Brendon Woodford, Our people, Department of Information Science

NZCDP(Stage III), BSc, PGCertTertT, PGDipSci, MSc, PhD (Otago)
Senior Lecturer

Room 3.47, Otago Business School
Tel +64 3 479 5432
Email brendon.woodford@otago.ac.nz

Background and interests

Dr Brendon Woodford lectures in the diverse areas of knowledge engineering, machine learning, information systems development, and health informatics.

He conducts research for the Knowledge, Intelligence and Web Informatics (KIWI) Laboratory in the areas of artificial neural networks, fuzzy systems, data visualisation, and image processing and recognition. Since 1998 the overall aim of this research is in the area of computational intelligence. The main objective of his work has been to create adaptive learning systems which generate new knowledge from data that they process and allow for improved decision making in difficult real-world domains.

In the past the application of this work has been improving upon existing machine learning techniques to support decision making and data mining primarily in the horticultural domain but the current focus is now in the health data analytics domain.

This research has contributed to 27 publications to date in both international journals and in some of the top international conferences. In 2008 he completed a PhD which primarily looked at the development and implementation of intelligent decision support systems for New Zealand's horticulture industry.

Dr Woodford is also a member of the research group:
Intelligent Computing and Networking

Papers

COMP 111 Information and Communications Technology
COMP 120 Practical Data Science
INFO 204 Introduction to Data Science
INFO 411 Machine Learning and Data Mining

Supervision

Currently supervising

Nuzla Ismail

Currently co-supervising

Ahmad Shahi

Publications

Ismail, F. N., Woodford, B. J., Licorish, S. A., & Miller, A. D. (2024). An assessment of existing wildfire danger indices in comparison to one-class machine learning models. Natural Hazards. Advance online publication. doi: 10.1007/s11069-024-06738-3 Journal - Research Article

Ghandour, A., Woodford, B. J., & Abusaimeh, H. (2024). Ethical considerations in the use of ChatGPT: An exploration through the lens of five moral dimensions. IEEE Access, 12, 60682-60693. doi: 10.1109/ACCESS.2024.3394243 Journal - Research Article

Ismail, F. N., Sengupta, A., Woodford, B. J., & Licorish, S. A. (2024). A comparison of one-class versus two-class machine learning models for wildfire prediction in California. In D. Benavides-Prado, S. Erfani, P. Fournier-Viger, Y. L. Boo & Y. S. Koh (Eds.), Data Science and Machine Learning: Proceedings of the 21st Australasian Conference, AusDM 2023 [Communications in Computer and Information Science 1943]. (pp. 239-253). Singapore: Springer. doi: 10.1007/978-981-99-8696-5_17 Conference Contribution - Published proceedings: Full paper

Woodford, B. J. (2023). Automatic parameter optimisation framework for ECoS-based models. Proceedings of the International Joint Conference on Neural Networks (IJCNN). IEEE. doi: 10.1109/IJCNN54540.2023.10191789 Conference Contribution - Published proceedings: Full paper

Ghandour, A., Woodford, B. J., Almutairy, A., & Al-Srehan, H. S. (2022). Means to support the ends: Forums that escape the official gaze in the educational institutions. Proceedings of the International Arab Conference on Information Technology (ACIT). IEEE. doi: 10.1109/ACIT57182.2022.9994133 Conference Contribution - Published proceedings: Full paper

2024

Journal - Research Article

Conference Contribution - Published proceedings: Full paper

2023

Conference Contribution - Published proceedings: Full paper

2022

Journal - Research Article

Cornwall, J., English, S., Woodford, B., Elliot, J., & McAuley, K. (2022). An exploration of Aotearoa New Zealanders' attitudes and perceptions on the use of posthumous healthcare data. New Zealand Medical Journal, 135(1554), 44-54. Retrieved from https://www.nzma.org.nz/journal

Conference Contribution - Published proceedings: Full paper

Woodford, B. J. (2022). Boosted self-evolving neural networks for pattern recognition. Advances in artificial intelligence: Lecture notes in artificial intelligence (Vol. 13728). (pp. 456-469). Cham, Switzerland: Springer. doi: 10.1007/978-3-031-22695-3_32

2021

Journal - Research Article

Ghandour, A., & Woodford, B. J. (2021). Regulating Internet of Things: The case of the United Arab Emirates. TEM Journal, 10(3), 1031-1038. doi: 10.18421/TEM103-04

