BSc(UESTC), MSc(SCUT), PhD(SCUT), MIEEE, MACM
Professor
Ōwheo Building, 2.24
Tel +64 3 479 8090
Email jeremiah.deng@otago.ac.nz
Web http://www.covic.otago.ac.nz/~jdeng
Background and interests
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. Professor 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.
Professor 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).
Papers
- INFO 204 Introduction to Data Science
- INFO 411 Machine Learning and Data Mining
- INFO 508 Research Project
Supervision
Currently supervising
- Sean Lee
- Ahmad Shahi
- Sophie Zareei
- Robert Hou
- Chontira Chumsaeng
Publications
Balasubramanian, L., Nguyen, T. T., & Deng, J. (2026). Maritime situational awareness: Harnessing big data and AI for textual risk mining and insights. Array, 32, 101229. doi: 10.1016/j.array.2026.101229 Journal - Research Article
Cook, S., Deng, J., De Ridder, D., & Song, J.-J. (2026). Tinnitus vs hearing loss without tinnitus classification using source-space EEG phase-amplitude coupling. In A. Banstola (Ed.), Proceedings of the 42nd International Australasian Winter Conference on Brain Research (AWCBR). 42, 4.2, (pp. 49). Retrieved from https://www.awcbr.org Conference Contribution - Published proceedings: Abstract
Zhang, H., Sun, B., Duan, D.-T., Liu, X.-F., Deng, J. D., & Jiang, J. (2026). A matrix-based evolutionary algorithm for electric vehicle charging station location. Proceedings of the 13th International Conference on Machine Intelligence Theory and Applications (MiTA). (pp. 544-550). Danvers, MA: IEEE. doi: 10.1109/MITA69365.2026.11582455 Conference Contribution - Published proceedings: Full paper
Qian, T., Liu, Y.-Y., Liu, X.-F., Bai, Y., Deng, J. D., Jiang, J., & Zhang, J. (2026). A comparative study on solution construction strategies for strategies for heterogeneous multi-agent scheduling. Proceedings of the 13th International Conference on Machine Intelligence Theory and Applications (MiTA). (pp. 256-263). Danvers, MA: IEEE. doi: 10.1109/MITA69365.2026.11582590 Conference Contribution - Published proceedings: Full paper
Chambers, T., Dean, F., Klavs, J., Stanger, N., Kim, A., Hales, S., Douwes, J., Baker, M. G., & Deng, J. (2026). A national-scale historical assessment of nitrate in public drinking water supplies in New Zealand: Data integration and machine learning imputation approaches. Water Environment Research, 98(2), e70296. doi: 10.1002/wer.70296 Journal - Research Article
2026
Authored Book - Research
Deng, J. D. (2026). Machine learning with Julia: An algorithmic exploration. Singapore: Springer, 422p. doi: 10.1007/978-981-96-9689-5
Journal - Research Article
Balasubramanian, L., Nguyen, T. T., & Deng, J. (2026). Maritime situational awareness: Harnessing big data and AI for textual risk mining and insights. Array, 32, 101229. doi: 10.1016/j.array.2026.101229
Chambers, T., Dean, F., Klavs, J., Stanger, N., Kim, A., Hales, S., Douwes, J., Baker, M. G., & Deng, J. (2026). A national-scale historical assessment of nitrate in public drinking water supplies in New Zealand: Data integration and machine learning imputation approaches. Water Environment Research, 98(2), e70296. doi: 10.1002/wer.70296
Briscoe, D. K., Velasco Castedo, R., Clarke, P., Di Stella, D., & Deng, J. D. (2026). Interpretable voice-based Parkinson’s detection: Ssex-specific acoustic biomarkers with subject-level nested cross-validation. Biomedical Signal Processing & Control, 126, 111014. doi: 10.1016/j.bspc.2026.111014
Yue, Y., Katare, R., Deng, J., Alencar da Costa, D., & Manning, P. (2026). Circulating microRNA signatures reveal core and reversible dysregulation in obesity via machine learning. Journal of Physiology. Advance online publication. doi: 10.1113/jp290345
Yue, Y., Manning, P., De Ridder, D., Hall, M., Adhia, D. B., Ross, S., Alencar da Costa, D., & Deng, J. D. (2026). Machine learning-based identification of abnormal functional connectivity in obesity across different metabolic states. Communications Medicine, 6, 241. doi: 10.1038/s43856-026-01518-5
Li, J., Deng, J. D., Adhia, D., Hall, M., Mani, R., & De Ridder, D. (2026). EEG connectivity features associated with fibromyalgia revealed by machine learning. Frontiers in Pain Research, 6, 1704444. doi: 10.3389/fpain.2025.1704444
Conference Contribution - Published proceedings: Full paper
Zhang, H., Sun, B., Duan, D.-T., Liu, X.-F., Deng, J. D., & Jiang, J. (2026). A matrix-based evolutionary algorithm for electric vehicle charging station location. Proceedings of the 13th International Conference on Machine Intelligence Theory and Applications (MiTA). (pp. 544-550). Danvers, MA: IEEE. doi: 10.1109/MITA69365.2026.11582455
