BCML Lab @Heriot-Watt

Academic Publications

Selected Journal publications

  1. Hassan, M., Wang, Y., Wang, D.,*Pang, W., Li, D., Zhou, Y, Xu, D, et al “Deep learning model for human-intuitive shoeprint reconstruction”, 2024, Expert Systems with Applications, Vol 249, DOI.

  2. Huang,L., Bai, X., Zeng, J., Yua, M., Pang, W., Wang, K., FAM: Improving Columnar Vision Transformer with Feature Attention Mechanism. 2024, Computer Vision and Image Understanding, Accepted, DOI

  3. Hou, W., Wang, Y., Zhao, Z., Cong Y., Pang, W, Tian, Y., Hierarchical Graph Neural Network with Subgraph Perturbations for Key Gene Cluster Discovery in Cancer Staging, 2023, Complex & Intelligent Systems, DOI(Impact Factor: 5.8).

  4. Awad, A., Coghill, G.M. & Pang, W. A novel Physarum-inspired competition algorithm for discrete multi-objective optimisation problems. Soft Computing (2023). DOI(Impact Factor: 3.732)

  5. Gherman,I.,Abdallah, Z., Pang, W, Gorochowski, T., Grierson, C., Marucci, L., Bridging the gap between mechanistic biological models and machine learning surrogates, PLOS Computational Biology, DOI(Impact Factor: 4.779)

  6. Huang L., Sun S., Zeng J., Wang W., Pang, W, Wang K., U-DARTS: Uniform-space differentiable architecture search,Information Sciences, Vol 628, Pages 339-349, DOI(Impact Factor: 8.233)

  7. Wang, Y., Pang, W, Jiao Z., An Adaptive Mutual K-nearest Neighbors Clustering Algorithm based on Maximizing Mutual Information, 2023, Pattern Recognition DOI (Impact Factor: 8.5).

  8. Gao, X., Taylor S. Pang, W. Hui, R., Lu, X., Oxford GI investigators, Braden, B., Fusion of colour contrasted images for early detection of oesophageal squamous cell dysplasia from endoscopic videos in real time, 2023, Information Fusion, Vol 92, pp. 64-29 DOI (Impact Factor: 17.56)

  9. Awad, A., Pang, W., Lusseau, D., Coghill G., 2022, A Survey on Physarum Polycephalum Intelligent Foraging Behaviour and Bio-Inspired Applications. Artificial Intelligence Review, DOI. (Impact Factor: 9.588)

  10. Wang Y.,Pang, W., Zhou, J., An Improved Density Peak Clustering Algorithm Guided by Pseudo Labels, 2022, Knowledge-Based Systems, DOI (Impact Factor: 8.038)

  11. Liu Q., Li J., Ren H., Pang, W., All particles driving particle swarm optimization: Superior particles pulling plus inferior particles pushing, 2022, Knowledge-Based Systems, Vol 249, 108849. DOI. (Impact Factor: 8.038)

  12. Hassan M., Wang. Y., Wang. D., Pang, W., Wang, K, Li, D., Zhou, Y., Xu D., 2022, Restorable-Inpainting: A Novel Deep Learning Approach for Shoeprint Restoration, Information Sciences, DOI (Impact Factor: 6.795)

  13. Wang, X., Liu, X., Pang, W., Jiang, A. 2022, Multiscale Increment Entropy: An approach for quantifying the physiological complexity of biomedical time series, Information Sciences, vol. 586, pp. 279-293. DOI (Impact Factor: 6.795)

  14. Hassan, M., Wang, Y., Pang, W., Di, W., Li, D., Xu, D., 2021 GUV-Net for high fidelity shoeprint generation. Complex & Intelligent Systems(2021). DOI (Impact Factor: 4.927)

  15. Usman, M.,Pang, W & Coghill, G. M, 2020 Inferring Structure and Parameters of Dynamic System Models using Swarm Intelligence, Memetic Computing, vol. 12, pp. 267-282, DOI (Impact Factor: 2.674)

  16. Liu, X, Wang X, Zhou, L, Xia, J, Pang, W, 2020 “Spatial Imputation for Air Pollutants Data Sets Via Low Rank Matrix Completion Algorithm”, Environment International, DOI (Impact Factor: 7.943, top journal in Environmental Sciences)

  17. Wang, Y, Wang, D, Pang, W, Miao, C, Tan, A, Zhou, Y, 2020, “A Systematic Density-based Clustering Method Using Anchor Points”,Neurocomputing, DOI (Impact Factor: 4.072)

