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2020 – today
- 2024
[j21]Michail Mamalakis
, Sarah C. Macfarlane, Scott V. Notley, Annica K. B. Gad, George Panoutsos:
A novel pipeline employing deep multi-attention channels network for the autonomous detection of metastasizing cells through fluorescence microscopy. Comput. Biol. Medicine 181: 109052 (2024)- [j20]Mohamed Atwya, George Panoutsos:
In-situ porosity prediction in metal powder bed fusion additive manufacturing using spectral emissions: a prior-guided machine learning approach. J. Intell. Manuf. 35(6): 2719-2742 (2024) - [j19]Michail Mamalakis, Abhirup Banerjee, Surajit Ray, Craig Wilkie, Richard H. Clayton, Andrew J. Swift, George Panoutsos, Bart Vorselaars:
Deep multi-metric training: the need of multi-metric curve evaluation to avoid weak learning. Neural Comput. Appl. 36(30): 18841-18862 (2024) - [c35]Zezhi Tang, J. Anthony Rossiter, Yi Dong, George Panoutsos:
Reinforcement Learning-Based Output Stabilization Control for Nonlinear Systems With Generalized Disturbances. ICIT 2024: 1-6 - [c34]Hongyu Zhao, Zezhi Tang, Zhenhong Li, Yi Dong, Yuancheng Si, Mingyang Lu, George Panoutsos:
Real-Time Object Detection and Robotic Manipulation for Agriculture Using a YOLO-Based Learning Approach. ICIT 2024: 1-6 - [e1]George Panoutsos, Mahdi Mahfouf, Lyudmila S. Mihaylova:
Advances in Computational Intelligence Systems - Contributions Presented at the 21st UK Workshop on Computational Intelligence, UKCI 2022, September 7-9, 2022, Sheffield, UK. Advances in Intelligent Systems and Computing 1454, Springer 2024, ISBN 978-3-031-55567-1 [contents] - [i2]Hongyu Zhao, Zezhi Tang, Zhenhong Li, Yi Dong, Yuancheng Si, Mingyang Lu, George Panoutsos:
Real-time object detection and robotic manipulation for agriculture using a YOLO-based learning approach. CoRR abs/2401.15785 (2024) - 2023
- [j18]Muhammad Zaiyad Muda, Adrian Rubio Solis, George Panoutsos:
An evolving feature weighting framework for radial basis function neural network models. Expert Syst. J. Knowl. Eng. 40(5) (2023) - [c33]Kai Eivind Wu, George Panoutsos:
High Dimensional Many Objective Optimisation through Diverse Creation and Categorisation of Reference Vectors. GECCO Companion 2023: 423-426 - [c32]Emad M. Grais, Scott V. Notley, George Panoutsos:
Reinforcement Learning for Multiple-Input Multiple-Output Control in Metal Additive Manufacturing. ICNSC 2023: 1-6 - [i1]Michail Mamalakis, Sarah C. Macfarlane, Scott V. Notley, Annica K. B. Gad, George Panoutsos:
A novel framework employing deep multi-attention channels network for the autonomous detection of metastasizing cells through fluorescence microscopy. CoRR abs/2309.00911 (2023) - 2022
- [j17]John Anthony Rossiter, Muhammad Saleheen Aftab, George Panoutsos, Oscar Gonzalez-Villarreal:
A novel approach to PFC for nonlinear systems. Eur. J. Control 68: 100668 (2022) - [j16]Mohamed Atwya, George Panoutsos:
Structure optimization of prior-knowledge-guided neural networks. Neurocomputing 491: 464-488 (2022) - [c31]Atakan Sahin, Pilar Rey, George Panoutsos:
Self-tuning multi-model statistical process control for process monitoring in additive manufacturing. CoDIT 2022: 1049-1054 - [c30]John Anthony Rossiter, Muhammad Saleheen Aftab, George Panoutsos:
