HollerithEnergyML : a prototype of a machine learning energy consumption recommender system
- 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.
| Author of HS Reutlingen | Zanger, Michael; Schulz, Alexander; Grodmeier, Lukas; Agaj, Dion; Schindler, Rafael; Weiss, Lukas; Möhring, Michael |
|---|---|
| URN: | urn:nbn:de:bsz:rt2-opus4-51439 |
| DOI: | https://doi.org/10.18420/inf2024_132 |
| ISBN: | 978-3-88579-746-3 |
| ISSN: | 1617-5468 |
| Published in: | Informatik 2024: Lock-in or log out? Wie digitale Souveränität gelingt, 24.-26. September 2024, Wiesbaden, proceedings (Lecture Notes in Informatics; vol. 352) |
| Publisher: | Gesellschaft für Informatik |
| Place of publication: | Bonn |
| Editor: | Maike Klein, Daniel Krupka, Cornelia Winter, Martin Gergeleit, Ludger Martin |
| Document Type: | Conference proceeding |
| Language: | English |
| Publication year: | 2024 |
| Tag: | AI; ML; energy consumption; recommender |
| Page Number: | 5 |
| First Page: | 1519 |
| Last Page: | 1523 |
| DDC classes: | 004 Informatik |
| Open access?: | Ja |
| Licence (German): |  Creative Commons - CC BY-SA - Namensnennung - Weitergabe unter gleichen Bedingungen 4.0 International |
