Dr Yuhua Li

2026

• Wang, S. and Li, Y. 2026. A smartphone-based two-stage approach for early screening of pulmonary health and COPD. Presented at: 10th International Conference, DMBD 2025 Beijing, China 19-22 December 2025. Published in: Tan, Y. and Shi, Y. eds. Vol. 2905.Springer. (10.1007/978-981-92-0229-4_2)
• Weng, J. , Karakuş, O. and Li, Y. 2026. Continual generalized category discovery via enhancing representation learning. Presented at: UKAIRS 2026 Edinburgh, Scotland, UK 24-25 November 2026.
• Zhang, R. et al., 2026. Spatial-temporal dynamics for enhanced temporal link prediction in stock market crisis forecasting. Presented at: 2026 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Bellevue, WA, USA 4-7 October 2026. SMC 2026 Conference Proceedings. IEEE.

2025

• Pan, X. et al., 2025. LLM-BCgrading: Large language model-based Chinese medical long text classification for bladder cancer grade prediction. Digital Health 2025 11. (10.1177/20552076251393290)
• Belarbi, O. et al., 2025. FedHDC-IDS: a hyperdimensional computing based network intrusion detection dystems for IoT networks. Presented at: 2025 IEEE 45th International Conference on Distributed Computing Systems Workshops (ICDCSW) Glasgow, United Kingdom 21-23 July 2025. 2025 IEEE 45th International Conference on Distributed Computing Systems Workshops (ICDCSW). IEEE. , pp.677-682. (10.1109/ICDCSW63273.2025.00126)
• Anggoro, A. et al. 2025. Harmonized system code classification using supervised contrastive learning with sentence BERT and multiple negative ranking loss. Data Technologies and Applications 59 (2), pp.279-301. (10.1108/DTA-01-2024-0052)

2024

• Lewis-Cheetham, J. et al. 2024. The impact of transaction costs on forecast-based trading strategy performance. Presented at: CIFEr 2024: IEEE Symposium on Computational Intelligence for Financial Engineering and Economics Hoboken, New Jersey, USA 22-23 October 2024. Proceedings of the IEEE Symposium on Computational Intelligence for Financial Engineering and Economics. IEEE. , pp.1-8. (10.1109/CIFEr62890.2024.10772810)
• Bent, G. A. et al., 2024. The transformative potential of vector symbolic architecture for cognitive processing at the network edge. Presented at: Security and Defence 2024 Edinburgh, UK 16-20 September 2024. Published in: Bouma, H. et al., Proceedings Artificial Intelligence for Security and Defence Applications II. Vol. 13206.SPIE. , pp.1320612. (10.1117/12.3030949)
• Bent, G. A. et al., 2024. A demonstration of vector symbolic architecture as an effective integrated technology for AI at the network edge. Presented at: Artificial Intelligence for Security and Defence Applications II Edinburgh, UK 17–19 September 2024. Published in: Bouma, H. et al., Proceedings of Artificial Intelligence for Security and Defence Applications II. Vol. 132061. SPIE. , pp.39-55. (10.1117/12.3030952)
• Wang, H. et al. 2024. Improving high-frequency details in cerebellum for brain MRI super-resolution. Presented at: Conference on ICT Solutions for eHealth (ICTS4eHealth 2024) Paris, France 26 - 29 June 2024. 2024 IEEE Symposium on Computers and Communications (ISCC). IEEE. , pp.1-7. (10.1109/ISCC61673.2024.10733580)
• Zhang, M. et al. 2024. Explaining the predictions of kernel SVM models for neuroimaging data analysis. Expert Systems with Applications 251 123993. (10.1016/j.eswa.2024.123993)
• Wu, F. et al. 2024. Analysing network dynamics: The contagion effects of SVB's collapse on the US tech industry. Journal of Risk and Financial Management 17 (10) 427. (10.3390/jrfm17100427)
• Alqurashi, N. , Li, Y. and Sidorov, K. 2024. Improving speech emotion recognition through hierarchical classification and text integration for enhanced emotional analysis and contextual understanding. Presented at: International Joint Conference on Neural Networks Yokohama, Japan 30 June – 5 July 2024. Proceedings of IJCNN. IEEE. , pp.1-8. (10.1109/IJCNN60899.2024.10650087)
• Zhang, M. et al. 2024. Fast explanation of RBF-Kernel SVM models using activation patterns. Presented at: International Joint Conference on Neural Networks Yokohama, Japan 30 June – 5 July 2024. Proceedings of IJCNN. IEEE. , pp.1-8. (10.1109/IJCNN60899.2024.10650697)
• Alqurashi, N. et al. 2024. Decision fusion based multimodal hierarchical method for speech emotion recognition from audio and text. Presented at: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning Bruges, Belgium 9-11 October 2024. ESANN 2024 - Proceedings.
• Li, S. , Li, Y. and Perera, C. 2024. Mobile sensing within smart buildings: A survey. Technical Report.