Qian, T., Liu, Y.-Y., Liu, X.-F., Bai, Y., Deng, J. D., Jiang, J., & Zhang, J. (2026). A comparative study on solution construction strategies for strategies for heterogeneous multi-agent scheduling. Proceedings of the 13th International Conference on Machine Intelligence Theory and Applications (MiTA). (pp. 256-263). Danvers, MA: IEEE. doi: 10.1109/MITA69365.2026.11582590
Conference Contribution - Published proceedings: Abstract
Cook, S., Deng, J., De Ridder, D., & Song, J.-J. (2026). Tinnitus vs hearing loss without tinnitus classification using source-space EEG phase-amplitude coupling. In A. Banstola (Ed.), Proceedings of the 42nd International Australasian Winter Conference on Brain Research (AWCBR). 42, 4.2, (pp. 49). Retrieved from https://www.awcbr.org
2025
Journal - Research Article
Yuan, S., Tian, X., Lin, W., Xia, S., & Deng, J. D. (2025). Quantum granular-ball generation methods and their application in KNN classification. Scientific Reports, 15, 29779. doi: 10.1038/s41598-025-14724-3
Yuan, S., Li, X., Xia, S., Qing, X., & Deng, J. D. (2025). Quantum color image edge detection algorithm based on Sobel operator. Quantum Information Processing, 24, 195. doi: 10.1007/s11128-025-04819-1
Tetereva, A., Knodt, A. R., Melzer, T. R., van der Vliet, W., Gibson, B., Hariri, A. R., … Lal Khakpoor, F., … Deng, J., Ireland, D., Ramrakha, S., & Pat, N. (2025). Improving predictability, reliability, and generalizability of brain-wide associations for cognitive abilities via multimodal stacking. PNAS Nexus, 4(6), pgaf175. doi: 10.1093/pnasnexus/pgaf175
Yuan, S., Li, X., Yinxia, S., Qing, X., & Deng, J. D. (2025). Improved quantum image weighted average filtering algorithm. Quantum Information Processing, 24, 125. doi: 10.1007/s11128-025-04741-6
Li, J., De Ridder, D., Adhia, D., Hall, M., Mani, R., & Deng, J. D. (2025). Modified feature selection for improved classification of resting-state raw EEG signals in chronic knee pain. IEEE Transactions on Biomedical Engineering, 72(5), 1688-1696. doi: 10.1109/TBME.2024.3517659
Conference Contribution - Published proceedings: Full paper
Yue, Y., De Ridder, D., Manning, P., & Deng, J. D. (2025). Variational autoencoder learns better feature representations for EEG-based obesity classification. In A. Antonacopoulos, S. Chaudhuri, R. Chellappa, C.-L. Liu, S. Bhattacharya & U. Pal (Eds.), Pattern Recognition: Lecture notes in computer science (Vol. 15323). (pp. 179-191). Cham, Switzerland: Springer. doi: 10.1007/978-3-031-78347-0_12
Conference Contribution - Published proceedings: Abstract
Buianova, I., Silvestrin, M., Deng, J., & Pat, N. (2025). Multimodal MRI marker of cognition explains the association between cognition and mental health in UK biobank. In A. Banstola (Ed.), Proceedings of the 41st International Australasian Winter Conference on Brain Research (AWCBR). 11.8, (pp. 58). Retrieved from https://www.awcbr.org
2024
Journal - Research Article
Yuan, S., Zhao, W., Deng, J. D., Xia, S., & Li, X. (2024). Quantum image edge detection based on Laplacian of Gaussian operator. Quantum Information Processing, 23(5), 178. doi: 10.1007/s11128-024-04392-z
Pang, Y., Zhang, H., Deng, J. D., Peng, L., & Teng, F. (2024). Collaborative learning with heterogeneous local models: A rule-based knowledge fusion approach. IEEE Transactions on Knowledge & Data Engineering, 36(11), 5768-5783. doi: 10.1109/TKDE.2023.3341808
2023
Conference Contribution - Published proceedings: Full paper
Fernando, E. N., & Deng, J. D. (2023). Enhancing hate speech detection in Sinhala language on social media using machine learning. Proceedings of the Australasian Conference on Information Systems (ACIS). 52. Retrieved from https://aisel.aisnet.org/acis2023
Zwanenburg, S. P., Vincent, K., Deng, J., Gurtner, M., Gage, R., Smith, M., & Signal, L. (2023). Kids’ digital distractions: An observation study of recorded everyday screentime by 12-year-olds. Proceedings of the Australasian Conference on Information Systems (ACIS). 110. Retrieved from https://aisel.aisnet.org/acis2023
Conference Contribution - Published proceedings: Abstract
Tetereva, A., Li, J., Gibson, B., van der Vliet, W., Deng, J., & Pat, N. (2023). Benchmark machine-learning based multimodal MRI to capture cognitive abilities across the lifespan: Predictability, reliability and generalisability. Proceedings of the Psycolloquy Symposium. (pp. 35). Dunedin, New Zealand: Department of Psychology, University of Otago. Retrieved from https://www.otago.ac.nz/psychology/research/otago059081.html
Tetereva, A., Li, J., Gibson, B., van der Vliet, W., Deng, J., & Pat, N. (2023). Capability of multimodal MRI to capture cognitive abilities across the lifespan: Predictability, reliability and generalizability. In K.-L. Horne (Ed.), Proceedings of the 39th International Australasian Winter Conference on Brain Research (AWCBR). (pp. 70). Retrieved from https://www.awcbr.org
