Wang, G., Huang, L., Wang, Y., & Pang, W. (2016). A Link Density Clustering Method based on Automatically Selecting Center Density Peaks For Overlapping Community Detection. World Scientific .

Jia, C., Pang, W., & Fu, Y. (2016). Multimodal Action Recognition. In Y. Fu (Ed.), Human Activity Recognition and Prediction. (pp. 71-85). Springer . 10.1007/978-3-319-27004-3_4

Du, W., Wang, Y., & Pang, W. (2016). PEPro: A Screening Pipeline for Accurate Prediction of Excretory Proteins in Urine.

Wu, Z., Pang, W., & Coghill, G. M. (2015). An Integrated Qualitative and Quantitative Biochemical Model Learning Framework Using Evolutionary Strategy and Simulated Annealing. Cognitive Computation, 7(6), 637-651. 10.1007/s12559-015-9328-x

Lin, C., Liu, D., Pang, W., & Wang, Z. (2015). Sherlock: a Semi-Automatic Framework for Quiz Generation Using a Hybrid Semantic Similarity Measure. Cognitive Computation, 7(6), 667-679. 10.1007/s12559-015-9347-7

Du, W., cao, Z., Wang, Y., Pang, W., zhou, F., Tian, Y., & Liang, Y. (2015). Specific biomarkers: detection of cancer biomarkers through high-throughput transcriptomics data. Cognitive Computation, 7(6), 652-666. 10.1007/s12559-015-9336-x

Ji, J., Pang, W., Zheng, Y., Wang, Z., & Ma, Z. (2015). An initialization method for clustering mixed numeric and categorical data based on the density and distance. International Journal of Pattern Recognition and Artificial Intelligence, 29(7), [1550024]. 10.1142/S021800141550024X

Wu, Z., Pang, W., & Coghill, G. M. (2015). An integrative top-down and bottom-up qualitative model construction framework for exploration of biochemical systems. Soft Computing, 19(6), 1595-1610. 10.1007/s00500-014-1467-6

Jiang, Y., Wang, Y., Pang, W., Chen, L., Sun, H., Liang, Y., & Blanzieri , E. (2015). Essential Protein Identification Based on Essential Protein-Protein Interaction Prediction by Integrated Edge Weights. Methods, 83, 51-62. 10.1016/j.ymeth.2015.04.013

Ji, J., Pang, W., Zheng, Y., Wang, Z., & Ma, Z. (2015). A novel artificial bee colony based clustering algorithm for categorical data. PLoS ONE, 10(5), [e0127125]. 10.1371/journal.pone.0127125

Emele, C. D., Spakov, V., Pang, W., Bone, J. D., & Coghill, G. M. (2015). ADOVA: Anomaly Detection in Online and Virtual spAces. 38-41.

Pang, W., & Coghill, G. M. (2015). Qualitative, Semi-quantitative, and Quantitative Simulation of the Osmoregulation System in Yeast. BioSystems, 131, 40-50. 10.1016/j.biosystems.2015.04.003

Jia, C., Pang, W., & Fu, Y. (2015). Mode-driven volume analysis based on correlation of time series. In L. Agapito, M. M. Bronstein, & C. Rother (Eds.), Computer Vision - ECCV 2014 Workshops: Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part I. (pp. 818-833). (Lecture Notes in Computing Science; Vol. 8925). Zurich: Springer . 10.1007/978-3-319-16178-5_57

Pang, W., & Coghill, G. M. (2015). QML-AiNet: an immune network approach to learning qualitative differential equation models. Applied Soft Computing, 27, 148-157. 10.1016/j.asoc.2014.11.008

Ji, J., Pang, W., Zheng, Y., Wang, Z., Ma, Z., & Zhang, L. (2015). A novel cluster center initialization method for the k-prototypes algorithms using centrality and distance. Applied Mathematics & Information Sciences, 9(6), 2933-2942. 10.12785/amis/090621

Ma, D., Yu, J., Yu, Z., & Pang, W. (2015). A novel object tracking algorithm based on compressed sensing and entropy of information. Mathematical Problems in Engineering, 2015, [ 628101]. 10.1155/2015/628101

Lin, C., Liu, D., Pang, W., & Apeh, E. (2015). Automatically Predicting Quiz Difficulty Level Using Similarity Measures. In Proceedings of The 8th International Conference on Knowledge Capture (K-Cap). (pp. 1-8). [1] ACM. 10.1145/2815833.2815842

Wang, G., Wang, Y., Huang, L., Pang, W., & Ma, Q. (2015). Link Community Detection Based on Line Graphs with A Novel Link Similarity Measure. International Journal of Modern Physics B.