2023

• Wang, H. et al. 2023. A skewed loss function for correcting predictive bias in brain age prediction. IEEE Transactions on Medical Imaging 42 (6), pp.1577-1589. (10.1109/TMI.2022.3231730)
• Zhang, F. et al., 2023. CBA-GAN: Cartoonization style transformation based on the convolutional attention module. Computers and Electrical Engineering 106 108575. (10.1016/j.compeleceng.2022.108575)
• Anggoro, A. W. et al. 2023. Using DistilBERT to assign HS codes to international trading transactions. Presented at: World Conference on Information Systems and Technologies Pisa, Italy 4 - 6 April 2023.

2022

• Sidorowicz, T. , Peres, P. and Li, Y. 2022. A novel approach for cross-selling insurance products using positive unlabelled learning. Presented at: 2022 International Joint Conference on Neural Networks (IJCNN) Padua, Italy 18 - 23 July 2022. 2022 International Joint Conference on Neural Networks (IJCNN). Institute of Electrical and Electronics Engineers. , pp.1-8. (10.1109/IJCNN55064.2022.9892762)
• Bent, G. et al., 2022. Energy efficient spiking neural network neuromorphic processing to enable decentralised service workflow composition in support of multi-domain operations. Presented at: SPIE Defense + Commercial Sensing 2022 Orlando, Florida, United States 3 April - 13 June 2022. Published in: Pham, T. and Solomon, L. eds. Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications. Vol. 12113.SPIE. , pp.121131M. (10.1117/12.2617362)
• Bent, G. et al., 2022. Hyperdimensional computing using time-to-spike neuromorphic circuits. Presented at: 2022 IEEE World Congress on Computational Intelligence (WCCI) Padova, Italy 18-23 July 2022. IEEE

2021

• Sohail, M. , Peres, P. and Li, Y. 2021. Feature importance analysis for customer management of insurance products. Presented at: 2021 International Joint Conference on Neural Networks (IJCNN) Shenzhen, China (Virtual) 18-22 July 2021. Proceedings of the International Joint Conference on Neural Networks. IEEE(10.1109/IJCNN52387.2021.9533893)

2020

• Zhao, H. et al., 2020. Portrait style transfer using deep convolutional neural networks and facial segmentation. Computers and Electrical Engineering 85 106655. (10.1016/j.compeleceng.2020.106655)
• Taherkhani, A. et al., 2020. A review of learning in biologically plausible spiking neural networks. Neural Networks 122 , pp.253-272. (10.1016/j.neunet.2019.09.036)

2018

• Taherkhani, A. et al., 2018. A supervised learning algorithm for learning precise timing of multiple spikes in multilayer spiking neural networks. IEEE Transactions on Neural Networks and Learning Systems 29 (11), pp.5394-5407. (10.1109/TNNLS.2018.2797801)
• Liu, S. et al., 2018. Quantitative analysis of breast cancer diagnosis using a probabilistic modelling approach. Computers in Biology and Medicine 92 , pp.168-175. (10.1016/j.compbiomed.2017.11.014)

2017

• Shynkevich, Y. et al., 2017. Forecasting price movements using technical indicators: Investigating the impact of varying input window length. Neurocomputing 264 , pp.71--88. (10.1016/j.neucom.2016.11.095)
• Zhai, J. et al., 2017. Coarse and fine identification of collusive clique in financial market. Expert Systems with Applications 69 , pp.225-238. (10.1016/j.eswa.2016.10.051)
• Zhai, J. et al., 2017. Computational intelligent hybrid model for detecting disruptive trading activity. Decision Support Systems 93 , pp.26--41. (10.1016/j.dss.2016.09.003)

2016

• Cao, Y. et al., 2016. Detecting wash trade in financial market using digraphs and dynamic programming. IEEE Transactions on Neural Networks and Learning Systems 27 (11), pp.2351-2363. (10.1109/TNNLS.2015.2480959)
• Raza, H. et al., 2016. Adaptive learning with covariate shift-detection for motor imagery-based brain–computer interface. Soft Computing 20 (8), pp.3085--3096. (10.1007/s00500-015-1937-5)
• Liu, J. et al., 2016. Fault-tolerant networks-on-chip routing with coarse and fine-grained look-ahead. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 35 (2), pp.260-273. (10.1109/TCAD.2015.2459050)