2022
Journal - Research Article
Tetereva, A., Li, J., Deng, J. D., Stringaris, A., & Pat, N. (2022). Capturing brain-cognition relationship: Integrating task-based fMRI across tasks markedly boosts prediction and test-retest reliability. NeuroImage, 263, 119588. doi: 10.1016/j.neuroimage.2022.119588
Hou, J., Ding, X., Deng, J. D., & Cranefield, S. (2022). Deep adversarial transition learning using cross-grafted generative stacks. Neural Networks, 149, 172-183. doi: 10.1016/j.neunet.2022.02.011
Journal - Research Other
Gurtner, M., Smith, M., Gage, R., Howey-Brown, A., Wang, X., Latavao, T., Deng, J. D., Zwanenburg, S. P., Stanley, J., & Signal, L. (2022). Objective assessment of the nature and extent of children’s internet-based world: Protocol for the Kids Online Aotearoa study. JMIR Research Protocols, 11(10), e39017. doi: 10.2196/39017
Conference Contribution - Published proceedings: Full paper
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
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
Conference Contribution - Published proceedings: Abstract
Tetereva, A., Li, J., Gibson, B., Deng, J., & Pat, N. (2022). Multimodal MRI predictive biomarkers for cognition across the lifespan. In K. Horne (Ed.), Proceedings of the 38th International Australasian Winter Conference on Brain Research (AWCBR). (pp. 66). Retrieved from https://www.queenstownresearchweek.org
2021
Chapter in Book - Research
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
Journal - Research Article
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
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
Conference Contribution - Published proceedings: Full paper
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
2020
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
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
Conference Contribution - Published proceedings: Full paper
Deng, J. D., & Parry, M. (2020). Data Science programmes: Is there an ideal design? Proceedings of the 62nd International Statistics Institute (ISI) World Statistics Congress: Special Topic Session. Vol. 3, (pp. 66-73). Putrajaya, Malaysia: Department of Statistics Malaysia. Retrieved from www.isi2019.org
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
Working Paper; Discussion Paper; Technical Report
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
2019
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
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
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
2018
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: PAKDD Workshops, revised selected papers: Lecture notes in artificial intelligence (Vol. 11154). (pp. 345-356). Cham, Switzerland: Springer. doi: 10.1007/978-3-030-04503-6_33
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
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
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
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., 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: Abstract
Lee, S. H.-S., Deng, J. D., Purvis, M., & Purvis, M. (2018). A cooperative model for renewable energy prosumer in smart-grids: A pathway to be a city of 100% renewable energy. Proceedings of the 12th Otago Energy Research Centre (OERC) Energy & Climate Change Symposium. (pp. 21). Retrieved from https://www.otago.ac.nz/oerc
2017
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
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
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
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
Gu, X., Deng, J. D., & Purvis, M. K. (2017). A hierarchical segmentation tree for superpixel-based image segmentation. Proceedings of the 2016 International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE. doi: 10.1109/ivcnz.2016.7804454
2016
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: Proceedings of the 20th Pacific-Asia Conference PAKDD, part 1: Lecture Notes in Artificial Intelligence (Vol. 9651). (pp. 165-176). Cham, Switzerland: Springer. doi: 10.1007/978-3-319-31753-3_14
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
Conference Contribution - Published proceedings: Full paper
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
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
Conference Contribution - Verbal presentation and other Conference outputs
Basubas, D. E., Ohlemuller, R. S., Lord, J. M., & Deng, J. D. (2016, February). Variation in flowering patterns and flowering phenology in alpine cushion plants in response to microclimate. Verbal presentation at the New Zealand Geographical Society Conference: Geographical Interactions, Dunedin, New Zealand.