Exploiting Laguerre polynomials and steady-state estimates to facilitate tuning of PFC. ECC 2022: 1641-1646 - [c29]Atakan Sahin, Pilar Rey, George Panoutsos:
A Fuzzy Logic-Based Framework for Statistical Process Control in Additive Manufacturing. UKCI 2022: 61-72 - [c28]Hesham Yusuf, Kai Yang, George Panoutsos:
Improving the Explainability of Multi-criteria Decision-Making Using Neutrosophic Logic. UKCI 2022: 551-562 - 2021
- [c27]Quadri Adewale, George Panoutsos:
Mental Workload Estimation using Wireless EEG Signals. BIOSIGNALS 2021: 200-207 - [c26]Kai Eivind Wu, George Panoutsos:
A New Diversity Performance Indicator for Many-Objective Optimisation Problems. CEC 2021: 144-152 - [c25]Kai Eivind Wu, George Panoutsos:
A Visualisation Method for Pareto Front Approximations in Many-objective Optimisation. CEC 2021: 1929-1937 - [c24]Scott V. Notley, Yunhui Chen, Peter D. Lee, George Panoutsos:
Variance Stabilised Optimisation of Neural Networks: A Case Study in Additive Manufacturing. IJCNN 2021: 1-7 - [c23]Taha Al-Saadi, J. Anthony Rossiter, George Panoutsos:
Control of selective laser melting processes: existing efforts, challenges, and future opportunities. MED 2021: 89-94 - [c22]Muhammad Zaiyad Muda, George Panoutsos:
An Evolving Feature Weighting Framework for Granular Fuzzy Logic Models. UKCI 2021: 3-14 - [c21]Hesham Yusuf, Kai Yang, George Panoutsos:
Fuzzy Multi-Criteria Decision-Making: Example of an Explainable Classification Framework. UKCI 2021: 15-26 - 2020
- [j15]Adrian Rubio-Solis, George Panoutsos, Carlos Beltran Perez, Uriel Martinez-Hernandez:
A Multilayer Interval Type-2 Fuzzy Extreme Learning Machine for the recognition of walking activities and gait events using wearable sensors. Neurocomputing 389: 42-55 (2020) - [j14]William Mycroft, Mordechai Katzman, Samuel Tammas-Williams, Everth Hernandez-Nava, George Panoutsos, Iain Todd, Visakan Kadirkamanathan:
A data-driven approach for predicting printability in metal additive manufacturing processes. J. Intell. Manuf. 31(7): 1769-1781 (2020) - [j13]Mohamed Atwya, George Panoutsos:
Transient Thermography for Flaw Detection in Friction Stir Welding: A Machine Learning Approach. IEEE Trans. Ind. Informatics 16(7): 4423-4435 (2020) - [c20]Kai Eivind Wu, George Panoutsos:
A New Method for Generating and Indexing Reference Points in Many Objective Optimisation. CEC 2020: 1-8 - [c19]Hesham Yusuf, George Panoutsos:
Multi-criteria decision making using Fuzzy Logic and ATOVIC with application to manufacturing. FUZZ-IEEE 2020: 1-7
2010 – 2019
- 2019
- [j12]Ali Baraka, George Panoutsos:
Long-term learning for type-2 neural-fuzzy systems. Fuzzy Sets Syst. 368: 59-81 (2019) - [j11]Adrian Rubio Solis, Patricia Melin, Uriel Martinez-Hernandez, George Panoutsos:
General Type-2 Radial Basis Function Neural Network: A Data-Driven Fuzzy Model. IEEE Trans. Fuzzy Syst. 27(2): 333-347 (2019) - [j10]Chengming Shi, George Panoutsos, Bo Luo, Hongqi Liu, Bin Li, Xu Lin:
Using Multiple-Feature-Spaces-Based Deep Learning for Tool Condition Monitoring in Ultraprecision Manufacturing. IEEE Trans. Ind. Electron. 66(5): 3794-3803 (2019) - 2018