Luo, C., Pang, W., & Wang, Z. (2014). Hete-CF: Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations. ArXiv.

Pang, W., & Coghill, G. M. (2014). QML-Morven: A Novel Framework for Learning Qualitative Differential Equation Models using Both Symbolic and Evolutionary Approaches. Journal of Computational Science, 5(5), 795–808. 10.1016/j.jocs.2014.06.002

Li, B., Pang, W., Liu, Y., Yu, X., Du, A., & Yu, Z. (2014). Dimension Reduction Using Samples’ Inner Structure Based Graph for Face Recognition. Mathematical Problems in Engineering, 2014, [603025]. 10.1155/2014/603025

Luo, C., Pang, W., & Wang, Z. (2014). Semi-supervised clustering on heterogeneous information networks. In V. S. Tseng, T. Bao Ho, Z-H. Zhou, A. L. P. Chen, & H-Y. Kao (Eds.), Advances in Knowledge Discovery and Data Mining: 18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, May 13-16, 2014. Proceedings, Part II. (pp. 548-559). (Lecture Notes in Computer Science; Vol. 8444). Springer . 10.1007/978-3-319-06605-9_45

Li, B., Pang, W., Liu, Y., Yu, X., & Yu, Z. (2014). Building recognition on subregion’s multi-scale gist feature extraction and corresponding columns information based dimensionality reduction. Journal of Applied Mathematics, 2014, [898705]. 10.1155/2014/898705

Pang, W., & Coghill, G. M. (2014). An immune network approach to learning qualitative models of biological pathways. In 2014 IEEE Congress on Evolutionary Computation (IEEE CEC 2014). (pp. 1030-1037). IEEE Press. 10.1109/CEC.2014.6900393

Jiang, Y., Wang, Y., Pang, W., Chen, L., Sun, H., Liang, Y., & Blanzieri , E. (2014). Essential Protein Identification based on Essential Protein-Protein Interaction Prediction by Integrated Edge Weights. In The IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2014). (pp. 480-483). Belfast, UK: IEEE Press. 10.1109/BIBM.2014.6999204

Pang, W., & Coghill, G. M. (2014). Fuzzy qualitative simulation with multivariate constraints. In 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2014). (pp. 575-582). IEEE Press. 10.1109/FUZZ-IEEE.2014.6891702

Luo, C., Pang, W., Wang, Z., & Lin, C. (2014). Hete-CF: social-based collaborative filtering recommendation using heterogeneous relations. In 2014 IEEE International Conference on Data Mining (ICDM). (pp. 917-922). IEEE Press. 10.1109/ICDM.2014.64

Pang, W., & Coghill, G. M. (2013). An Immune Network Approach to Qualitative System Identification of Biological Pathways. In M. Bhatt, P. Struss, & C. Freksa (Eds.), 27th International Workshop on Qualitative Reasoning (QR 2013). (pp. 77-84). Bremen, Germany: Universität Bremen / Universität Freiburg.

Wu, Z., Pang, W., & Coghill, G. M. (2013). Stepwise modelling of biochemical pathways based on qualitative model learning. In Y. Jin, & S. A. Thomas (Eds.), Proceeding of the 13th UK Workshop on Computational Intelligence. Computational Intelligence (UKCI 2013). (pp. 31-37). IEEE Explore. 10.1109/UKCI.2013.6651284

Kaloriti, D., Tillmann, A., Cook, E., Jacobsen, M., You, T., Lenardon, M., ... Brown, A. J. P. (2012). Combinatorial stresses kill pathogenic Candida species. Medical Mycology, 50(7), 699-709. 10.3109/13693786.2012.672770

Ji, J., Pang, W., Han, X., Zhou, C., & Wang, Z. (2012). A fuzzy k-prototype clustering algorithm for mixed numeric and categorical data. Knowledge-Based Systems, 30, 129-135. 10.1016/j.knosys.2012.01.006

Kaloriti, D., Tillmann, A., Jacobsen, M., Yin, Z., Patterson, M., Radmaneshfar, E., ... Brown, A. J. P. (2012). Impact of combinatorial stresses upon Candida albicans. Mycoses, 55(Suppl. 4), 15. 10.1111/j.1439-0507.2012.02204.x

Pang, W., Coghill, G. M., & Bruce, A. M. (2012). Non-constructive interval simulation of dynamic systems. (Technical Report ABDN–CS–12–02). Aberdeen: Department of Computing Science, University of Aberdeen.