Working Paper; Discussion Paper; Technical Report
Gu, X., Deng, J. D., & Purvis, M. K. (2016). Image segmentation with superpixel-based covariance descriptors in low-rank representation. arXiv. 7p. Retrieved from http://arxiv.org/abs/1605.05466
2015
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
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
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
Conference Contribution - Published proceedings: Full paper
Khan, S., Yong, S.-P., & Deng, J. D. (2015). Ensemble classification with modified SIFT descriptor for medical image modality. Proceedings of the Image and Vision Computing New Zealand (IVCNZ) International Conference. 118. IEEE. doi: 10.1109/IVCNZ.2015.7761517
Lin, H., Deng, J. D., & Woodford, B. J. (2015). Anomaly detection in crowd scenes via online adaptive one-class support vector machines. Proceedings of the International Conference on Image Processing (ICIP). (pp. 2434-2438). IEEE. doi: 10.1109/icip.2015.7351239
Shahi, A., Woodford, B. J., & Deng, J. D. (2015). Event classification using adaptive cluster-based ensemble learning of streaming sensor data. In B. Pfahringer & J. Renz (Eds.), Advances in artificial intelligence: Lecture notes in artificial intelligence (Vol. 9457). (pp. 505-516). Cham, Switzerland: Springer. doi: 10.1007/978-3-319-26350-2_45
Javed, A., Huang, Z., Zhang, H., & Deng, J. D. (2015). CAMS: Consensus-based Anchor-node Management Scheme for train localisation. In S. Papavassiliou & S. Ruehrup (Eds.), Ad-hoc, mobile, and wireless networks: Lecture Notes in Computer Science (Vol. 9143). (pp. 107-120). Cham, Switzerland: Springer International. doi: 10.1007/978-3-319-19662-6_8
2014
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
Conference Contribution - Published proceedings: Full paper
Gu, X., Deng, J. D., & Purvis, M. K. (2014). Improving superpixel-based image segmentation by incorporating color covariance matrix manifolds. Proceedings of the International Conference on Image Processing (ICIP). (pp. 4403-4406). IEEE. doi: 10.1109/ICIP.2014.7025893
Gu, X., Deng, J. D., & Purvis, M. K. (2014). Superpixel-based segmentation using multi-layer bipartite graphs and Grassmann manifolds. Proceedings of the 29th International Conference on Image and Vision Computing New Zealand (IVCNZ). (pp. 119-123). New York: ACM. doi: 10.1145/2683405.2683428
Deng, J. D. (2014). Empirical capacity modeling and evaluation of delay tolerant network routing protocols. Proceedings of the 33rd International Performance Computing and Communications (IPCC) Conference. doi: 10.1109/PCCC.2014.7017046
Lin, H., Deng, J. D., & Woodford, B. J. (2014). Spatial-temporal pyramid matching for crowd scene analysis. In A. Rahman, J. Deng & J. Li (Eds.), Proceedings of the 2nd Workshop on Machine Learning for Sensory Data Analysis (MLSDA). (pp. 12-18). New York: ACM. doi: 10.1145/2689746.2689751
Deng, J. D., Lee, H.-S., McMillan, C., Rimoni, A., & Zhang, M. (2014). Analyzing wind speed data through Markov chain based profiling and clustering. In A. Rahman, J. Deng & J. Li (Eds.), Proceedings of the 2nd Workshop on Machine Learning for Sensory Data Analysis (MLSDA). (pp. 67-73). New York: ACM. doi: 10.1145/2689746.2689756
Javed, A., Zhang, H., Huang, Z., & Deng, J. D. (2014). BWS: Beacon-driven wake-up scheme for train localization using wireless sensor networks. Proceedings of the International Conference on Communications (ICC). (pp. 276-281). IEEE. doi: 10.1109/icc.2014.6883331
Conference Contribution - Edited volume of conference proceedings
Rahman, A., Deng, J., & Li, J. (Eds.). (2014). Proceedings of the 2nd Workshop on Machine Learning for Sensory Data Analysis (MLSDA). New York: ACM. 81p.
2013
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 International Workshops, revised selected papers, part 1: Lecture notes in computer science (Vol. 7728). (pp. 308-314). Berlin, Germany: Springer. doi: 10.1007/978-3-642-37410-4_27
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
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
Conference Contribution - Published proceedings: Full paper
Shah, M., Deng, J. D., & Woodford, B. J. (2013). Improving mixture of Gaussians background model through adaptive learning and spatio-temporal voting. Proceedings of the International Conference on Image Processing (ICIP). (pp. 3436-3440). IEEE. doi: 10.1109/ICIP.2013.6738709
Deng, J. D., & Zhang, Y. (2013). Light-weight online predictive data aggregation for wireless sensor networks. Proceedings of Workshop on Machine Learning for Sensory Data Analysis (MLSDA). (pp. 35-42). New York: ACM. doi: 10.1145/2542652.2542657
Lin, H., Deng, J. D., & Woodford, B. J. (2013). Event detection using quantized binary code and spatial-temporal locality preserving projections. In S. Cranefield & A. Nayak (Eds.), Advances in artificial intelligence: Lecture notes in artificial intelligence (Vol. 8272). (pp. 123-134). Heidelberg, Germany: Springer. doi: 10.1007/978-3-319-03680-9_14
Shah, M., Deng, J. D., & Woodford, B. J. (2013). Growing neural gas video background model (GNG-BM). In S. Cranefield & A. Nayak (Eds.), Advances in artificial intelligence: Lecture notes in artificial intelligence (Vol. 8272). (pp. 135-147). Heidelberg, Germany: Springer. doi: 10.1007/978-3-319-03680-9_15
Aderohunmu, F. A., Paci, G., Benini, L., Deng, J. D., & Brunelli, D. (2013). SWIFTNET: A data acquisition protocol for fast-reactive monitoring applications. Proceedings of the 8th International Symposium on Industrial Embedded Systems (SIES). (pp. 93-96). IEEE. doi: 10.1109/SIES.2013.6601478
Aderohunmu, F. A., Paci, G., Brunelli, D., Deng, J. D., Benini, L., & Purvis, M. (2013). Trade-offs of forecasting algorithm for extending WSN lifetime in a real-world deployment. Proceedings of the International Conference on Distributed Computing in Sensor Systems (DCOSS). (pp. 283-285). IEEE. doi: 10.1109/DCOSS.2013.45
Xu, Y., Deng, J. D., & Nowostawski, M. (2013). Quality of service for video streaming over multi-hop wireless networks: Admission control approach based on analytical capacity estimation. Proceedings of the 8th International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). (pp. 345-350). IEEE. doi: 10.1109/ISSNIP.2013.6529814
Aderohunmu, F. A., Paci, G., Brunelli, D., Deng, J. D., & Benini, L. (2013). Prolonging the lifetime of wireless sensor networks using light-weight forecasting algorithms. Proceedings of the 8th International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). (pp. 461-466). IEEE. doi: 10.1109/ISSNIP.2013.6529834
Aderohunmu, F. A., Paci, G., Brunelli, D., Deng, J. D., Benini, L., & Purvis, M. (2013). An Application-specific forecasting algorithm for extending WSN lifetime. Proceedings of the International Conference on Distributed Computing in Sensor Systems (DCOSS). (pp. 374-381). IEEE. doi: 10.1109/DCOSS.2013.51
Conference Contribution - Edited volume of conference proceedings
Deng, J. D., & Zhang, H. (Eds.). (2013). Proceedings of the 1st Workshop on Machine Learning for Sensory Data Analysis (MLSDA). New York: ACM. 55p.
