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8 Publikationen
2024 | Dissertation | FH-PUB-ID: 4392 | 
Grumbach, F. (2024). Feldsynchrone Ablaufplanung dynamischer Fertigungsprozesse mit Techniken des maschinellen Lernens [kumulative Dissertation]. Bernburg: Universitäts- und Landesbibliothek Sachsen-Anhalt. https://doi.org/10.25673/115290
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2024 | Artikel | FH-PUB-ID: 4881
Wiegraebe, F., Schönbeck, M., Wunderlich, P., Nauerth, A., & Dörksen, H. (2024). KI-basiertes Unterstützungstool für pflegende Erwerbstätige. Pflege und Gesellschaft, 3, 271–285. https://doi.org/10.3262/P&G2403271
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2023 | Konferenzbeitrag | FH-PUB-ID: 4207 |
Sanaullah, S., & Jungeblut, T. (2023). Analysis of MR Images for Early and Accurate Detection of Brain Tumor using Resource Efficient Simulator Brain Analysis. Presented at the 19th International Conference on Machine Learning and Data Mining MLDM, New York USA. https://doi.org/10.5281/zenodo.10457930
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2023 | Kurzbeitrag Konferenz | FH-PUB-ID: 4206 |
Sanaullah, S., Amanullah, A., Roy, K., Lee, J.-A., Chul-Jun, S., & Jungeblut, T. (2023). A Hybrid Spiking-Convolutional Neural Network Approach for Advancing High-Quality Image Inpainting. Presented at the International Conference on Computer Vision (ICCV) 2023, Paris France. https://doi.org/10.5281/zenodo.10458019
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2023 | Konferenzbeitrag | FH-PUB-ID: 4394 |
Müller, A., & Grumbach, F. (2023). Predicting processing times in high mix low volume job shops. In E. Glistau & S. Trojahn (Eds.), 16th International Doctoral Students Workshop on Logistics, Supply Chain and Production Management. Magdeburg: Otto von Guericke University Library, Magdeburg, Germany. https://doi.org/10.25673/103491
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2023 | Diskussionspapier | FH-PUB-ID: 3729 |
Kösters, J., Schöne, M., & Kohlhase, M. (n.d.). Benchmarking of Machine Learning Models for Tabular Scarce Data.
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2022 | Konferenzbeitrag | FH-PUB-ID: 2953 |
Behrens, G., Hepp, D., Hempelmann, S., & Friedrich, W. (2022). Detection of snow-coverage on PV-modules with images based on CNN-techniques. In V. Wohlgemuth, S. Naumann, H.-K. Arnd, G. Behrens, M. Höb, & Gesellschaft für Informatik e.V. (Eds.), ENVIROINFO 2022 (Vol. P328). Bonn.
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2022 | Artikel | FH-PUB-ID: 1799 |
Vandevoorde, K., Vollenkemper, L., Schwan, C., Kohlhase, M., & Schenck, W. (2022). Using Artificial Intelligence for Assistance Systems to Bring Motor Learning Principles into Real World Motor Tasks. Sensors, 22(7). https://doi.org/10.3390/s22072481
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