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My Erdös Number is at most 4; my Einstein Number is at most 6

Last updated: 27 July 2019

Selected Publications (2014-present)

Selected Journal publications (2014-present)

  1. Pang, W. and Coghill, GM. 'QML-AiNet: an immune network approach to learning qualitative differential equation models'. Applied Soft Computing, vol 27, pp. 148-157, 2015. (Impact Factor: 2.857, Five-year Impact Factor: 3.288).
  2. Hu, X, Huang, L, Wang, Y & Pang, W 2019, 'Explosion gravitation field algorithm with dust sampling for unconstrained optimization' Applied Soft Computing, vol. 81, 105500. https://doi.org/10.1016/j.asoc.2019.105500 (Impact Factor: 4.873, Corresponding Author)
  3. Wang, W, Moreau, NG, Yuan, Y, Race, PR & Pang, W 2019, 'Towards machine learning approaches for predicting the self-healing efficiency of materials' Computational Materials Science, vol. 168, pp. 180-187 (Impact Factor: 2.644, corresponding author)
  4. Wu, Z., Pang, W. & Coghill, GM. 'An Integrative Top-down and Bottom-up Qualitative Model Construction Framework for Exploration of Biochemical Systems'. Soft Computing, vol 19, no. 6, pp. 1595-1610, Springer, 2015.(Impact Factor: 1.630, Co-first Author)
  5. Tian, X, Pang, W, Wang, Y, Guo, K & Zhou, Y 2019, 'LatinPSO: An algorithm for simultaneously inferring structure and parameters of ordinary differential equations models ' BioSystems, vol. 182, pp. 8-16. (Impact Factor: 1.623)
  6. Pang, W. & Coghill, GM. 'Qualitative, Semi-quantitative, and Quantitative Simulation of the Osmoregulation System in Yeast'. BioSystems, vol. 131, pp. 40-50, 2015. (Impact Factor: 1.495).
  7. Pang, W. . & Coghill, GM. 'QML-Morven: A Novel Framework for Learning Qualitative Differential Equation Models using Both Symbolic and Evolutionary Approaches'. Journal of Computational Science, vol 5, no. 5, pp. 795-808, 2014. (Impact Factor: 1.231).
  8. Wu, Z., Pang, W. & Coghill, GM. 'An Integrated Qualitative and Quantitative Biochemical Model Learning Framework Using Evolutionary Strategy and Simulated Annealing'. Cognitive Computation, vol 7, no. 6, pp. 637-651, Springer, 2015. (Impact Factor: 1.933, Co-first Author).
  9. Ji, J., Pang, W., Zheng, Y., Wang, Z. & Ma, Z. 'An initialization method for clustering mixed numeric and categorical data based on the density and distance'. International Journal of Pattern Recognition and Artificial Intelligence. vol 29, no. 7, pp.1550024, 2015. (Impact Factor: 0.915)
  10. Wang, Y.Z., Pang, W., Zhou Y., Density Propagation Based Adaptive Multi-density Clustering Algorithm, PLoS ONE 13(7): e0198948, 2018 (Impact Factor: 2.806)
  11. Xue, Y., Zhao B., Ma T., and Pang, W. ' A Self-adaptive Fireworks Algorithm for Classification Problems', IEEE Access, vol. 6, pp. 44406-44416, 2018 (Impact Factor: 3.557)
  12. Li, D, Huang, L, Wang, K, Pang, W, Zhou, Y & Zhang, R 2018, 'A General Framework for Accelerating Swarm Intelligence Algorithms on FPGAs, GPUs and Multi-core CPUs' IEEE Access, vol. 6, pp. 72327 - 72344. (Impact Factor: 3.557)
  13. Ji, J., Pang, W., Zheng, Y., Wang, Z. & Ma, Z. (2015). 'A novel artificial bee colony based clustering algorithm for categorical data'. PLoS ONE, vol 10, no. 5, e0127125, PLOS, 2015 (Impact Factor: 3.057, Corresponding Author).
  14. Yu, X., Yu, Z., Pang, W., Li, M. & Wu, L. 'An improved EMD-based dissimilarity Metric for Unsupervised Linear Subspace Learning'. Complexity, Volume 2018, Article ID: 8917393 (Impact Factor: 4.621)
  15. Huang, L., Wang, G., Wang, Y., Pang, W. & Ma, Q. 'A link density clustering algorithm based on automatically selecting density peaks for overlapping community detection'. International Journal of Modern Physics B, Vol 30, No. 24, pp.1650167 (1-15). World Scientific Publishing, 2016. (Impact Factor: 0.850)

Selected Conference Publications (2014-present)