Working Paper; Discussion Paper; Technical Report
Javed, A., Zhang, H., Huang, Z., & Deng, J. (2013). BWS: Beacon-driven wake-up scheme for train localization using wireless sensor networks [Technical Report OUCS-2013-13]. Dunedin, New Zealand: Department of Computer Science, University of Otago. 8p.
2012
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
Conference Contribution - Published proceedings: Full paper
Lin, H., Deng, J. D., & Woodford, B. J. (2012). Video manifold modelling: Finding the right parameter settings for anomaly detection. In B. McCane, S. Mills & J. D. Deng (Eds.), Proceedings of the 27th Image and Vision Computing New Zealand Conference (IVCNZ). (pp. 168-173). New York: ACM. [Full Paper]
Shah, M., Deng, J. D., & Woodford, B. J. (2012). Enhancing the Mixture of Gaussians background model with local matching and local adaptive learning. In B. McCane, S. Mills & J. D. Deng (Eds.), Proceedings of the 27th Image and Vision Computing New Zealand Conference (IVCNZ). (pp. 103-108). New York: ACM. [Full Paper]
Xu, Y., Deng, J. D., & Nowostawski, M. (2012). Optimizing routing in multi-hop wireless networks using analytical capacity estimation: A study on video streaming. Proceedings of the 14th International Conference on High Performance Computing and Communication and the 9th International Conference on Embedded Software and Systems (HPCC-ICESS). (pp. 748-755). doi: 10.1109/HPCC.2012.106
Aderohunmu, F. A., Deng, J. D., & Purvis, M. K. (2012). Optimization of energy-efficient protocols with energy-heterogeneity for coverage preservation in wireless sensor networks: An empirical study. Proceedings of the 14th International Conference on High Performance Computing and Communication and the 9th International Conference on Embedded Software and Systems (HPCC-ICESS). (pp. 1173-1178). doi: 10.1109/HPCC.2012.172
Yong, S.-P., Deng, J. D., & Purvis, M. K. (2012). Key-frame extraction of wildlife video based on semantic context modeling. Proceedings of the International Joint Conference on Neural Networks (IJCNN). (pp. 1702-1709). IEEE. doi: 10.1109/IJCNN.2012.6252602
Conference Contribution - Edited volume of conference proceedings
McCane, B., Mills, S., & Deng, J. D. (Eds.). (2012). Proceedings of the 27th Image and Vision Computing New Zealand Conference (IVCNZ). New York: ACM. 547p.
