| Opposition-based learning: a new scheme for machine intelligence HR Tizhoosh International Conference on Computational Intelligence for Modelling …, 2005 | 2052 | 2005 |
| Opposition-based differential evolution S Rahnamayan, HR Tizhoosh, MMA Salama IEEE Transactions on Evolutionary computation 12 (1), 64-79, 2008 | 1795 | 2008 |
| A novel population initialization method for accelerating evolutionary algorithms S Rahnamayan, HR Tizhoosh, MMA Salama Computers & Mathematics with Applications 53 (10), 1605-1614, 2007 | 396 | 2007 |
| Artificial Intelligence and Digital Pathology: Challenges and Opportunities HR Tizhoosh, L Pantanowitz Journal of Pathology Informatics 9 (38), 2018 | 377 | 2018 |
| Opposition versus randomness in soft computing techniques S Rahnamayan, HR Tizhoosh, MMA Salama Applied Soft Computing 8 (2), 906-918, 2008 | 373 | 2008 |
| Image thresholding using type II fuzzy sets HR Tizhoosh Pattern recognition 38 (12), 2363-2372, 2005 | 369 | 2005 |
| Quasi-oppositional differential evolution S Rahnamayan, HR Tizhoosh, MMA Salama 2007 IEEE congress on evolutionary computation, 2229-2236, 2007 | 356 | 2007 |
| Fuzzy image processing HR Tizhoosh Publisher: Springer-Verlag. Kartoniert (TB), Deutsch 10, 1997 | 266* | 1997 |
| Opposition-based differential evolution algorithms S Rahnamayan, HR Tizhoosh, MMA Salama 2006 IEEE international conference on evolutionary computation, 2010-2017, 2006 | 231 | 2006 |
| Opposition-based reinforcement learning HR Tizhoosh Journal of Advanced Computational Intelligence and Intelligent Informatics …, 2006 | 230 | 2006 |
| Fuzzy image processing: an overview HR Tizhoosh, H Haußecker Handbook on computer vision and applications, Academic Press, Boston, 1998 | 222* | 1998 |
| Reinforcement learning based on actions and opposite actions HR Tizhoosh International conference on artificial intelligence and machine learning 414, 2005 | 156 | 2005 |
| Improving the convergence of backpropagation by opposite transfer functions M Ventresca, HR Tizhoosh The 2006 IEEE International Joint Conference on Neural Network Proceedings …, 2006 | 153 | 2006 |
| Convolutional neural networks for histopathology image classification: Training vs. using pre-trained networks B Kieffer, M Babaie, S Kalra, HR Tizhoosh 2017 Seventh International Conference on Image Processing Theory, Tools and …, 2017 | 152 | 2017 |
| Opposition-based differential evolution for optimization of noisy problems S Rahnamayan, HR Tizhoosh, MMA Salama 2006 IEEE International Conference on Evolutionary Computation, 1865-1872, 2006 | 145 | 2006 |
| Ignorance functions. An application to the calculation of the threshold in prostate ultrasound images H Bustince, M Pagola, E Barrenechea, J Fernández, P Melo-Pinto, ... Fuzzy sets and Systems 161 (1), 20-36, 2010 | 129 | 2010 |
| Applying opposition-based ideas to the ant colony system AR Malisia, HR Tizhoosh 2007 IEEE swarm intelligence symposium, 182-189, 2007 | 119 | 2007 |
| Fast fuzzy edge detection HR Tizhoosh 2002 Annual Meeting of the North American Fuzzy Information Processing …, 2002 | 115 | 2002 |
| A comparative study of CNN, BoVW and LBP for classification of histopathological images MD Kumar, M Babaie, S Zhu, S Kalra, HR Tizhoosh 2017 IEEE symposium series on computational intelligence (SSCI), 1-7, 2017 | 113 | 2017 |
| Iris segmentation: Detecting pupil, limbus and eyelids EM Arvacheh, HR Tizhoosh 2006 International Conference on Image Processing, 2453-2456, 2006 | 108 | 2006 |