2015

• Liu, J. et al., 2015. Low cost fault-tolerant routing algorithm for Networks-on-Chip. Microprocessors and Microsystems 39 (6), pp.358-372. (10.1016/j.micpro.2015.06.002)
• Ding, X. et al., 2015. Novelty detection using level set methods. IEEE Transactions on Neural Networks and Learning Systems 26 (3), pp.576-588. (10.1109/TNNLS.2014.2320293)
• Raza, H. , Prasad, G. and Li, Y. 2015. EWMA model based shift-detection methods for detecting covariate shifts in non-stationary environments. Pattern Recognition 48 (3), pp.659-669. (10.1016/j.patcog.2014.07.028)
• Cao, Y. et al., 2015. Adaptive hidden Markov model with anomaly states for price manipulation detection. IEEE Transactions on Neural Networks and Learning Systems 26 (2), pp.318-330. (10.1109/TNNLS.2014.2315042)
• Taherkhani, A. et al., 2015. DL-ReSuMe: A delay learning-based remote supervised method for spiking neurons. IEEE Transactions on Neural Networks and Learning Systems 26 (12), pp.3137-3149. (10.1109/TNNLS.2015.2404938)

2014

• Liu, J. et al., 2014. Low overhead monitor mechanism for fault-tolerant analysis of NoC. Presented at: 8th International Symposium On Embedded Multicore/manycore Socs Aizu-Wakamatsu, Japan 23-25 Sep 2014. 8th International Symposium on Embedded Multicore/manycore Socs. IEEE. , pp.189-196. (10.1109/MCSoC.2014.35)
• Liu, J. et al., 2014. Online fault detection for Networks-on-Chip interconnect. Presented at: 2014 NASA/ESA Conference on Adaptive Hardware and Systems (AHS) Leicester, UK 14-17 Jul 2014. 2014 NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2014). Piscataway, NJ: IEEE. , pp.31-38. (10.1109/AHS.2014.6880155)
• Ding, X. et al., 2014. An experimental evaluation of novelty detection methods. Neurocomputing 135 , pp.313-327. (10.1016/j.neucom.2013.12.002)
• Cao, Y. et al., 2014. A hidden Markov model with abnormal states for detecting stock price manipulation. Presented at: SMC 2013 Manchester, UK 13-16 Oct 2013. 2013 IEEE International Conference on Systems, Man and Cybernetics (SMC 2013). Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics Piscataway, NJ: IEEE. , pp.3014-3019. (10.1109/SMC.2013.514)
• Ding, X. et al., 2014. Novelty detection using level set methods with adaptive boundaries. Presented at: SMC 2013 Manchester, UK 13-16 Oct 2013. 2013 IEEE International Conference on Systems, Man and Cybernetics (SMC 2013). Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics Piscataway, NJ: IEEE. , pp.3020-3025. (10.1109/SMC.2013.515)
• Raza, H. , Prasad, G. and Li, Y. 2014. Dataset shift detection in non-stationary environments using EWMA charts. Presented at: 2013 IEEE International Conference on Systems, Man, and Cybernetics Manchester, UK 13-16 Oct 2013. 2013 IEEE International Conference on Systems, Man, and Cybernetics. Piscataway, New Jersey: IEEE. , pp.3151-3156. (10.1109/SMC.2013.537)
• Liu, J. et al., 2014. Online traffic-aware fault detection for networks-on-chip. Journal of Parallel and Distributed Computing 74 (1), pp.1984-1993. (10.1016/j.jpdc.2013.09.001)
• McDonald, S. et al., 2014. A hybrid forecasting approach using ARIMA models and self-organising fuzzy neural networks for capital markets. Presented at: 2013 International Joint Conference on Neural Networks (IJCNN) Dallas, TX. USA 4-9 August 2013. The 2013 International Joint Conference on Neural Networks (IJCNN). IEEE. , pp.1-7. (10.1109/IJCNN.2013.6706965)

2013

• Goel, G. et al., 2013. Evaluation of sampling methods for learning from imbalanced data. Presented at: ICIC 2013 Nanning, China 28-31 Jul 2013. Published in: Huang, D. et al., Intelligent Computing Theories. Vol. 7995.Information Systems and Applications, incl. Internet/Web, and HCI Berlin, Heidelberg: Springer. , pp.392-401. (10.1007/978-3-642-39479-9_47)
• Raza, H. , Prasad, G. and Li, Y. 2013. EWMA based two-stage dataset shift-detection in non-stationary environments. Presented at: AIAI 2013 Paphos, Cyprus 30 Sep - 2 Oct 2013. Published in: Papadopoulos, H. et al., Artificial Intelligence Applications and Innovations. Vol. 412.IFIP Advances in Information and Communication Technology Berlin, Heidelberg: Springer. , pp.625-635. (10.1007/978-3-642-41142-7_63)

