| Nonlinear curve fitting to stopping power data using RBF neural networks MM Li, B Verma Expert Systems with Applications 45, 161-171, 2016 | 42 | 2016 |
| RBF neural networks for solving the inverse problem of backscattering spectra MM Li, B Verma, X Fan, K Tickle Neural Computing and Applications 17, 391-397, 2008 | 31 | 2008 |
| Intelligent methods for solving inverse problems of backscattering spectra with noise: a comparison between neural networks and simulated annealing MM Li, W Guo, B Verma, K Tickle, J O’Connor Neural Computing and Applications 18, 423-430, 2009 | 20 | 2009 |
| A novel method of curve fitting based on optimized extreme learning machine M Li, LD Li Applied artificial intelligence 34 (12), 849-865, 2020 | 16 | 2020 |
| A survey of computational intelligence in educational timetabling K Zhu, LD Li, M Li International Journal of Machine Learning and Computing 11 (1), 40-47, 2021 | 14 | 2021 |
| Mutual complement between statistical and neural network approaches for rock magnetism data analysis WW Guo, MM Li, G Whymark, ZX Li Expert Systems with Applications 36 (6), 9678-9682, 2009 | 13 | 2009 |
| School timetabling optimisation using artificial bee colony algorithm based on a virtual searching space method K Zhu, LD Li, M Li Mathematics 10 (1), 73, 2021 | 7 | 2021 |
| Quantitative spectral data analysis using extreme learning machines algorithm incorporated with pca M Li, S Wibowo, W Li, LD Li Algorithms 14 (1), 18, 2021 | 6 | 2021 |
| Impact of variability in data on accuracy and diversity of neural network based ensemble classifiers CY Chiu, B Verma, M Li The 2013 International Joint Conference on Neural Networks (IJCNN), 1-5, 2013 | 6 | 2013 |
| Approximating nonlinear relations between susceptibility and magnetic contents in rocks using neural networks WW Guo, M Li, Z Li, G Whymark Tsinghua Science and Technology 15 (3), 281-287, 2010 | 6 | 2010 |
| Nonlinear curve fitting using extreme learning machines and radial basis function networks M Li, S Wibowo, W Guo Computing in Science & Engineering 21 (5), 6-15, 2018 | 5 | 2018 |
| An improved RBF neural network approach to nonlinear curve fitting MM Li, B Verma Advances in Computational Intelligence: 13th International Work-Conference …, 2015 | 5 | 2015 |
| Simulation of multiple scattering background in heavy ion backscattering spectrometry MM Li, DJ O’Connor Nuclear Instruments and Methods in Physics Research Section B: Beam …, 1999 | 5 | 1999 |
| The evolution of chemometrics coupled with near infrared spectroscopy for fruit quality evaluation. II. The rise of convolutional neural networks J Walsh, A Neupane, A Koirala, M Li, N Anderson Journal of Near Infrared Spectroscopy 31 (3), 109-125, 2023 | 4 | 2023 |
| Principal Component Analysis and Neural Networks for Analysis of Complex Spectral Data from Ion Backscattering. MM Li, X Fan, K Tickle Artificial Intelligence and Applications, 228-234, 2006 | 4 | 2006 |
| A neural networks-based fitting to high energy stopping power data for heavy ions in solid matter M Li, W Guo, B Verma, H Lee The 2012 International Joint Conference on Neural Networks (IJCNN), 1-6, 2012 | 3 | 2012 |
| Machine learning techniques in handwriting recognition: problems and solutions H Lee, B Verma, M Li, A Rahman Machine Learning Algorithms for Problem Solving in Computational …, 2012 | 3 | 2012 |
| A study of the charge state approach to the stopping power of MeV B, N and O ions in carbon MM Li, DJ O'Connor, H Timmers Nuclear Instruments and Methods in Physics Research Section B: Beam …, 2004 | 3 | 2004 |
| Modeling Socioeconomic Determinants of Building Fires through Backward Elimination by Robust Final Prediction Error Criterion A Untadi, LD Li, M Li, R Dodd Axioms 12 (6), 524, 2023 | 2 | 2023 |
| A Novel Framework Incorporating Socioeconomic Variables into the Optimisation of South East Queensland Fire Stations Coverages A Untadi, LD Li, R Dodd, M Li Conference on Innovative Technologies in Intelligent Systems and Industrial …, 2022 | 2 | 2022 |
