Anzahl der Einträge: 7.
2023
Riedelbauch, Dominik ; Höllerich, Nico ; Henrich, Dominik:
Benchmarking Teamwork of Humans and Cobots : An Overview of Metrics, Strategies, and Tasks.
In: IEEE Access.
Bd. 11
(2023)
.
- S. 43648-43674.
ISSN 2169-3536
DOI der Verlagsversion: https://doi.org/10.1109/ACCESS.2023.3271602
Farah, Niklas ; Drack, Nicolas ; Dawel, Hannah ; Büttner, Ricardo:
A Deep Learning-Based Approach for the Detection of Infested Soybean Leaves.
In: IEEE Access.
Bd. 11
(2023)
.
- S. 99670-99679.
ISSN 2169-3536
DOI der Verlagsversion: https://doi.org/10.1109/ACCESS.2023.3313978
Kreuzberger, Dominik ; Kühl, Niklas ; Hirschl, Sebastian:
Machine learning operations (mlops) : Overview, definition, and architecture.
In: IEEE Access.
Bd. 11
(2023)
.
- S. 31866-31879.
ISSN 2169-3536
DOI der Verlagsversion: https://doi.org/10.1109/ACCESS.2023.3262138
Braig, Niklas ; Benz, Alina ; Voth, Soeren ; Breitenbach, Johannes ; Büttner, Ricardo:
Machine Learning Techniques for Sentiment Analysis of COVID-19-Related Twitter Data.
In: IEEE Access.
Bd. 11
(2023)
.
- S. 14778-14803.
ISSN 2169-3536
DOI der Verlagsversion: https://doi.org/10.1109/ACCESS.2023.3242234
Penava, Pascal ; Büttner, Ricardo:
A Novel Small-Data Based Approach for Decoding Yes/No-Decisions of Locked-In Patients Using Generative Adversarial Networks.
In: IEEE Access.
Bd. 11
(2023)
.
- S. 118849-118864.
ISSN 2169-3536
DOI der Verlagsversion: https://doi.org/10.1109/ACCESS.2023.3326720
Principato, Marc ; Hasselwander, Lisa ; Stangner, Michael ; Büttner, Ricardo:
Unlocking the Potential of Wind Energy With Machine Learning-Based Avian Detection : A Call to Action.
In: IEEE Access.
Bd. 11
(2023)
.
- S. 64026-64048.
ISSN 2169-3536
DOI der Verlagsversion: https://doi.org/10.1109/ACCESS.2023.3287861
2022
Gross, Jan ; Büttner, Ricardo ; Baumgartl, Hermann:
Benchmarking Transfer Learning Strategies in Time-Series Imaging : Recommendations for Analyzing Raw Sensor Data.
In: IEEE Access.
Bd. 10
(2022)
.
- S. 16977-16991.
ISSN 2169-3536
DOI der Verlagsversion: https://doi.org/10.1109/ACCESS.2022.3148711