Stamou, G., Garcia-Palacios, A., Woodford, B. J., Suso-Ribera, C., & Botella, C. (2021). The combination of cognitive-behavioural therapy with virtual reality for the treatment of postnatal depression in a brief intervention context: A single-case study trial. Journal of Healthcare Engineering, 2021, 5514770. doi: 10.1155/2021/5514770

Conference Contribution - Published proceedings: Full paper

Woodford, B. J., & Ghandour, A. (2021). An information retrieval-based approach to activity recognition in Smart Homes. In H. Hacid, F. Outay, H.-Y. Paik, A. Alloum, M. Petrocchi, M. R. Bouadjenek, … A. Maaradji (Eds.), Service-oriented computing: ICSOC 2020 Workshops: Lecture notes in computer science (Vol. 12632). 12632 LNCS, (pp. 583-595). Cham, Switzerland: Springer. doi: 10.1007/978-3-030-76352-7_51

2020

Conference Contribution - Published proceedings: Full paper

Ghandour, A., & Woodford, B. J. (2020). COVID-19 impact on e-commerce in UAE. Proceedings of the 21st International Arab Conference on Information Technology (ACIT). IEEE. doi: 10.1109/ACIT50332.2020.9300077

2019

Conference Contribution - Published proceedings: Full paper

Ghandour, A., & Woodford, B. J. (2019). Ethical issues in artificial intelligence in UAE. Proceedings of the International Arab Conference on Information Technology (ACIT). (pp. 262-266). IEEE. [Full Paper]

Ismail, F. N., Woodford, B. J., & Licorish, S. A. (2019). Evaluating the boundaries of big data environments for machine learning. In J. Liu & J. Bailey (Eds.), Advances in artificial intelligence: Lecture notes in artificial intelligence (Vol. 11919). (pp. 253-264). Cham, Switzerland: Springer. doi: 10.1007/978-3-030-35288-2_21

2017

Chapter in Book - Research

Shahi, A., Woodford, B. J., & Lin, H. (2017). Dynamic real-time segmentation and recognition of activities using a multi-feature windowing approach. In U. Kang, E.-P. Lim, J. X. Yu & Y.-S. Moon (Eds.), Trends and applications in knowledge discovery and data mining: PAKDD Workshops, revised selected papers: Lecture notes in artificial intelligence (Vol. 10526). (pp. 26-38). Cham, Switzerland: Springer. doi: 10.1007/978-3-319-67274-8_3

Conference Contribution - Published proceedings: Full paper

Shahi, A., Deng, J. D., & Woodford, B. J. (2017). Online hidden conditional random fields to recognize activity-driven behavior using adaptive resilient gradient learning. In D. Liu, S. Xie, Y. Li, D. Zhao & E.-S. M. El-Alfy (Eds.), Neural Information Processing: Lecture notes in computer science (Vol. 10634). (pp. 515-525). Cham, Switzerland: Springer. doi: 10.1007/978-3-319-70087-8

Shahi, A., Deng, J. D., & Woodford, B. J. (2017). A streaming ensemble classifier with multi-class imbalance learning for activity recognition. Proceedings of the International Joint Conference on Neural Networks (IJCNN). (pp. 3983-3990). IEEE. doi: 10.1109/IJCNN.2017.7966358

2016

Chapter in Book - Research

Lin, H., Deng, J. D., & Woodford, B. J. (2016). Shot boundary detection using multi-instance incremental and decremental one-class support vector machine. In J. Bailey, L. Khan, T. Washio, G. Dobbie, J. Z. Huang & R. Wang (Eds.), Advances in knowledge discovery and data mining: Proceedings of the 20th Pacific-Asia Conference PAKDD, part 1: Lecture Notes in Artificial Intelligence (Vol. 9651). (pp. 165-176). Cham, Switzerland: Springer. doi: 10.1007/978-3-319-31753-3_14

Conference Contribution - Published proceedings: Full paper

Lin, H., Deng, J. D., Woodford, B. J., & Shahi, A. (2016). Online weighted clustering for real-time abnormal event detection in video surveillance. Proceedings of the Association for Computing Machinery (ACM) on Multimedia Conference. (pp. 536-540). New York, NY: ACM. doi: 10.1145/2964284.2967279

2015

Journal - Research Article

Shah, M., Deng, J. D., & Woodford, B. J. (2015). A Self-adaptive CodeBook (SACB) model for real-time background subtraction. Image & Vision Computing, 38, 52-64. doi: 10.1016/j.imavis.2015.02.001

