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dc.contributor.authorSikora, Axel
dc.date.accessioned2025-09-16T08:28:44Z
dc.date.available2025-09-16T08:28:44Z
dc.date.issued2025
dc.identifier.citationAdvances in electrical and electronic engineering. 2025, vol. 23, no. 2, p. 83-90 : ill.cs
dc.identifier.issn1336-1376
dc.identifier.issn1804-3119
dc.identifier.urihttp://hdl.handle.net/10084/158025
dc.description.abstractGenerative modeling is an artificial intelligence (AI) technique to generate synthetic artifacts from analyzing training examples and from learning their patterns and distribution. Generative AI (GenAI) uses generative modeling and advances in Deep Learning to produce diverse content at scale by utilizing existing data. Whereas traditionally GenAI is mostly using media contents, such as text, graphics, audio, and video, it can additionally be used also for data from the (Industrial) Internet of Things. This article provides a systematic overview on the manifold different practical opportunities and challenges GenAI brings for the IIoT. It also presents selected examples from the author’s research with his teams. In doing so, it covers the relevance of GenAI for the complete lifecycle of IIoT, from design and development, over testing to deployment. This paper summarizes a keynote presentation from the 13th International Conference on Green and Human Information Technology (ICGHIT) in January 2025 held in Nha Trang, Vietnam.cs
dc.language.isoencs
dc.publisherVysoká škola báňská - Technická univerzita Ostravacs
dc.relation.ispartofseriesAdvances in electrical and electronic engineeringcs
dc.relation.urihttps://doi.org/10.15598/aeee.v23i2.250310cs
dc.rights© Vysoká škola báňská - Technická univerzita Ostrava
dc.rightsAttribution-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nd/4.0/*
dc.subjectgenerative artificial intelligencecs
dc.subjectinternet of thingscs
dc.subjectindustrial internet of thingscs
dc.subjectgenerative internet of thingscs
dc.titleHqw Generative AI and the Intelligent Industrial Internet of Thimgs Complement Each othercs
dc.typearticlecs
dc.identifier.doi10.15598/aeee.v23i2.250310
dc.rights.accessopenAccesscs
dc.type.versionpublishedVersioncs
dc.type.statusPeer-reviewedcs


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