- [c18]Adrian Rubio Solis, Uriel Martinez-Hernandez, George Panoutsos:
Evolutionary Extreme Learning Machine for the Interval Type-2 Radial Basis Function Neural Network: A Fuzzy Modelling Approach. FUZZ-IEEE 2018: 1-8 - [c17]Zhen Xi, George Panoutsos:
Interpretable Machine Learning: Convolutional Neural Networks with RBF Fuzzy Logic Classification Rules. IEEE Conf. on Intelligent Systems 2018: 448-454 - 2017
- [c16]Uriel Martinez-Hernandez, Adrian Rubio Solis, George Panoutsos, Abbas A. Dehghani-Sanij:
A combined Adaptive Neuro-Fuzzy and Bayesian strategy for recognition and prediction of gait events using wearable sensors. FUZZ-IEEE 2017: 1-6 - [c15]Adrian Rubio Solis, George Panoutsos:
An ensemble data-driven fuzzy network for laser welding quality prediction. FUZZ-IEEE 2017: 1-6 - 2016
- [c14]Georgios N. Tzagarakis, George Panoutsos:
Model-based feature selection based on Radial Basis Functions and information measures. FUZZ-IEEE 2016: 401-407 - [c13]Adrian Rubio Solis, George Panoutsos:
Iterative information granulation for novelty detection in complex datasets. FUZZ-IEEE 2016: 953-960 - [c12]Adrian Rubio Solis, George Panoutsos, Steve Thornton:
A Data-driven fuzzy modelling framework for the classification of imbalanced data. IEEE Conf. on Intelligent Systems 2016: 302-307 - 2015
- [j9]Adrian Rubio Solis, George Panoutsos:
Interval Type-2 Radial Basis Function Neural Network: A Modeling Framework. IEEE Trans. Fuzzy Syst. 23(2): 457-473 (2015) - 2014
- [c11]Adrian Rubio Solis, George Panoutsos:
Fuzzy uncertainty assessment in RBF Neural Networks using neutrosophic sets for multiclass classification. FUZZ-IEEE 2014: 1591-1598 - [c10]Ali Baraka, George Panoutsos, Mahdi Mahfouf, Stephen Cater:
A Shannon entropy-based conflict measure for enhancing granular computing-based information processing. GrC 2014: 13-18 - [c9]Adriana Gonzalez-Rodriguez, George Panoutsos, Mahdi Mahfouf, Kathryn Beamish:
A Novelty Detection Framework Based on Fuzzy Entropy for a Complex Manufacturing Process. IEEE Conf. on Intelligent Systems (2) 2014: 453-464 - 2013
- [j8]Adrian Rubio Solis, George Panoutsos:
Granular computing neural-fuzzy modelling: A neutrosophic approach. Appl. Soft Comput. 13(9): 4010-4021 (2013) - [c8]Julio De Alejandro Montalvo, George Panoutsos, Mahdi Mahfouf, James W. Catto:
Radial Basis Function Neural-fuzzy Model for Microarray Signature Identification. BIOINFORMATICS 2013: 134-139 - 2012
- [c7]Guangrui Zhang, Mahdi Mahfouf, George Panoutsos, Shen Wang:
A multi-objective particle swarm optimization algorithm with a dynamic hypercube archive, mutation and population competition. IEEE Congress on Evolutionary Computation 2012: 1-7 - 2011
- [j7]Yong Yao Yang, Mahdi Mahfouf, George Panoutsos:
Development of a parsimonious GA-NN ensemble model with a case study for Charpy impact energy prediction. Adv. Eng. Softw. 42(7): 435-443 (2011) - [c6]Suzani Mohamad-Samuri, George Panoutsos, Mahdi Mahfouf, Gary H. Mills, Mouloud Azzedine Denaï, Brian H. Brown:
Neural-fuzzy Modelling of Lung Volume using Absolute Electrical Impedance Tomography. BIOSIGNALS 2011: 43-50 - [c5]Suzani Mohamad-Samuri, George Panoutsos, Mahdi Mahfouf, Gary H. Mills, Mouloud Azzedine Denaï, Brian H. Brown:
