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Jeremiah Deng

BSc(UESTC), MSc(SCUT), PhD(SCUT), MIEEE, MACM

Position
Associate Professor
Room
8.13, Commerce Building
Phone
+64 3 479 8090
Email
jeremiah.deng@otago.ac.nz
Web page
http://www.covic.otago.ac.nz/~jdeng
Supervising
Sean Lee, Ahmad Shahi, Sophie Zareei
Papers
2017 S2: INFO411
2017 FY: INFO501, INFO580
2018 FY: INFO580, INFO501
Research group
Intelligent Computing and Networking

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.

Publications

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

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

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

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

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

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

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

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., Deng, J. D., Li, Y., Gu, T., & Zhang, J. (2017). A level-based learning swarm optimizer for large scale optimization. IEEE Transactions on Evolutionary Computation. Advance online publication. doi: 10.1109/TEVC.2017.2743016

Liu, X.-F., Zhang, Z.-H., Deng, J. D., Li, Y., Gu, T., & Zhang, J. (2016). An energy efficient ant colony system for virtual machine placement in cloud computing. IEEE Transactions on Evolutionary Computation. Advance online publication. doi: 10.1109/TEVC.2016.2623803

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

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

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

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

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

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. (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

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

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.

More publications...