| Methods for rapid pore classification in metal additive manufacturing R Snell, S Tammas-Williams, L Chechik, A Lyle, E Hernández-Nava, ... Jom 72, 101-109, 2020 | 186 | 2020 |
| Using multiple-feature-spaces-based deep learning for tool condition monitoring in ultraprecision manufacturing C Shi, G Panoutsos, B Luo, H Liu, B Li, X Lin IEEE Transactions on industrial electronics 66 (5), 3794-3803, 2018 | 139 | 2018 |
| Real-time adaptive automation system based on identification of operator functional state in simulated process control operations CH Ting, M Mahfouf, A Nassef, DA Linkens, G Panoutsos, P Nickel, ... IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and …, 2009 | 120 | 2009 |
| Interval type-2 radial basis function neural network: a modeling framework A Rubio-Solis, G Panoutsos IEEE Transactions on Fuzzy Systems 23 (2), 457-473, 2014 | 89 | 2014 |
| A neural-fuzzy modelling framework based on granular computing: Concepts and applications G Panoutsos, M Mahfouf Fuzzy Sets and Systems 161 (21), 2808-2830, 2010 | 66 | 2010 |
| A data-driven approach for predicting printability in metal additive manufacturing processes W Mycroft, M Katzman, S Tammas-Williams, E Hernandez-Nava, ... Journal of Intelligent Manufacturing 31, 1769-1781, 2020 | 62 | 2020 |
| Granular computing neural-fuzzy modelling: A neutrosophic approach AR Solis, G Panoutsos Applied Soft Computing 13 (9), 4010-4021, 2013 | 58 | 2013 |
| Modeling and optimal design of machining-induced residual stresses in aluminium alloys using a fast hierarchical multiobjective optimization algorithm Q Zhang, M Mahfouf, JR Yates, C Pinna, G Panoutsos, S Boumaiza, ... Materials and manufacturing processes 26 (3), 508-520, 2011 | 51 | 2011 |
| General type-2 radial basis function neural network: a data-driven fuzzy model A Rubio-Solis, P Melin, U Martinez-Hernandez, G Panoutsos IEEE Transactions on Fuzzy Systems 27 (2), 333-347, 2018 | 44 | 2018 |
| Absolute electrical impedance tomography (aEIT) guided ventilation therapy in critical care patients: simulations and future trends MA Denaï, M Mahfouf, S Mohamad-Samuri, G Panoutsos, BH Brown, ... IEEE Transactions on Information Technology in Biomedicine 14 (3), 641-649, 2009 | 44 | 2009 |
| A real-time quality monitoring framework for steel friction stir welding using computational intelligence A Baraka, G Panoutsos, S Cater Journal of Manufacturing Processes 20, 137-148, 2015 | 42 | 2015 |
| A multilayer interval type-2 fuzzy extreme learning machine for the recognition of walking activities and gait events using wearable sensors A Rubio-Solis, G Panoutsos, C Beltran-Perez, U Martinez-Hernandez Neurocomputing 389, 42-55, 2020 | 36 | 2020 |
| Physics-informed regularisation procedure in neural networks: An application in blast protection engineering JJ Pannell, SE Rigby, G Panoutsos International Journal of Protective Structures 13 (3), 555-578, 2022 | 30 | 2022 |
| Intelligent model-based advisory system for the management of ventilated intensive care patients. Part II: advisory system design and evaluation A Wang, M Mahfouf, GH Mills, G Panoutsos, DA Linkens, K Goode, ... Computer methods and programs in biomedicine 99 (2), 208-217, 2010 | 29 | 2010 |
| Predicting specific impulse distributions for spherical explosives in the extreme near-field using a Gaussian function JJ Pannell, G Panoutsos, SB Cooke, DJ Pope, SE Rigby International Journal of Protective Structures 12 (4), 437-459, 2021 | 27 | 2021 |
| Interpretable machine learning: convolutional neural networks with RBF fuzzy logic classification rules Z Xi, G Panoutsos 2018 International conference on intelligent systems (IS), 448-454, 2018 | 27 | 2018 |
| Probabilistic characterisation of model error using Gaussian mixture model—With application to Charpy impact energy prediction for alloy steel YY Yang, M Mahfouf, G Panoutsos Control engineering practice 20 (1), 82-92, 2012 | 26 | 2012 |
| Development of a parsimonious GA–NN ensemble model with a case study for Charpy impact energy prediction YY Yang, M Mahfouf, G Panoutsos Advances in Engineering Software 42 (7), 435-443, 2011 | 26 | 2011 |
| Transient thermography for flaw detection in friction stir welding: A machine learning approach M Atwya, G Panoutsos IEEE Transactions on Industrial Informatics 16 (7), 4423-4435, 2019 | 23 | 2019 |
| An ensemble data-driven fuzzy network for laser welding quality prediction A Rubio-Solis, G Panoutsos 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1-6, 2017 | 23 | 2017 |
Mahdi MahfoufProfessor of Intelligent Systems, The University of SheffieldVahvistettu sähköpostiosoite verkkotunnuksessa sheffield.ac.uk
