| Artificial intelligent approaches in petroleum geosciences C Cranganu, H Luchian, ME Breaban Springer International Publishing, 2015 | 56 | 2015 |
| An adaptive RL based approach for dynamic resource provisioning in Cloud virtualized data centers F Bahrpeyma, H Haghighi, A Zakerolhosseini Computing 97, 1209-1234, 2015 | 31 | 2015 |
| Using IDS fitted Q to develop a real-time adaptive controller for dynamic resource provisioning in Cloud's virtualized environment F Bahrpeyma, A Zakerolhoseini, H Haghighi Applied Soft Computing 26, 285-298, 2015 | 27 | 2015 |
| A review of the applications of multi-agent reinforcement learning in smart factories F Bahrpeyma, D Reichelt Frontiers in Robotics and AI 9, 1027340, 2022 | 19 | 2022 |
| Fast fuzzy modeling method to estimate missing logsin hydrocarbon reservoirs F Bahrpeyma, B Golchin, C Cranganu Journal of Petroleum Science and Engineering 112, 310-321, 2013 | 16 | 2013 |
| A methodology for validating diversity in synthetic time series generation F Bahrpeyma, M Roantree, P Cappellari, M Scriney, A McCarren MethodsX 8, 101459, 2021 | 11 | 2021 |
| A systematic mapping study on machine learning techniques applied for condition monitoring and predictive maintenance in the manufacturing sector TLJ Phan, I Gehrhardt, D Heik, F Bahrpeyma, D Reichelt Logistics 6 (2), 35, 2022 | 8 | 2022 |
| Active learning method for estimating missing logs in hydrocarbon reservoirs F Bahrpeyma, C Cranganu, BZ Dadaneh Artificial Intelligent Approaches in Petroleum Geosciences, 209-224, 2015 | 8 | 2015 |
| Use of active learning method to determine the presence and estimate the magnitude of abnormally pressured fluid zones: a case study from the Anadarko Basin, Oklahoma C Cranganu, F Bahrpeyma Artificial Intelligent Approaches in Petroleum Geosciences, 191-208, 2015 | 8 | 2015 |
| Multi-Resolution Forecast Aggregation for Time Series in Agri Datasets F Bahrpeyma, M Roantree, A McCarren Irish Conference on Artificial Intelligence and Cognitive Science 25, 2017 | 7 | 2017 |
| A bipolar resource management framework for resource provisioning in Cloud’s virtualized environment F Bahrpeyma, H Haghighi, A Zakerolhosseini Applied Soft Computing 46, 487-500, 2016 | 7 | 2016 |
| Multistep-ahead prediction: A comparison of analytical and algorithmic approaches F Bahrpeyma, M Roantree, A McCarren International Conference on Big Data Analytics and Knowledge Discovery, 345-354, 2018 | 6 | 2018 |
| Dynamic job shop scheduling in an industrial assembly environment using various reinforcement learning techniques D Heik, F Bahrpeyma, D Reichelt International Conference on Intelligent Systems Design and Applications, 523-533, 2022 | 3 | 2022 |
| An Application of Reinforcement Learning in Industrial Cyber-Physical Systems D Heik, F Bahrpeyma, D Reichelt OVERLAY 2022, 4th Workshop on Artificial Intelligence and Formal …, 2022 | 3 | 2022 |
| Improving the Accuracy of Active Learning Method via Noise Injection for Estimating Hydraulic Flow Units: An Example from a Heterogeneous Carbonate Reservoir F Bahrpeyma, C Cranganu, B Golchin Artificial Intelligent Approaches in Petroleum Geosciences, 225-244, 2015 | 3 | 2015 |
| Application of Reinforcement Learning to UR10 Positioning for Prioritized Multi-Step Inspection in NVIDIA Omniverse F Bahrpeyma, A Sunilkumar, D Reichelt 2023 IEEE Symposium on Industrial Electronics & Applications (ISIEA), 1-6, 2023 | 2 | 2023 |
| Multistep ahead time series prediction F Bahrpeyma Dublin City University, 2021 | 2 | 2021 |
| Application of multi-agent reinforcement learning to the dynamic scheduling problem in manufacturing systems D Heik, F Bahrpeyma, D Reichelt International Conference on Machine Learning, Optimization, and Data Science …, 2023 | 1 | 2023 |
| Anwendung von Reinforcement Learning in industriellen cyberphysischen Systemen D Heik, F Bahrpeyma, D Reichelt | 1 | 2023 |
| An overview of the applications of reinforcement learning to robot programming: discussion on the literature and the potentials A Sunilkumar, F Bahrpeyma, D Reichelt | | 2024 |
Constantin CranganuProfessor of Geophysics and Petroleum Geology, The City University of New YorkDirección de correo verificada de brooklyn.cuny.edu
