| Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions GB Huang, HA Babri IEEE transactions on neural networks 9 (1), 224-229, 1998 | 694 | 1998 |
| Hierarchical clustering for software architecture recovery O Maqbool, H Babri IEEE Transactions on Software Engineering 33 (11), 759-780, 2007 | 501 | 2007 |
| Classification ability of single hidden layer feedforward neural networks GB Huang, YQ Chen, HA Babri IEEE transactions on neural networks 11 (3), 799-801, 2000 | 295 | 2000 |
| The weighted combined algorithm: A linkage algorithm for software clustering O Maqbool, HA Babri Eighth European Conference on Software Maintenance and Reengineering, 2004 …, 2004 | 169 | 2004 |
| Feature selection based on a normalized difference measure for text classification A Rehman, K Javed, HA Babri Information Processing & Management 53 (2), 473-489, 2017 | 156 | 2017 |
| Feature selection based on class-dependent densities for high-dimensional binary data K Javed, HA Babri, M Saeed IEEE Transactions on Knowledge and Data Engineering 24 (3), 465-477, 2010 | 142 | 2010 |
| Software clustering techniques and the use of combined algorithm M Saeed, O Maqbool, HA Babri, SZ Hassan, SM Sarwar Seventh European Conference onSoftware Maintenance and Reengineering, 2003 …, 2003 | 96 | 2003 |
| Relative discrimination criterion–A novel feature ranking method for text data A Rehman, K Javed, HA Babri, M Saeed Expert Systems with Applications 42 (7), 3670-3681, 2015 | 92 | 2015 |
| A two-stage Markov blanket based feature selection algorithm for text classification K Javed, S Maruf, HA Babri Neurocomputing 157, 91-104, 2015 | 81 | 2015 |
| Selection of the most relevant terms based on a max-min ratio metric for text classification A Rehman, K Javed, HA Babri, MN Asim Expert Systems with Applications 114, 78-96, 2018 | 58 | 2018 |
| Machine learning using Bernoulli mixture models: Clustering, rule extraction and dimensionality reduction M Saeed, K Javed, HA Babri Neurocomputing 119, 366-374, 2013 | 39 | 2013 |
| General approximation theorem on feedforward networks GB Huang, HA Babri Proceedings of ICICS, 1997 International Conference on Information …, 1997 | 33 | 1997 |
| Automated software clustering: An insight using cluster labels O Maqbool, HA Babri Journal of Systems and Software 79 (11), 1632-1648, 2006 | 31 | 2006 |
| Improving text classification performance with random forests-based feature selection S Maruf, K Javed, HA Babri Arabian Journal for Science and Engineering 41, 951-964, 2016 | 28 | 2016 |
| Metarule-guided association rule mining for program understanding O Maqbool, HA Babri, A Karim, M Sarwar IEE Proceedings-Software 152 (6), 281-296, 2005 | 24 | 2005 |
| Interpreting clustering results through cluster labeling O Maqbool, HA Babri Proceedings of the IEEE Symposium on Emerging Technologies, 2005., 429-434, 2005 | 22 | 2005 |
| Classification of degraded traffic signs using flexible mixture model and transfer learning A Mannan, K Javed, AU Rehman, HA Babri, SK Noon IEEe Access 7, 148800-148813, 2019 | 21 | 2019 |
| Bayesian learning for software architecture recovery O Maqbool, HA Babri 2007 International Conference on Electrical Engineering, 1-6, 2007 | 19 | 2007 |
| A stochastic backpropagation algorithm for training neural networks YQ Chen, T Yin, HA Babri Proceedings of ICICS, 1997 International Conference on Information …, 1997 | 19 | 1997 |
| Deep feedforward networks: application to pattern recognition HA Babri, Y Tong Proceedings of International Conference on Neural Networks (ICNN'96) 3, 1422 …, 1996 | 16 | 1996 |