Pang, W., & Coghill, G. M. (2012). QML-Morven: A Novel Framework for Learning Qualitative Models. (Technical Report ABDN–CS–12–03). Aberdeen: Department of Computing Science, University of Aberdeen.

Jia, C-C., Wang, S-J., Peng, X-J., Pang, W., Zhang, C-Y., Zhou, C., & Yu, Z-Z. (2012). Incremental multi-linear discriminant analysis using canonical correlations for action recognition. Neurocomputing, 83(-), 56-63. 10.1016/j.neucom.2011.11.006

Yu, Z-Z., Jia, C-C., Pang, W., & Zhang, C-Y. (2012). Tensor Discriminant Analysis with Multi-Scale Features for Action Modeling and Categorization. IEEE Signal Processing Letters, 19(2), 95-98. 10.1109/LSP.2011.2180018

Pang, W., & Coghill, G. M. (2012). Extended Kernel Subsets Analysis for Qualitative Model Learning. In P. De Wilde, G. M. Coghill, & A. V. Kononova (Eds.), Proceeding of the 12th UK Workshop on Computational Intelligence. (pp. 1-7). Edinburgh, UK: IEEE Explore. 10.1109/UKCI.2012.6335774

Pang, W., & Coghill, G. M. (2011). A fast opt-AINet approach to qualitative model learning with a modified mutation operator.In Proceedings of the 11th UK Workshop on Computational Intelligence (UKCI). University of Manchester.

Pang, W., & Coghill, G. M. (2011). An immune-inspired approach to qualitative system identification of biological pathways. Natural Computing, 10(1), 189-207. 10.1007/s11047-010-9212-2

Liu, Y., Zhou, C., Guo, D., Wang, K., Pang, W., & Zhai, Y. (2010). A decision support system using soft computing for modern international container transportation services. Applied Soft Computing, 10(4), 1087-1095. 10.1016/j.asoc.2009.06.015

Pang, W., & Coghill, G. M. (2010). Learning Qualitative Differential Equation models: a survey of algorithms and applications. Knowledge Engineering Review, 25(1), 69-107. 10.1017/S0269888909990348

Pang, W., & Coghill, G. M. (2010). Learning Qualitative Metabolic Models Using Evolutionary Methods. In 2010 Fifth International Conference on Frontier of Computer Science and Technology. (pp. 436-441). Changchun, Jilin Province : IEEE Computer Society. 10.1109/FCST.2010.57

Pang, W., & Coghill, G. M. (2010). QML-AiNet: An Immune-inspired Network Approach to Qualitative Model Learning. In E. Hart, C. McEwan, J. Timmis, & A. Hone (Eds.), proc. of 8th International Conference on Artificial Immune Systems (ICARIS 2010). (pp. 223-236). (Lecture Notes in Computer Science; Vol. 6209). Berlin Heidelberg: Springer-Verlag. 10.1007/978-3-642-14547-6_18

Pang, W., & Coghill, G. M. (2009). An Immune-Inspired Approach to Qualitative System Identification of the Detoxification Pathway of Methylglyoxal. In Lecture Notes in Computer Science. (Vol. 5666, pp. 151-164). (Lecture Notes in Computer Science; Vol. 5666). Springer . 10.1007/978-3-642-03246-2_17

Liu, Y., Wang, K., Guo, D., Pang, W., & Zhou, C. (2008). Multi-agent ERA Model Based on Belief Solves Multi-port Container Stowage Problem. In Seventh Mexican International Conference on Artificial Intelligence (MICAI '08). (pp. 287-292). (Lecture Notes in Artificial Intelligence; No. 5137). IEEE Explore. 10.1109/MICAI.2008.10

Pang, W., & Coghill, G. M. (2008). Learning qualitative models of the detoxification pathway of methylglyoxal. In the 8th annual UK Workshop on Computational Intelligence. (pp. CD). De Montford University, UK.

Pang, W., & Coghill, G. M. (2007). Advanced experiments for learning qualitative compartment models. In Proceeding of the 21st International Workshop on Qualitative Reasoning. (pp. 109-117). Aberystwyth, UK.