Conference Contribution - Published proceedings: Full paper

Lin, H., Deng, J. D., & Woodford, B. J. (2015). Anomaly detection in crowd scenes via online adaptive one-class support vector machines. Proceedings of the International Conference on Image Processing (ICIP). (pp. 2434-2438). IEEE. doi: 10.1109/icip.2015.7351239

Shahi, A., Woodford, B. J., & Deng, J. D. (2015). Event classification using adaptive cluster-based ensemble learning of streaming sensor data. In B. Pfahringer & J. Renz (Eds.), Advances in artificial intelligence: Lecture notes in artificial intelligence (Vol. 9457). (pp. 505-516). Cham, Switzerland: Springer. doi: 10.1007/978-3-319-26350-2_45

2014

Journal - Research Article

Shah, M., Deng, J. D., & Woodford, B. J. (2014). Video background modeling: Recent approaches, issues and our proposed techniques. Machine Vision & Applications, 25(5), 1105-1119. doi: 10.1007/s00138-013-0552-7

Conference Contribution - Published proceedings: Full paper

Lin, H., Deng, J. D., & Woodford, B. J. (2014). Spatial-temporal pyramid matching for crowd scene analysis. In A. Rahman, J. Deng & J. Li (Eds.), Proceedings of the 2nd Workshop on Machine Learning for Sensory Data Analysis (MLSDA). (pp. 12-18). New York: ACM. doi: 10.1145/2689746.2689751

2013

Chapter in Book - Research

Shah, M., Deng, J., & Woodford, B. (2013). Illumination invariant background model using mixture of Gaussians and SURF features. In J.-I. Park & J. Kim (Eds.), Computer vision: ACCV 2012 International Workshops, revised selected papers, part 1: Lecture notes in computer science (Vol. 7728). (pp. 308-314). Berlin, Germany: Springer. doi: 10.1007/978-3-642-37410-4_27

Conference Contribution - Published proceedings: Full paper

Shah, M., Deng, J. D., & Woodford, B. J. (2013). Improving mixture of Gaussians background model through adaptive learning and spatio-temporal voting. Proceedings of the International Conference on Image Processing (ICIP). (pp. 3436-3440). IEEE. doi: 10.1109/ICIP.2013.6738709

Lin, H., Deng, J. D., & Woodford, B. J. (2013). Event detection using quantized binary code and spatial-temporal locality preserving projections. In S. Cranefield & A. Nayak (Eds.), Advances in artificial intelligence: Lecture notes in artificial intelligence (Vol. 8272). (pp. 123-134). Heidelberg, Germany: Springer. doi: 10.1007/978-3-319-03680-9_14

Shah, M., Deng, J. D., & Woodford, B. J. (2013). Growing neural gas video background model (GNG-BM). In S. Cranefield & A. Nayak (Eds.), Advances in artificial intelligence: Lecture notes in artificial intelligence (Vol. 8272). (pp. 135-147). Heidelberg, Germany: Springer. doi: 10.1007/978-3-319-03680-9_15

Conference Contribution - Published proceedings: Abstract

Woodford, B. J., Gillis, W., & Cornwall, J. (2013). Who accesses clinical anatomy websites? Data from a New Zealand anatomy department. Clinical Anatomy, 26, (pp. 659). doi: 10.1002/ca.22235

2012

Journal - Research Article

Mann, S. L., Marshall, M. R., Woodford, B. J., Holt, A., & Williams, A. B. (2012). Predictive performance of Acute Physiological and Chronic Health Evaluation releases II to IV: A single New Zealand centre experience. Anaesthesia & Intensive Care, 40(3), 479-489.

Conference Contribution - Published proceedings: Full paper

Lin, H., Deng, J. D., & Woodford, B. J. (2012). Video manifold modelling: Finding the right parameter settings for anomaly detection. In B. McCane, S. Mills & J. D. Deng (Eds.), Proceedings of the 27^th Image and Vision Computing New Zealand Conference (IVCNZ). (pp. 168-173). New York: ACM. [Full Paper]

Shah, M., Deng, J. D., & Woodford, B. J. (2012). Enhancing the Mixture of Gaussians background model with local matching and local adaptive learning. In B. McCane, S. Mills & J. D. Deng (Eds.), Proceedings of the 27^th Image and Vision Computing New Zealand Conference (IVCNZ). (pp. 103-108). New York: ACM. [Full Paper]

