Kevin Mallinger
Projektass. Mag.rer.nat.
Role
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PreDoc Researcher
Data Science, E194-04
Projects
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FarmIT - Digital transformation for sustainable and resilient agriculture
2021 / Vienna Business Agency (WAW)
Publications
- Tabular Reinforcement learning for Robust, Explainable CropRotation Policies Matching Deep Reinforcement LearningPerformance / Goldenits, G., Mallinger, K., Neubauer, T., & Weippl, E. (2024). Tabular Reinforcement learning for Robust, Explainable CropRotation Policies Matching Deep Reinforcement LearningPerformance. In EGU General Assembly 2024. EGU General Assembly 2024, Wien, Austria. EGU. https://doi.org/10.5194/egusphere-egu24-9018
- Nachhaltige Digitale Zwillinge in der Landwirtschaft / Neubauer, T., Bauer, A., Heurix, J., Iwersen, M., Mallinger, K., Manschadi, A. M., Purcell, W., & Rauber, A. (2024). Nachhaltige Digitale Zwillinge in der Landwirtschaft. Zeitschrift für Hochschulentwicklung, 19, 165–188. https://doi.org/10.21240/zfhe/SH-A/10
- Unsupervised and supervised machine learning approach to assess user readiness levels for precision livestock farming technology adoption in the pig and poultry industries / Mallinger, K., Corpaci, L., Neubauer, T., Tikasz, I. E., & Banhazi, T. (2023). Unsupervised and supervised machine learning approach to assess user readiness levels for precision livestock farming technology adoption in the pig and poultry industries. Computers and Electronics in Agriculture, 213, Article 108239. https://doi.org/10.1016/j.compag.2023.108239
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Digital Twins in agriculture: challenges and opportunities for environmental sustainability
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Purcell, W., Neubauer, T., & Mallinger, K. (2023). Digital Twins in agriculture: challenges and opportunities for environmental sustainability. Current Opinion in Environmental Sustainability, 61, Article 101252. https://doi.org/10.34726/4522
Download: Manuscript (459 KB)
Project: DiLaAg (2018–2022) -
Combining Fractional Derivatives and Machine Learning: A Review
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Raubitzek, S., Mallinger, K., & Neubauer, T. (2023). Combining Fractional Derivatives and Machine Learning: A Review. Entropy, 25(1), Article 35. https://doi.org/10.3390/e25010035
Download: PDF (402 KB) -
Root Cause Analysis of Software Aging in Critical Information Infrastructure
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König, P., Obermann, F., Mallinger, K., & Schatten, A. (2022). Root Cause Analysis of Software Aging in Critical Information Infrastructure [Conference Presentation]. Critis 2022, Germany. https://doi.org/10.34726/3745
Download: Conference Paper (234 KB) -
When legacy code turns into senescent code: Assessing software aging and its implications
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König, P., Mallinger, K., & Schatten, A. (2022). When legacy code turns into senescent code: Assessing software aging and its implications [Conference Presentation]. 5th European Conference on Technology Assessment (TA), Germany. https://doi.org/10.34726/3762
Download: Conference Paper (161 KB) -
Informatics & Sustainability. Aktuelle Herausforderungen der nachhaltigen Informatik
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Mallinger, K., Tjoa, A. M., & Tjoa, S. (2021). Informatics & Sustainability. Aktuelle Herausforderungen der nachhaltigen Informatik. OCG Journal, 2021(03–04), 16–18. https://doi.org/10.34726/3468
Download: PDF (200 KB) - Anforderungen und Potentiale resilienter IoT-Systeme / Mallinger, K., Ullrich, J., & Schatten, A. (2021). Anforderungen und Potentiale resilienter IoT-Systeme. Elektrotechnik und Informationstechnik : e & i.