Pang, W. (2007). Clonal selection algorithm for learning qualitative compartmental models of metabolic systems. In 7th annual UK Workshop on Computational Intelligence. (pp. CD). London.

Pang, W., & Coghill, G. M. (2007). Modified clonal selection algorithm for learning qualitative compartmental models of metabolic systems. In Proceedings of the 2007 GECCO conference companion on Genetic and evolutionary computation. (pp. 2887-2894). ACM Press. 10.1145/1274000.1274049

Meng, Y., Li, W., Wang, Y., Guo, W., & Pang, W. (2006). An Evolution Computation Based Approach to Synthesize Video Texture. In V. N. Alexandrov, G. D. van Albada, P. M. A. Sloot, & J. Dongarra (Eds.), Computational Science – ICCS 2006: Proceedings of the 6th International Conference, Part II. (Vol. 3992, pp. 223-230). (Lecture Notes in Computer Science - Computer Science and General Issues; Vol. 3992). Berlin, Germany: Springer . 10.1007/11758525_30

Liu, M., Pang, W., Wang, K. P., & Zhou, C. G. (2006). Improved Immune Genetic Algorithm For Solving Flow Shop Scheduling Problem. In G. R. Liu, V. B. C. Tan, & X. Han (Eds.), Computational methods. (pp. 1057-1062). Dordrecht, Netherlands: Springer . 10.1007/978-1-4020-3953-9_7

Pang, W., & Coghill, G. M. (2006). EQML- An Evolutionary Qualitative Model Learning Framework. In 2nd European Symposium on Nature-inspired Smart Information Systems. (pp. 1-7). Puerto de la Cruz, Tenerife, Spain.

Pang, W., & Coghill, G. M. (2006). Evolutionary approaches for learning qualitative compartment metabolic models. In Proceeding of the 6th annual UK Workshop on Computational Intelligence. (pp. 11-16). Leeds, UK.

Lv, C., Yu, Z., Zhou, C., Wang, K., & Pang, W. (2005). A Dynamic and Adaptive Ant Algorithm Applied to Quadratic Assignment Problems. Journal of Jilin University (Science Edition), 43(4), 477-480.

Pang, W., Wang, K., Zhou, C., Huang, L., & Ji, X. (2005). Fuzzy Discrete Particle Swarm Optimization for Solving Travel Salesman Problem. Journal of Chinese Computer Systems, 26(8), 1331-1334.

Huang, L., Pang, W., Wang, K., Zhou, C., & Lv, Y. (2005). New Genetic Algorithm for Vehicle Routing Problem with Time Window. Journal of Chinese Computer Systems, 26(2), 214-217.

Pang, W., Wang, K., Zhou, C., Dong, L., & Yin, Z. (2004). Fuzzy discrete particle swarm optimization for solving traveling salesman problem. In Proceedings of the 2004 International Conference on Computer and Information Technology (CIT2004). (pp. 796-800). Los Alamos, CA, USA: IEEE Press. 10.1109/CIT.2004.1357292

Pang, W., Wang, K., Zhou, C., Dong, L., Liu, M., Zhang, H., & Wang, J. (2004). Modified particle swarm optimization based on space transformation for solving traveling salesman problem. In Proceedings of 2004 International Conference on Machine Learning and Cybernetics. (Vol. 4, pp. 2342-2346). New York, NY, USA: IEEE Press. 10.1109/ICMLC.2004.1382191

Huang, L., Pang, W., Wang, K., Zhou, C., & Xiao, Y. (2004). Improved Genetic Algorithm for Vehicle Routing Problem with Time Windows. Advances in Systems Science and Applications, 4(1), 118-124.

Wang, K., Huang, L., Zhou, C., & Pang, W. (2003). Particle swarm optimization for traveling salesman problem. In 2003 International Conference on Machine Learning and Cybernetics. (Vol. 3, pp. 1583-1585). IEEE Press. 10.1109/ICMLC.2003.1259748

Huang, L., Wang, K., Zhou, C., Pang, W., & Dong, L. (2003). Particle Swarm Optimization for Traveling Salesman Problems. Acta Scientiarium Naturalium Universitatis Jilinensis, 41(4), 477-480.

Huang, L., Wang, K., Zhou, C., Yuan, Y., & Pang, W. (2002). Hybrid Approach Based on Ant Algorithm for Solving Traveling Salesman Problem. Acta Scientiarium Naturalium Universitatis Jilinensis, 40(4), 369-373.