  1. Luo, C., Pang, W., Wang, Z. & Lin, C. (2015). 'Hete-CF: Social-Based Collaborative Filtering Recommendation Using Heterogeneous Relations'. in The IEEE International Conference on Data Mining (ICDM 2014). IEEE Press, pp. 917-922. (Top Data Mining Conference, CORE Ranking A*__, acceptance rate: 19.53%, citations: 52).
  2. Wa_ng, Y., Du, W_., Liang, Y., Chen, X., Zhang, C., Pang, W. & Xu, Y. (2016) 'PUEPro: A Computational Pipeline for Prediction of Urine Excretory Proteins'. in Proceedings of ADMA 2016. Lecture Notes in Artificial Intelligence (LNAI) Vol 10086, Springer, Gold Coast, Australia, pp. 714-725, 2016. Best Paper Runner Up Award among 70 accepted papers (I presented the paper at the conference, and the presentation was one important criterion of the award).
  3. Wang, Y. Ou, G., Huang L., Pang, W., Coghill, G.M. (2018) 'e-Distance Weighted Support Vector Regression', in Proceedings of The 2018 Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2018), Springer, Melbourne, Australia, 2018. (CORE Ranking A, Acceptance Rate__: 17.8%)
  4. Pang, W. & Coghill, GM. (2014). 'Fuzzy qualitative simulation with multivariate constraints'. in 2014 IEEE International Conference on Fuzzy Systems (IEEE-FUZZ 2014). IEEE Press, pp. 575-582 (CORE Ranking A)
  5. Pang, W. & Coghill, GM. (2014). 'An immune network approach to learning qualitative models of biological pathways'. in 2014 IEEE Congress on Evolutionary Computation (IEEE CEC 2014). IEEE Press, pp. 1030-1037.
  6. Awad, A., Pang, W., Coghill G.M., Lusseau, D., A Hexagonal Cell Automaton Model to Imitate Physarum Polycephalum Competitive Behaviour, The 2019 Conference on Artificial Life (ALIFE 2019), Newcastle, 2019, in press. (CORE Rank A).
  7. Pang, W., Bruce A., Coghill G.M., 'Non-constructive interval simulation of dynamic systems', 31st International Workshop on Qualitative Reasoning (IJCAI 2018 QR Workshop), 2018, Stockholme, Sweden, pp 70-77.
  8. Luo, C., Pang, W. & Wang, Z. (2014). 'Semi-supervised clustering on heterogeneous information networks', Advances in Knowledge Discovery and Data Mining: 18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, May 13-16, 2014. Lecture Notes in Computer Science, vol. 8444, Springer, pp. 548-559. (CORE Rank A_,_ Acceptance Rate: 26.9%,)
  9. Ma, M., Pang, W. , Huang, L. & Wang, Z. 'A Novel Diversity Measure for Understanding Movie Ranks in Movie Collaboration Networks'. in Proceedings of The 2017 Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2017). Lecture Notes in Computing Science (LNCS) Vol 10234, Springer, Jeju, South Korea. pp 750-761, (CORE Rank A_,_ Acceptance Rate: 28.2%).

Full Publication List (until 27 July 2019)

