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Jeremiah Deng

Jeremiah Deng imageBSc(UESTC), MSc(SCUT), PhD(SCUT), MIEEE, MACM
Associate Professor

Room 8.10, Otago Business School
Tel +64 3 479 8090
Email jeremiah.deng@otago.ac.nz
Web http://www.covic.otago.ac.nz/~jdeng

Background and interests

Associate Professor Jeremiah Deng is interested in developing intelligent algorithms for pattern recognition, machine learning, and optimization of computer and network systems. His recent work investigates online adaptive learning algorithms for anomaly detection, scene categorization, semantic video analysis, event detection, and performance modeling and optimization of wireless networks. He has authored/co-authored more than 100 papers published in peer-reviewed journals and conference proceedings, or as book chapters. Dr. Deng is a member of ACM and IEEE, and serves on the editorial board of Cognitive Computation (Springer). He co-chairs the Machine Learning for Sensory Data Analysis (MLSDA) workshops (in conjunction with PAKDD), and has served on the program committees of a number of international conferences such as IJCAI, PRICAI, ACCV, GlobeCom, ICC and ECE.

Dr. Deng teaches a variety of undergraduate and postgraduate courses in Information Science and Telecommunications (Applied Science). He is currently the Director of the Telecommunications Programme and supports ongoing curriculum development for BAppSc/PGDip/MAppSc qualifications.

For more information, including recent publications, see his personal website (link above).

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Papers

Supervision

Currently supervising

  • Sean Lee
  • Ahmad Shahi
  • Sophie Zareei
  • Robert Hou
  • Chontira Chumsaeng

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Publications

Lin, H., Deng, J. D., Albers, D., & Siebert, F. W. (2020). Helmet use detection of tracked motorcycles using CNN-based multi-task learning. IEEE Access, 8, 162073-162084. doi: 10.1109/ACCESS.2020.3021357

Gu, X., & Deng, J. D. (2020). A multi-feature bipartite graph ensemble for image segmentation. Pattern Recognition Letters, 131, 98-104. doi: 10.1016/j.patrec.2019.12.017

Zhang, H., & Deng, J. D. (2020). Design and management solutions to emergent networking technologies. International Journal of Parallel, Emergent & Distributed Systems. Advance online publication. doi: 10.1080/17445760.2020.1767102

Li, J., Deng, J. D., De Ridder, D., & Adhia, D. (2020). Gender classification of EEG signals using a motif attribute classification ensemble. Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN). (pp. 1-8). IEEE. doi: 10.1109/IJCNN48605.2020.9207695

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

Chapter in Book - Research

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

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

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 workshops: Lecture notes in computer science (Vol. 7728). (pp. 308-314). Berlin, Germany: Springer. doi: 10.1007/978-3-642-37410-4_27

Deng, J. D. (2011). Feature analysis for object and scene categorization. In H. Kwasnicka & L. C. Jain (Eds.), Innovations in intelligent image analysis: Studies in computational intelligence (Vol. 339). (pp. 225-244). Berlin, Germany: Springer. doi: 10.1007/978-3-642-17934-1_10

Kasabov, N. K., Erzegovezi, L., Fedrizzi, M., Beber, A., & Deng, D. (2000). Hybrid intelligent decision support systems and applications for risk analysis and prediction of evolving economic clusters in Europe. In N. Kasabov (Ed.), Future Directions for Intelligent Information Systems and Information Sciences. (pp. 347-372). Heidleberg, Germany: Springer Verlag.

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Journal - Research Article

Lin, H., Deng, J. D., Albers, D., & Siebert, F. W. (2020). Helmet use detection of tracked motorcycles using CNN-based multi-task learning. IEEE Access, 8, 162073-162084. doi: 10.1109/ACCESS.2020.3021357

Gu, X., & Deng, J. D. (2020). A multi-feature bipartite graph ensemble for image segmentation. Pattern Recognition Letters, 131, 98-104. doi: 10.1016/j.patrec.2019.12.017

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

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

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

Deng, J. D., & Purvis, M. K. (2015). Teaching service modelling to a mixed class: An integrated approach. Informatics in Education, 14(1), 35-50. doi: 10.15388/infedu.2015.03

Aderohunmu, F. A., Brunelli, D., Deng, J. D., & Purvis, M. K. (2015). A data acquisition protocol for a reactive wireless sensor network monitoring application. Sensors, 15(5), 10221-10254. doi: 10.3390/s150510221

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

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

Guan, G., Wang, Z., Lu, S., Deng, J. D., & Feng, D. D. (2013). Keypoint-based keyframe selection. IEEE Transactions on Circuits & Systems for Video Technology, 23(4), 729-734. doi: 10.1109/TCSVT.2012.2214871

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. doi: 10.1007/s11042-011-0902-2

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: 10.1016/j.patcog.2012.02.036

Deng, J. D., Purvis, M. K., & Purvis, M. A. (2011). Software effort estimation: Harmonizing algorithms and domain knowledge in an integrated data mining approach. International Journal of Intelligent Information Technologies, 7(3), 41-53. doi: 10.4018/jiit.2011070104

Deng, J. D., & Purvis, M. K. (2011). Multi-core application performance optimization using a constrained tandem queueing model. Journal of Network & Computer Applications, 34(6), 1990-1996. doi: 10.1016/j.jnca.2011.07.004

Aderohunmu, F. A., Deng, J. D., & Purvis, M. K. (2011). Enhancing clustering in wireless sensor networks with energy heterogeneity. International Journal of Business Data Communications & Networking, 7(4), 18-31. doi: 10.4018/jbdcn.2011100102

Deng, J. D. (2010). Controlling chaotic associative memory for multiple pattern retrieval. Cognitive Computation, 2(4), 257-264. doi: 10.1007/s12559-010-9043-6

Deng, J. D., Simmermacher, C., & Cranefield, S. (2008). A study on feature analysis for musical instrument classification. IEEE Transactions on Systems, Man & Cybernetics: Part B, 38(2), 429-438. doi: 10.1109/TSMCB.2007.913394

Deng, D. (2007). Content-based image collection summarization and comparison using self-organizing maps. Pattern Recognition, 40, 718-727.

