TY  - CPAPER
U1  - Konferenzveröffentlichung
A1  - Zanger, Michael
A1  - Schulz, Alexander
A1  - Grodmeier, Lukas
A1  - Agaj, Dion
A1  - Schindler, Rafael
A1  - Weiss, Lukas
A1  - Möhring, Michael
ED  - Klein, Maike
ED  - Krupka, Daniel
ED  - Winter, Cornelia
ED  - Gergeleit, Martin
ED  - Martin, Ludger
T1  - HollerithEnergyML : a prototype of a machine learning energy consumption recommender system
T2  - Informatik 2024: Lock-in or log out? Wie digitale Souveränität gelingt, 24.-26. September 2024, Wiesbaden, proceedings (Lecture Notes in Informatics; vol. 352)
N2  - Energy consumption aspects of machine learning classifiers are important for research and practice as well. Due to sparse research in this area, a prototype of a recommender system was developed to provide energy consumption recommendations of different possible classifiers. The prototype is demonstrated as well as discussed and future research points are derived.
KW  - AI
KW  - energy consumption
KW  - ML
KW  - recommender
Y1  - 2024
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-51439
SN  - 1617-5468
SS  - 1617-5468
SN  - 978-3-88579-746-3
SB  - 978-3-88579-746-3
U6  - https://doi.org/10.18420/inf2024_132
DO  - https://doi.org/10.18420/inf2024_132
SP  - 1519
EP  - 1523
S1  - 5
PB  - Gesellschaft für Informatik
CY  - Bonn
ER  -