2011
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
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
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
Conference Contribution - Published proceedings: Full paper
Aderohunmu, F. A., Deng, J. D., & Purvis, M. K. (2011). A deterministic energy-efficient clustering protocol for wireless sensor networks. Proceedings of the Seventh International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). (pp. 341-346). doi: 10.1109/ISSNIP.2011.6146592
Shah, M., Deng, J., & Woodford, B. (2011). Enhanced codebook model for real-time background subtraction. In B.-L. Lu, L. Zhang & J. Kwok (Eds.), Neural information processing: Lecture notes in computer science (Vol. 7064). (pp. 449-458). Berlin, Germany: Springer. doi: 10.1007/978-3-642-24965-5_51
2010
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
Conference Contribution - Published proceedings: Full paper
Yong, S.-P., Deng, J. D., & Purvis, M. K. (2010). Modeling semantic context for key-frame extraction in wildlife video. Proceedings of the 25th International Conference of Image and Vision Computing New Zealand (IVCNZ). doi: 10.1109/IVCNZ.2010.6148855
Shah, M., Deng, J., & Woodford, B. J. (2010). Localized adaptive learning of Mixture of Gaussians models for background extraction. Proceedings of the 25th International Conference of Image and Vision Computing New Zealand (IVCNZ). doi: 10.1109/IVCNZ.2010.6148870
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. doi: 10.1109/ICME.2010.5583899
Conference Contribution - Published proceedings: Abstract
Shah, M., Deng, J., & Woodford, B. J. (2010). Localized adaptive learning of mixture of Gaussians models for background extraction (LAL-MoG). Proceedings of the Information Science Postgraduate Day. (pp. 11-12). Retrieved from http://infosci.otago.ac.nz/information-science-postgrad-day-2010/
Manimala, J. M., & Deng, J. D. (2010). Chaotic neural networks: Incremental learning and capacity study. Proceedings of the Information Science Postgraduate Day. (pp. 45-46). Retrieved from http://infosci.otago.ac.nz/information-science-postgrad-day-2010/
Yong, V., Deng, J., & Purvis, M. (2010). Modeling semantic context for novelty detection and key-frame extraction. Proceedings of the Information Science Postgraduate Day. (pp. 33-34). Retrieved from http://infosci.otago.ac.nz/information-science-postgrad-day-2010/
Aderohunmu, F. A., Deng, J. D., & Purvis, M. K. (2010). Managing energy heterogeneity in wireless sensor networks. Proceedings of the Information Science Postgraduate Day. (pp. 15-16). Retrieved from http://infosci.otago.ac.nz/information-science-postgrad-day-2010/
Working Paper; Discussion Paper; Technical Report
Yong, S.-P., Deng, J. D., & Purvis, M. K. (2010). Modelling semantic context for novelty detection in wildlife scenes [Information Science Discussion Paper: No. 2010/02]. Dunedin, New Zealand: Department of Information Science, University of Otago. 13p.
2009
Conference Contribution - Published proceedings: Full paper
Deng, J. D. (2009). Improving feature extraction for automatic medical image categorization. In D. Bailey (Ed.), Proceedings of the 24th International Conference Image and Vision Computing New Zealand. (pp. 379-384). IEEE. doi: 10.1109/IVCNZ.2009.5378376
Working Paper; Discussion Paper; Technical Report
Aderohunmu, F. A., Deng, J. D., & Purvis, M. K. (2009). Enhancing clustering in wireless sensor networks with energy heterogeneity [Information Science Discussion Paper: No. 2009/07]. Dunedin, New Zealand: Department of Information Science, University of Otago. 18p.
Deng, J. D., Purvis, M. K., & Purvis, M. A. (2009). Software effort estimation: Harmonizing algorithims and domain knowledge in an integrated data mining approach [Discussion Paper Series No. 2009/05]. Dunedin, New Zealand: Department of Information Science, University of Otago. 13p.
Deng, J. (2009). Automatic sapstain detection in processed timber through image feature analysis [Discussion Paper Series No. 2009/04]. Dunedin, New Zealand: Department of Information Science, University of Otago. 8p.
2008
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
Conference Contribution - Published proceedings: Full paper
Deng, J. D., & Li, S. (2008). Improving the pattern retrieval characteristics of the Aihara Chaotic Neural Network model. In D. Tien & M. Kavakli (Eds.), Proceedings of the 5th International Conference on Information Technology and Applications. [CD-ROM], (pp. 732-737). Sydney, Australia: Macquarie Scientific Publishing. [Full Paper]
Yong, S. P., Deng, J. D., & Purvis, M. K. (2008). A new keypoint matching method for object classification using local descriptors. In D. Tien & M. Kavakli (Eds.), Proceedings of the 5th International Conference on Information Technology and Applications. [CD-ROM], (pp. 343-348). Sydney, Australia: Macquarie Scientific Publishing. [Full Paper]
Deng, J. D., Brinkworth, R. S. A., & O'Carroll, D. C. (2008). Assessing the naturalness of scenes: An approach using statistics of local features. Proceedings of the 23rd Image and Vision Computing New Zealand International Conference. IEEE. Retrieved from http://ieeexplore.ieee.org/servlet/opac?punumber=4740206
2007
Journal - Research Article
Deng, D. (2007). Content-based image collection summarization and comparison using self-organizing maps. Pattern Recognition, 40, 718-727.
Conference Contribution - Published proceedings: Full paper
Deng, J. D., & Purvis, M. K. (2007). Queueing analysis for multi-core performance improvement: Two case studies. Proceedings of the Australasian Telecommunication Networks and Applications Conference. (pp. 390-395). IEEE. [Full Paper]
Deng, D., Purvis, M., & Purvis, M. (2007). Software metric estimation: An empirical study using an integrated data analysis approach. In J. Chen (Ed.), Proceedings of the International Conference on Service Systems and Service Management. (pp. 692-697). IEEE. [Full Paper]
Deng, J. D., & Gleeson, M. T. (2007). Automatic sapstain detection in processed timber. In M. A. Orgun & J. Thornton (Eds.), AI 2007: Advances in Artificial Intelligence: Proceedings of the 20th Australian Joint Conference on Artificial Intellingence. LNAI 4830, (pp. 637-641). Berlin, Germany: Springer. [Full Paper]
Working Paper; Discussion Paper; Technical Report
Deng, D., Simmermacher, C., & Cranefield, S. (2007). A study on feature analysis for musical instrument classification [Discussion Paper Series No. 2007/04]. Dunedin, New Zealand: Department of Information Science, University of Otago. 18p.
