Dr David Donald ~ Lecturer, Data Science

Teaching

• MA5800: Foundations for Data Science (Level 5; CNS & ONL)
• MA5810: Introduction to Data Mining (Level 5; ONL)
• MA5820: Statistical Methods for Data Scientists (Level 5; ONL)
• MA5830: Data Visualisation (Level 5; ONL)
• MA5832: Data Mining and Machine Learning (Level 5; CNS)
• MA5851: Data Science Master Class 1 (Level 5; CNS)

Research Disciplines

• Statistics (4905)
• Agriculture, land and farm management (3002)

These are the most recent publications associated with this author. To see a detailed profile of all publications stored at JCU, visit ResearchOnline@JCU. Hover over Altmetrics badges to see social impact.

Journal Articles

• Sexton J, Everingham Y, Donald D, Staunton S and White R (2020) Investigating the identification of atypical sugarcane using NIR analysis of online mill data. Computers and Electronics in Agriculture, 168, Article: 105111, DOI:10.1016/j.compag.2019.105111.
• Sexton J, Everingham Y, Donald D, Staunton S and White R (2018) A comparison of non-linear regression methods for improved on-line near infrared spectroscopic analysis of a sugarcane quality measure. Journal of Near Infrared Spectroscopy, 26 (5), pp. 297-310, DOI:10.1177/0967033518802448.
• Donald D, Coomans D and Everingham Y (2011) Joint multiple adaptive wavelet regression ensembles. Chemometrics and Intelligent Laboratory Systems, 108 (2), pp. 133-141, DOI:10.1016/j.chemolab.2011.06.006.
• Everingham Y, Lowe K, Donald D, Coomans D and Markley J (2007) Advanced satellite imagery to classify sugarcane crop characteristics. Agronomy for Sustainable Development, 27 (2), pp. 111-117, DOI:10.1051/agro:2006034.
• Donald D, Coomans D, Everingham Y, Cozzolino D, Gishen M and Hancock T (2006) Adaptive wavelet modelling of a nested 3 factor experimental design in NIR chemometrics. Chemometrics and Intelligent Laboratory Systems, 82 (1-2), pp. 122-129, DOI:10.1016/j.chemolab.2005.05.013.
• Donald D, Hancock T, Coomans D and Everingham Y (2006) Bagged super wavelets reduction for boosted prostate cancer classification of seldi-tof mass spectral serum profiles. Chemometrics and Intelligent Laboratory Systems, 82 (1-2), pp. 2-7, DOI:10.1016/j.chemolab.2005.08.007.
• Donald D, Everingham Y and Coomans D (2005) Integrated wavelet principal component mapping for unsupervised clustering on near infra-red spectra. Chemometrics and Intelligent Laboratory Systems, 77 (1-2), pp. 32-42, DOI:10.1016/j.chemolab.2004.12.012.

Conference Papers

• Sexton J, Everingham Y, Donald D, Staunton S and White R (2018) A feasibility test for detection of atypical cane samples using near infrared spectroscopy. Proceedings of the 40th Annual Conference of the Australian Society of Sugar Cane Technologists. In: ASSCT 2018: 40th Annual Conference of the Australian Society of Sugar Cane Technologists, 17-20 April 2018, Mackay, QLD, Australia
• Sexton J, Everingham Y and Donald D (2017) A comparison of data mining algorithms for improving NIR models of cane quality measures. Proceedings of the 39th Annual Conference of the Australian Society of Sugar Cane Technologists. In: ASSCT 2017: 39th Annual Conference of the Australian Society of Sugar Cane Technologists, 3-5 May 2017, Cairns, QLD, Australia

Other research outputs

• Donald D, Everingham Y, McKinna L and Coomans D (2009) Feature selection in the wavelet domain: adaptive wavelets. In: Comprehensive Chemometrics: chemical and biochemical data analysis. Elsevier, Oxford, UK, pp. 647-679