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Data Science Research Group

Below is a list of selected publications of the research group. A more detailed list of members’ publications can be found on the Kent Academic Repository (KAR) or on their individual staff profiles.

Alterkawi, L.; Migliavacca, M. (2019), ‘Parallelism and partitioning in large-scale GAs using spark’, GECCO ’19 Proceedings of the Genetic and Evolutionary Computation Conference, 736–744.

Aniceto, N.; Freitas, A. A.; Bender, A. & Ghafourian, T. (2016), ‘A novel applicability domain technique for mapping predictive reliability across the chemical space of a QSAR: reliability-density neighbourhood‘, Journal of Cheminformatics 8(1).

Asensio-Cubero, J.; Gan, .J.Q; Palaniappan, R. (2016), ‘Multiresolution analysis over graphs for a motor imagery based online BCI game’, Computers in Biology and Medicine 68, 21–26.

Barros, R. C.; Basgalupp, M. P.; Freitas, A. A. & de Carvalho, A. C. P. L. F. (2013), ‘Evolutionary design of decision-tree algorithms tailored to microarray gene expression data sets‘, IEEE Transactions on Evolutionary Computation 18(6), 873–892.

Castro Fernandez, R.; Migliavacca, M.; Kalyvianaki, E.; Pietzuch, P. (2014), ‘Making State Explicit for Imperative Big Data Processing’, 2014 USENIX Annual Technical Conference (USENIX ATC 14).

Castro Fernandez, R.; Migliavacca, M.; Kalyvianaki, E.; Pietzuch, P. (2013), ‘Integrating scale out and fault tolerance in stream processing using operator state management’, Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, 725–736.

Chen, H.; Wang, F.; Helian, N. (2018), ‘Entropy4Cloud: Using Entropy-Based Complexity To Optimize Cloud Service Resource Management’, IEEE Transactions on Emerging Topics in Computational Intelligence 2(1), 13–24.

Chen, J.; Wang, F.; Zhou, J.; Li, L.; Crookes, D. & Zhou, H. (2018), ‘Short-Time Velocity Identification and Coherent-Like Detection of Ultrahigh Speed Targets‘, IEEE Transactions on Signal Processing 66(18), 4811–4825.

Chu, D. & Barnes, D. J. (2016), ‘The lag-phase during diauxic growth is a trade-off between fast adaptation and high growth rate‘, Scientific Reports 6.

Chu, D.; Kazana, E.; Bellanger, N.; Singh, T.; Tuite, M. F. & von der Haar, T. (2014), ‘Translation elongation can control translation initiation on eukaryotic mRNAs‘, EMBO Journal 33(1), 21–34.

Cramer, S.; Kampouridis, M. & Freitas, A. A. (2018), ‘Decomposition Genetic Programming: An Extensive Evaluation on Rainfall Prediction in the Context of Weather Derivatives‘, Applied Soft Computing 70, 208–224.

Cramer, S.; Kampouridis, M.; Freitas, A. A. & Alexandridis, A. (2019), ‘Stochastic Model Genetic Programming: Deriving Pricing Equations for Rainfall Weather Derivatives‘, Swarm and Evolutionary Computation 46, 184–200.

Deng, Y.; Huang, X.; Song, L.; Zhou, Y.; Wang, F. (2017), ‘Memory Deduplication: An Effective Approach to Improve the Memory System’, Journal of Information Science and Engineering 33(5), 1103–1120.

Fabris, F. & Freitas, A. A. (2018), ‘A new approach for interpreting Random Forest models and its application to the biology of ageing‘, Bioinformatics, 34(14), 2449-2456

Fabris, F. & Freitas, A. A. (2016), ‘New KEGG pathway-based interpretable features for classifying ageing-related mouse proteins‘, Bioinformatics 32(19), 2988–2995.

Fabris, F.; Palmer, D.; Salama, K. M.; de Magalhaes, J. P. & Freitas, A. A. (2020), ‘Using deep learning to associate human genes with age-related diseases‘, Bioinformatics, 36(7), 2202-2208

Figueredo, G.P.; Agrawal, U.; Mase, J.M.; Mesgarpour, M.; Wagner, C.; Soria, D.; Garibaldi, J.M.; Siebers, P.O.; John R.I. (2019), ‘Identifying Heavy Goods Vehicle Driving Styles in the United Kingdom, IEEE Transactions on Intelligent Transportation Systems 20(9), 3324–3336.

Friston, K. J.; Rosch, R.; Parr, T.; Price, C. & Bowman, H. (2017), ‘Deep temporal models and active inference‘, Neuroscience & Biobehavioral Reviews 77, 388–402.

Hanslmayr, S.; Staresina, B. & Bowman, H. (2016), ‘Oscillations and Episodic Memory: Addressing the Synchronization/Desynchronization Conundrum‘, Trends in Neuroscience 39(1), 16–25.

Hong, X.; Li, H.; Miller, P.; Zhou, J.; Li, L.; Crookes, D.; Lu, Y.; Li, X. & Zhou, H. (2019), ‘Component-based Feature Saliency for Clustering‘, IEEE Transactions on Knowledge and Data Engineering.

Intarasirisawat, J.; Ang, C.S., Efstratiou, C.; Dickens, L.; Sriburapar, N.; Sharma, D.; Asawathaweeboon. B. (2020), ‘An Automated Mobile Game-based Screening Tool for Patients with Alcohol Dependence’, Proceedings of the ACM Conference on Interactive, Mobile, Wearable and Ubiquitous Technologies.

Jiang, Z.; Crookes, D.; Green, B. D.; Zhao, Y.; Ma, H.; Li, L.; Zhang, S.; Tao, D. & Zhou, H. (2018), ‘Context-aware Mouse Behaviour Recognition using Hidden Markov Models‘, IEEE Transactions on Image Processing 28(3), 1133–1148.

Jordanous, A. & Keller, B. (2016), ‘Modelling Creativity: Identifying Key Components through a Corpus-Based Approach‘, PLoS ONE 11(10).

Kampouridis, M. & Otero, F. E. B. (2017), ‘Evolving Trading Strategies Using Directional Changes‘, Expert Systems with Applications 73, 145–160.

Kanjo, E.; Younis, E.M.G.; Ang, C.S. (2018) ‘Deep Learning Analysis of Mobile Physiological, Environmental and Location Sensor Data for Emotion Detection.’ Information Fusion 49, 46-56.

Lee, Y.; Nicholls, B.; Lee, D.S.; Chen, Y.; Chun, Y.; Ang, C.S.; Yeo, W-H. (2017) ‘Soft Electronics Enabled Ergonomic Human-Computer Interaction for Swallowing Training.’ Scientific Reports 7.

Liu, J.; Xu, B.; Zheng, C.; Gong, Y.; Garibaldi, J.M.; Soria, D.; Green, A.; Ellis, I.O.; Zou, W.; Qiu, G. (2018), ‘An end-to-end deep learning histochemical scoring system for breast cancer TMA’, IEEE transactions on medical imaging 38(2), 617–628.

Ma, H.; Fei, M.; Jiang, Z.; Li, L.; Zhou, H. & Crookes, D. (2018), ‘A Multipopulation-Based Multiobjective Evolutionary Algorithm‘, IEEE Transactions on Cybernetics, 1–14.

Mahmood, M.; Mzurikwao, D.; Kim, Y-S.; Lee, Y.; Mishra, S.; Herbert, R.; Duarte, A.; Ang, C.S.; Yeo, W-H. (2019) ‘Fully portable and wireless universal brain-machine interfaces enabled by flexible scalp electronics and deep-learning algorithm.’ Nature Machine Intelligence 1, 412–422.

McLoughlin, I.V.; Xie, Z-P.; Song, Y.; Phan, H.; Palaniappan, R. (2019), ‘Time-Frequency feature fusion for noise-robust audio event classification’, Circuits Systems and Signal Processing 39, 1672–1687.

Mishra, S.; Kim, Y-S.; Intarasirisawat, J.; Kwon, Y-T.; Lee, Y.; Mahmood, Y.; Lim, H.R.; Yu, K.J.; Ang, C.S.; Yeo, W-H. (2020) ‘Soft, wireless periocular wearable electronics for real-time detection of eye vergence in a virtual reality toward mobile eye therapies.’ Science Advances 6(11).

Otero, F. E. B. & Freitas, A. A. (2016), ‘Improving the Interpretability of Classification Rules Discovered by an Ant Colony Algorithm: Extended Results‘, Evolutionary Computation 24(3), 385–409.

Palaniappan, R.; Phon-Amnuaisuk, S.; Eswaran, C. (2015), ‘On the binaural brain entrainment indicating lower heart rate variability’, International Journal of Cardiology 190, 262–263.

Parish, G.; Hanslmayr, S. & Bowman, H. (2018), ‘The Sync/deSync model: How a synchronized hippocampus and a de-synchronized neocortex code memories‘, The Journal of Neuroscience 38(14), 3428–3440.

Rakha, E.; Soria, D.; Green, A.R.; Lemetre, C.; Powe, D.G.; Nolan, C.C.; Garibaldi, J.M.; Ball, G.R.; Ellis I.O. (2014), ‘Nottingham Prognostic Index Plus (NPI+): A Modern Clinical Decision Making Tool in Breast Cancer’, British Journal of Cancer 110(7), 1688–1697.

Razak, T.R.; Garibaldi, J.M.; Wagner, C.; Pourabdollah, A.; Soria, D. (2020), ‘Towards a Framework for Capturing Interpretability of Hierarchical Fuzzy Systems – A Participatory Design Approach’, IEEE Transactions on Fuzzy Systems, DOI: 10.1109/TFUZZ.2020.2969901.

Rose, V.; Stewart, I; Jenkins, K.G.; Tabbaa, L.; Ang, C.S.; Matsangidou, M. (2019) ‘Bringing the outside in: The feasibility of virtual reality with people with dementia in an inpatient psychiatric care setting.’ Dementia.

Wang, F.; Li, L.; Shi, L.; Wu, H.; Chua, L. (2019), ‘Φ memristor: Real memristor found’, Journal of Applied Physics 125(5), 54504.

Wang, F.; Shi, L.; Wu, H.; Helian, N.; Chua, L.O. (2017), ‘Fractional memristor’, Applied Physics Letters 111(24), Article number 243502.

Wang, P.; Peng, D.; Li, L.; Chen, L.; Wu, C.; Wang, X.; Childs, P. & Guo, Y. (2019), ‘Human-in-the-Loop Design with Machine Learning‘, Proceedings of the Design Society: International Conference on Engineering Design 1(1), 2577–2586.

Yuvaraj, R.; Murugappan, M.; Ibrahim, N. M.; Sundaraj, K.; Omar, M. I.; Mohamad, K.; Palaniappan, R.; Satiyan, M. (2014), ‘Inter-hemispheric EEG coherence analysis in Parkinson’s disease : Assessing brain activity during emotion processing’, Journal of Neural Transmission 122(2), 237–252.