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Intelligent Computing and Networking

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).

Members

Core members:

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

Adjunct members:

Professor Stephen Cranefield: semantics analysis

Dr. Haibo Zhang (Computer Science): sensor network optimization

Dr. Ralf Ohlemüller (Geology): plant image analysis

Dr. Ashfaqur Rahman (CSIRO): data mining

Assoc. 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

Selected Publications

  • 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

Publications

Afshar Sedigh, A. H., Frantz, C. K., Savarimuthu, B. T. R., Purvis, M. K., & Purvis, M. A. (2019). A comparison of two historical trader societies: An agent-based simulation study of English East India Company and New-Julfa. In P. Davidsson & H. Verhagen (Eds.), Multi-agent-based simulation XIX (MABS): Multi-agent-based simulation XIX: Lecture notes in artificial intelligence (Vol. 11463). (pp. 17-31). Cham, Switzerland: Springer. doi: 10.1007/978-3-030-22270-3_2

Zareei, S., & Deng, J. D. (2019). Energy harvesting modelling for self-powered fitness gadgets: A feasibility study. International Journal of Parallel, Emergent & Distributed Systems, 34(4), 412-429. doi: 10.1080/17445760.2017.1410817

Lee, S. H.-S., Deng, J. D., Purvis, M. K., Purvis, M., & Peng, L. (2018). An improved PBIL algorithm for optimal coalition structure generation of smart grids. In M. Ganji, L. Rashidi, B. C. M. Fung & C. Wang (Eds.), Trends and applications in knowledge discovery and data mining: Lecture notes in artificial intelligence (Vol. 11154). (pp. 345-356). Cham, Switzerland: Springer. doi: 10.1007/978-3-030-04503-6_33

Yasir, M., Purvis, M., Purvis, M., & Savarimuthu, T. B. R. (2018). Complementary-based coalition formation for energy microgrids. Computational Intelligence. doi: 10.1111/coin.12171

Yang, Q., Chen, W.-N., Deng, J. D., Li, Y., Gu, T., & Zhang, J. (2018). A level-based learning swarm optimizer for large scale optimization. IEEE Transactions on Evolutionary Computation, 22(4), 578-594. doi: 10.1109/TEVC.2017.2743016

Chapter in Book - Research

Afshar Sedigh, A. H., Frantz, C. K., Savarimuthu, B. T. R., Purvis, M. K., & Purvis, M. A. (2019). A comparison of two historical trader societies: An agent-based simulation study of English East India Company and New-Julfa. In P. Davidsson & H. Verhagen (Eds.), Multi-agent-based simulation XIX (MABS): Multi-agent-based simulation XIX: Lecture notes in artificial intelligence (Vol. 11463). (pp. 17-31). Cham, Switzerland: Springer. doi: 10.1007/978-3-030-22270-3_2

Lee, S. H.-S., Deng, J. D., Purvis, M. K., Purvis, M., & Peng, L. (2018). An improved PBIL algorithm for optimal coalition structure generation of smart grids. In M. Ganji, L. Rashidi, B. C. M. Fung & C. Wang (Eds.), Trends and applications in knowledge discovery and data mining: Lecture notes in artificial intelligence (Vol. 11154). (pp. 345-356). Cham, Switzerland: Springer. doi: 10.1007/978-3-030-04503-6_33

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: Lecture notes in artificial intelligence (Vol. 10526). (pp. 26-38). Cham, Switzerland: Springer. doi: 10.1007/978-3-319-67274-8_3

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: Lecture Notes in Artificial Intelligence (Vol. 9651). (pp. 165-176). Cham, Switzerland: Springer. doi: 10.1007/978-3-319-31753-3_14

Gu, X., & Purvis, M. (2016). Image segmentation with superpixel based covariance descriptor. In H. Cao, J. Li & R. Wang (Eds.), Trends and applications in knowledge discovery and data mining: Lecture notes in artificial intelligence (Vol. 9794). (pp. 154-165). Cham, Switzerland: Springer. doi: 10.1007/978-3-319-42996-0_13