Deng, D., & Wolf, H. (2005). POISE: Achieving content-based picture organisation for image search engines. Lecture Notes in Computer Science, 3682, 1-7.

Deng, D. (2004). Content-based profiling of image collections: A SOM-based approach. International Journal of Computers, Systems & Signals, 5(2), 44-52.

Wang, X., Whigham, P., Deng, D., & Purvis, M. K. (2004). ″Time-line″ hidden Markov experts for time series prediction. Neural Information Processing - Letters & Reviews, 3(2), 39-48.

Deng, D., & Kasabov, N. K. (2003). On-line pattern analysis by evolving self-organizing maps. Neurocomputing, 51, 87-103.

Deng, D., Zhang, L., Dong, S. B., & Yu, Y. L. (1999). Digital video library: Key techniques and its implementation. Journal of South China University of Technology (Natural Science), 27(3), 19-24.

Huang, Q., Deng, D., & Pan, D. (1999). Texture classification analysis for casting defects based on wavelet theory. Journal of South China University of Technology (Natural Science), 27(4), 42-47.

Xiao, P., Deng, D., & Yu, Y. L. (1999). Fuzzy associative inference and its implementation. Control Theory & Applications, 16(4), 562-565.

Deng, D., & Yu, Y. (1998). An algorithm of nonlinear competitive Hebbian learning. Journal of South China University of Technology (Natural Science), 26(9), 6-11.

Wang, Z., Deng, D., & Yu, Y. (1998). Fractal technique for color image segmentation. Journal of South China University of Technology (Natural Science), 26(10), 64-70.

Deng, D., Yu, Y. L., & Chan, K. P. (1997). Self-organized Hebbian learning of receptive fields from image pyramids. Journal of Circuits & Systems, 2(3), 8-12.

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Journal - Research Other

Zhang, H., & Deng, J. D. (2020). Design and management solutions to emergent networking technologies. International Journal of Parallel, Emergent & Distributed Systems. Advance online publication. doi: 10.1080/17445760.2020.1767102

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Conference Contribution - Published proceedings: Full paper

Li, J., Deng, J. D., De Ridder, D., & Adhia, D. (2020). Gender classification of EEG signals using a motif attribute classification ensemble. Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN). (pp. 1-8). IEEE. doi: 10.1109/IJCNN48605.2020.9207695

Deng, J. D. (2019). Performance modelling of synchronized predictive sensing for clustered wireless sensor networks. Proceedings of the 25th Asia-Pacific Conference on Communications (APCC). (pp. 165-170). IEEE. doi: 10.1109/APCC47188.2019.9026508

Hou, J., Ding, X., Deng, J. D., & Cranefield, S. (2019). Unsupervised domain adaptation using deep networks with cross-grafted stacks. Proceedings of the IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). (pp. 3257-3264). IEEE. doi: 10.1109/ICCVW.2019.00407

Zareei, S., Afshar Sedigh, A. H., Deng, J. D., & Purvis, M. (2018). Buffer management using integrated queueing models for mobile energy harvesting sensors. Proceedings of the IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC). 17581028 . IEEE. doi: 10.1109/PIMRC.2017.8292636

Lee, S. H.-S., Deng, J. D., Purvis, M. K., & Purvis, M. (2018). Hierarchical population-based learning for optimal large-scale coalition structure generation in smart grids. In T. Mitrovic, B. Xue & X. Li (Eds.), Advances in artifical intelligence: Lecture notes in artificial intelligence (Vol. 11320). (pp. 16-28). Cham, Switzerland: Springer. doi: 10.1007/978-3-030-03991-2_2

Zareei, S., & Deng, J. D. (2018). Impact of compression ratio and reconstruction methods on ECG classification for E-health gadgets: A preliminary study. In T. Mitrovic, B. Xue & X. Li (Eds.), Advances in artifical intelligence: Lecture notes in artificial intelligence (Vol. 11320). (pp. 85-97). Cham, Switzerland: Springer. doi: 10.1007/978-3-030-03991-2_9

Lee, S. H.-S., Deng, J. D., Peng, L., Purvis, M. K., & Purvis, M. (2017). Top-k merit weighting PBIL for optimal coalition structure generation of smart grids. In D. Liu, S. Xie, Y. Li, D. Zhao & E.-S. M. El-Alfy (Eds.), Neural information processing: Lecture notes in computer science (Vol. 10637). (pp. 171-181). Cham, Switzerland: Springer International Publishing. doi: 10.1007/978-3-319-70093-9_18

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

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

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

Deng, J. D. (2016). Online outlier detection of energy data streams using incremental and kernel PCA algorithms. Proceedings of the 16th International Conference on Data Mining Workshops. (pp. 390-397). doi: 10.1109/ICDMW.2016.0062

Zareei, S., & Deng, J. D. (2016). Energy management policy for fitness gadgets: A case study of human daily routines. Proceedings of the International Telecommunication Networks and Applications Conference (ITNAC). IEEE. doi: 10.1109/ATNAC.2016.7878774

Gu, X., Deng, J. D., & Purvis, M. K. (2016). A hierarchical segmentation tree for superpixel-based image segmentation. Proceedings of the Image and Vision Computing New Zealand (IVCNZ) International Conference. IEEE. doi: 10.1109/ivcnz.2016.7804454

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