DI (FH) Michael Roßbory
Research Team Lead AI-Assisted Prescriptive Analytics
Publikationen
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2022
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Challenges in Mass Flow Estimation on Conveyor Belts in the Mining Industry: A Case Study
Heinzl, B., Hinterreiter, C., Roßbory, M., & Hinterdorfer, C. (2022). Challenges in Mass Flow Estimation on Conveyor Belts in the Mining Industry: A Case Study. Database and Expert Systems Applications - DEXA 2022 Workshops, 90–99.
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2021
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Indirect Mass Flow Estimation based on Power Measurements of Conveyor Belts in Mineral Processing Applications
Heinzl, B., Martinez-Gil, J., Himmelbauer, J., Rosbory, M., Hinterdorfer, C., & Hinterreiter, C. (2021). Indirect Mass Flow Estimation based on Power Measurements of Conveyor Belts in Mineral Processing Applications. 2021 IEEE 19th International Conference on Industrial Informatics (INDIN).
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2020
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Differentially private learning of distributed deep models
Kumar, M., Rossbory, M., Moser, B. A., & Freudenthaler, B. (2020). Differentially Private Learning of Distributed Deep Models. Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization.
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An optimal (∊,δ)-differentially private learning of distributed deep fuzzy model
Kumar, M., Rossbory, M., Moser, B. A., & Freudenthaler, B. (2021). An optimal (∊,δ)-differentially private learning of distributed deep fuzzy models. Information Sciences, 546, 87–120.
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The SERUMS tool-chain: Ensuring security and privacy of medical data in smart patient-centric healthcare systems
Janjic, V., Bowles, J. K. F., Vermeulen, A. F., Silvina, A., Belk, M., Fidas, C., Pitsillides, A., Kumar, M., Rossbory, M., Vinov, M., Given-Wilson, T., Legay, A., Blackledge, E., Arredouani, R., Stylianou, G., & Huang, W. (2019). The SERUMS tool-chain: Ensuring Security and Privacy of Medical Data in Smart Patient-Centric Healthcare Systems. 2019 IEEE International Conference on Big Data (Big Data).
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2019
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Deriving an optimal noise adding mechanism for privacy-preserving machine learning.
Kumar, M., Rossbory, M., Moser, B. A., & Freudenthaler, B. (2019). Deriving an Optimal Noise Adding Mechanism for Privacy-Preserving Machine Learning. Database and Expert Systems Applications, 108–118.
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Learning-based dynamic pinning of parallelized applications in many-core systems
Chasparis, G. C., Rossbory, M., Janjic, V., & Hammond, K. (2019). Learning-Based Dynamic Pinning of Parallelized Applications in Many-Core Systems. 2019 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP).