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Dr. Donald Hong

Dr. Don Hong

Publications

Books/Proceedings/Professional Report

  1. QUANTITATIVE MEDICAL DATA ANALYSIS USING MATHEMATICAL TOOLS AND STATISTICAL TECHNIQUES, (with Yu Shyr), World Scientific Publication, 2007. ISBN: 978-981-270-461-0.
  2. Real Analysis with an introduction to wavelets and applications, Graduate Textbook, (with Jianzhong Wang and Robert Gardner), Elsevier/Academic Press, 2004. ISBN: 0-12-354861-6.
  3. Proceedings of Wavelets and Approximation Theory, (edited with Michael Prophet), A Special Issue in Journal of Computational Analysis and Applications, 5:1 (2003). 
  4. Proceedings of Approximation Theory and Numerical Analysis, (edited with Tianxiao He), A Special Issue in Journal of Computational and Applied Mathematics, 155 (2003).
  5. Wavelets and Statistical Applications, a special issue in the Journal of Concrete and Applicable Mathematics, (editing with Yu Shyr), 4 (2006).
  6. 2010 Special Issue on Computational Biology and Data Mining for IJMCS, (edited with Ji-Ping Wang). 
  7. RATE REVIEW REPORT for Tennessee Department of Commerce and Insurance (TDCI) for Health Insurance Rate Review Project (Don Hong, Qiang Wu, Kenneth Hollman, and Harold Tuthill), MTSU Actuarial Science Program, 2012.

Articles in Journals

  1. D. Hong, Spaces of bivariate spline functions over triangulations, Approx. Theory and Applications, 7 (1) 1991, 56-75. MR 92f: 65016.
  2. C. K. Chui, D. Hong, and S. T. Wu, On the degree of multivariate Bernstein polynomial operators, J. of Approx. Theory, 78 (1994), 77-86. MR 95j: 41032.
  3. D. Hong and S. T. Wu, Some properties of Bernstein polynomials on a simplex, J. of Math. Res. and Expos., 14 (1994), 355-366. MR 95h: 41057.
  4. D. Hong , A Bernstein-B\’ezier based smoothness condition for $pp$ functions, Approx. Theory and Applications, 11 (1995), 67-75. CMP 1 370 767 (96:07).
  5. C. K. Chui, D. Hong, and R. Q. Jia, Stability of optimal order approximation by bivariate splines over arbitrary triangulations, Trans. of Amer. Math. Soc. , 347 (1995), 3301-3318. MR 96d: 41012.
  6. D. Hong , Optimal triangulations using edge swappings, in Approximation Theory VIII, Vol. 1, C. K. Chui and L. L. Schumaker (eds.), pp. 249-256, World Scientific Publishing Co., Inc., 1995. CMP 1 471 736 (98:01), MR 98d: 41001.
  7. C. K. Chui and D. Hong, Construction of the local $C^1$ quartic spline elements for optimal order approximation, Math. of Computation , 65 (01/1996). 85-98. MR 96d: 65023.
  8. C. K. Chui and D. Hong, Swapping edges of arbitrary triangulations to achieve the optimal order of approximation, SIAM J. Numer. Anal. 34 (08/1997), 1472-1482. MR 98h: 41036.
  9. D. Hong, Recent Progress on Multivariate Splines , in Approximation Theory: In Memory of A.K. Varma (N.K. Govil et al. Eds.), pp.265-291, Marcel Dekker, Inc., New York, NY, 1998. MR 99c: 41022.
  10. D. Hong and R. Mohapatra, Optimal order of approximation by mixed three-directional spline elements, Computers and Mathematics with Applications, 40 (2000), 127-135. CMP 1 768 969 (2000:15)
  11. D. Hong and Huan-Wen Liu, Some new formulation of smoothness conditions and conformality conditions of bivariate splines, Computers and Mathematics with Applications, 40 (2000), 117-125. CMP 1 768 968 (2000:15).
  12. D. Hong, On optimal order of approximation from bivariate spline spaces, In ” Proceedings of the Sixth International Colloquium on Numerical Analysis and Computers with Applications” (D.D. Bainov Ed.), pp.219-226, VSP, International Sciences Publishers, Utrecht, The Netherlands, 1998. CMP 1 644 943 (99:01), MR 99d: 00026. 
  13. D. Hong, Huanwen Liu, and Ram Mohapatra, Optimal triangulations and smoothness conditions for bivariate splines, in “Approximation Theory IX, Vol. 2: Computational Aspects”, Charles K. Chui and Larry L. Schumaker (eds.), pp. 129-136, Vanderbilt University Press, Nashville, TN, 1998. CMP 1 743 040 (2000:11), MR 00k: 41002.
  14. H.-W., Liu and D. Hong, Some smoothness conditions and conformality conditions for bivariate quartic and quintic splines, <a, href=”http://link.springer-ny.com/link/service/journals/10092/index.htm”>CALCOLO: A Quarterly on Numerical Analysis and Theory of Computation , (1999), 43 — 61. CMP 1 742 430.
  15. Don Hong and Yuchun Anna Mu, On construction of minimum supported piecewise linear prewavelets over triangulations, In: “Wavelets and Multiresolution Methods,” (T.X. He ed.), pp. 181–201, Marcel Decker Pub., New York, 2000.
  16. H.W. Liu and D. Hong , Bivariate $C^1$ cubic spline spaces over even stratified triangulations, J. of Computational Analysis and Applications, B> 4:1 (2002), 19-35.
  17. D. Hong, On scattered data representation using bivariate splines, In: Handbook on Analytic-Computational Methods in Applied Mathematics, pp.997–1029, Chapman & Hall/CRC Press, Boca Raton, 2000. CMP 1 770 944 (2000:16).
  18. Don Hong and Aidi Wu, Orthogonal multiwavelets of multiplicity four, Computers and Mathematics with Applications, 40 (2000), 1153–1169.
  19. Lutai Guan, Don Hong, and Aidi Wu, On smoothness and convergence of orthogonal multiwavelets, Journal of Engineering Matheamtics, 18:2 (2001), 1–11.
  20. Hao Gu, Don Hong , and M. Barrett, On still image compression, J. of Computational Analysis and Applications 5(2003), 45–75.
  21. Bradley Dyer and Don Hong, An algorithm for optimal triangulations on $C^1$ quartic spline approxiamtion and MatLab implementation, J. of Computational Analysis and Applications 5(2003), 25-43.
  22. Jiansheng Cao and Don Hong, Best approximation for symmetric semi-definite positive solutions of the left and right inverse problems on a subspace, Journal of Concrete and Applicable Mathematics, 1:3 (2003) 217-227.
  23. Huanwen Liu and Don Hong, An explicit representation of a local basis in $C^1$ cubic spline space over a triangulated quadrangulation, Journal of Computational and Applied Mathematics, 155 (2003), 187-200.
  24. Doug Hardin and Don Hong, On Piecewise Linear Wavelets and Prewavelets over Triangulations, Journal of Computational and Applied Mathematics, 155 (2003), 91-109.
  25. Don Hong, Martin Barrett, and Panrong Xiao, Biorthogonal Spline Wavelets and EZW Coding for Image Compression, Lecture Notes in Control and Information Sciences, Vol. 299 (Tarn, Tzyh-Jong; Chen, Shan-Ben; Zhou, Changjiu (Eds.)), Springer-Verlag Berlin Heidelberg, 2004, pp.281-303.
  26. Don Hong and Larry L. Schumaker, Surface Compression Using a Basis of $C^1$ Cubic Bivariate Spline Spaces, Computing, 72 (2004), 79-92.
  27. Don Hong and Qingbo Xue, Construction of Piecewise Linear Prewavelets over Regular Triangulations, Journal of Concrete and Applicable Mathematics, 4 (2006), 451-471.
  28. Don Hong and Yu Shyr, Wavelets in Biostatistics, Journal of Concrete and Applicable Mathematics, 4 (2006), 505-521.
  29. Huan-Wen Liu, Don Hong, and Dun-Qian Cao, Bivariate $C^1$ cubic spline space over non-uniform type-2 triangulation and its subspaces with boundary conditions, Journal of Computers and Mathematics with Applications, 49 (2005), 1853-1865.
  30. Renee Ferguson and Don Hong, On airline revenue optimization problems, Journal of Concrete and Applicable Math, 5 (2007), 153-167.
  31. Jiansheng Cao, Don Hong, and Wenjun Huang, Construction of Small Support Piecewise Linear Prewavelets over Type-2 Triangulations, In: Splines and Wavelets: Athens 2005 (G.R. Chen and M.J. Lai Eds.), pp. 105-122, Nashboro Press, Brentwood, 2006.
  32. Xingchen Yuan, Don Hong, and Yu Shyr, Survival Model and Estimation for Lung Cancer Patients, In: Quantitative Medical Data Analysis Using Math Tools and Statistical Techniques, pp. 201-222, World Scientific Publications, LLC., Singapore, 2007 
  33. Don Hong, Huiming Li, Ming Li, and Yu Shyr, Wavelets and Projecting Spectrum Binning for Proteomic Data Processing, In: Quantitative Medical Data Analysis Using Math Tools and Statistical Techniques, pp. 159-178, World Scientific Publications, LLC., Singapore, 2007.
  34. Jiansheng Cao and Don Hong, Parameterized Piecewise Linear Prewavelets over Type-2 Triangulations, Journal of Applicable Analysis, 86 (2007) 83-98.
  35. Shuo Chen, Don Hong, and Yu Shyr, Wavelet-Based Procedures for Proteomic MS Data Processing, Computational Statistics and Data Analysis, 52 (2007), 211-220.
  36. Don Hong and Yu Shyr, Mathematical Framework and Wavelets Applications in Proteomics for Cancer Study, In: Handbook of Cancer Models With Applications to Cancer Screening, Cancer Treatment and Risk Assessment, (Wai-Yuan Tan and Leonid Hannin Eds.), pp. 471-499, World Scientific Publication, Singapore, 2008. ISBN: 981-277-947-7.
  37. Shuo Chen, Ming Li, Don Hong, Dean Billheimer, Huiming Li, Baogang J. Xu, and Yu Shyr, A Novel Comprehensive Wave-form MS Data Processing Method, Bioinformatics, Vol. 25, no. 6, 2009, 808–814.
  38. D. Hong and F. Zhang, Weighted Elastic Net Model for Mass Spectrometry Imaging Processing, Math. Model. Nat. Phenom., Vol. 5, No. 3, 2010, pp. 115-133.
  39. Don Hong, Shiyin Qin, and Fengqing (Zoe) Zhang, Mathematical Tools and Statistical Techniques for Proteomic Data Mining, for International Journal of Mathematics and Computer Science, 5(2010), no. 2, 123-140. 
  40. Wenjun Huang, Huanwen Liu, and Don Hong, Triangulation based method for constructing molecular surfaces, Proceedings on Multimedia and Computational Intelligence of IEEE, 2010 2nd International Conference on MCI of IEEE, Wuhan, China, September 29-30, 2010, pp. 423-426.
  41. Fengqing (Zoe) Zhang and Don Hong, Elastic net-based framework for imaging mass spectrometry data biomarker selection and classification, Statistics in Medicine, 30 (2011), 753-768. DOI: 10.1002/sim.4147, March 2011. 
  42. Lu Xiong and Don Hong, Multi-Resolution Analysis Method for IMS Proteomic Data Biomarker Selection and Classification, British Journal of Mathematics & Computer Science, 5(1): 64-80, 2015.
  43. Don Hong, Zhihui Yang, and Jiancheng Zou, Hyperspectral Imaging Type Medical Data Processing: Spectral and Spatial Analysis, Journal of Health & Medical Informatics, 2015, 6: e135, doi: 10.4172/2157-7420.1000e135.
  44. JS. Liang, FZ Zhang, JC. Zou, and D. Hong, IMSmining: A Tool for Imaging Mass Spectrometry Data Biomarker Selection and Classification, Springer Proceedings in Mathematics & Statistics, Volume 139: Mathematics and Computing, pp.155-162, Springer, New York, 2015.
