In the age of Big Data and the Internet of Things, both the opportunities and challenges to intelligent data analysis are unprecedented. The tight coupling of computing and networking raises new research questions on to the optimal designs of the information systems.
We are interested in utilizing cutting-edge techniques of machine learning, computational intelligence, and modeling and simulation, to design algorithms and systems for real-world computing and networking problems. Current research encompasses a wide range of topics, including object and event detection from video, activity recognition, semantic image and video retrieval, and energy-conservation design in wireless sensor networks.
The ICoN Lab has been the technical sponsor of the Machine Learning for Sensory Data Analysis (MLSDA) workshops (2013-2017), the Data Mining for Energy Modeling and Optimization (DaMEMO’16/ICDM’16), and the International Telecommunication Networks and Applications Conference (ITNAC’16).
- Associate Professor Jeremiah Deng
Dr Deng's topics include signal analysis, anomaly detection, online learning, self-organized learning in neural networks; performance optimization in computer networks. He has published more than 100 journal articles and conference papers and has served on the program committees of leading conferences such as IJCAI, GlobeCom and ICC. He is on the Editorial Board of the Springer journal of Cognitive Computation, and has organized a number of workshops on notable venues such as IEEE ICDM and PAKDD.
- Dr Brendon Woodford
Dr Woodford's topics include on-line adaptive learning systems for activity recognition in smart homes, concept drift detection in large data streams, anomaly detection in video surveillance, computer vision applications in horticulture, and health data analytics.
- Emeritus Professor Martin Purvis
- Professor Stephen Cranefield: semantics analysis
- Associate Professor Haibo Zhang (Computer Science): sensor network optimization
- Dr Ralf Ohlemüller (Geology): plant image analysis
- Dr Ashfaqur Rahman (CSIRO): data mining
- Associate Professor Irena Koprinska (University of Sydney): neural networks, machine learning
- Professor Jun Zhang (South China University of Technology): computational intelligence, cloud computing
- Professor Amir Hussain (Stirling University, UK): cognitive computation, machine learning
Postgraduate Research students and topics:
- Juan Zhang, PhD: Quality of Experience/Services design of networks
- Sepideh Zareei, PhD: Energy-efficient wireless sensing
- Ahmad Shahi, PhD: Event classification and detection in smart-homes
- Hsin-Shyuan Lee, PhD: Optimization-based design of smart-grids
- Xianbin Gu, PhD: Image segmentation
- X-F Liu, Z-H Zhan, J. D. Deng, Y. Li, J. Zhang, An Energy Efficient Ant Colony System for Virtual Machine Placement in Cloud Computing, IEEE Transactions on Evolutionary Computation (2016) [ERA A*] DOI
- Lin, J. D. Deng, B. J. Woodford, A. Shahi: Online Weighted Clustering for Real-time Abnormal Event Detection in Video Surveillance. ACM Multimedia 2016: 536-540 [ERA A*] DOI
- Lin, J. D. Deng, B. J. Woodford, Shot boundary detection using multi-instance incremental and decremental one-class support vector machine, Proc. of Pacific-Asia Conf. Advances in Knowledge Discovery and Data Mining (PAKDD), 2016, Part I, pp.165-176. [ERA A] DOI
- Yang; W. N. Chen; T. Gu; H. Zhang; J. D. Deng; Y. Li; J. Zhang, Segment-Based Predominant Learning Swarm Optimizer for Large-Scale Optimization, IEEE Transactions on Cybernetics, online first, pp.1-15, (2016) [ERA A] DOI
- Shah, J. D. Deng, B. J. Woodford: A Self-adaptive CodeBook (SACB) model for real-time background subtraction. Image Vision Computing 38: 52-64 (2015) [ERA B]DOI
- Xu, J. D. Deng, M. Nowostawski, M. K. Purvis: Optimized routing for video streaming in multi-hop wireless networks using analytical capacity estimation. J. Comput. Syst. Sci. 81(1): 145-157 (2015) [ERA A*]DOI
- A. Aderohunmu, D. Brunelli, J. D. Deng, M. K. Purvis: A data acquisition protocol for a reactive wireless sensor network monitoring application, MDPI Sensors 15: 10221-10254 (2015)DOI
- Shah, J. D. Deng, B. J. Woodford: Video background modeling: recent approaches, issues and our proposed techniques. Mach. Vis. Appl. 25(5): 1105-1119 (2014) [ERA B]DOI
- Gu, J. D. Deng, M. K. Purvis: Improving superpixel-based image segmentation by incorporating color covariance matrix manifolds. IEEE Inter. Conf. Image Processing 2014: 4403-4406 (Top 10% Paper) [ERA B]DOI
- Yong, S.-P., Deng, J. D., & Purvis, M. K. (2013). Wildlife video key-frame extraction based on novelty detection in semantic context.Multimedia Tools & Applications. 62(2): 359-376 (2013). [ERA B] DOI
- Yong, S.-P., Deng, J. D., & Purvis, M. K. (2012). Novelty detection in wildlife scenes through semantic context modelling.Pattern Recognition, 45(9), 3439-3450. DOI [ERA A*]
- Guan, Z. Wang, S. Lu, J. D. Deng, D. D. Feng (2012). Keypoint based key frame selection,IEEE Transactions on Circuits and Systems for Video Technology, 2012. [ERA B] DOI
- S-P Yong, J. D. Deng, M. K. Purvis: Key-frame extraction of wildlife video based on semantic context modeling. IEEE Joint Conf. Neural Networks 2012: 1-8 [ERA A] DOI
- Yong, S.-P., Deng, J. D., & Purvis, M. K. (2010). Modelling semantic context for novelty detection in wildlife scenes.Proceedings of the IEEE International Conference on Multimedia and Expo (ICME), (pp. 1254-1259). IEEE. [ERA B] DOI
Pang, Y., Zhang, H., Deng, J. D., Peng, L., & Teng, F. (2022). Rule-based collaborative learning with heterogeneous local learning models. In J. Gama, T. Li, Y. Yu, E. Chen, Y. Zheng & F. Teng (Eds.), Advances in knowledge discovery and data mining: Proceedings of the 26th Pacific-Asia Conference, PAKDD (Part 1): Lecture notes in artificial intelligence (Vol. 13280). (pp. 639-651). Cham, Switzerland: Springer. doi: 10.1007/978-3-031-05933-9_50
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
Afshar Sedigh, A. H., Purvis, M., Savarimuthu, T. B. R., Frantz, C. K., & Purvis, M. (2022). A comparative study on apprenticeship systems using agent-based simulation. Journal of Artificial Societies & Social Simulation, 25(1), 1. doi: 10.18564/jasss.4733
Hou, J., Ding, X., & Deng, J. D. (2022). Semi-supervised semantic segmentation of vessel images using leaking perturbations. Proceedingsof the IEEE/CFV Winter Conference on Applications of Computer Vision (WACV). (pp. 1769-1778). IEEE. doi: 10.1109/WACV51458.2022.00183
Hou, J., Ding, X., Deng, J. D., & Cranefield, S. (2022). Deep adversarial transition learning using cross-grafted generative stacks. Neural Networks. Advance online publication. doi: 10.1016/j.neunet.2022.02.011