Journal publications

  1. Parmar, MD, Pang, W, Hao, D, Jang, J, Liupu, W & Zhou, Y 2019, 'FREDPC: a feasible residual error-based density peak clustering algorithm with a fragment measuring strategy' IEEE Access. DOI: 10.1109/ACCESS.2019.2926579 (Impact Factor: 4.098) Early Access: https://ieeexplore.ieee.org/document/8754775
  2. Wang, W, Moreau, NG, Yuan, Y, Race, PR & Pang, W 2019, 'Towards machine learning approaches for predicting the self-healing efficiency of materials' Computational Materials Science, vol. 168, pp. 180-187(Impact Factor: 2.644, corresponding author)
  3. Tian, X, Pang, W, Wang, Y, Guo, K & Zhou, Y 2019, 'LatinPSO: An algorithm for simultaneously inferring structure and parameters of ordinary differential equations models ' BioSystems, vol. 182, pp. 8-16. (Impact Factor: 1.623)
  4. Hu, X, Huang, L, Wang, Y & Pang, W 2019, 'Explosion gravitation field algorithm with dust sampling for unconstrained optimization' Applied Soft Computing, vol. 81, 105500. https://doi.org/10.1016/j.asoc.2019.105500 (Impact Factor: 4.873, Corresponding Author)
  5. Yu, X, Yu, Z, Wu, L, Pang, W & Lin, C 2019, 'Data-driven two-layer visual dictionary structure learning' Journal of Electronic Imaging, vol. 28, no. 2, 023006. (Impact Factor: 0.78)
  6. Xue, Y, Jia, W, Zhao, X & Pang, W 2018, 'An Evolutionary Computation Based Feature Selection Method for Intrusion Detection' Security and Communication Networks, vol. 2018, 2492956. (Impact Factor: 0.90)
  7. Wang, Y.Z., Pang, W., Zhou Y., Density Propagation Based Adaptive Multi-density Clustering Algorithm, PLoS ONE 13(7): e0198948, 2018 (Impact Factor: 2.806)
  8. Xue, Y., Zhao B., Ma T., and Pang, W. ' A Self-adaptive Fireworks Algorithm for Classification Problems', IEEE Access, vol. 6, pp. 44406-44416, 2018 (Impact Factor: 3.557)
  9. Yu, X., Yu, Z., Pang, W., Li, M. & Wu, L. 'An improved EMD-based dissimilarity Metric for Unsupervised Linear Subspace Learning'. Complexity (Impact Factor: 4.621), Volume 2018, Article ID: 8917393, https://doi.org/10.1155/2018/8917393, 2018
  10. Li, D, Huang, L, Wang, K, Pang, W, Zhou, Y & Zhang, R 2018, 'A General Framework for Accelerating Swarm Intelligence Algorithms on FPGAs, GPUs and Multi-core CPUs' IEEE Access, vol. 6, pp. 72327-72344. (Impact Factor: 3.557)
  11. Xue, Y., Jiang, J., Ma, T., Liu, J., Geng, H. & Pang, W.'A Self-adaptive Artificial Bee Colony Algorithm with Symmetry Initialization'. Journal of Internet Technology, In Press. (Impact Factor: 1.930)
  12. Huang, L., Wang, G., Wang, Y., Pang, W. & Ma, Q. 'A link density clustering algorithm based on automatically selecting density peaks for overlapping community detection'. International Journal of Modern Physics B, Vol 30, No. 24, pp.1650167 (1-15). World Scientific Publishing, 2016. (Impact Factor: 0.850)
  13. Wang, G., Wang, Y., Huang, L., Pang, W. & Ma, Q. 'Link Community Detection Based on Line Graphs with A Novel Link Similarity Measure'. International Journal of Modern Physics B, Vol 30, No. 6, pp. 1650023 (1-18). World Scientific Publishing, 2016. (Impact Factor: 0.850).
  14. Bone, JD., Emele, CD., Abdul, AO., Coghill, GM. & Pang, W. (2016). 'The social sciences and the web: From 'Lurking' to interdisciplinary 'Big Data' research'. Methodological Innovations, vol 9, pp. 1-14. SAGE Publications, 2016.
  15. Lin, C., Liu, D., Pang, W. & Wang, Z. 'Sherlock: a Semi-Automatic Framework for Quiz Generation Using a Hybrid Semantic Similarity Measure'. Cognitive Computation, vol 7, no. 6, pp. 667-679, Springer, 2015.Corresponding Author.(Impact Factor: 1.933).
  16. Ji, J., Pang, W., Zheng, Y., Wang, Z. & Ma, Z. 'An initialization method for clustering mixed numeric and categorical data based on the density and distance'. International Journal of Pattern Recognition and Artificial Intelligence. vol 29, no. 7, pp.1550024, World Scientific Publishing, 2015. (Impact Factor: 0.915)
  17. Ji, J., Pang, W. , Zheng, Y., Wang, Z., Ma, Z. & Zhang, L. 'A novel cluster center initialization method for the k-prototypes algorithms using centrality and distance'. Applied Mathematics & Information Sciences, vol 9, no. 6, pp. 2933-2942, Natural Sciences Publishing, 2015.
  18. Jiang Y., Wang Y., Pang, W., Chen L., Sun H., Liang Y. Blanzierib E. (2015). 'Essential Protein Identification Based on Essential Protein-Protein Interaction Prediction by Integrated Edge Weights'. Methods, Vol 83, pp. 51-62, Elsevier, 2015. (Impact Factor: 3.503, Five-year Impact Factor: 3.789)
  19. Wu, Z., Pang, W. & Coghill, GM. 'An Integrative Top-down and Bottom-up Qualitative Model Construction Framework for Exploration of Biochemical Systems'. Soft Computing, vol 19, no. 6, pp. 1595-1610, Springer, 2015. Co-first Author, (Impact Factor: 1.630,Five-year Impact Factor: 1.732)
  20. Ma, D., Yu, J., Yu, Z. & Pang, W.'A novel object tracking algorithm based on compressed sensing and entropy of information'. Mathematical Problems in Engineering. vol 2015, ID. 628101, 2015. (Impact Factor: 0.644)
  21. Du, W., Cao Z., Wang Y., Zhou F.F., Pang, W. , Tian Y., Liang Y.C. Specific biomarkers: detection of cancer biomarkers through high-throughput transcriptomics, Cognitive Computation, vol 7, no. 6, pp. 652-666, Springer, 2015. (Impact Factor: 1.933)