2011

Conference Contribution - Published proceedings: Full paper

Shah, M., Deng, J., & Woodford, B. (2011). Enhanced codebook model for real-time background subtraction. In B.-L. Lu, L. Zhang & J. Kwok (Eds.), Neural information processing: Lecture notes in computer science (Vol. 7064). (pp. 449-458). Berlin, Germany: Springer. doi: 10.1007/978-3-642-24965-5_51

2010

Journal - Research Article

Mann, S. L., Marshall, M. R., Holt, A., Woodford, B., & Williams, A. B. (2010). Illness severity scoring for Intensive Care at Middlemore Hospital, New Zealand: Past and future. New Zealand Medical Journal, 123(1316). Retrieved from http://journal.nzma.org.nz/journal/123-1316/4157/content.pdf

Conference Contribution - Published proceedings: Full paper

Shah, M., Deng, J., & Woodford, B. J. (2010). Localized adaptive learning of Mixture of Gaussians models for background extraction. Proceedings of the 25th International Conference of Image and Vision Computing New Zealand (IVCNZ). doi: 10.1109/IVCNZ.2010.6148870

Woodford, B. J. (2010). Automatic optimization of pruning in evolving fuzzy neural networks using an entropy measure. Proceedings of the IEEE World Congress on Computational Intelligence (WCCI). (pp. 1053-1059). IEEE. [Full Paper]

Conference Contribution - Published proceedings: Abstract

Shah, M., Deng, J., & Woodford, B. J. (2010). Localized adaptive learning of mixture of Gaussians models for background extraction (LAL-MoG). Proceedings of the Information Science Postgraduate Day. (pp. 11-12). Retrieved from http://infosci.otago.ac.nz/information-science-postgrad-day-2010/

2008

Journal - Research Article

Woodford, B. J. (2008). Evolving neurocomputing systems for horticulture applications. Applied Soft Computing, 8, 564-578. doi: 10.1016/j.asoc.2006.05.006

Conference Contribution - Published proceedings: Full paper

Woodford, B. J. (2008). Rule extraction from spatial data using a entropy-based evolving fuzzy neural network. In P. A. Whigham, I. Drecki & A. Moore (Eds.), Proceedings of the 20th Annual Colloquium of the Spatial Information Research Centre in conjunction with the New Zealand Cartographic Society Inc and GeoComp. (pp. 55-66). Dunedin, New Zealand: Spatial Information Research Centre and the New Zealand Cartographic Society Inc. [Full Paper]

2007

Journal - Research Article

Shaw, D., Woodford, B. J., & Benwell, G. L. (2007). Educating future IS professionals through real-world integration. International Journal of Teaching & Case Studies, 1(1/2), 66-83.

2005

Conference Contribution - Published proceedings: Full paper

Woodford, B. J. (2005). Rule extraction from spatial data using local learning techniques. In P. A. Whigham (Ed.), Proceedings of the 17th Annual Colloquium of the Spatial Information Research Centre. (pp. 125-130). Dunedin, New Zealand: University of Otago. [Full Paper]

2004

Conference Contribution - Published proceedings: Full paper

Woodford, B. J., Deng, D., & Benwell, G. L. (2004). A wavelet-based neuro-fuzzy system for data mining small image sets. Proceedings of the Australasian Workshop on Data Mining and Web Intelligence. (pp. 139-143). [Full Paper]

2003

Awarded Doctoral Degree

Woodford, B. J. (2003). Connectionist-Based Intelligent Information Systems for image analysis and knowledge engineering: Applications in horticulture (PhD). University of Otago, Dunedin, New Zealand. 243p.

2001

Conference Contribution - Published proceedings: Full paper

Woodford, B. J., & Kasabov, N. K. (2001). Ensembles of EFuNNs: An architecture for a multi module classifier. The Proceedings of FUZZ-IEEE 2001 - The 10th IEEE International Conference on Fuzzy Systems. (pp. 441-445). Melbourne: IEEE Press. [Full Paper]

Woodford, B. J., & Kasabov, N. K. (2001). A wavelet-based neural network classifier for temporal data. Proceedings of the 5th Australia-Japan Joint Workshop on Intelligent and Evolutionary Systems. (pp. 79-85). Japan: Mitsuwa. [Full Paper]

Woodford, B. J. (2001). Comparative analysis of the EFuNN and the support vector machine models for the classification of horticulture data. In N. Kasabov & B. Woodford (Eds.), Proceedings of the Fifth Biannual Conference on Artificial Neural Networks and Expert Systems. (pp. 70-75). Dunedin: University of Otago Printery. [Full Paper]

Conference Contribution - Edited volume of conference proceedings

Kasabov, N. K., & Woodford, B. J. (Eds.). (2001). Proceedings of the Fifth Biannual Conference on Artificial Neural Networks and Expert Systems (ANNES'2001). Dunedin, New Zealand: University of Otago Printery. 236p.

