| Machine learning and deep learning C Janiesch, P Zschech, K Heinrich Electronic markets 31 (3), 685-695, 2021 | 2763 | 2021 |
| Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability LV Herm, K Heinrich, J Wanner, C Janiesch International Journal of Information Management 69, 102538, 2023 | 117 | 2023 |
| The effect of transparency and trust on intelligent system acceptance: Evidence from a user-based study J Wanner, LV Herm, K Heinrich, C Janiesch Electronic Markets 32 (4), 2079-2102, 2022 | 75 | 2022 |
| Process data properties matter: Introducing gated convolutional neural networks (GCNN) and key-value-predict attention networks (KVP) for next event prediction with deep learning K Heinrich, P Zschech, C Janiesch, M Bonin Decision Support Systems 143, 113494, 2021 | 59 | 2021 |
| How Much AI Do You Require? Decision Factors for Adopting AI Technology. J Wanner, K Heinrich, C Janiesch, P Zschech ICIS, 2020 | 50 | 2020 |
| White, Grey, Black: Effects of XAI Augmentation on the Confidence in AI-based Decision Support Systems. J Wanner, LV Herm, K Heinrich, C Janiesch, P Zschech ICIS, 2020 | 44 | 2020 |
| Intelligent user assistance for automated data mining method selection P Zschech, R Horn, D Höschele, C Janiesch, K Heinrich Business & Information Systems Engineering 62, 227-247, 2020 | 35 | 2020 |
| Prognostic model development with missing labels: A condition-based maintenance approach using machine learning P Zschech, K Heinrich, R Bink, JS Neufeld Business & Information Systems Engineering 61, 327-343, 2019 | 30 | 2019 |
| Analyzing customer sentiments in microblogs–A topic-model-based approach for Twitter datasets S Sommer, A Schieber, A Hilbert, K Heinrich | 26 | 2011 |
| Everything counts: a Taxonomy of Deep Learning Approaches for Object Counting. K Heinrich, A Roth, P Zschech ECIS, 2019 | 22 | 2019 |
| Demystifying the black box: A classification scheme for interpretation and visualization of deep intelligent systems K Heinrich, P Zschech, T Skouti, J Griebenow, S Riechert | 21 | 2019 |
| Stop ordering machine learning algorithms by their explainability! An empirical investigation of the tradeoff between performance and explainability J Wanner, LV Herm, K Heinrich, C Janiesch Conference on e-Business, e-Services and e-Society, 245-258, 2021 | 20 | 2021 |
| A picture is worth a collaboration: Accumulating design knowledge for computer-vision-based hybrid intelligence systems P Zschech, J Walk, K Heinrich, M Vössing, N Kühl arXiv preprint arXiv:2104.11600, 2021 | 20 | 2021 |
| Towards a Taxonomic Benchmarking Framework for Predictive Maintenance: The Case of NASA's Turbofan Degradation. P Zschech, J Bernien, K Heinrich ICIS, 2019 | 18 | 2019 |
| A social evaluation of the perceived goodness of explainability in machine learning J Wanner, LV Herm, K Heinrich, C Janiesch Journal of Business Analytics 5 (1), 29-50, 2022 | 17 | 2022 |
| What is the conversation about?: A topic-model-based approach for analyzing customer sentiments in twitter S Sommer, A Schieber, K Heinrich, A Hilbert International Journal of Intelligent Information Technologies (IJIIT) 8 (1 …, 2012 | 17 | 2012 |
| Algorithms as a manager: A critical literature review of algorithm management K Heinrich, MA Vu, A Vysochyna | 16 | 2022 |
| Yield prognosis for the agrarian management of vineyards using deep learning for object counting K Heinrich, A Roth, L Breithaupt, B Möller, J Maresch | 16 | 2019 |
| Are you up for the challenge? Towards the development of a big data capability assessment model P Zschech, K Heinrich, M Pfitzner, A Hilbert | 15 | 2017 |
| Adoption Barriers of AI: a Context-Specific Acceptance Model for Industrial Maintenance. J Wanner, L Popp, K Fuchs, K Heinrich, LV Herm, C Janiesch ECIS, 2021 | 14 | 2021 |
Andreas SchieberTU DresdenVerificeret mail på tu-dresden.de