  45. Y. (Zoe) Ye, Q. Wu and D. Hong, Tail Conditional Expectations for Extended Exponential Dispersion Models, American Research Journal of Mathematics, 1:4 (2015), 28-33.
  46. Le Yin, Qiang Wu, and Don Hong, Statistical Methods for Medical Trend Analysis in Health Rate Review Process, Journal of Health & Medical Informatics, 7:219 (2016). https://www.hilarispublisher.com/open-access/statistical-methods-and-software-package-for-medical-trend-analysis-inhealth-rate-review-process-2157-7420-1000219.pdf .
  47. Xin Yang, Q. Wu, J.C. Zou, and D. Hong, Spatial Regularization for Multitask Learning and Application in fMRI Data Analysis, British Journal of Mathematics & Computer Science, 14:4 (2016), 1-13.
  48. Lu Xiong and Don Hong, An MCMC-MRF Algorithm for Incorporating Spatial Information in IMS Proteomic Data Processing, In “Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry,” Datta, Susmita, Mertens, Bart J. A. (Eds.), Springer International Publishing, Switzerland, pp.81-99, 2017.
  49. Xin Yang, Qiang Wu, Jiancheng Zou, and Don Hong, Spatial Regularization for Neural Network and Application in Alzheimer’s Disease Classification, Proceedings of FTC 2016 – Future Technologies Conference 2016, 6-7 December 2016, San Francisco, United States, IEEE, pp.831-837, 2017.
  50. J.C. Zou, Z.Z. Li, Z.J. Guo, and D. Hong, Super-resolution Reconstruction of Images Based on Microarray Camera, Computers, Materials and Continua, 58(2):163-177 DOI: 10.32604/cmc.2019.05795. 
  51. Jingsai Liang, J.C. Zou, and D. Hong, Non-Gaussian Penalized PARAFAC Analysis for fMRI Data, Frontiers in Applied Mathematics and Statistics, doi: 10.3389/fams.2019.00040.
  52. Shuzhe Xu, Sal Barbosa, and Don Hong, BERT Feature Based Model for Predicting the Helpfulness Scores of Online Customers Reviews, The Future of Information and Communication Conference (FICC) 2020 Proceedings, “Advances in Intelligent Systems and Computing,” Springer Nature Switzerland AG, 2020, pp.270-281. org/10.1007/978-3-030-39442-4,
  53. PZ Yan, YY Sun, ZZ Li, JC Zou, and D. Hong, Driver Fatigue Detection System Based on Colored and Infrared Eye Features Fusion, Computers, Materials & Continua (CMC), 63, No.3, 2020, pp.1563-1574, doi:10.32604/cmc.2020.09763.
  54. Lu Xiong and Don Hong, Using Monte Carlo Simulation to Predict Captive Insurance Solvency, ICCDA 2020: Proceedings of the 2020 the 4th International Conference on Compute and Data Analysis, March 2020, pp. 84–88. https://doi.org/10.1145/3388142.3388171.
  55. ZZ. Li, JC. Zou, PZ. Yan, and D. Hong, Non-contact Real-time Monitoring of Driver’s Physiological Parameters under Ambient Light Condition, Intelligent Automation & Soft Computing: An International Journal, 28, No.3, 2021, pp.811-822, doi:10.32604/iasc.2021.016516.
  56. JC. Zou, Na Zhu, BL Ge, and D. Hong, Elderly Fall Detection Based on Improved SSD Algorithm, Journal of New Media, Vol.3, No.1, pp. 1-10, 2021, DOI:10.32604/jnm.2021.017763.
  57. Donglin Wang,  Don Hong, and Qiang Wu, Prediction of Loan Rate for Mortgage Data: Deep LearningVersus Robust Regression, Computational Economics, 2022. https://doi.org/10.1007/s10614-022-10239-5
  58. Donglin Wang, Don Hong, and Qiang Wu, Attention Deficit Hyperactivity Disorder Classification Based on Deep Learning, the IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2022. DOI: 10.1109/TCBB.2022.3170527
  59. Shuzhe Xu, Chuanlong Zhang, Don Hong, BERT BASED NLP TECHNIQUES FOR CLASSIFICATION AND SEVERITY MODELING IN BASIC WARRANTY DATA STUDY, Insurance: Mathematics and Economics, Volume 107 (2022), 57-67. doi.org/10.1016/j.insmatheco.2022.07.013
  60. Donglin Wang, Qiang Wu, and Don Hong, Extracting Default Mode Network Based on Graph Neural Network for Resting State fMRI Study, Frontiers in Neuroimaging, 2022. https://doi.org/10.3389/fnimg.2022.963125
  61. Lu Xiong and Don Hong, A Solvency Assessment and Prediction Framework for Workers’ Compensation Captive Insurance Companies, Journal of Insurance Issues. 45, 2 (Nov. 2022), 82-113. 
  62. Toban, Gabriel, Khem Poudel, and Don Hong. 2023. “REM Sleep Stage Identification with Raw Single-Channel EEG” Bioengineering 10, no. 9: 1074. https://doi.org/10.3390/bioengineering10091074 
  63. Shuzhe Xu, Vajira Manthunga, Don Hong, Framework for BERT Based NLP Models with Applications to Warranty Policy Loss Prediction, Variance, the Casualty Actuarial Society Research Journal, November 2023. https://variancejournal.org/article/89002 

If you are interested in my papers, please Send Me Email. I’ll send you PostSript or PDF files via email.