About: Machine Learning for Predictive and Prescriptive Analytics of Operational Data in Smart Manufacturing

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Machine Learning for Predictive and Prescriptive Analytics of Operational Data in Smart Manufacturing
Creator	Anastasiou, Stathis Apostolou, Dimitris Bousdekis, Alexandros Lampathaki, Fenareti Lepenioti, Katerina Louca, Andreas Mentzas, Gregoris Pertselakis, Minas
Source	PMC
abstract	Perceiving information and extracting insights from data is one of the major challenges in smart manufacturing. Real-time data analytics face several challenges in real-life scenarios, while there is a huge treasure of legacy, enterprise and operational data remaining untouched. The current paper exploits the recent advancements of (deep) machine learning for performing predictive and prescriptive analytics on the basis of enterprise and operational data aiming at supporting the operator on the shopfloor. To do this, it implements algorithms, such as Recurrent Neural Networks for predictive analytics, and Multi-Objective Reinforcement Learning for prescriptive analytics. The proposed approach is demonstrated in a predictive maintenance scenario in steel industry.
has issue date	2020-04-29(xsd:dateTime)
bibo:doi	10.1007/978-3-030-49165-9_1
has license	no-cc
sha1sum (hex)	0b0aba24cbeabc0249d2483731d8f5e8f613c116
schema:url	https://doi.org/10.1007/978-3-030-49165-9_1
resource representing a document's title	Machine Learning for Predictive and Prescriptive Analytics of Operational Data in Smart Manufacturing
has PubMed Central identifier	PMC7225513
schema:publication	Advanced Information Systems Engineering Workshops
resource representing a document's body	covid:0b0aba24cbeabc0249d2483731d8f5e8f613c116#body_text
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