| Virtual sensors D Martin, N Kühl, G Satzger Business & Information Systems Engineering 63, 315-323, 2021 | 99 | 2021 |
| Human vs. supervised machine learning: Who learns patterns faster? N Kühl, M Goutier, L Baier, C Wolff, D Martin Cognitive Systems Research 76, 78-92, 2022 | 50 | 2022 |
| Service systems, smart service systems and cyber-physical systems—what’s the difference? towards a unified terminology D Martin, R Hirt, N Kühl | 15 | 2019 |
| A new metric for lumpy and intermittent demand forecasts: Stock-keeping-oriented prediction error costs D Martin, P Spitzer, N Kühl arXiv preprint arXiv:2004.10537, 2020 | 13 | 2020 |
| System-wide learning in cyber-physical service systems: A research agenda D Martin, N Kühl, JK von Bischhoffshausen, G Satzger Designing for Digital Transformation. Co-Creating Services with Citizens and …, 2020 | 11 | 2020 |
| Igniting the spark: Overcoming organizational change resistance to advance innovation adoption–The case of data-driven services T Enders, D Martin, GG Sehgal, R Schüritz Exploring Service Science: 10th International Conference, IESS 2020, Porto …, 2020 | 10 | 2020 |
| Holistic system-analytics as an alternative to isolated sensor technology: a condition monitoring use case D Martin, N Kühl | 10 | 2019 |
| Deep learning strategies for industrial surface defect detection systems D Martin, S Heinzel, JK von Bischhoffshausen, N Kühl arXiv preprint arXiv:2109.11304, 2021 | 9 | 2021 |
| " Healthy surveillance": Designing a concept for privacy-preserving mask recognition AI in the age of pandemics N Kühl, D Martin, C Wolff, M Volkamer arXiv preprint arXiv:2010.12026, 2020 | 9 | 2020 |
| Grasping the terminology: Smart services, smart service systems, and cyber-physical systems D Martin, N Kühl, M Maleshkova Smart service management: Design guidelines and best practices, 7-21, 2020 | 4 | 2020 |
| On the Effect of Contextual Information on Human Delegation Behavior in Human-AI collaboration P Spitzer, J Holstein, P Hemmer, M Vössing, N Kühl, D Martin, G Satzger arXiv preprint arXiv:2401.04729, 2024 | 3 | 2024 |
| Enabling inter-organizational analytics in business networks through meta machine learning R Hirt, N Kühl, D Martin, G Satzger Information Technology and Management, 1-25, 2023 | 3 | 2023 |
| Deep domain adaptation for detecting bomb craters in aerial images M Geiger, D Martin, N Kühl arXiv preprint arXiv:2209.11299, 2022 | 3 | 2022 |
| A reference architecture for cyber-physical fluid power systems: towards a smart ecosystem D Martin, JK von Bischoffshausen, A Hensel, J Strandberg | 3 | 2020 |
| White Spots in Business and IT: An Explorative Study for E-Mobility Services D Martin, N Kühl, C Stryja, J Haude World Electric Vehicle Journal 9 (2), 27, 2018 | 2 | 2018 |
| Towards a reference architecture for future industrial Internet of Things networks D Martin, N Kühl, M Schwenk 2021 IEEE 23rd Conference on Business Informatics (CBI) 2, 1-9, 2021 | 1 | 2021 |
| Looking Through the Deep Glasses: How Large Language Models Enhance Explainability of Deep Learning Models P Spitzer, S Celis, D Martin, N Kühl, G Satzger Proceedings of Mensch und Computer 2024, 566-570, 2024 | | 2024 |
| Human Delegation Behavior in Human-AI Collaboration: The Effect of Contextual Information J Holstein, P Spitzer, P Hemmer, M Vössing, N Kühl, D Martin, G Satzger | | 2024 |
| Virtual Sensors: Enhancing Sensor Capabilities Via Machine Learning Across Internet of Things Ecosystems D Martin Karlsruher Institut für Technologie (KIT), 2023 | | 2023 |
| Smart Services: A Condition Monitoring Use Case Utilizing System-Wide Analyses D Martin, N Kühl, JK Bischhoffshausen Smart Service Management: Design Guidelines and Best Practices, 179-191, 2020 | | 2020 |
Niklas KühlUniversity of BayreuthVerified email at uni-bayreuth.de
