| A review on the long short-term memory model G Van Houdt, C Mosquera, G Nápoles Artificial Intelligence Review 53 (8), 5929-5955, 2020 | 1298 | 2020 |
| A review on methods and software for fuzzy cognitive maps G Felix, G Nápoles, R Falcon, W Froelich, K Vanhoof, R Bello Artificial intelligence review 52, 1707-1737, 2019 | 307 | 2019 |
| Detecting malicious URLs using machine learning techniques F Vanhoenshoven, G Nápoles, R Falcon, K Vanhoof, M Köppen 2016 IEEE Symposium Series on Computational Intelligence (SSCI), 1-8, 2016 | 139 | 2016 |
| On the convergence of sigmoid fuzzy cognitive maps G Nápoles, E Papageorgiou, R Bello, K Vanhoof Information Sciences 349, 154-171, 2016 | 115 | 2016 |
| Two-steps learning of Fuzzy Cognitive Maps for prediction and knowledge discovery on the HIV-1 drug resistance G Nápoles, I Grau, R Bello, R Grau Expert Systems with Applications 41 (3), 821-830, 2014 | 104 | 2014 |
| Learning and convergence of fuzzy cognitive maps used in pattern recognition G Nápoles, E Papageorgiou, R Bello, K Vanhoof Neural Processing Letters 45, 431-444, 2017 | 83 | 2017 |
| Fuzzy cognitive maps based models for pattern classification: Advances and challenges G Nápoles, M Leon Espinosa, I Grau, K Vanhoof, R Bello Soft Computing Based Optimization and Decision Models: To Commemorate the …, 2018 | 81 | 2018 |
| FCM expert: software tool for scenario analysis and pattern classification based on fuzzy cognitive maps G Nápoles, ML Espinosa, I Grau, K Vanhoof International Journal on Artificial Intelligence Tools 27 (07), 1860010, 2018 | 80 | 2018 |
| Rough cognitive networks G Nápoles, I Grau, E Papageorgiou, R Bello, K Vanhoof Knowledge-Based Systems 91, 46-61, 2016 | 55 | 2016 |
| Fuzzy-rough cognitive networks G Nápoles, C Mosquera, R Falcon, I Grau, R Bello, K Vanhoof Neural Networks 97, 19-27, 2018 | 50 | 2018 |
| On the accuracy–convergence tradeoff in sigmoid fuzzy cognitive maps G Nápoles, L Concepción, R Falcon, R Bello, K Vanhoof IEEE Transactions on Fuzzy Systems 26 (4), 2479-2484, 2017 | 48 | 2017 |
| How to improve the convergence on sigmoid fuzzy cognitive maps? G Nápoles, R Bello, K Vanhoof Intelligent Data Analysis 18 (6S), S77-S88, 2014 | 47 | 2014 |
| Deep neural network to extract high-level features and labels in multi-label classification problems M Bello, G Nápoles, R Sánchez, R Bello, K Vanhoof Neurocomputing 413, 259-270, 2020 | 42 | 2020 |
| Constricted Particle Swarm Optimization based algorithm for global optimization G Nápoles, I Grau, R Bello Polibits, 05-11, 2012 | 41 | 2012 |
| Pseudoinverse learning of fuzzy cognitive maps for multivariate time series forecasting F Vanhoenshoven, G Nápoles, W Froelich, JL Salmeron, K Vanhoof Applied Soft Computing 95, 106461, 2020 | 39 | 2020 |
| Explicit methods for attribute weighting in multi-attribute decision-making: a review study J Pena, G Nápoles, Y Salgueiro Artificial Intelligence Review 53, 3127-3152, 2020 | 37 | 2020 |
| Data quality measures based on granular computing for multi-label classification M Bello, G Nápoles, K Vanhoof, R Bello Information Sciences 560, 51-67, 2021 | 36 | 2021 |
| Unveiling the dynamic behavior of fuzzy cognitive maps L Concepción, G Nápoles, R Falcon, K Vanhoof, R Bello IEEE Transactions on Fuzzy Systems 29 (5), 1252-1261, 2020 | 32 | 2020 |
| Modeling implicit bias with fuzzy cognitive maps G Nápoles, I Grau, L Concepción, LK Koumeri, JP Papa Neurocomputing 481, 33-45, 2022 | 31 | 2022 |
| Short-term cognitive networks, flexible reasoning and nonsynaptic learning G Nápoles, F Vanhoenshoven, K Vanhoof Neural Networks 115, 72-81, 2019 | 31 | 2019 |
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