| A systematic literature review on state-of-the-art deep learning methods for process prediction DA Neu, J Lahann, P Fettke Artificial Intelligence Review 55 (2), 801-827, 2022 | 174 | 2022 |
| Time series classification using deep learning for process planning: A case from the process industry N Mehdiyev, J Lahann, A Emrich, D Enke, P Fettke, P Loos Procedia Computer Science 114, 242-249, 2017 | 155 | 2017 |
| Utilizing machine learning techniques to reveal vat compliance violations in accounting data J Lahann, M Scheid, P Fettke 2019 IEEE 21st conference on business informatics (CBI) 1, 1-10, 2019 | 51 | 2019 |
| Multivariate business process representation learning utilizing gramian angular fields and convolutional neural networks P Pfeiffer, J Lahann, P Fettke Business Process Management: 19th International Conference, BPM 2021, Rome …, 2021 | 26 | 2021 |
| Adaptive social media skills trainer for vocational education and training: concept and implementation of a recommender system C Di Valentin, A Emrich, J Lahann, D Werth, P Loos 2015 48th Hawaii International Conference on System Sciences, 1951-1960, 2015 | 19 | 2015 |
| Deep learning‐based clustering of processes and their visual exploration: An industry 4.0 use case for small, medium‐sized enterprises N Mehdiyev, L Mayer, J Lahann, P Fettke Expert Systems 41 (2), e13139, 2024 | 10 | 2024 |
| LSTM-based anomaly detection of process instances: benchmark and tweaks J Lahann, P Pfeiffer, P Fettke International Conference on Process Mining, 229-241, 2022 | 10 | 2022 |
| A case study on the application of process mining in combination with journal entry tests for financial auditing S Stephan, J Lahann, P Fettke | 8 | 2021 |
| The label ambiguity problem in process prediction P Pfeiffer, J Lahann, P Fettke International Conference on Business Process Management, 37-44, 2022 | 7 | 2022 |
| Business Process Intelligence Challenge 2020: Analysis and evaluation of a travel process S Klein, J Lahann, L Mayer, D Neu, P Pfeiffer, A Rebmann, M Scheid, ... 10th business process intelligence challenge at the int. conf. on process mining, 2020 | 5 | 2020 |
| Towards Optimal Free Trade Agreement Utilization through Deep Learning Techniques. J Lahann, M Scheid, P Fettke HICSS, 1-10, 2020 | 4 | 2020 |
| Walkable graph: An immersive augmented reality interface for performing the memory palace method R Raso, J Lahann, P Fettke, P Loos | 3 | 2019 |
| Multimodal Process Prediction J Lahann ICPM Doctoral Consortium/Demo, 32-36, 2022 | 1 | 2022 |
| Exploring the Potentials of Artificial Intelligence Techniques for Business Process Analysis S Dadashnia, P Fettke, P Hake, J Lahann, P Loos, S Klein, N Mehdiyev, ... BPI Challenge, 2017 | 1 | 2017 |
| Adaptive recommendations to foster social media skills in teaching and learning scenarios C Di Valentin, A Emrich, J Lahann, M Schmidt, U Schwertel, D Werth, ... Proceedings of the 14th International Conference on Knowledge Technologies …, 2014 | 1 | 2014 |
| Utilizing Deep Learning for Field-Level Information Extraction from German Real Estate Tax Notices AM Rombach, J Lahann, T Niesen, P Fettke Journal of Emerging Technologies in Accounting, 1-18, 2024 | | 2024 |
| Efficient and Compliant Purchase Order HandlingA Contribution to BPI Challenge 2019 O Gutermuth, J Lahann, JR Rehse, M Scheid, S Schuhmann, S Stephan, ... | | 2019 |