Jahanbazi, M., Frantz, C., Purvis, M., & Purvis, M. (2016). The role of knowledge keepers in an artificial primitive human society: An agent-based approach. In V. Dignum, P. Noriega, M. Sensoy & J. S. Sichman (Eds.), Coordination, organizations, institutions, and norms in agent systems XI (COIN): Lecture notes in artificial intelligence (Vol. 9628). (pp. 154-172). Cham, Switzerland: Springer International. doi: 10.1007/978-3-319-42691-4_9

Frantz, C., Purvis, M. K., Purvis, M. A., Nowostawski, M., & Lewis, N. D. (2015). Fuzzy modeling of economic institutional rules. In A. Sadeghian & H. Tahayori (Eds.), Frontiers of higher order fuzzy sets. (pp. 87-129). New York: Springer.

Farhangian, M., Purvis, M., Purvis, M., & Savarimuthu, B. T. R. (2015). Agent-based modeling of resource allocation in software projects based on personality and skill. In F. Koch, C. Guttmann & D. Busquets (Eds.), Advances in social computing and multiagent systems: Communications in computer and information science (Vol. 541). (pp. 130-146). Cham, The Netherlands: Springer. doi: 10.1007/978-3-319-24804-2_9

Frantz, C., Purvis, M., Savarimuthu, B. T. R., & Nowostawski, M. (2015). Modelling the impact of role specialisation on cooperative behaviour in historic trader scenarios. In A. Ghose, N. Oren, P. Telang & J. Thangarajah (Eds.), Coordination, organizations, institutions and norms in agent systems X (COIN): Lecture notes in artificial intelligence (Vol. 9372). (pp. 53-71). Cham, Switzerland: Springer. doi: 10.1007/978-3-319-25420-3_16

Yasir, M., Purvis, M., Purvis, M., & Savarimuthu, B. T. R. (2015). Improving energy outcomes in dynamically formed micro-grid coalitions. In A. Ghose, N. Oren, P. Telang & J. Thangarajah (Eds.), Coordination, organizations, institutions and norms in agent systems X (COIN): Lecture notes in artificial intelligence (Vol. 9372). (pp. 251-267). Cham, Switzerland: Springer. doi: 10.1007/978-3-319-25420-3_16

Jahanbazi, M., Frantz, C., Purvis, M., & Purvis, M. (2015). Building an artificial primitive human society: An agent-based approach. In A. Ghose, N. Oren, P. Telang & J. Thangarajah (Eds.), Coordination, organizations, institutions and norms in agent systems X (COIN): Lecture notes in artificial intelligence (Vol. 9372). (pp. 89-96). Cham, Switzerland: Springer. doi: 10.1007/978-3-319-25420-3_16

Frantz, C., Purvis, M. K., Nowostawski, M., & Savarimuthu, B. T. R. (2015). Analysing the apprenticeship system in the Maghribi Traders Coalition. In F. Grimaldo & E. Norling (Eds.), Multi-agent-based simulation XV (MABS): Lecture notes in artificial intelligence (Vol. 9002). (pp. 180-196). Heidelberg, Germany: Springer. doi: 10.1007/978-3-319-14627-0_13

Farhangian, M., Purvis, M., Purvis, M., & Savarimuthu, B. T. R. (2015). The effects of temperament and team formation mechanism on collaborative learning of knowledge and skill in short-term projects. In F. Koch, C. Guttmann & D. Busquets (Eds.), Advances in social computing and multiagent systems: Communications in computer and information science (Vol. 541). (pp. 48-65). Cham, The Netherlands: Springer. doi: 10.1007/978-3-319-24804-2_4

Yasir, M., Purvis, M., Purvis, M., & Savarimuthu, B. T. R. (2014). An intelligent learning mechanism for trading strategies for local energy distribution. In S. Ceppi, E. David, V. Podobnik, V. Robu, O. Shehory, S. Stein & I. A. Vetsikas (Eds.), Agent-mediated electronic commerce: Designing trading strategies and mechanisms for electronic markets (LNBIP 187). (pp. 159-170). Cham, Switzerland: Springer. doi: 10.1007/978-3-319-13218-1_12

