Sangam: A Confluence of Knowledge Streams

Development of machine learning techniques and evaluation of analysis results

Show simple item record

dc.contributor University of Aberdeen.Vice Principals
dc.contributor University of Aberdeen.Computing Science
dc.contributor University of Aberdeen.Natural & Computing Sciences
dc.creator Kollias, Stefanos
dc.creator Stafylopatis, Andreas
dc.creator Leontidis, Georgios
dc.creator Alexandridis, Georgios
dc.creator Tabouratzis, Tatiana
dc.creator Durrant, Aiden Mark
dc.date 2022-05-21T19:39:01Z
dc.date 2022-05-21T19:39:01Z
dc.date 2019-08-12
dc.date.accessioned 2022-05-24T06:09:05Z
dc.date.available 2022-05-24T06:09:05Z
dc.identifier Kollias , S , Stafylopatis , A , Leontidis , G , Alexandridis , G , Tabouratzis , T & Durrant , A M 2019 , Development of machine learning techniques and evaluation of analysis results . European Commission .
dc.identifier PURE: 215631169
dc.identifier PURE UUID: 483543ec-1a06-4940-afb8-6fb5a8d671d2
dc.identifier https://hdl.handle.net/2164/18603
dc.identifier https://cortex-h2020.eu/wp-content/uploads/2020/04/CORTEX_D3_4_Development_of_machine_learning_techniques_and_evaluation_of_analysis_results_V1.pdf
dc.identifier.uri http://localhost:8080/xmlui/handle/CUHPOERS/117821
dc.description CORTEX - Research and Innovation Action (RIA) This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 754316.
dc.description Publisher PDF
dc.format 42
dc.format application/pdf
dc.language eng
dc.publisher European Commission
dc.subject SDG 9 - Industry, Innovation, and Infrastructure
dc.subject SDG 7 - Affordable and Clean Energy
dc.subject 2040 Data and Artificial Intelligence
dc.subject nuclear reactors
dc.subject Machine learning
dc.subject QA75 Electronic computers. Computer science
dc.subject QA75
dc.title Development of machine learning techniques and evaluation of analysis results
dc.type Other


Files in this item

Files Size Format View
CORTEX_D3_4_Dev ... of_analysis_results_V1.pdf 1.431Mb application/pdf View/Open

This item appears in the following Collection(s)

Show simple item record

Search DSpace


Advanced Search

Browse