  22. Ji, J., Pang, W., Zheng, Y., Wang, Z. & Ma, Z. (2015). 'A novel artificial bee colony based clustering algorithm for categorical data'. PLoS ONE, vol 10, no. 5, e0127125, PLOS, 2015. Corresponding Author. (Impact Factor: 3.057).
  23. Wu, Z., Pang, W. & Coghill, GM. 'An Integrated Qualitative and Quantitative Biochemical Model Learning Framework Using Evolutionary Strategy and Simulated Annealing'. Cognitive Computation, vol 7, no. 6, pp. 637-651, Springer, 2015. Co-first Author (Impact Factor: 1.933).
  24. Pang, W. & Coghill, GM. 'Qualitative, Semi-quantitative, and Quantitative Simulation of the Osmoregulation System in Yeast'. BioSystems, vol. 131, pp. 40-50, Elsevier, 2015. (Impact Factor: 1.495, Five-year Impact Factor: 1.620).
  25. Pang, W. and Coghill, GM. 'QML-AiNet: an immune network approach to learning qualitative differential equation models'. Applied Soft Computing, vol 27, pp. 148-157. Elsevier, 2015. (Impact Factor: 2.857, Five-year Impact Factor: 3.288).
  26. Pang, W. . & Coghill, GM. 'QML-Morven A Novel Framework for Learning Qualitative Differential Equation Models using Both Symbolic and Evolutionary Approaches'. Journal of Computational Science, vol 5, no. 5, pp. 795-808, Elsevier, 2014. (Impact Factor: 1.231).
  27. Li, B., Pang, W., Liu, Y., Yu, X., Du, A. & Yu, Z. 'Dimension Reduction Using Samples' Inner Structure Based Graph for Face Recognition'. Mathematical Problems in Engineering, vol 2014, 603025, 2014. (Impact Factor: 0.644)
  28. Li, B., Pang, W., Liu, Y., Yu, X. & Yu, Z. 'Building recognition on subregion's multi-scale gist feature extraction and corresponding columns information based dimensionality reduction'. Journal of Applied Mathematics, vol 2014, 898-705, 2014. (Impact Factor: 0.720)
  29. Kaloriti, D. Tillmann A. Cook, E. Jacobsen, M. You, T. Lenardon, M. Ames, L. Barahona, M. Chandrasekaran, K. Coghill, G. Goodman, D. Gow, N.A.R. Grebogi, C. Ho, H. Ingram, P. McDonagh, A. de Moura, A. Pang, W. Puttnam, M. Radmaneshfar, E. Romano, M. Silk, D. Stark, J. Stumpf, M. Thiel M. Thorne, T. Usher, J. Haynes, K. Brown, A.J.P. (2012). 'Combinatorial stresses kill pathogenic Candida species'. Medical Mycology, Vol 50, No.7, pp. 699-709, Oxford University Press, 2012. (Impact Factor: 1.979, Cited 62 times)
  30. Ji, J., Pang, W. Han X. Zhou, C.G. Wang Z. 'A fuzzy k-prototype clustering algorithm for mixed numeric and categorical data'. Knowledge-Based Systems, 30, pp. 129-135, Elsevier, 2012. (Impact Factor: 4.104, cited 111 times).
  31. Jia, C. Wang, S.J. Peng, X. Pang, W. Zhang, C. Zhou, C.G., Yu, Z. Z. 'Incremental Multi-linear Discriminant Analysis Using Canonical Correlations for Action Recognition'. Neurocomputing, 83, pp. 56-63, Elseveir, 2012. (Impact Factor: 1.634).
  32. Yu, Z. Jia, C. Pang, W. Zhang, C. Zhong, L. 'Tensor Discriminant Analysis with Multi-Scale Features for Action Modeling and Categorization'. IEEE Signal Processing Letters, 19(2), pp.95-98, IEEE, 2012. (Impact Factor: 1.674)
  33. Pang, W. and Coghill, G.M. 'An immune-inspired approach to qualitative system identification of biological pathways'. Natural Computing, 10(1), pp. 189-207, Springer, 2011. (Impact Factor: 0.683).
  34. Liu, Y.B. Zhou, C.G. Guo, D.W. Wang, K.P. Pang, W. Zhai, Y.D. 'A decision support system using soft computing for modern international container transportation services'. Applied Soft Computing 10(4), pp. 1087-1095, Elsevier, 2010. (Impact Factor: 2.097, Cited 18 times).
  35. Pang, W. and Coghill, G.M. 'Learning Qualitative Differential Equation models: a survey of algorithms and applications', knowledge engineering review, 25(1), pp. 69–107, Cambridge University Press, 2010. (Impact Factor: 1.257)
  36. Pang, W. , Wang, K.P. Zhou, C.G. Huang, L. Ji, X.H. 'Fuzzy Discrete Particle Swarm Optimization for Solving Travel Salesman Problem'. Journal of Chinese Computer Systems, 26(8), pp.1331-1334, China Academy of Sciences, 2005. (In Chinese with English Abstract)
  37. Huang, L, Pang, W. , Wang, K.P., Zhou, C.G. Lv, Y.H. 'New Genetic Algorithm for Vehicle Routing Problem with Time Window'. Journal of Chinese Computer Systems, 26(2), pp. 214-217, China Academy of Sciences, 2005. (In Chinese with English Abstract)
  38. Lv, C.Y. Yu, Z.Z. Zhou, C.G. Wang, K.P. Pang, W. ' A Dynamic and Adaptive Ant Algorithm Applied to Quadratic Assignment Problems'. Journal of Jilin University (Science Edition), 43(4), pp. 477-480, Jilin University Press, 2005. (In Chinese with English Abstract)
  39. Huang, L. Pang, W. Wang, K.P. Zhou, C.G. Xiao, Y. 'Improved Genetic Algorithm for Vehicle Routing Problem with Time Windows'. Advances in Systems Science and Applications, 4(1), 118-124, International Institute For General Systems Studies, 2004.
  40. Huang, L. Wang, K.P. Zhou, C.G. Pang, W. Dong, L.J. Peng, L. 'Particle Swarm Optimization forTraveling Salesman Problems'. Acta Scientiarium Naturalium Universitatis Jilinensis, 41(4), pp. 477-480, Jilin University Press, 2003. (In Chinese with English Abstract), cited 82 times
  41. Huang, L. Wang, K.P. Zhou, C.G. Yuang, Y. Pang, W. ' Hybrid Approach Based on Ant Algorithm for Solving Traveling Salesman Problem'. Acta Scientiarium Naturalium Universitatis Jilinensis, 40(4), pp. 369-373, Jilin University Press, 2002. (In Chinese with English Abstract)

