Prof. Dr.-Ing. Morris Riedel
Prof. Dr.-Ing. Morris Riedel
Professor of High Performance Computing & Machine Learning, Forschungszentrum Juelich & UoIceland
Verified email at - Homepage
Cited by
Cited by
Smart medical information technology for healthcare (SMITH)
A Winter, S Stäubert, D Ammon, S Aiche, O Beyan, V Bischoff, P Daumke, ...
Methods of information in medicine 57 (S 01), e92-e105, 2018
On understanding big data impacts in remotely sensed image classification using support vector machine methods
G Cavallaro, M Riedel, M Richerzhagen, JA Benediktsson, A Plaza
IEEE journal of selected topics in applied earth observations and remote …, 2015
UNICORE—from project results to production grids
A Streit, D Erwin, T Lippert, D Mallmann, R Menday, M Rambadt, M Riedel, ...
Advances in Parallel Computing 14, 357-376, 2005
UNICORE 6—recent and future advancements
A Streit, P Bala, A Beck-Ratzka, K Benedyczak, S Bergmann, R Breu, ...
Annals of Telecommunications-annales des Télécommunications 65, 757-762, 2010
Interoperation of world‐wide production e‐Science infrastructures
M Riedel, E Laure, T Soddemann, L Field, JP Navarro, J Casey, ...
Concurrency and Computation: Practice and Experience 21 (8), 961-990, 2009
HPDBSCAN: highly parallel DBSCAN
M Götz, C Bodenstein, M Riedel
Proceedings of the workshop on machine learning in high-performance …, 2015
Future medical artificial intelligence application requirements and expectations of physicians in German university hospitals: web-based survey
O Maassen, S Fritsch, J Palm, S Deffge, J Kunze, G Marx, M Riedel, ...
Journal of medical Internet research 23 (3), e26646, 2021
Attitudes and perception of artificial intelligence in healthcare: A cross-sectional survey among patients
SJ Fritsch, A Blankenheim, A Wahl, P Hetfeld, O Maassen, S Deffge, ...
Digital health 8, 20552076221116772, 2022
Classification of different approaches for e-science applications in next generation computing infrastructures
M Riedel, A Streit, F Wolf, T Lippert, D Kranzlmüller
2008 ieee fourth international conference on escience, 198-205, 2008
Gridbeans: Support e-science and grid applications
R Ratering, A Lukichev, M Riedel, D Mallmann, A Vanni, C Cacciari, ...
2006 Second IEEE International Conference on e-Science and Grid Computing (e …, 2006
Deisa—distributed european infrastructure for supercomputing applications
W Gentzsch, D Girou, A Kennedy, H Lederer, J Reetz, M Riedel, A Schott, ...
Journal of grid computing 9, 259-277, 2011
Research advances by using interoperable e-science infrastructures: the infrastructure interoperability reference model applied in e-science
M Riedel, F Wolf, D Kranzlmüller, A Streit, T Lippert
Cluster computing 12, 357-372, 2009
Improving e-Science with Interoperability of the e-Infrastructures EGEE and DEISA
M Riedel, A Memon, M Memon, D Mallmann, A Streit, F Wolf, T Lippert, ...
Proceedings of the 31st International Convention MIPRO, Conference on Grid …, 2008
Remote sensing big data classification with high performance distributed deep learning
R Sedona, G Cavallaro, J Jitsev, A Strube, M Riedel, JA Benediktsson
Remote Sensing 11 (24), 3056, 2019
Open standards-based interoperability of job submission and management interfaces across the grid middleware platforms glite and unicore
M Marzolla, P Andreetto, V Venturi, A Ferraro, S Memon, S Memon, ...
Third IEEE International Conference on e-Science and Grid Computing (e …, 2007
Approaching remote sensing image classification with ensembles of support vector machines on the d-wave quantum annealer
G Cavallaro, D Willsch, M Willsch, K Michielsen, M Riedel
IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium …, 2020
Standardization processes of the unicore grid system
M Riedel, D Mallmann
Proceedings of 1st Austrian Grid Symposium 2006, 2005
Quantum support vector machine algorithms for remote sensing data classification
A Delilbasic, G Cavallaro, M Willsch, F Melgani, M Riedel, K Michielsen
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2608 …, 2021
Cloud Deep Networks for Hyperspectral Image Analysis
M Haut, J.M., Gallardo, J.A., Paoletti, M.E., Cavallaro, G., Plaza, J ...
IEEE Transactions on Geoscience and Remote Sensing, …, 2019
The influence of sampling methods on pixel-wise hyperspectral image classification with 3D convolutional neural networks
J Lange, G Cavallaro, M Götz, E Erlingsson, M Riedel
IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium …, 2018
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