{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T05:12:18Z","timestamp":1760332338055,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032084613","type":"print"},{"value":"9783032084620","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:00:00Z","timestamp":1760400000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:00:00Z","timestamp":1760400000000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-08462-0_12","type":"book-chapter","created":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T04:47:38Z","timestamp":1760330858000},"page":"143-154","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["IIoT-Driven Time Series Imputation for\u00a0Sustainable Metalworking Fluid Monitoring"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-9607-110X","authenticated-orcid":false,"given":"F\u00e9lix","family":"De Miguel","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2988-757X","authenticated-orcid":false,"given":"Nuria","family":"Velasco","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3142-1426","authenticated-orcid":false,"given":"F\u00e9lix","family":"Movilla","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2662-798X","authenticated-orcid":false,"given":"Daniel","family":"Urda","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5567-9194","authenticated-orcid":false,"given":"Carlos","family":"Cambra","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2444-5384","authenticated-orcid":false,"given":"\u00c1lvaro","family":"Herrero","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,14]]},"reference":[{"issue":"1","key":"12_CR1","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1186\/s10033-021-00667-z","volume":"35","author":"L Tang","year":"2022","unstructured":"Tang, L., et al.: Biological stability of water-based cutting fluids: progress and application. Chin. J. Mech. Eng. 35(1), 3 (2022). https:\/\/doi.org\/10.1186\/s10033-021-00667-z","journal-title":"Chin. J. Mech. Eng."},{"key":"12_CR2","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.jclepro.2014.07.071","volume":"83","author":"S Debnath","year":"2014","unstructured":"Debnath, S., Reddy, M.M., Yi, Q.S.: Environmental friendly cutting fluids and cooling techniques in machining: a review. J. Clean. Prod. 83, 33\u201347 (2014). https:\/\/doi.org\/10.1016\/j.jclepro.2014.07.071. ISSN 0959-6526","journal-title":"J. Clean. Prod."},{"key":"12_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2022.133766","volume":"373","author":"H Ling","year":"2022","unstructured":"Ling, H., et al.: Antimony recovery from antimony-rich slag by top blowing nitrogen into the molten slag bath. J. Clean. Prod. 373, 133766 (2022). https:\/\/doi.org\/10.1016\/j.jclepro.2022.133766","journal-title":"J. Clean. Prod."},{"issue":"2","key":"12_CR4","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1016\/j.cirp.2015.05.003","volume":"64","author":"E Brinksmeier","year":"2015","unstructured":"Brinksmeier, E., et al.: Metalworking fluids\u2013mechanisms and performance. CIRP Ann. 64(2), 605\u2013628 (2015). https:\/\/doi.org\/10.1016\/j.cirp.2015.05.003","journal-title":"CIRP Ann."},{"key":"12_CR5","doi-asserted-by":"publisher","first-page":"1119","DOI":"10.1007\/s00253-012-4055-7","volume":"94","author":"R Saha","year":"2012","unstructured":"Saha, R., Donofrio, R.S.: The microbiology of metalworking fluids. Appl. Microbiol. Biotechnol. 94, 1119\u20131130 (2012)","journal-title":"Appl. Microbiol. Biotechnol."},{"issue":"2","key":"12_CR6","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1016\/j.cirp.2015.05.003","volume":"64","author":"E Brinksmeier","year":"2015","unstructured":"Brinksmeier, E., et al.: Metalworking fluids\u2013mechanisms and performance. CIRP Ann. 64(2), 605\u2013628 (2015). https:\/\/doi.org\/10.1016\/j.cirp.2015.05.003. ISSN 0007-8506","journal-title":"CIRP Ann."},{"key":"12_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00170-006-0806-x","volume":"36","author":"KM Hubbard","year":"2008","unstructured":"Hubbard, K.M., Callahan, R.N., Strong, S.D.: A standardized model for the evaluation of machining coolant\/lubricant costs. Int. J. Adv. Manuf. Technol. 36, 1\u201310 (2008)","journal-title":"Int. J. Adv. Manuf. Technol."},{"issue":"1","key":"12_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s40899-021-00589-7","volume":"8","author":"HV Puneeth","year":"2022","unstructured":"Puneeth, H.V., Prasad, M.S.G.: Sustainable in-situ recycling and iot-based monitoring system of water-soluble metal working fluids. Sustain. Water Res. Manag. 