Peer Reviewed Conference Papers

  1. _1._Hadeel, B., Pang, W., Coghill G.M., Swarm Inspired Approaches for K-prototypes clustering, 19th Annual UK Workshop on Computational Intelligence (UKCI 2019), Portsmouth, UK, 2019, in press
  2. _2._Byla, E., Pang, W., DeepSwarm: Optimising Convolutional Neural Networks using Swarm Intelligence, 19th Annual UK Workshop on Computational Intelligence (UKCI 2019), Portsmouth, UK, 2019, in press
  3. _3._Awad, A., Lusseau, D., Usman, M., Coghill G.M., Pang, W., A Physarum-Inspired Competition Algorithm for Solving Discrete Multi-Objective Optimization Problems, The Genetic and Evolutionary Computation Conference (GECCO 2019), Prague, 2019, in press.
  4. _4._Usman, M., Awad, A., Pang, W., Coghill G.M., Inferring Structure and Parameters of Dynamic Systems using Particle Swarm Optimization, The Genetic and Evolutionary Computation Conference (GECCO 2019), Prague, 2019, in press.
  5. _5._Awad, A., Pang, W., Coghill G.M., Lusseau, D., A Hexagonal Cell Automaton Model to Imitate Physarum Polycephalum Competitive Behaviour, The 2019 Conference on Artificial Life (ALIFE 2019), Newcastle, 2019, in press.
  6. 6._Forrest, J, Sripada, S, Pang, W & Coghill, G 2018, Towards making NLG a voice for interpretable Machine Learning, _Proceedings of The 11th International Natural Language Generation Conference (INLG 2018), W18-6522, Association for Computational Linguistics (ACL), pp. 177-182, Tilburg, Netherlands, 5/11/18.
  7. _7._Awad, A. Pang, W., Coghill G.M. Physarum Inspired Model for Mobile Sensor Nodes Deployment in the Presence of Obstacles (iCETiC '18), Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST), Vol 200, pp. 153-160.
  8. 8. Pang, W., Bruce A., Coghill G.M., 'Non-constructive interval simulation of dynamic systems', 31st International Workshop on Qualitative Reasoning (IJCAI), 2018, Stockholme, Sweden, pp 70-77.
  9. _9._Chapman, A., Pang W., Cogill G.M. 'Towards a Robust Imputation Evaluation Framework', The Seventh International Conference on Intelligent Systems and Applications (INTELLI 2018), In Press.
  10. 10._Awad, A. Pang, W., Coghill G.M., "Physarum Inspired Connectivity and Restoration for Wireless Sensor and Actor Networks', _the 13th UK Workshop on Computational Intelligence. Computational Intelligence (UKCI 2018), Advances in Intelligent Systems and Computing (AISC), vol. 840, pp. 327-338.
  11. 11._Chapman, A, Pang, W & Coghill, G 2018, CLEMI-imputation evaluation. in _SACI 2018 - IEEE 12th International Symposium on Applied Computational Intelligence and Informatics, Timisoara, Romania, 2018
  12. 12._Chapman, A, Pang, W & Coghill, G 2018, Towards a Robust Imputation Evaluation Framework. in GM Magalhães & A Gonçalves (eds), _Proceedings of The Seventh International Conference on Intelligent Systems and Applications. INTELLI, IARIA, pp. 7-13, Venice, Italy
  13. 13._Karatu, MT, Pang, W & Coghill, GM 2018, A Conceptual Framework of Starlings Swarm Intelligence Intrusion Prevention for Software Defined Networks, _ReaLX'18: Reasoning, Learning & Explainability in AI. vol. 2151, CEUR Workshop Proceedings, CEUR-WS, ReaLX'18: Reasoning, Learning & Explainability in AI, Aberdeen, United Kingdom
  14. 14._Ou, G., Wang, Y. , Huang L., Pang, W., Coghill, G.M. 'e-Distance Weighted Support Vector Regression', in Proceedings of The 2018 Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2018), Springer, Melbourne, Australia, 2018, _Acceptance Rate__: 17.8%
  15. 15.__Ou, G., Wang, Y., Pang, W., _Coghill, G.M. ''_Large Margin Distribution Machine Recursive Feature Elimination', in Proceedings of the 4th International Conference on Systems and Informatics (ICSAI 2017), IEEE, Hangzhou, China, 2017.
  16. 16.__Huang, L., Hu, X., Wang, Y.,Zhang F., Liu Z, Pang, W.';Gravitation Field Algorithm with Optimal Detection for Unconstrained Optimization', in Proceedings of the 4th International Conference on Systems and Informatics (ICSAI 2017), IEEE, Hangzhou, China, 2017. Coresponding Author
