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Computational Intelligence Group

abstract image of a human head with interconnections and binary background representing artificial intelligence

Publications

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.

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).

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.

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.

Dib, F. K. & Rodgers, P. (2018), ‘Graph drawing using tabu search coupled with path relinking‘, PLOS ONE 13(5), e0197103.

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

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

Grzes, M. (2017), ‘Reward Shaping in Episodic Reinforcement Learning‘, in Sixteenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2017)’, ACM, pp. 565–573.

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.

Liza, F. F. & Grzes, M. (2018), Improving Language Modelling with Noise Contrastive Estimation, in The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18)‘, AAAI Press, Palo Alto, California, USA, pp. 5277–5284.

Lloyd, P. B.; Rodgers, P. & Roberts, M. J. (2018), ‘Metro Map Colour-Coding: Effect on Usability in Route Tracing‘, in Peter Chapman; Gem Stapleton; Amirouche Moktefi; Sarah Perez-Kriz & Francesco Bellucci, ed., 10th International Conference on the Theory and Application of Diagrams (Diagrams 2018), Springer, pp. 411–428.

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.

Micallef, L. & Rodgers, P. (2014), ‘eulerAPE: Drawing Area-proportional 3-Venn Diagrams Using Ellipses‘, PLoS ONE 9(7).

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.

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.

Rodgers, P.; Stapleton, G.; Alsallakh, B.; Micallef, L.; Baker, R. & Thompson, S. (2016), ‘A Task-Based Evaluation of Combined Set and Network Visualization‘, Information Sciences 367(8), 58–79.

Rodgers, P.; Stapleton, G. & Chapman, P. (2015), ‘Visualizing Sets with Linear Diagrams‘, ACM Transactions on Computer-Human Interaction 22(6).

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, 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.