2006
Conference Contribution - Published proceedings: Full paper
Simmermacher, C., Deng, D., & Cranefield, S. (2006). Feature analysis and classification of classical musical instruments: An empirical study. In P. Perner (Ed.), Proceedings of the 6th Industrial Conference on Data Mining (LNAI 4065). (pp. 444-458). Berlin, Germany: Springer. [Full Paper]
Koprinska, I., Deng, D., & Feger, F. (2006). Image classification using labelled and unlabelled data. In E. Bagnoli & M. Casula (Eds.), Proceedings of the 14th European Signal Processing Conference. Florence, Italy: EUSIPCO. Retrieved from http://www.eurasip.org/Proceedings/Eusipco/Eusipco2006/papers/1568982187.pdf
Deng, D., Simmermacher, C., & Cranefield, S. (2006). Finding the right features for instrument classification of classical music. In K.-L. Ong, K. Smith-Miles, V. Lee & W.-K. Ng (Eds.), Proceedings of the International Workshop on Integrating AI and Data Mining. (pp. 34-41). Los Alamitos, California: IEEE. [Full Paper]
Working Paper; Discussion Paper; Technical Report
Deng, D., & Zhang, J. (2006). Combining multiple precision-boosted classifiers for indoor-outdoor scene classification [Discussion Paper Series No. 2006/09]. Dunedin, New Zealand: Department of Information Science, University of Otago. 13p.
Simmermacher, C., Deng, D., & Cranefield, S. (2006). Feature analysis and classification of classical musical instruments: An empirical study [Discussion Paper Series No. 2006/10]. Dunedin, New Zealand: Department of Information Science, University of Otago. 15p.
2005
Conference Contribution - Published proceedings: Full paper
Deng, D., & Wolf, H. (2005). POISE: Achieving content-based picture organisation for image search engines. In R. Khosla, R. J. Howlett & L. C. Jain (Eds.), Knowledge-based Intelligent Information and Engineering Systems: Proceedings of the 9th International KES Conference (part II): Lecture Notes in Artificial Intelligence (Vol. 3682). (pp. 1-7). Berlin, Germany: Springer. doi: 10.1007/11552451_1
Wolf, H., & Deng, D. (2005). How interesting is this? Finding interest hotspots and ranking images using an MPEG-7 visual attention model. Proceedings of the 17th Annual Colloquium of the Spatial Information Research Centre. (pp. 67-76). Dunedin, New Zealand: University of Otago. [Full Paper]
Deng, D., & Zhang, J. (2005). Combining multiple precision-bosted classifiers for indoor-outdoor scene classification. Proceedings of the 3rd International Conference on Information Technology and Applications. (pp. 720-725). Sydney, Australia: IEEE Computer Society Press. [Full Paper]
Working Paper; Discussion Paper; Technical Report
Wolf, H., & Deng, D. (2005). Image saliency mapping and ranking using an extensible visual attention model based on MPEG-7 feature descriptors. The Information Science Discussion Paper Series. Dunedin, New Zealand: Department of Information Science, University of Otago. 19p.
Deng, D. (2005). Content-based image collection summarization and comparison using self-organizing maps. The Information Science Discussion Paper Series. Dunedin, New Zealand: Department of Information Science, University of Otago. 18p.
2004
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.
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.
Conference Contribution - Published proceedings: Full paper
Zhang, J., & Deng, D. (2004). Indoor-outdoor scene classification by combining multiple precision-boosted classifiers. In D. Pairman, H. North & S. McNeill (Eds.), Proceedings of the Image and Vision Computing New Zealand. (pp. 393-398). [Full Paper]
Deng, D. (2004). Braving the semantic gap: Mapping visual concepts from images and videos. Lecture Notes in Computer Science (LNAI 3275). (pp. 50-59). Berlin, Heidelberg: Springer. [Full Paper]
Deng, D. (2004). Content-based comparison of image collections via distance measuring of self-organised maps. Proceedings of the 10th International Multimedia Modelling Conference. IEEE Computer Society. [Full Paper]
Woodford, B. J., Deng, D., & Benwell, G. L. (2004). A wavelet-based neuro-fuzzy system for data mining small image sets. Proceedings of the Australasian Workshop on Data Mining and Web Intelligence. (pp. 139-143). [Full Paper]
Deng, D., Zhang, J., & Purvis, M. K. (2004). Visualisation and comparison of image collections based on self-organised maps. Conferences in Research and Practice in Information Technology. 32, (pp. 97-102). [Full Paper]
2003
Journal - Research Article
Deng, D., & Kasabov, N. K. (2003). On-line pattern analysis by evolving self-organizing maps. Neurocomputing, 51, 87-103.
Conference Contribution - Published proceedings: Full paper
Wang, X., Whigham, P., Deng, D., & Purvis, M. K. (2003). Time-line hidden Markov experts for time series prediction. Proceedings of the International Conference on Neural Networks and Signal Processing. 1, (pp. 786-789). Nanjing, China: IEEE Press. [Full Paper]
Deng, D. (2003). Content-based image collection profiling and comparison via self-organised maps. In A. Abraham, M. Köppen & K. Franke (Eds.), Frontiers in artificial intelligence and application series: Design and application of hybrid intelligence systems. 104, (pp. 811-819). Amsterdam: IOS Press. [Full Paper]
Working Paper; Discussion Paper; Technical Report
Wang, X., Whigham, P., & Deng, D. (2003). Time-line Hidden Markov Experts and its application in time series prediction (Information Science Discussion Paper Series, University of Otago).