  17. 17.__Ma, M., Pang, W. , Huang, L. & Wang, Z. 'A Novel Diversity Measure for Understanding Movie Ranks in Movie Collaboration Networks'. in Proceedings of The 2017 Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2017). Lecture Notes in Computing Science (LNCS) Vol 10234, Springer, Jeju, South Korea. pp 750-761, Acceptance Rate: 28.2%
  18. 18._Wa_ng, Y., Du, W., Liang, Y., Chen, X., Zhang, C., Pang, W. & Xu, Y. 'PUEPro: A Computational Pipeline for Prediction of Urine Excretory Proteins'. in Proceedings of ADMA 2016. Lecture Notes in Artificial Intelligence (LNAI) Vol 10086, Springer, Gold Coast, Australia, pp. 714-725, 2016. Best Paper Runner Up Award.
  19. 19. Peng, Q., Wang, Y., Ou, G., Huang, L. & Pang, W. Partitioning Clustering Based on Support Vector Ranking. in ADMA 2016 Conference Proceedings. Lecture Notes in Artificial Intelligence (LNAI) Vol 10086, Springer, Gold Coast, Australia, pp 726-737, 2016. Corresponding author
  20. 20.__Han, L., Huang, L., Yang, X., Pang, W. & Wang, K. 'A Novel Spatio-temporal Data Storage and Index Method for ARM-based Hadoop Server'. The 2nd International Conference on Cloud Computing and Security, LNCS Vol 10039, Nanjing, China, pp. 206-216, Springer, 2016.
  21. 21.__Mukhtar N., Coghill, G., Pang, W. (2016) 'FDCA: A novel Fuzzy Deterministic Dedritic Cell Algorithm', GECCO 2016 (Genetic and Evolutionary Computation Conference), Denver, USA, ACM, 2016.
  22. 22.__Emele C.D., Spakov V., Pang, W., Bone J., Coghill G.M. (2015), "ADOVA: Anomaly Detection in Online and Virtual spAces", COOS'15@AAMAS 2015, Istanbul, Turkey, pp. 38-41.
  23. 23.__Lin, C., Liu, D., Pang, W. & Apeh, E. (2015). 'Predicting quiz difficulty level using a hybrid semantic similarity measure'. in Proceedings of The 8th International Conference on Knowledge Capture (K-CAP 2015). ACM, pp.1-8. Acceptance Rate: 28.5%
  24. 24.__Luo, C., Pang, W., Wang, Z. & Lin, C. (2015). 'Hete-CF: Social-Based Collaborative Filtering Recommendation Using Heterogeneous Relations'. in The IEEE International Conference on Data Mining (ICDM 2014). IEEE Press, pp. 917-922. (Top Data Mining Conference, acceptance rate: 19.53%,__cited 30 times in two years).
  25. 25.__Jia, C., Pang, W. & Fu, Y. (2015). 'Mode-driven volume analysis based on correlation of time series'. in European Conference on Computer Vision (ECCV 2014) : The 6th International Workshop on Video Event Categorization, Tagging and Retrieval towards Big Data, Springer, Zurich, LNCS Vol 8925, pp. 818-833.
  26. 26. Pang, W., Wang K., Ge O., Li H., Wang, Y., Huang L., (2015) 'Clonal Selection Algorithm for Solving Permutation Optimisation Problems: A Case Study of Travelling Salesman Problem', International Conference on Logistics Engineering, Management and Computer Science (LEMCS 2015), Atlantis Press, pp. 575-580.
  27. 27.__Luo, C., Pang, W. & Wang, Z. (2014). 'Semi-supervised clustering on heterogeneous information networks', Advances in Knowledge Discovery and Data Mining: 18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, May 13-16, 2014. Lecture Notes in Computer Science, vol. 8444, Springer, pp. 548-559. Acceptance Rate: 26.9%
  28. 28. Pang, W. & Coghill, GM. (2014). 'Fuzzy qualitative simulation with multivariate constraints'. in 2014 IEEE International Conference on Fuzzy Systems (IEEE-FUZZ 2014). IEEE Press, pp. 575-582.
  29. 29. Pang, W. & Coghill, GM. (2014). 'An immune network approach to learning qualitative models of biological pathways'. in 2014 IEEE Congress on Evolutionary Computation (IEEE CEC 2014). IEEE Press, pp. 1030-1037.
  30. 30.__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). IEEE Press, Belfast, UK, pp.480-483. Acceptance Rate: 38%, recommended for an extended journal submission.
  31. 31.__Wu, Z., Pang, W. & Coghill, GM. (2013). 'Stepwise modelling of biochemical pathways based on qualitative model learning'. in Y Jin & SA Thomas (eds), Proceeding of the 13th UK Workshop on Computational Intelligence. Computational Intelligence (UKCI 2013). IEEE Explore, pp. 31-37.
  32. 32. Pang, W. and Coghill, GM. (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). Universität Bremen / Universität Freiburg, Bremen, Germany, pp. 77-84.
