Accessibility Skip to Global Navigation Skip to Local Navigation Skip to Content Skip to Search Skip to Site Map Menu

Jeremiah Deng

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

Room 2.09 (temporary office), 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).

^ Top of page

Papers

Supervision

Currently supervising

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

^ Top of page

^ Top of page

Publications

Hu, X.-M., Zhang, S.-R., Li, M., & Deng, J. D. (2021). Multimodal particle swarm optimization for feature selection. Applied Soft Computing, 113, 107887. doi: 10.1016/j.asoc.2021.107887

Li, J., Deng, J. D., Adhia, D., & de Ridder, D. (2021). Resting-state EEG sex classification using selected brain connectivity representation. In T. D. Pham, H. Yan, M. W. Ashraf & F. Sjöberg (Eds.), Advances in artificial intelligence, computation, and data science: For medicine and life science. (pp. 319-329). Cham, Switzerland: Springer. doi: 10.1007/978-3-030-69951-2_13

Hou, J., Deng, J. D., Cranefield, S., & Ding, X. (2021). Cross-domain latent modulation for variational transfer learning. Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV). (pp. 3148-3157). Piscataway, NJ: IEEE. doi: 10.1109/WACV48630.2021.00319

Wei, F.-F., Chen, W.-N., Yang, Q., Deng, J., Luo, X.-N., Jin, H., & Zhang, J. (2021). A classifier-assisted level-based learning swarm optimizer for expensive optimization. IEEE Transactions on Evolutionary Computation, 25(2), 219-233. doi: 10.1109/TEVC.2020.3017865

Li, J., Deng, J. D., Adhia, D., & de Ridder, D. (2020). Resting-state EEG sex classification using selected brain connectivity representation. arXiv. Retrieved from https://arxiv.org/abs/2012.11105

Li, J., Deng, J. D., Adhia, D., & de Ridder, D. (2021). Resting-state EEG sex classification using selected brain connectivity representation. In T. D. Pham, H. Yan, M. W. Ashraf & F. Sjöberg (Eds.), Advances in artificial intelligence, computation, and data science: For medicine and life science. (pp. 319-329). Cham, Switzerland: Springer. doi: 10.1007/978-3-030-69951-2_13

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

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

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

Chapter in Book - Research

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

Chapter in Book - Research

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.

Chapter in Book - Research

Hu, X.-M., Zhang, S.-R., Li, M., & Deng, J. D. (2021). Multimodal particle swarm optimization for feature selection. Applied Soft Computing, 113, 107887. doi: 10.1016/j.asoc.2021.107887

Journal - Research Article

Wei, F.-F., Chen, W.-N., Yang, Q., Deng, J., Luo, X.-N., Jin, H., & Zhang, J. (2021). A classifier-assisted level-based learning swarm optimizer for expensive optimization. IEEE Transactions on Evolutionary Computation, 25(2), 219-233. doi: 10.1109/TEVC.2020.3017865

Journal - Research Article

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

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

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

Journal - Research Article

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

Journal - Research Article

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

Journal - Research Article

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

Journal - Research Article

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

Journal - Research Article

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

Journal - Research Article

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

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

Journal - Research Article

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

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

Journal - Research Article

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

Journal - Research Article

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

Journal - Research Article

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

Journal - Research Article

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

Journal - Research Article

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

Journal - Research Article

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

Journal - Research Article

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

Journal - Research Article

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

Journal - Research Article

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

Journal - Research Article

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

Journal - Research Article

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

Journal - Research Article

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.

Journal - Research Article

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

Journal - Research Article

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.

Journal - Research Article

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.

Journal - Research Article

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

Journal - Research Article

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.

Journal - Research Article

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.

Journal - Research Article

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.

Journal - Research Article

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

Journal - Research Other

Hou, J., Deng, J. D., Cranefield, S., & Ding, X. (2021). Cross-domain latent modulation for variational transfer learning. Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV). (pp. 3148-3157). Piscataway, NJ: IEEE. doi: 10.1109/WACV48630.2021.00319

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 International Joint Conference on Neural Networks (IJCNN). (pp. 1-8). IEEE. doi: 10.1109/IJCNN48605.2020.9207695

Conference Contribution - Published proceedings: Full paper

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

Conference Contribution - Published proceedings: Full paper

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

Conference Contribution - Published proceedings: Full paper

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

Conference Contribution - Published proceedings: Full paper

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

Conference Contribution - Published proceedings: Full paper

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

Conference Contribution - Published proceedings: Full paper

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

Conference Contribution - Published proceedings: Full paper

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

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

Conference Contribution - Published proceedings: Full paper

More publications...