Yasir, M., Purvis, M. K., Purvis, M., & Savarimuthu, B. T. R. (2014). Intelligent battery strategies for local energy distribution. In T. Balke, F. Dignum, M. Birna van Riemsdijk & A. K. Chopra (Eds.), Coordination, organizations, institutions, and norms in agent systems IX (COIN): Lecture notes in artificial intelligence (Vol. 8386). (pp. 63-80). Cham, Switzerland: Springer. doi: 10.1007/978-3-319-07314-9_4

Farhangian, M., Purvis, M. K., Purvis, M., & Savarimuthu, B. T. R. (2014). Modelling the effects of personality and temperament in small teams. In T. Balke, F. Dignum, M. Birna van Riemsdijk & A. K. Chopra (Eds.), Coordination, organizations, institutions, and norms in agent systems IX (COIN): Lecture notes in artificial intelligence (Vol. 8386). (pp. 25-41). Cham, Switzerland: Springer. doi: 10.1007/978-3-319-07314-9_2

Frantz, C., Purvis, M. K., Nowostawski, M., & Savarimuthu, B. T. R. (2014). Modelling institutions using dynamic deontics. In T. Balke, F. Dignum, M. Birna van Riemsdijk & A. K. Chopra (Eds.), Coordination, organizations, institutions, and norms in agent systems IX (COIN): Lecture notes in artificial intelligence (Vol. 8386). (pp. 211-233). Cham, Switzerland: Springer. doi: 10.1007/978-3-319-07314-9_12

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Journal - Research Article

Zareei, S., & Deng, J. D. (2019). Energy harvesting modelling for self-powered fitness gadgets: A feasibility study. International Journal of Parallel, Emergent & Distributed Systems, 34(4), 412-429. doi: 10.1080/17445760.2017.1410817

Yasir, M., Purvis, M., Purvis, M., & Savarimuthu, T. B. R. (2018). Complementary-based coalition formation for energy microgrids. Computational Intelligence. doi: 10.1111/coin.12171

Yang, Q., Chen, W.-N., Deng, J. D., Li, Y., Gu, T., & Zhang, J. (2018). A level-based learning swarm optimizer for large scale optimization. IEEE Transactions on Evolutionary Computation, 22(4), 578-594. doi: 10.1109/TEVC.2017.2743016

Liu, X.-F., Zhan, Z.-H., Deng, J. D., Li, Y., Gu, T., & Zhang, J. (2018). An energy efficient ant colony system for virtual machine placement in cloud computing. IEEE Transactions on Evolutionary Computation, 22(1), 113-128. doi: 10.1109/TEVC.2016.2623803

Yasir, M., Purvis, M., Purvis, M., & Savarimuthu, B. T. R. (2017). Agent-based modelling of coalition formation in energy micro-grids. International Journal of Agent-Oriented Software Engineering, 5(4), 399-432. doi: 10.1504/IJAOSE.2017.087639

Liu, Q., Chen, W.-N., & Deng, J. D. (2017). Benchmarking stochastic algorithms for global optimization problems by visualizing confidence intervals. IEEE Transactions on Cybernetics, 47(9), 2924-2937. doi: 10.1109/tcyb.2017.2659659

Yang, Q., Chen, W.-N., Gu, T., Zhang, H., Deng, J. D., Li, Y., & Zhang, J. (2016). Segment-based predominant learning swarm optimizer for large-scale optimization. IEEE Transactions on Cybernetics, 47(9), 2896-2910. doi: 10.1109/TCYB.2016.2616170

Xu, Y., Deng, J. D., Nowostawski, M., & Purvis, M. K. (2015). Optimized routing for video streaming in multi-hop wireless networks using analytical capacity estimation. Journal of Computer & System Sciences, 81(1), 145-157. doi: 10.1016/j.jcss.2014.06.015

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