| 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 | 152 | 2017 |
| Stock market prediction with multiple regression, fuzzy type-2 clustering and neural networks D Enke, M Grauer, N Mehdiyev Procedia Computer Science 6, 201-206, 2011 | 126 | 2011 |
| Evaluating forecasting methods by considering different accuracy measures N Mehdiyev, D Enke, P Fettke, P Loos Procedia Computer Science 95, 264-271, 2016 | 114 | 2016 |
| A Novel Business Process Prediction Model Using a Deep Learning Method N Mehdiyev, J Evermann, P Fettke Business & Information Systems Engineering, 1-15, 2018 | 110 | 2018 |
| Towards Explainable Process Predictions for Industry 4.0 in the DFKI-Smart-Lego-Factory JR Rehse, N Mehdiyev, P Fettke KI-Künstliche Intelligenz, 1-7, 2019 | 99 | 2019 |
| Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring N Mehdiyev, P Fettke Interpretable Artificial Intelligence: A Perspective of Granular Computing …, 2021 | 97 | 2021 |
| Determination of rule patterns in complex event processing using machine learning techniques N Mehdiyev, J Krumeich, D Enke, D Werth, P Loos Procedia Computer Science 61, 395-401, 2015 | 96 | 2015 |
| A multi-stage deep learning approach for business process event prediction N Mehdiyev, J Evermann, P Fettke 19th IEEE Conference on Business Informatics (CBI) 1, 119-128, 2017 | 82 | 2017 |
| Stock market prediction using a combination of stepwise regression analysis, differential evolution-based fuzzy clustering, and a fuzzy inference neural network D Enke, N Mehdiyev Intelligent Automation & Soft Computing 19 (4), 636-648, 2013 | 76 | 2013 |
| A hybrid neuro-fuzzy model to forecast inflation D Enke, N Mehdiyev Procedia Computer Science 36, 254-260, 2014 | 57 | 2014 |
| Prescriptive process analytics with deep learning and explainable artificial intelligence N Mehdiyev, P Fettke 28th European Conference on Information Systems (ECIS 2020), 2020 | 40 | 2020 |
| Determination of event patterns for complex event processing using fuzzy unordered rule induction algorithm with multi-objective evolutionary feature subset selection N Mehdiyev, J Krumeich, D Werth, P Loos 49th Hawaii International Conference on System Sciences (HICSS), 1719-1728, 2016 | 25 | 2016 |
| iPRODICT–Intelligent Process Prediction based on Big Data Analytics N Mehdiyev, A Emrich, B Stahmer, P Fettke, P Loos | 21 | 2017 |
| Local Post-Hoc Explanations for Predictive Process Monitoring in Manufacturing N Mehdiyev, P Fettke 29th European Conference on Information Systems (ECIS 2021), 2021 | 20 | 2021 |
| Explainable Artificial Intelligence (XAI) Supporting Public Administration Processes–On the Potential of XAI in Tax Audit Processes N Mehdiyev, C Houy, O Gutermuth, L Mayer, P Fettke 16th International Conference on Wirtschaftsinformatik 2021, 2021 | 19 | 2021 |
| Manufacturing execution systems driven process analytics: A case study from individual manufacturing L Mayer, N Mehdiyev, P Fettke Procedia CIRP 97, 284-289, 2021 | 18 | 2021 |
| Quantifying and explaining machine learning uncertainty in predictive process monitoring: an operations research perspective N Mehdiyev, M Majlatow, P Fettke Annals of Operations Research, 1-40, 2024 | 16 | 2024 |
| Towards an extended metamodel of event-driven process chains to model complex event patterns J Krumeich, N Mehdiyev, D Werth, P Loos Advances in Conceptual Modeling: ER 2015 Workshops AHA, CMS, EMoV, MoBID …, 2015 | 16 | 2015 |
| Identification of distinct usage patterns and prediction of customer behavior S Dadashnia, T Niesen, P Hake, P Fettke, N Mehdiyev, J Evermann BPI Challenge, 2016 | 12 | 2016 |
| 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, 2022 | 10 | 2022 |