8(1), 1 (2022)","journal-title":"Sustain. Water Res. Manag."},{"key":"12_CR9","first-page":"1","volume":"1","author":"M Zafar","year":"2023","unstructured":"Zafar, M., et al.: Smart microalgae wastewater treatment: IoT and edge computing applications with LCA and technoeconomic analysis. Next-Gener. Algae 1, 1\u201347 (2023)","journal-title":"Next-Gener. Algae"},{"issue":"1","key":"12_CR10","doi-asserted-by":"publisher","DOI":"10.1002\/met.1873","volume":"27","author":"E Afrifa-Yamoah","year":"2020","unstructured":"Afrifa-Yamoah, E., et al.: Missing data imputation of high-resolution temporal climate time series data. Meteorol. Appl. 27(1), e1873 (2020). https:\/\/doi.org\/10.1002\/met.1873","journal-title":"Meteorol. Appl."},{"key":"12_CR11","unstructured":"Cao, W., et al.: BRITS: bidirectional recurrent imputation for time series. In: Bengio, S., et al. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc. (2018)"},{"issue":"8","key":"12_CR12","doi-asserted-by":"publisher","first-page":"6855","DOI":"10.1109\/JIOT.2020.2970467","volume":"7","author":"Y Liu","year":"2020","unstructured":"Liu, Y., et al.: Missing value imputation for industrial iot sensor data with large gaps. IEEE Internet Things J. 7(8), 6855\u20136867 (2020). https:\/\/doi.org\/10.1109\/JIOT.2020.2970467","journal-title":"IEEE Internet Things J."},{"key":"12_CR13","doi-asserted-by":"publisher","unstructured":"Zhou, Z., Mo, J., Shi, Y.: Data imputation and dimensionality reduction using deep learning in industrial data. In: 2017 3rd IEEE International Conference on Computer and Communications (ICCC), pp. 2329\u20132333 (2017). https:\/\/doi.org\/10.1109\/CompComm.2017.8322951","DOI":"10.1109\/CompComm.2017.8322951"},{"key":"12_CR14","unstructured":"Fang, C., Wang, C.: Time series data imputation: a survey on deep learning approaches. CoRR arxiv:2011.11347 (2020)"},{"key":"12_CR15","unstructured":"de Miguel, F., et al.: Time Series Sensor Data for Cutting Fluid Analysis from the SmarTaladrine Project (2025). http:\/\/hdl.handle.net\/10259\/10492"},{"key":"12_CR16","unstructured":"Goswami, M., et al.: Moment: a family of open time-series foundation models (2024). arXiv: 2402.03885"},{"key":"12_CR17","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes (2022). arXiv: 1312.6114"},{"issue":"8","key":"12_CR18","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997). https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput."},{"issue":"11","key":"12_CR19","doi-asserted-by":"publisher","first-page":"2541","DOI":"10.1016\/j.jss.2012.05.073","volume":"85","author":"S Zhang","year":"2012","unstructured":"Zhang, S.: Nearest neighbor selection for iteratively knn imputation. J. Syst. Softw. 85(11), 2541\u20132552 (2012)","journal-title":"J. Syst. Softw."},{"issue":"10","key":"12_CR20","doi-asserted-by":"publisher","first-page":"913","DOI":"10.1080\/08839514.2019.1637138","volume":"33","author":"A Jadhav","year":"2019","unstructured":"Jadhav, A., Pramod, D., Ramanathan, K.: Comparison of performance of data imputation methods for numeric dataset. Appl. Artif. Intell. 33(10), 913\u2013933 (2019)","journal-title":"Appl. Artif. Intell."},{"key":"12_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.compenvurbsys.2022.101823","volume":"95","author":"Y Eum","year":"2022","unstructured":"Eum, Y., Yoo, E.: Imputation of missing time-activity data with long-term gaps: a multi-scale residual cnn-lstm network model. Comput. Environ. Urban Syst. 95, 101823 (2022)","journal-title":"Comput. Environ. Urban Syst."}],"container-title":["Lecture Notes in Computer Science","Hybrid Artificial Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-08462-0_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T04:47:40Z","timestamp":1760330860000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-08462-0_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,14]]},"ISBN":["9783032084613","9783032084620"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-08462-0_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,14]]},"assertion":[{"value":"14 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HAIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Hybrid Artificial Intelligence Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Salamanca","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hais2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/haisconference.eu","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}