Towards a Patient-Specific Model of Lung Volume Using Absolute Electrical Impedance Tomography (aEIT). BIOSTEC (Selected Papers) 2011: 191-204 - [c4]Yong Yao Yang, Mahdi Mahfouf, George Panoutsos, Qian Zhang, Steve Thornton:
Adaptive neural-fuzzy inference system for classification of rail quality data with bootstrapping-based over-sampling. FUZZ-IEEE 2011: 2205-2212 - [c3]Qian Zhang, Mahdi Mahfouf, George Panoutsos, Kathryn Beamish, Ian Norris:
Multiple characterisation modelling of friction stir welding using a genetic multi-objective data-driven fuzzy modelling approach. FUZZ-IEEE 2011: 2288-2295 - 2010
- [j6]Ang Wang, Mahdi Mahfouf, Gary H. Mills, George Panoutsos, Derek A. Linkens, Kevin M. Goode, Hoi-Fei Kwok, Mouloud Azzedine Denaï:
Intelligent model-based advisory system for the management of ventilated intensive care patients: Hybrid blood gas patient model. Comput. Methods Programs Biomed. 99(2): 195-207 (2010) - [j5]Ang Wang, Mahdi Mahfouf, Gary H. Mills, George Panoutsos, Derek A. Linkens, Kevin M. Goode, Hoi-Fei Kwok, Mouloud Azzedine Denaï:
Intelligent model-based advisory system for the management of ventilated intensive care patients. Part II: Advisory system design and evaluation. Comput. Methods Programs Biomed. 99(2): 208-217 (2010) - [j4]George Panoutsos, Mahdi Mahfouf:
A neural-fuzzy modelling framework based on granular computing: Concepts and applications. Fuzzy Sets Syst. 161(21): 2808-2830 (2010) - [j3]George Panoutsos, Mahdi Mahfouf:
Modelling steel heat treatment data using granular data compression and multiple granularity modelling. Int. J. Granul. Comput. Rough Sets Intell. Syst. 1(4): 382-392 (2010) - [j2]Mouloud Azzedine Denaï, Mahdi Mahfouf, Suzani Mohamad-Samuri, George Panoutsos, Brian H. Brown, Gary H. Mills:
Absolute electrical impedance tomography (aEIT) guided ventilation therapy in critical care patients: simulations and future trends. IEEE Trans. Inf. Technol. Biomed. 14(3): 641-649 (2010) - [j1]Ching-Hua Ting, Mahdi Mahfouf, Ashraf Nassef, Derek A. Linkens, George Panoutsos, Peter Nickel, Adam Charles Roberts, G. Robert J. Hockey:
Real-Time Adaptive Automation System Based on Identification of Operator Functional State in Simulated Process Control Operations. IEEE Trans. Syst. Man Cybern. Part A 40(2): 251-262 (2010) - [c2]George Panoutsos, Mahdi Mahfouf, Gary H. Mills, Brian H. Brown:
A generic framework for enhancing the interpretability Of granular computing-based information. IEEE Conf. of Intelligent Systems 2010: 19-24
2000 – 2009
- 2008
- [c1]George Panoutsos, Mahdi Mahfouf:
Modelling Imprecise and Scattered Multidimensional Data using Granular Data Compression and Multiple Granularity Modelling. GrC 2008: 512-517 - [p1]George Panoutsos, Mahdi Mahfouf:
An Incremental Learning Structure Using Granular Computing and Model Fusion with Application to Materials Processing. Intelligent Techniques and Tools for Novel System Architectures 2008: 139-153
Coauthor Index
aka: Adrian Rubio-Solis
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last updated on 2024-12-10 21:42 CET by the dblp team
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