25_CR6","doi-asserted-by":"crossref","unstructured":"Gregor, S., Hevner, A.R.: Positioning and presenting design science research for maximum impact. MIS Q. 37, 337\u2013355 (2013)","DOI":"10.25300\/MISQ\/2013\/37.2.01"},{"key":"25_CR7","unstructured":"Gregor, S., Jones, D., et al.: The anatomy of a design theory. Assoc. Inf. Syst. (2007)"},{"key":"25_CR8","doi-asserted-by":"crossref","unstructured":"Gupta, S., Mittal, M., Padha, A.: Predictive analytics of sensor data based on supervised machine learning algorithms. In: 2017 International Conference on Next Generation Computing and Information Systems (ICNGCIS), pp. 171\u2013176. IEEE (2017)","DOI":"10.1109\/ICNGCIS.2017.12"},{"key":"25_CR9","unstructured":"Hirt, R., Koehl, N.J., Satzger, G.: An end-to-end process model for supervised machine learning classification: from problem to deployment in information systems. In: Designing the Digital Transformation: DESRIST 2017 Research in Progress Proceedings of the 12th International Conference on Design Science Research in Information Systems and Technology. Karlsruhe, Germany, 30 May\u20131 June, pp. 55\u201363. Karlsruher Institut f\u00fcr Technologie (KIT) (2017)"},{"key":"25_CR10","doi-asserted-by":"crossref","unstructured":"Paolanti, M., Romeo, L., Felicetti, A., Mancini, A., Frontoni, E., Loncarski, J.: Machine learning approach for predictive maintenance in industry 4.0. In: 2018 14th IEEE\/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA), pp. 1\u20136. IEEE (2018)","DOI":"10.1109\/MESA.2018.8449150"},{"key":"25_CR11","doi-asserted-by":"crossref","unstructured":"Podder, I., Fischl, T., Bub, U.: Smart feature selection for fault detection in the mems sensor production process using machine learning methods. In: 2nd International Conference on Industry 4.0 and Artificial Intelligence (ICIAI 2021), pp. 21\u201325. Atlantis Press (2022)","DOI":"10.2991\/aisr.k.220201.005"},{"key":"25_CR12","doi-asserted-by":"publisher","unstructured":"Podder, I., Fischl, T., Bub, U.: Artificial intelligence applications for mems-based sensors and manufacturing process optimization. Telecom 4(1), 165\u2013197 (2023). https:\/\/doi.org\/10.3390\/telecom4010011, https:\/\/www.mdpi.com\/2673-4001\/4\/1\/11","DOI":"10.3390\/telecom4010011"},{"key":"25_CR13","doi-asserted-by":"publisher","unstructured":"Saltz, J.S.: CRISP-DM for data science: strengths, weaknesses and potential next steps. In: 2021 IEEE International Conference on Big Data (Big Data), pp. 2337\u20132344 (2021). https:\/\/doi.org\/10.1109\/BigData52589.2021.9671634","DOI":"10.1109\/BigData52589.2021.9671634"},{"key":"25_CR14","doi-asserted-by":"crossref","unstructured":"Scalabrini Sampaio, G., Vallim Filho, A.R.D.A., Santos da Silva, L., Augusto da Silva, L.: Prediction of motor failure time using an artificial neural network. Sensors 19(19), 4342 (2019)","DOI":"10.3390\/s19194342"},{"key":"25_CR15","doi-asserted-by":"crossref","unstructured":"Shiau, J.K., Ma, D.M., Huang, C.X., Chang, M.Y.: Mems gyroscope null drift and compensation based on neural network. Adv. Mater. Res. 255, 2077\u20132081. Trans Tech Publ (2011)","DOI":"10.4028\/www.scientific.net\/AMR.255-260.2077"},{"key":"25_CR16","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/978-3-642-33681-2_7","volume-title":"Practical Aspects of Design Science","author":"C Sonnenberg","year":"2012","unstructured":"Sonnenberg, C., vom Brocke, J.: Evaluation patterns for design science research artefacts. In: Helfert, M., Donnellan, B. (eds.) EDSS 2011. CCIS, vol. 286, pp. 71\u201383. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-33681-2_7"},{"issue":"1","key":"25_CR17","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1108\/JQME-05-2020-0029","volume":"28","author":"W Tiddens","year":"2022","unstructured":"Tiddens, W., Braaksma, J., Tinga, T.: Exploring predictive maintenance applications in industry. J. Qual. Maint. Eng. 28(1), 68\u201385 (2022)","journal-title":"J. Qual. Maint. Eng."},{"key":"25_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.sna.2020.112393","volume":"316","author":"S Wang","year":"2020","unstructured":"Wang, S., Zhu, W., Shen, Y., Ren, J., Gu, H., Wei, X.: Temperature compensation for mems resonant accelerometer based on genetic algorithm optimized backpropagation neural network. Sens. Actuators A 316, 112393 (2020)","journal-title":"Sens. Actuators A"},{"key":"25_CR19","unstructured":"Wirth, R., Hipp, J.: CRISP-DM: towards a standard process model for data mining. In: Proceedings of the 4th International Conference on the Practical Applications of Knowledge Discovery and Data Mining, vol. 1, pp. 29\u201339. Manchester (2000)"},{"issue":"10","key":"25_CR20","doi-asserted-by":"publisher","first-page":"8349","DOI":"10.3390\/s91008349","volume":"9","author":"D Xia","year":"2009","unstructured":"Xia, D., Chen, S., Wang, S., Li, H.: Temperature effects and compensation-control methods. Sensors 9(10), 8349\u20138376 (2009)","journal-title":"Sensors"},{"issue":"10","key":"25_CR21","doi-asserted-by":"publisher","first-page":"2335","DOI":"10.3390\/s17102335","volume":"17","author":"H Xing","year":"2017","unstructured":"Xing, H., Hou, B., Lin, Z., Guo, M.: Modeling and compensation of random drift of mems gyroscopes based on least squares support vector machine optimized by chaotic particle swarm optimization. Sensors 17(10), 2335 (2017)","journal-title":"Sensors"},{"key":"25_CR22","doi-asserted-by":"crossref","unstructured":"Yang, Y., Liu, Y., Liu, Y., Zhao, X.: Temperature compensation of mems gyroscope based on support vector machine optimized by GA. In: 2019 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 2989\u20132994. IEEE (2019)","DOI":"10.1109\/SSCI44817.2019.9003139"}],"container-title":["Communications in Computer and Information Science","Advances in Computational Collective Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-41774-0_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T06:33:25Z","timestamp":1695278005000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-41774-0_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031417733","9783031417740"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-41774-0_25","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"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":"ICCCI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Collective Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Budapest","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hungary","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":"27 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":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccci2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccci.pwr.edu.pl\/2023\/","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":"218","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":"59","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":"27% - 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":"3.01","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":"1.86","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)"}}]}}