Michael M. Li

Publication Activity (10 Years)

Years Active: 2005-2024
Publications (10 Years): 8

Top Topics

Top Venues

Publications

Qing Huang, Michael M. Li, Dan Agustin, Lily D. Li, Meena Jha
A Novel CNN Model for Classification of Chinese Historical Calligraphy Styles in Regular Script Font. Sensors 24 (1) (2024)
Albertus Untadi, Lily D. Li, Michael M. Li, Roland Dodd
Incorporating Socio-Economic Factors in Maximizing Two-Dimensional Demand Coverage and Minimizing Distance to Uncovered Demand: A Dual-Objective MCLP Approach for Fire Station Location Selection. Axioms 13 (1) (2024)
Albertus Untadi, Lily D. Li, Michael M. Li, Roland Dodd
Modeling Socioeconomic Determinants of Building Fires through Backward Elimination by Robust Final Prediction Error Criterion. Axioms 12 (6) (2023)
Michael M. Li, Santoso Wibowo, Wei Li, Lily D. Li
Quantitative Spectral Data Analysis Using Extreme Learning Machines Algorithm Incorporated with PCA. Algorithms 14 (1) (2021)
Kaixiang Zhu, Lily D. Li, Michael M. Li
Developing an Online Examination Timetabling System Using Artificial Bee Colony Algorithm in Higher Education. BROADNETS (2021)
Michael M. Li, Lily D. Li
A Novel Method of Curve Fitting Based on Optimized Extreme Learning Machine. Appl. Artif. Intell. 34 (12) (2020)
Michael M. Li, Brijesh K. Verma
Nonlinear curve fitting to stopping power data using RBF neural networks. Expert Syst. Appl. 45 (2016)
Michael M. Li
The Development of a Nonlinear Curve Fitter Using RBF Neural Networks with Hybrid Neurons. ISNN (2016)
Michael M. Li, Brijesh K. Verma
An Improved RBF Neural Network Approach to Nonlinear Curve Fitting. IWANN (2) (2015)
Paul Moore, Brijesh K. Verma, Michael M. Li
A neural network based method to determine initial object positions for segmentation. ICNC (2015)
Chien-Yuan Chiu, Brijesh K. Verma, Michael M. Li
Impact of variability in data on accuracy and diversity of neural network based ensemble classifiers. IJCNN (2013)
Michael M. Li, William W. Guo, Brijesh K. Verma, Hong Lee
A neural networks-based fitting to high energy stopping power data for heavy ions in solid matter. IJCNN (2012)
William W. Guo, Michael M. Li, Gregory K. Whymark, Zheng-Xiang Li
Mutual complement between statistical and neural network approaches for rock magnetism data analysis. Expert Syst. Appl. 36 (6) (2009)
Michael M. Li, William W. Guo, Brijesh K. Verma, Kevin Tickle, John O'Connor
Intelligent methods for solving inverse problems of backscattering spectra with noise: a comparison between neural networks and simulated annealing. Neural Comput. Appl. 18 (5) (2009)
Michael M. Li, Brijesh K. Verma, Xiaolong Fan, Kevin Tickle
RBF neural networks for solving the inverse problem of backscattering spectra. Neural Comput. Appl. 17 (4) (2008)
Michael M. Li, Xiaolong Fan, Kevin Tickle
Principal Component Analysis and Neural Networks for Analysis of Complex Spectral Data from Ion Backscattering. Artificial Intelligence and Applications (2006)
Michael M. Li, Xiaolong Fan, Brijesh K. Verma, Ron Balsys, John O'Connor
Artificial neural network techniques for analysis of ion backscattering spectra. IC-AI (2005)