  33. 33. Pang, W. and Coghill, GM. (2012). 'Extended Kernel Subsets Analysis for Qualitative Model Learning', Proceeding of the 12th UK Workshop on Computational Intelligence. IEEE Explore, Edinburgh, UK, pp. 1-7, Computational Intelligence (UKCI), 2012 12th UK Workshop on, United Kingdom, 2012.
  34. 34. Pang, W. and Coghill, G.M. (2011). 'A Fast Opt-AINet approach to Qualitative Model Learning with Modified Mutation Operator', Proceedings of the 2011 UK workshop on Computational Intelligence (UKCI 2011), Manchester, pp. 43-48.
  35. 35. Pang, W. and Coghill, G.M. (2010). 'QML-AiNet: An Immune-inspired Network Approach to Qualitative Model Learning'. proc. of 8th International Conference on Artificial Immune Systems (ICARIS 2010), Edinburgh, LNCS 6209, pp. 223-236.
  36. 36. Pang, W. and Coghill, G.M. (2010). 'Learning Qualitative Metabolic Models Using Evolutionary Methods'. 5th International Conference on Frontier of Computer Science and Technology, Changchun, China, pp. 436-441.
  37. 37. Pang, W. and Coghill, G.M. (2009). 'An Immune-inspired Approach to Qualitative System Identification of the Detoxification Pathway of Methylglyoxal'. proc. of 8th International Conference on Artificial Immune Systems (ICARIS 2009), LNCS 5666, 2009, pp. 151-164.
  38. 38. Pang, W. and Coghill, G.M. (2008). 'Learning qualitative models of the detoxification pathway of methylglyoxal'. Proceedings of the 2008 UK workshop on Computational Intelligence (De Montford University): pages CD.
  39. 39.__Liu, Y. Wang, K.P. Guo D.W. Pang, W.. Zhou, C.G. (2008). 'Multi-agent ERA Model Based on Belief Solves Multi-port Container Stowage Problem'. Proceedings of the 2008 Seventh Mexican International Conference on Artificial Intelligence, pp. 287-292.
  40. 40. Pang, W. and Coghill, G.M. (2007). 'Clonal selection algorithm for learning qualitative compartmental models of metabolic systems'. Proceeding of the 7th annual UK Workshop on Computational Intelligence, Imperial College, London, UK, 2007, pages CD.
  41. 41. Pang, W. and Coghill, G.M. (2007). 'Modified clonal selection algorithm for learning qualitative compartmental models of metabolic systems', proceeding of Genetic and Evolutionary Computation Conference (GECCO07), London, UK, pp. 2887-2894.
  42. 42. Pang, W. and Coghill, G.M. (2007). 'Advanced experiments for learning qualitative compartment models'. Proceeding of the 21st International Workshop on Qualitative Reasoning, Aberystwyth, UK, 2007, pp. 109-117.
  43. 43. Pang, W. and Coghill, G.M. (2006). 'Evolutionary approaches for learning qualitative compartment metabolic models'. Proceeding of the 6th annual UK Workshop on Computational Intelligence, University of Leeds, Leeds, UK, pp. 11-16.
  44. 44. Pang, W. and Coghill, G.M. (2006). 'EQML- An Evolutionary Qualitative Model Learning Framework', 2nd Annual Symposium on Nature-inspired smart information systems, Puerto de la Cruz, Tenerife, Spain.
  45. 45.Meng, Y.Li, W.H.Wang, Y. Guo.Y. Pang, W. (2006). 'An Evolution Computation Based Approach to Synthesize Video Texture'. International Conference on Computational Science (2), pp 223-230.
  46. 46.__Liu, M. Pang, W. Wang, K.P. Song, Y.Z. Zhou, C.G (2006)., Improved Immune Genetic Algorithm For Solving Flow Shop Scheduling Problem, computational methods, 1057-1062, Springer.
  47. 47. Pang, W. . Wang, K.P. Zhou, C.G. Dong, L.J. Liu, M. Zhang, H.Y. Wang, J.Y. (2004). 'Modified Particle Swarm Optimization Based on Space Transformation For Solving Travelling Salesman Problem'. Proceedings of 2004 International Conference on Machine Learning and Cybernetics, Shanghai, China_,_ pp. 2342-2346.Cited 62 times.
  48. 48. Pang, W., Wang, K.P. Zhou, C.G. Dong, L.J. (2004). 'Fuzzy discrete particle swarm optimization for solving travelling salesman problem'. Proceedings of The Fourth International Conference on Computer and Information Technology (CIT 2004), Wuhan, China, pp. 796-800. Cited 190 times.
  49. 49.__Wang, K.P. Huang, L. Zhou, C.G. Pang, W. (2003). 'Particle Swarm Optimization for Travelling Salesman Problem'. Proceedings of the Second International Conference on Machine Learning and Cybernetics, Xi'an, China, pp. 583-1585. Cited 416 times.