2001
Conference Contribution - Published proceedings: Full paper
Deng, D., & Kasabov, N. K. (2001). On-line pattern analysis by evolving self-organizing maps. In N. Kasabov & B. Woodford (Eds.), Proceedings of the Fifth Biannual Conference on Artificial Neural Networks and Expert Systems. (pp. 46-51). Dunedin: University of Otago Printery. [Full Paper]
Deng, D., & Kasabov, N. K. (2001). Evolving localised learning model for on-line image colour quantisation. International Conference on Image Processing (ICIP 2001). 1, (pp. 906-909). IEEE Press. [Full Paper]
Deng, D. (2001). Mining visual concepts for image retrieval: A case study. In N. Kasabov & B. Woodford (Eds.), Proceedings of the Fifth Biannual Conference on Artificial Neural Networks and Expert Systems (ANNES). (pp. 169-174). Dunedin: University of Otago Printery. [Full Paper]
2000
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.
Conference Contribution - Published proceedings: Full paper
Kasabov, N. K., Deng, D., Erzegovezi, L., Fedrizzi, M., & Beber, A. (2000). On-line decision making and prediction of financial and macroeconomic parameters on the case study of the European monetary union. In H. Bothe & R. Rojas (Eds.), Proceedings of the Second ICSC Symposium on Neural Computation. (pp. 301-307). Canada/Switzerland: ICSC Academic Press. [Full Paper]
Deng, D., & Kasabov, N. K. (2000). ESOM: An algorithm to evolve self-organizing maps from on-line data streams. In S. Amari, C. Giles, M. Gori & V. Piuri (Eds.), Proceedings of the IEEE-INNS International Joint Conference 2000 on Neural Networks Neural Computing. VI, (pp. 3-8). Los Alamitos, CA, USA: IEEE Computer Society. [Full Paper]
Deng, D., & Kasabov, N. K. (2000). Evolving localised learning for on-line colour image quantisation. In M. J. Cree & A. Steyn-Ross (Eds.), Proceedings of the Image and Vision Computing New Zealand 2000 Conference. (pp. 186-191). Hamilton, New Zealand: University of Waikato. [Full Paper]
1999
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.
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.
Conference Contribution - Published proceedings: Full paper
Wang, Z., Chi, Z., Deng, D., & Yu, Y. (1999). Block-constrained fractal coding scheme for image retrieval. In D. P. Huijsmans & A. W. M. Smeulders (Eds.), Visual Information and Information Systems,. 1614, (pp. 673-680). Springer-Verlag. [Full Paper]
Kasabov, N. K., Deng, D., Erzegovesi, L., Fedrizzi, M., & Beber, A. (1999). Hybrid intelligent decision support systems and applications for risk analysis and prediction. In C. Tan & K. Kumar (Eds.), International Conference on Intelligent Systems for Investment Decision Making. (pp. 172-177). Bond University, Gold Coast, Australia. [Full Paper]
Deng, D., Koprinska, I., & Kasabov, N. K. (1999). RICBIS: New Zealand repository for intelligent connectionist-based information systems. In N. K. Kasabov & K. Ko (Eds.), Emerging Knowledge Engineering and Connectionist-based Information Systems - Proceedings of the ICONIP/ANZIIS/ANNES'99 International Workshop on Future Directions for Intelligent Systems and Information Sciences. (pp. 182-187). Dunedin, New Zealand: University of Otago. [Full Paper]
Deng, D., & Kasabov, N. K. (1999). Evolving self-organizing map and its application in generation of a world macroeconomic map. In N. K. Kasabov & K. Ko (Eds.), Emerging Knowledge Engineering and Connectionist-based Information Systems - Proceedings of the ICONIP/ANZIIS/ANNES'99 International Workshop on Future Directions for Intelligent Systems and Information Sciences. (pp. 7-12). Dunedin, New Zealand: University of Otago. [Full Paper]
1998
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.
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.
Conference Contribution - Published proceedings: Full paper
Li, S., & Deng, D. (1998). Pattern retrieval control in an associative chaotic neural network. Proceedings of the International Conference on Neural Networks and Brain. (pp. 173-176). China: Publishing House of Electronics Industry. [Full Paper]
Deng, D., & Li, S. (1998). Knowledge manipulation in a Hebbian network for fault diagnosis. Applications and Science of Computational Intelligence. 3390, (pp. 143-150). [Full Paper]
Deng, D. (1998). Fractal texture signatures for segmentation of multi-spectral remote sensing images. Proceedings of International SYmposium on Multispectral Image processing. 3545, (pp. 461-464). [Full Paper]
1997
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.
Conference Contribution - Published proceedings: Full paper
Deng, D. (1997). A feedback PCA network with powered correlation memory. Proceedings of International Conference on Neural Information Processing (ICONIP97). (pp. 620-623). Singapore: Springer-Verlag. [Full Paper]