| Imitation learning: A survey of learning methods A Hussein, MM Gaber, E Elyan, C Jayne ACM Computing Surveys (CSUR) 50 (2), 1-35, 2017 | 1527 | 2017 |
| Random forests: from early developments to recent advancements K Fawagreh, MM Gaber, E Elyan Systems Science & Control Engineering: An Open Access Journal 2 (1), 602-609, 2014 | 760 | 2014 |
| Neighbourhood-based undersampling approach for handling imbalanced and overlapped data P Vuttipittayamongkol, E Elyan Information Sciences 509, 47-70, 2020 | 267 | 2020 |
| On the class overlap problem in imbalanced data classification P Vuttipittayamongkol, E Elyan, A Petrovski Knowledge-based systems 212, 106631, 2021 | 243 | 2021 |
| MFC-GAN: Class-imbalanced dataset classification using multiple fake class generative adversarial network A Ali-Gombe, E Elyan Neurocomputing 361, 212-221, 2019 | 194 | 2019 |
| New trends on digitisation of complex engineering drawings CF Moreno-García, E Elyan, C Jayne Neural computing and applications 31, 1695-1712, 2019 | 135 | 2019 |
| Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward E Elyan, P Vuttipittayamongkol, P Johnston, K Martin, K McPherson, ... Artificial Intelligence Surgery 2 (1), 24-45, 2022 | 133 | 2022 |
| Data stream mining: methods and challenges for handling concept drift S Wares, J Isaacs, E Elyan SN Applied Sciences 1, 1-19, 2019 | 130 | 2019 |
| Artificial intelligence surgery: How do we get to autonomous actions in surgery? AA Gumbs, I Frigerio, G Spolverato, R Croner, A Illanes, E Chouillard, ... Sensors 21 (16), 5526, 2021 | 128 | 2021 |
| Deep learning for symbols detection and classification in engineering drawings E Elyan, L Jamieson, A Ali-Gombe Neural networks 129, 91-102, 2020 | 114 | 2020 |
| CDSMOTE: class decomposition and synthetic minority class oversampling technique for imbalanced-data classification E Elyan, CF Moreno-Garcia, C Jayne Neural computing and applications 33, 2839-2851, 2021 | 99 | 2021 |
| A genetic algorithm approach to optimising random forests applied to class engineered data E Elyan, MM Gaber Information sciences 384, 220-234, 2017 | 91 | 2017 |
| Panchromatic and multispectral image fusion for remote sensing and earth observation: Concepts, taxonomy, literature review, evaluation methodologies and challenges ahead K Zhang, F Zhang, W Wan, H Yu, J Sun, J Del Ser, E Elyan, A Hussain Information Fusion 93, 227-242, 2023 | 79 | 2023 |
| Improved overlap-based undersampling for imbalanced dataset classification with application to epilepsy and parkinson’s disease P Vuttipittayamongkol, E Elyan International journal of neural systems 30 (08), 2050043, 2020 | 75 | 2020 |
| Overlap-based undersampling for improving imbalanced data classification P Vuttipittayamongkol, E Elyan, A Petrovski, C Jayne Intelligent Data Engineering and Automated Learning–IDEAL 2018: 19th …, 2018 | 72 | 2018 |
| Deep imitation learning for 3D navigation tasks A Hussein, E Elyan, MM Gaber, C Jayne Neural computing and applications 29, 389-404, 2018 | 68 | 2018 |
| A review of state-of-the-art in Face Presentation Attack Detection: From early development to advanced deep learning and multi-modal fusion methods F Abdullakutty, E Elyan, P Johnston Information fusion 75, 55-69, 2021 | 55 | 2021 |
| A review of digital video tampering: From simple editing to full synthesis P Johnston, E Elyan Digital Investigation 29, 67-81, 2019 | 55 | 2019 |
| The advances in computer vision that are enabling more autonomous actions in surgery: a systematic review of the literature AA Gumbs, V Grasso, N Bourdel, R Croner, G Spolverato, I Frigerio, ... Sensors 22 (13), 4918, 2022 | 52 | 2022 |
| Symbols classification in engineering drawings E Elyan, CM Garcia, C Jayne 2018 International joint conference on neural networks (IJCNN), 1-8, 2018 | 51 | 2018 |
Chrisina JayneTeesside Universityایمیل تأیید شده در tees.ac.uk