  18. Wang, Y, Wang, D, Zhang, X, Pang，W, Miao, C, Tan, A & Zhou, Y, 2020, ‘McDPC: Multi-center Density Peak Clustering’, Neural Computing and Applications DOI (Impact Factor: 4.664)

  19. Xue, Y, Tang, T, Pang, W & Liu, AX 2020, ‘Self-adaptive Parameter and Strategy based Particle Swarm Optimization for Large-scale Feature Selection Problems with Multiple Classifiers’, Applied Soft Computing, Vol. 88, 106031. DOI (Impact Factor: 4.873)

  20. 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. DOI (Impact Factor: 4.098)

  21. 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. DOI (Impact Factor: 1.623)

  22. 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. DOI (Impact Factor: 4.873)

  23. Wu, Z, Pang, W & Coghill, GM 2015, 'An Integrated Qualitative and Quantitative Biochemical Model Learning Framework Using Evolutionary Strategy and Simulated Annealing' Cognitive Computation, vol. 7, no. 6, pp. 637-651. DOI (Impact Factor: 1.933)

  24. Wu, Z, Pang, W & Coghill, GM 2015, '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. DOI (Impact Factor: 1.63)

  25. Pang, W & Coghill, GM 2015, 'Qualitative, Semi-quantitative, and Quantitative Simulation of the Osmoregulation System in Yeast' BioSystems, vol. 131, pp. 40-50. DOI [CODE]JMorven (Impact Factor: 1.495)

  26. Pang, W & Coghill, GM 2015, 'QML-AiNet: an immune network approach to learning qualitative differential equation models' Applied Soft Computing, vol. 27, pp. 148-157. DOI (Impact Factor: 4.38)

  27. Pang, W & Coghill, GM 2014, '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. DOI (Impact Factor: 1.231)

  28. 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, vol. 30, pp. 129-135. DOI (Impact Factor: 4.104)

  29. 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, vol. 83, no. -, pp. 56-63. DOI (Impact Factor: 1.634)

  30. 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, vol. 19, no. 2, pp. 95-98. DOI (Impact Factor: 1.674)

  31. Pang, W & Coghill, GM 2011, 'An immune-inspired approach to qualitative system identification of biological pathways' Natural Computing, vol. 10, no. 1, pp. 189-207. DOI

  32. Pang, W & Coghill, GM 2010, 'Learning Qualitative Differential Equation models: a survey of algorithms and applications' Knowledge Engineering Review, vol. 25, no. 1, pp. 69-107. DOI (Impact Factor: 1.257)

Selected Conference papers

  1. Yuan, Y., Wang, W., Li, X, Chen,X., Zhang, Y.,Pang W.,. Evolving Molecular Graph Neural Networks with Hierarchical Evaluation Strategy, GECCO 2024, Accepted, DOI

  2. Yang F., Li X., Wang M., Zang H, Pang W., Wang M., WaveForM: Graph Enhanced Wavelet Learning for Long Sequence Forecasting of Multivariate Time Series, AAAI’23: Proceedings of the AAAI Conference on Artificial Intelligence, 37(9), 10754-10761. DOI

  3. Markovic, M, Naja, I., Edwards, P. & Pang , W 2021 , The Accountability Fabric : A Suite of Semantic Tools For Managing AI System Accountability and Audit, In Proceedings of The 20th International Semantic Web Conference (ISWC 2021). URL. Demo Video and code

  4. Yuan, Y., Wang, W. & Pang, W, 6 Apr 2021, ‘A Genetic Algorithm with Tree-structured Mutation for Hyperparameter Optimisation of Graph Neural Networks’, 2021 IEEE Congress on Evolutionary Computation (CEC), 2021, pp. 482-489, DOI, Open Access, arXiv

  5. Frachon, L., Pang, W & Coghill, G., 2021 ‘An Immune-Inspired Approach to Macro-Level Neural Ensemble Search’ 2021 IEEE Congress on Evolutionary Computation (CEC), 2021, pp. 2491-2498, DOI, arXiv

  6. Yuan, Y., Wang, W. & Pang, W, 2021, ‘A Systematic Comparison Study on Hyperparameter Optimisation of Graph Neural Networks for Molecular Property Prediction’, GECCO ‘21: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 386–394 DOI open access arXiv

  7. Wang, W, Pang, W, Bingham, P, Mania, M, Chen T, Perry, J. ‘Evolutionary Learning for Soft Margin Problems: A Case Study on Practical Problems with Kernels’, 2020, IEEE Congress on Evolutionary Computation (CEC 2000), Glasgow, United Kingdom. pp. 1-7, DOI.