2000

Journal - Research Article

Kasabov, N. K., Israel, S. A., & Woodford, B. J. (2000). The application of hybrid evolving connectionist systems to image classification. International Journal of Advanced Computational Intelligence, 4(1), 57-65.

1999

Chapter in Book - Research

Kasabov, N. K., Israel, S. A., & Woodford, B. J. (1999). Adaptive, evolving, hybrid connectionist systems for image pattern recognition. In S. Pal, A. Ghosh & M. Kundu (Eds.), Soft Computing for Image Processing. (pp. 318-336). Heidleberg, Germany: Springer Verlag.

Conference Contribution - Published proceedings: Full paper

Kasabov, N. K., & Woodford, B. J. (1999). Rule insertion and rule extraction from evolving fuzzy neural networks: Algorithms and applications for building adaptive, intelligent expert systems. IEEE International Fuzzy Systems Conference Proceedings. III, (pp. 1406-1411). Seoul, Korea: IEEE Press. [Full Paper]

Watts, M., Woodford, B. J., & Kasabov, N. K. (1999). FuzzyCOPE: A software environment for building intelligent systems - the past, the present and the future. In N. K. Kasabov & K. Ko (Eds.), Emerging Knowledge Engineering and Connectionist-based Information Systems - Proceedings of the ICONIP/ANZIIS/ANNES'99 International Workshop on Future Directions for Intelligent Systems and Information Sciences. (pp. 188-191). Dunedin, New Zealand: University of Otago. [Full Paper]

Woodford, B. J., Wearing, C. H., Walker, J. T. S., & Kasabov, N. K. (1999). An adaptive agent-based distributed system for pest management. In N. K. Kasabov & K. Ko (Eds.), Emerging Knowledge Engineering and Connectionist-based Information Systems - Proceedings of the ICONIP/ANZIIS/ANNES'99 International Workshop on Future Directions for Intelligent Systems and Information Sciences. (pp. 207-212). Dunedin, New Zealand: University of Otago. [Full Paper]

Woodford, B. J., Kasabov, N. K., & Wearing, C. H. (1999). Fruit image analysis using wavelets. In N. K. Kasabov & K. Ko (Eds.), Emerging Knowledge Engineering and Connectionist-based Information Systems - Proceedings of the ICONIP/ANZIIS/ANNES'99 International Workshop on Future Directions for Intelligent Systems and Information Sciences. (pp. 88-91). Dunedin, New Zealand: University of Otago. [Full Paper]

Woodford, B. J. (1999). An overview of virtual reality. In N. K. Kasabov & K. Ko (Eds.), Emerging Knowledge Engineering and Connectionist-based Information Systems - Proceedings of the ICONIP/ANZIIS/ANNES'99 International Workshop on Future Directions for Intelligent Systems and Information Sciences. (pp. 111-114). Dunedin, New Zealand: University of Otago. [Full Paper]

Woodford, B. J. (1999). Analysing Images of Pest Damage to Apples using Wavelets. In S. Yeates (Ed.), Proceedings of the Third New Zealand Computer Science Research Students' Conference. (pp. 101-108). University of Waikato: University of Waikato. [Full Paper]

1997

Conference Contribution - Published proceedings: Full paper

Woodford, B. J. (1997). An intelligent knitwear design aid for novice knitters. In R. Kozma, A. R. Gray, R. I. Kilgour & B. J. Woodford (Eds.), Proceedings of the Addendum Session of the 1997 International Conference on Neural Information Processing and Intelligent Information Systems (ICONIP'97/ANZIIS/ANNES'97). (pp. 65-68). University of Otago, Dunedin, New Zealand: Information Science Department. [Full Paper]

Conference Contribution - Edited volume of conference proceedings

Kozma, R., Gray, A. R., Kilgour, R. I., & Woodford, B. J. (Eds.). (1997). Proceedings of the Addendum Session of the 1997 International Conference on Neural Information Processing and Intelligent Information Systems (ICONIP/ANZIIS/ANNES'97). Dunedin, New Zealand: University of Otago. 108p.