Stock Market Prediction with Multiple Regression, Fuzzy Type-2 Clustering and Neural Networks. - David Enke; Manfred Grauer; Nijat Mehdiyev (Complex Adaptive Systems, 2011)

A New Hybrid Approach For Forecasting Interest Rates. - David Enke; Nijat Mehdiyev (Complex Adaptive Systems, 2012)

Type-2 Fuzzy Clustering and a Type-2 Fuzzy Inference Neural Network for the Prediction of Short-term Interest Rates. - David Enke; Nijat Mehdiyev (Complex Adaptive Systems, 2013)

Stock Market Prediction Using a Combination of Stepwise Regression Analysis, Differential Evolution-based Fuzzy Clustering, and a Fuzzy Inference Neural Network. - David Enke; Nijat Mehdiyev (Intell. Autom. Soft Comput., 2013)

A Hybrid Neuro-fuzzy Model to Forecast Inflation. - David Enke; Nijat Mehdiyev (Complex Adaptive Systems, 2014)

Interest rate prediction: a neuro-hybrid approach with data preprocessing. - Nijat Mehdiyev; David Enke (Int. J. Gen. Syst., 2014)

Sensor event mining with hybrid ensemble learning and evolutionary feature subset selection model. - Nijat Mehdiyev; Julian Krumeich; Dirk Werth; Peter Loos (IEEE BigData, 2015)

Determination of Rule Patterns in Complex Event Processing Using Machine Learning Techniques. - Nijat Mehdiyev; Julian Krumeich; David Enke; Dirk Werth; Peter Loos (Complex Adaptive Systems, 2015)

Towards an Extended Metamodel of Event-Driven Process Chains to Model Complex Event Patterns. - Julian Krumeich; Nijat Mehdiyev; Dirk Werth; Peter Loos (ER Workshops, 2015)

Determination of Event Patterns for Complex Event Processing Using Fuzzy Unordered Rule Induction Algorithm with Multi-objective Evolutionary Feature Subset Selection. - Nijat Mehdiyev; Julian Krumeich; Dirk Werth; Peter Loos (HICSS, 2016)

iPRODICT - Intelligent Process Prediction based on Big Data Analytics. - Nijat Mehdiyev; Andreas Emrich; Björn P. Stahmer; Peter Fettke; Peter Loos (BPM (Industry Track), 2017)

A Multi-stage Deep Learning Approach for Business Process Event Prediction. - Nijat Mehdiyev; Joerg Evermann; Peter Fettke (CBI (1), 2017)

Substitution of hazardous chemical substances using Deep Learning and t-SNE. - Patrick Lübbecke; Nijat Mehdiyev; Peter Fettke (Wirtschaftsinformatik, 2019)

Towards Explainable Process Predictions for Industry 4.0 in the DFKI-Smart-Lego-Factory. - Jana-Rebecca Rehse; Nijat Mehdiyev; Peter Fettke (Künstliche Intell., 2019)

Prescriptive Process Analytics with Deep Learning and Explainable Artificial Intelligence. - Nijat Mehdiyev; Peter Fettke (ECIS, 2020)

Explainable Process Predictions (xPP): A Holistic Framework and Applications (Extended Abstract). - Nijat Mehdiyev (ICPM Doctoral Consortium / Tools, 2020)

A Novel Business Process Prediction Model Using a Deep Learning Method. - Nijat Mehdiyev; Joerg Evermann; Peter Fettke (Bus. Inf. Syst. Eng., 2020)

Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring. - Nijat Mehdiyev; Peter Fettke (CoRR, 2020)

Local Post-Hoc Explanations for predictive Process Monitoring in manufacturing. - Nijat Mehdiyev; Peter Fettke (ECIS, 2021)

Explainable Artificial Intelligence (XAI) Supporting Public Administration Processes - On the Potential of XAI in Tax Audit Processes. - Nijat Mehdiyev; Constantin Houy; Oliver Gutermuth; Lea Mayer; Peter Fettke (Wirtschaftsinformatik, 2021)

Explainable Artificial Intelligence Meets Uncertainty Quantification for Predictive Process Monitoring. - Nijat Mehdiyev; Maxim Majlatow; Peter Fettke (PMAI@IJCAI, 2023)

Interpretable and Explainable Machine Learning Methods for Predictive Process Monitoring: A Systematic Literature Review. - Nijat Mehdiyev; Maxim Majlatow; Peter Fettke (CoRR, 2023)

Communicating Uncertainty in Machine Learning Explanations: A Visualization Analytics Approach for Predictive Process Monitoring. - Nijat Mehdiyev; Maxim Majlatow; Peter Fettke (xAI (3), 2024)

Deep learning-based clustering of processes and their visual exploration: An industry 4.0 use case for small, medium-sized enterprises. - Nijat Mehdiyev; Lea Mayer; Johannes Lahann; Peter Fettke (Expert Syst. J. Knowl. Eng., 2024)

Uncertainty-aware multi-criteria decision analysis for evaluation of explainable artificial intelligence methods: A use case from the healthcare domain. - Kamala Aliyeva; Nijat Mehdiyev (Inf. Sci., 2024)

Counterfactual Explanations in the Big Picture: An Approach for Process Prediction-Driven Job-Shop Scheduling Optimization. - Nijat Mehdiyev; Maxim Majlatow; Peter Fettke (Cogn. Comput., 2024)

Conversational Business Process Modeling using LLMs: Initial Results and Challenges. - Chantale Lauer; Peter Pfeiffer; Alexander Rombach; Nijat Mehdiyev (EMISA, 2025)

Augmenting post-hoc explanations for predictive process monitoring with uncertainty quantification via conformalized Monte Carlo dropout. - Nijat Mehdiyev; Maxim Majlatow; Peter Fettke (Data Knowl. Eng., 2025)

Integrating permutation feature importance with conformal prediction for robust Explainable Artificial Intelligence in predictive process monitoring. - Nijat Mehdiyev; Maxim Majlatow; Peter Fettke (Eng. Appl. Artif. Intell., 2025)

Quantifying and explaining machine learning uncertainty in predictive process monitoring: an operations research perspective. - Nijat Mehdiyev; Maxim Majlatow; Peter Fettke (Ann. Oper. Res., 2025)

From Theory to Practice: Real-World Use Cases on Trustworthy LLM-Driven Process Modeling, Prediction and Automation. - Peter Pfeiffer; Alexander Rombach; Maxim Majlatow; Nijat Mehdiyev (CoRR, 2025)