  8. Byla, E & Pang, W 2019, 'DeepSwarm: Optimising Convolutional Neural Networks using Swarm Intelligence' Paper presented at 19th Annual UK Workshop on Computational Intelligence, Portsmouth, United Kingdom, 4/09/19-6/09/19. CODE DOI Best Paper Award among 45 papers

  9. Awad, A, Usman, M, Lusseau, D, Coghill, GM & Pang, W 2019, A Physarum-Inspired Competition Algorithm for Solving Discrete Multi-Objective Optimization Problems. in Genetic and Evolutionary Computation Conference Companion (GECCO '19 Companion), July 13–17, 2019, Prague, Czech Republic. ACM, New York, USA, The Genetic and Evolutionary Computation Conference GECCO 2019, Prague, Czech Republic, 13/07/19. DOI

  10. Usman, M, Awad, A, Pang, W & Coghill, GM 2019, Inferring Structure and Parameters of Dynamic Systems using Latin Hypercube Sampling Multi Dimensional Uniformity-Particle Swarm Optimization. in Genetic and Evolutionary Computation Conference Companion (GECCO '19 Companion), July 13–17, 2019, Prague, Czech Republic. ACM, New York, USA, The Genetic and Evolutionary Computation Conference GECCO 2019, Prague, Czech Republic, 13/07/19. DOI

  11. Pang, W , Bruce, AM & Coghill, GM 2018, Non-constructive interval simulation of dynamic systems. in Z Falomir, GM Coghill & W Pang (eds), Proceeding of the 31st International Workshop on Qualitative Reasoning (co-located at IJCAI'18). pp. 70-77, 31st International Workshop on Qualitative Reasoning (IJCAI'18), Stockholm, Sweden, 13/07/18. URL

  12. Ma, M, Pang, W , Huang, L & Wang, Z 2017, A Novel Diversity Measure for Understanding Movie Ranks in Movie Collaboration Networks. in J Kim, K Shim, L Cao, JG Lee, X Lin & YS Moon (eds), The Pacific-Asia Conference on Knowledge Discovery and Data Mining: PAKDD 2017. Lecture Notes in Computer Science, vol. 10234, Springer , Cham, pp. 750-761, The Pacific-Asia Conference on Knowledge Discovery and Data Mining, Jeju, Korea, Republic of, 23/05/16. DOI

  13. Mukhtar, N, Coghill, GM & Pang, W 2016, FdDCA: A Novel Fuzzy Deterministic Dendritic Cell Algorithm. in T Friedrich, F Neumann & AM Sutton (eds), GECCO '16 Companion Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion. ACM, pp. 1007-1010. DOI

  14. Wang, 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 J Li, X Li, S Wang, J Li & QZ Sheng (eds), Advanced Data Mining and Applications: 2th International Conference, ADMA 2016, Gold Coast, QLD, Australia, December 12-15, 2016, Proceedings. Lecture Notes in Artificial Intelligence (LNAI), Springer International Publishing, pp. 714-725, ADMA 2016, Gold Coast, Australia, 12/12/16. DOI Best Paper Runner Up Award

  15. Jia, C, Pang, W & Fu, Y 2015, Mode-Driven Volume Analysis Based on Correlation of Time Series. in L Agapito, MM Bronstein & C Rother (eds), Computer Vision - ECCV 2014 Workshops: Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part I. Lecture Notes in Computing Science, vol. 8925, Springer , Zurich, pp. 818-833. DOI

  16. 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)., 1, ACM, pp. 1-8, K-CAP 2015 - The 8th International Conference on Knowledge Capture, New York, United States, 7/10/15. DOI

  17. Luo, C, Pang, W , Wang, Z & Lin, C 2014, Hete-CF: Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations. in R Kumar, H Toivonen, J Pei, JZ Huang & X Wu (eds), 2014 IEEE International Conference on Data Mining (ICDM 2014). IEEE proceedings, IEEE Explore, pp. 917-922, 14th IEEE International Conference on Data Mining, Shenzhen, China, 14/12/14. DOI,[CODE]Hete-CF ` top data mining conference`

  18. Luo, C, Pang, W & Wang, Z 2014, Semi-supervised clustering on heterogeneous information networks. in VS Tseng, T Bao Ho, Z-H Zhou, ALP 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. Lecture Notes in Computer Science, vol. 8444, Springer , pp. 548-559. DOI

  19. 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. DOI

  20. 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. DOI

  21. Pang, W & Coghill, GM 2014, Fuzzy qualitative simulation with multivariate constraints. in 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2014). IEEE Press, pp. 575-582. DOI

  22. Pang, W & 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, 27th International Workshop on Qualitative Reasoning, Bremen, United Kingdom, 27/08/13.

BCML Lab @Heriot-Watt

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