Book Chapters

  1. Jia, C., Pang, W., & Fu, Y. 'Multimodal Action Recognition'. In Y Fu (ed.), Human Activity Recognition and Prediction. Springer, pp. 71-85, Springer, 2015.

Abstracts

1. Kaloriti, D., Tillmann, A., Jacobsen, M., Yin, Z., Patterson, M., Radmaneshfar, E., You, T., Chandrasekaran, K., Pang, W., Coghill, G., de Moura, APS., Thiel, M., Romano, MC., Grebogi, C., Haynes, K., Quinn, J., Gow, NAR. & Brown, AJP. 'Impact of combinatorial stresses upon Candida albicans'. Mycoses, vol 55, no. Suppl. 4, pp. 15, John Wiley & Sons, Inc., 11 June 2012.

Departmental Technical Reports

  1. Pang, W., Coghill, GM. & Bruce, AM. (2012). 'Non-constructive interval simulation of dynamic systems', Technical Report ABDN–CS–12–02, vol. ABDN–CS–12–02, Department of Computing Science, University of Aberdeen, Aberdeen.

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

Arxiv Papers

  1. Wang Y., Ou G., Pang, W., Huang L., Coghill G.M. (2016), 'e-Distance Weighted Support Vector Regression', https://arxiv.org/abs/1607.06657

  2. Luo, C., Pang, W., & Wang, Z. (2014). 'Hete-CF: Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations'. HTTP://ARXIV.ORG/ABS/1412.7610