Michael M. Li - researchr alias

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Viewing Publication 1 - 16 from 16

2024

A Novel CNN Model for Classification of Chinese Historical Calligraphy Styles in Regular Script FontQing Huang, Michael M. Li, Dan Agustin, Lily D. Li, Meena Jha. sensors, 24(1):197, 2024. [doi]

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 SelectionAlbertus Untadi, Lily D. Li, Michael M. Li, Roland Dodd. axioms, 13(1):13, January 2024. [doi]

2023

Modeling Socioeconomic Determinants of Building Fires through Backward Elimination by Robust Final Prediction Error CriterionAlbertus Untadi, Lily D. Li, Michael M. Li, Roland Dodd. axioms, 12(6):524, June 2023. [doi]

2021

Quantitative Spectral Data Analysis Using Extreme Learning Machines Algorithm Incorporated with PCAMichael M. Li, Santoso Wibowo, Wei Li 0005, Lily D. Li. algorithms, 14(1):18, 2021. [doi]

Developing an Online Examination Timetabling System Using Artificial Bee Colony Algorithm in Higher EducationKaixiang Zhu, Lily D. Li, Michael M. Li. broadnets 2022: 112-131 [doi]

2016

Nonlinear curve fitting to stopping power data using RBF neural networksMichael M. Li, Brijesh Verma. eswa, 45:161-171, 2016. [doi]

The Development of a Nonlinear Curve Fitter Using RBF Neural Networks with Hybrid NeuronsMichael M. Li. isnn 2016: 434-443 [doi]

2015

An Improved RBF Neural Network Approach to Nonlinear Curve FittingMichael M. Li, Brijesh Verma. iwann 2015: 262-275 [doi]

A neural network based method to determine initial object positions for segmentationPaul Moore, Brijesh Verma, Michael M. Li. icnc 2015: 620-626 [doi]

2013

Impact of variability in data on accuracy and diversity of neural network based ensemble classifiersChien-Yuan Chiu, Brijesh Verma, Michael M. Li. ijcnn 2013: 1-5 [doi]

2012

A neural networks-based fitting to high energy stopping power data for heavy ions in solid matterMichael M. Li, William W. Guo, Brijesh Verma, Hong Lee. ijcnn 2012: 1-6 [doi]

2009

Mutual complement between statistical and neural network approaches for rock magnetism data analysisWilliam W. Guo, Michael M. Li, Gregory K. Whymark, Zheng-Xiang Li. eswa, 36(6):9678-9682, 2009. [doi]

Intelligent methods for solving inverse problems of backscattering spectra with noise: a comparison between neural networks and simulated annealingMichael M. Li, William W. Guo, Brijesh Verma, Kevin Tickle, John O Connor. nca, 18(5):423-430, 2009. [doi]

2008

RBF neural networks for solving the inverse problem of backscattering spectraMichael M. Li, Brijesh Verma, Xiaolong Fan, Kevin Tickle. nca, 17(4):391-397, 2008. [doi]

2006

Principal Component Analysis and Neural Networks for Analysis of Complex Spectral Data from Ion BackscatteringMichael M. Li, X. Fan, Kevin Tickle. aia 2006: 228-234

2005

Artificial neural network techniques for analysis of ion backscattering spectraMichael M. Li, Xiaolong Fan, Brijesh Verma, Ron Balsys, John O Connor. icai 2005: 175-182

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