{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T07:17:44Z","timestamp":1760426264809,"version":"3.27.0"},"reference-count":26,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T00:00:00Z","timestamp":1725926400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T00:00:00Z","timestamp":1725926400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,9,10]]},"DOI":"10.1109\/etfa61755.2024.10711142","type":"proceedings-article","created":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T17:51:22Z","timestamp":1729101082000},"page":"01-07","source":"Crossref","is-referenced-by-count":1,"title":["Optimizing Lstm-Based Temperature Prediction Algorithm for Embedded System Deployment"],"prefix":"10.1109","volume":"77","author":[{"given":"Pietro","family":"d'Agostino","sequence":"first","affiliation":[{"name":"Politecnico di Torino,DAUIN,Computer and control department,Turin,Italy"}]},{"given":"Massimo","family":"Violante","sequence":"additional","affiliation":[{"name":"Politecnico di Torino,DAUIN,Computer and control department,Turin,Italy"}]},{"given":"Gianpaolo","family":"Macario","sequence":"additional","affiliation":[{"name":"R&#x0026;D AROL Closure Systems,Canelli,Italy"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/iraset52964.2022.9737901"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/emr.2019.2958037"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/iihc55949.2022.10059639"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3042874"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/icecaa58104.2023.10212238"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/AEECA55500.2022.9918957"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICECONF57129.2023.10083595"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICCCNT56998.2023.10306927"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2023.3318684"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3050441"},{"key":"ref11","first-page":"13494","article-title":"Smart predictive maintenance for high-performance computing systems: a literature review","volume-title":"J Supercomput","volume":"77","author":"Lima","year":"2021"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3447568.3448537"},{"volume-title":"Time Series Forecasting: Definition, Applications, and Examples","author":"Taubleau","key":"ref13"},{"key":"ref14","first-page":"76","article-title":"Machine learning advances for time series forecasting","volume-title":"J Econ Surv","volume":"37","author":"Masini","year":"2023"},{"key":"ref15","first-page":"93","article-title":"Deep learning for anomaly detection in multivariate time series: Approaches, applications, and challenges","volume-title":"Information Fusion","volume":"91","author":"Li","year":"2023"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICECCT52121.2021.9616662"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.23919\/ConTEL52528.2021.9495986"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/COMSNETS48256.2020.9027454"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICECONF57129.2023.10083755"},{"key":"ref20","first-page":"12374","article-title":"Time Series Prediction in Industry 4.0: A Comprehensive Review and Prospects for Future Advancements","volume-title":"Appl. Sci","volume":"13","author":"Kashpruk","year":"2023"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.114598"},{"key":"ref22","doi-asserted-by":"crossref","DOI":"10.2991\/978-94-6463-136-4_67","article-title":"A Review on Machine Learning Techniques for Predictive Maintenance in Industry 4.0","volume-title":"Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022)","author":"Sisode","year":"2023"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ISIE51358.2023.10227939"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/FMEC59375.2023.10306045"},{"key":"ref25","article-title":"Fog Computing?","volume-title":"African Women in Technology","author":"Ndanu","year":"2021"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/KhPIWeek61412.2023.10312948"}],"event":{"name":"2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA)","start":{"date-parts":[[2024,9,10]]},"location":"Padova, Italy","end":{"date-parts":[[2024,9,13]]}},"container-title":["2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10710336\/10710347\/10711142.pdf?arnumber=10711142","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T08:02:33Z","timestamp":1729152153000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10711142\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,10]]},"references-count":26,"URL":"https:\/\/doi.org\/10.1109\/etfa61755.2024.10711142","relation":{},"subject":[],"published":{"date-parts":[[2024,9,10]]}}}