{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T20:36:57Z","timestamp":1743107817520,"version":"3.40.3"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031831164"},{"type":"electronic","value":"9783031831171"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-83117-1_19","type":"book-chapter","created":{"date-parts":[[2025,2,26]],"date-time":"2025-02-26T19:17:41Z","timestamp":1740597461000},"page":"200-209","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Application of\u00a0Federated Learning and\u00a0xAI in\u00a0I4.0 - A Case Study"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2457-1567","authenticated-orcid":false,"given":"Jos\u00e9","family":"Ribeiro","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2139-5414","authenticated-orcid":false,"given":"Ricardo","family":"Santos","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7796-644X","authenticated-orcid":false,"given":"Cesar","family":"Analide","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9872-7117","authenticated-orcid":false,"given":"F\u00e1bio","family":"Silva","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,27]]},"reference":[{"key":"19_CR1","unstructured":"Explainable AI (XAI): benefits and use cases $$|$$ birlasoft. https:\/\/www.birlasoft.com\/articles\/demystifying-explainable-artificial-intelligence"},{"key":"19_CR2","doi-asserted-by":"publisher","unstructured":"Ariesen-Verschuur, N., Verdouw, C., Tekinerdogan, B.: Digital twins in greenhouse horticulture: a review. Comput. Electron. Agric. 199, 107183 (2022). https:\/\/doi.org\/10.1016\/J.COMPAG.2022.107183","DOI":"10.1016\/J.COMPAG.2022.107183"},{"key":"19_CR3","doi-asserted-by":"publisher","unstructured":"Directorate-General for Internal Market, Industry, Entrepreneurship and SMEs (European Commission): A vision for the European industry until 2030 - final report of the industry 2030 high level industrial roundtable. J. Ind. Insights 1(1), 1\u201320 (2019). https:\/\/doi.org\/10.2873\/34695","DOI":"10.2873\/34695"},{"key":"19_CR4","doi-asserted-by":"publisher","unstructured":"Golightly, L., Modesti, P., Garcia, R., Chang, V.: Securing distributed systems: a survey on access control techniques for cloud, blockchain, IoT and SDN. Cyber Secur. Appl. 1, 100015 (2023). https:\/\/doi.org\/10.1016\/J.CSA.2023.100015","DOI":"10.1016\/J.CSA.2023.100015"},{"key":"19_CR5","doi-asserted-by":"publisher","unstructured":"Hjort, A., Scheel, I., Sommervoll, D.E., Pensar, J.: Locally interpretable tree boosting: an application to house price prediction. Decis. Support Syst. 114106 (2023). https:\/\/doi.org\/10.1016\/J.DSS.2023.114106","DOI":"10.1016\/J.DSS.2023.114106"},{"key":"19_CR6","doi-asserted-by":"publisher","unstructured":"Kraus, M., Feuerriegel, S.: Forecasting remaining useful life: interpretable deep learning approach via variational Bayesian inferences. Decis. Support Syst. 125, 113100 (2019). https:\/\/doi.org\/10.1016\/J.DSS.2019.113100","DOI":"10.1016\/J.DSS.2019.113100"},{"key":"19_CR7","doi-asserted-by":"publisher","unstructured":"Li, S., et al.: Proactive human-robot collaboration: mutual-cognitive, predictable, and self-organising perspectives. Robot. Comput.-Integr. Manuf. 81, 102510 (2023). https:\/\/doi.org\/10.1016\/J.RCIM.2022.102510","DOI":"10.1016\/J.RCIM.2022.102510"},{"key":"19_CR8","doi-asserted-by":"publisher","unstructured":"Perno, M., Hvam, L., Haug, A.: Implementation of digital twins in the process industry: a systematic literature review of enablers and barriers. Comput. Ind. 134, 103558 (2022). https:\/\/doi.org\/10.1016\/J.COMPIND.2021.103558","DOI":"10.1016\/J.COMPIND.2021.103558"},{"key":"19_CR9","doi-asserted-by":"publisher","unstructured":"Pinto, R., Gon\u00e7alves, G.: Application of artificial immune systems in advanced manufacturing. Array 15, 100238 (2022). https:\/\/doi.org\/10.1016\/J.ARRAY.2022.100238","DOI":"10.1016\/J.ARRAY.2022.100238"},{"key":"19_CR10","doi-asserted-by":"publisher","unstructured":"Rejeb, A., Rejeb, K., Zailani, S., Keogh, J.G., Appolloni, A.: Examining the interplay between artificial intelligence and the agri-food industry. Artif. Intell. Agric. 6, 111\u2013128 (2022). https:\/\/doi.org\/10.1016\/J.AIIA.2022.08.002","DOI":"10.1016\/J.AIIA.2022.08.002"},{"key":"19_CR11","doi-asserted-by":"publisher","unstructured":"Ribeiro, J., Ramos, J.: Forecasting system and project monitoring in industry. Lecture Notes in Networks and Systems, vol. 583, pp. 249\u2013259 (2023). https:\/\/doi.org\/10.1007\/978-3-031-20859-1_25\/COVER","DOI":"10.1007\/978-3-031-20859-1_25\/COVER"},{"key":"19_CR12","doi-asserted-by":"publisher","unstructured":"Silva, F., Analide, C.: Information asset analysis: credit scoring and credit suggestion. Int. J. Electron. Bus. 9(3), 203 (2011). https:\/\/doi.org\/10.1504\/IJEB.2011.042542","DOI":"10.1504\/IJEB.2011.042542"},{"key":"19_CR13","doi-asserted-by":"publisher","unstructured":"Somers, R.J., Douthwaite, J.A., Wagg, D.J., Walkinshaw, N., Hierons, R.M.: Digital-twin-based testing for cyber-physical systems: a systematic literature review. Inf. Softw. Technol. 156, 107145 (2023). https:\/\/doi.org\/10.1016\/J.INFSOF.2022.107145","DOI":"10.1016\/J.INFSOF.2022.107145"},{"key":"19_CR14","doi-asserted-by":"publisher","unstructured":"Wang, B., Li, W., Bradlow, A., Bazuaye, E., Chan, A.T.: Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning. Decis. Support Syst. 166, 113899 (2023). https:\/\/doi.org\/10.1016\/J.DSS.2022.113899","DOI":"10.1016\/J.DSS.2022.113899"}],"container-title":["Lecture Notes in Networks and Systems","Ambient Intelligence \u2013 Software and Applications \u2013 15th International Symposium on Ambient Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-83117-1_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,26]],"date-time":"2025-02-26T19:17:48Z","timestamp":1740597468000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-83117-1_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031831164","9783031831171"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-83117-1_19","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"27 February 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISAmI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Ambient Intelligence","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":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 June 2024","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":"isaml2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.isami-conference.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}