2012

• Ding, X. et al., 2012. Constructing minimum volume surfaces using level set methods for novelty detection. Presented at: IJCNN 2012 International Join Conference on Neural Networks Brisbane, QLD, Australia 10-15 June 2012. The 2012 International Joint Conference on Neural Networks (IJCNN). IEEE International Joint Conference on Neural Networks (IJCNN) IEEE. , pp.1-6. (10.1109/IJCNN.2012.6252804)

2011

• Zhang, K. et al., 2011. Feature selection for high-dimensional machinery fault diagnosis data using multiple models and Radial Basis Function networks. Neurocomputing 74 (17), pp.2941-2952. (10.1016/j.neucom.2011.03.043)
• Zhang, K. et al., 2011. A novel feature selection algorithm for high-dimensional condition monitoring data. International Journal of Condition Monitoring 1 (1), pp.33-43. (10.1784/204764211798089075)
• Li, Y. 2011. Selecting training points for one-class support vector machines. Pattern recognition letters 32 (11), pp.1517-1522. (10.1016/j.patrec.2011.04.013)
• Li, Y. and Maguire, L. 2011. Selecting critical patterns based on local geometrical and statistical information. IEEE Transactions on Pattern Analysis and Machine Intelligence 33 (6), pp.1189-1201. (10.1109/TPAMI.2010.188)

2009

• Li, Y. 2009. A surface representation approach for novelty detection. Presented at: International Conference on Information and Automation 2008 Changsha, China 20-23 June 2008. 2008 International Conference on Information and Automation. IEEE. , pp.1464-1468. (10.1109/ICINFA.2008.4608233)

2007

• Zhang, K. et al., 2007. A hybrid model with a weighted voting scheme for feature selection in machinery condition monitoring. Presented at: IEEE International Conference on Automation Science and Engineering 2007 Scottsdale, AZ, United States 22-25 September 2007. 2007 IEEE International Conference on Automation Science and Engineering. IEEE. , pp.174-179. (10.1109/COASE.2007.4341697)

2006

• Ben Sasi, A. Y. et al., 2006. A validated model for the prediction of rotor bar failure in squirrel-cage motors using instantaneous angular speed. Mechanical Systems and Signal Processing 20 (7), pp.1572-1589. (10.1016/j.ymssp.2005.09.010)
• Li, Y. et al. 2006. Sentence similarity based on semantic nets and corpus statistics. IEEE Transactions on Knowledge and Data Engineering 18 (8), pp.1138-1150. (10.1109/TKDE.2006.130)

2005

• Li, Y. et al. 2005. The measurement of instantaneous angular speed. Mechanical Systems and Signal Processing 19 (4), pp.786-805. (10.1016/j.ymssp.2004.04.003)
• Gu, F. et al., 2005. An investigation of the effects of measurement noise in the use of instantaneous angular speed for machine diagnosis. Mechanical Systems and Signal Processing 20 (6), pp.1444-1460. (10.1016/j.ymssp.2005.02.001)

2003

• Li, Y. , Bandar, Z. and McLean, D. 2003. An approach for measuring semantic similarity between words using multiple information sources. IEEE Transactions on Knowledge and Data Engineering 15 (4), pp.871-882. (10.1109/TKDE.2003.1209005)

2002

• Li, Y. , Bandar, Z. and Mclean, D. 2002. Measuring semantic similarity between words using lexical knowledge and neural networks. Presented at: 3rd International Conference on Intelligent Data Engineering and Automated Learning — IDEAL 2002 Manchester, England, UK 12-14 August 2002. Intelligent Data Engineering and Automated Learning — IDEAL 2002. Vol. 2412.Lecture Notes in Computer Science Berlin and Heidelberg: Springer. , pp.111-116. (10.1007/3-540-45675-9_19)
• Li, Y. , Pont, M. J. and Barrie Jones, N. 2002. Improving the performance of radial basis function classifiers in condition monitoring and fault diagnosis applications where `unknown' faults may occur. Pattern recognition letters 23 (5), pp.569-577. (10.1016/S0167-8655(01)00133-7)

2001

• Li, Y. et al. 2001. Applying MLP and RBF classifiers in embedded condition monitoring and fault diagnosis systems. Transactions of the Institute of Measurement and Control 23 (5), pp.315-343. (10.1177/014233120102300504)

2000

• Li, Y. et al. 2000. Comparing the performance of three neural classifiers for use in embedded applications. Presented at: Workshop 99 on Recent Advances in Soft Computing Leicester, England 01-02 July 1999. Published in: John, R. and Birkenhead, R. eds. Soft Computing Techniques and Applications. Advances in Soft Computing Physica. , pp.34-29.