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

(last updated: 4 April 2024). Publication in total: 160, of which peer reviewed: 153

Journal Publications (80)

  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. Sun, M., Hu, H., Pang, W., Zhou, Y. ACP-BC: A Model for Accurate Identification of Anticancer Peptides Based on Fusion Features of Bidirectional Long Short-Term Memory and Chemically Derived Information, International Journal of Molecular Sciences, 2023, Vol.24,No 20, 15447 DOI(Impact Factor: 5.6)
  4. Hassan, M., Zhang, H., Fateh, A.A. Ma, S. Liang, W.,Shang, D., Deng, J.,Zhang, Z., Lam T., Xu, M., Huang, Q., Yu, D., Zhang, C., Zhou, Y, Pang, W, Yang, C., Qin, P., Retinal disease projection conditioning by biological traits. 2023, Complex & Intelligent Systems. DOI(Impact Factor 5.8)
  5. 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).
  6. 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)
  7. 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)
  8. 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)
  9. Cong, Y., Wang, Y., Hou, W., Pang, W, Feature Correspondences Increase and Hybrid Terms Optimization Warp for Image Stitching, 2023, Entropy, Vol 25(1), 106, DOI(Impact Factor: 2.738)
  10. 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).
  11. 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)
  12. 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)
  13. Naja Y., Markovic, M., Edwards P., Pang, W., Cottrill C., Williams, R., Using Knowledge Graphs to Unlock Practical Collection, Integration, and Audit of AI Accountability Information, 2022, IEEE Access, DOI (Impact Factor: 3.367)
  14. 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)
  15. 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)
  16. Hassan M., Wang, Y., Pang, W., Wang, D., Li, D., Zhou, Y., Xu, D., 2022, IPAS-Net: A deep-learning model for high fidelity shoeprints from low-quality images with no natural references, Journal of King Saud University - Computer and Information Sciences, doi.(Impact Factor: 13.473)
  17. Zhou Y., Wang Y., Wu J., Hassan M., Pang, W., Lv L., Wang, L., Cui H.,2022, ErythroidCounter: an automatic pipeline for erythroid cell detection, identification and counting based on deeplearning, Multimedia Tools and Applications (2022), DOI. (Impact Factor: 2.757)
  18. 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)
  19. Alnabulsi, A., Wang, T.,Pang, W., Ionescu, M., Craig, S., Humphries, M., McCombe, K., Tellez, M., Alnabulsi, A., Murray, G., 2022, Identification of a prognostic signature in colorectal cancer using combinatorial algorithms driven analysis, The Journal of Pathology: Clinical Research, DOI (Impact factor: 5.638, top journal in Pathology)
  20. Williams, R., Cloete, R., Cobbe, J., Cotterill, C., Edwards, P., Markovic, M., Naja, I., Ryan, F., Singh, J., Pang, W, 2022, From Transparency to Accountability of Intelligent Systems–moving beyond aspirations, Data & Policy, vol. 4, pp E7. DOI(Impact Factor: awaiting).
  21. 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)
  22. Gao X., Taylor S., Pang, W, Hui, R.,Lu, X.,Braden, B., Computational colour contrast-enhancement improves endoscopic visibility of oesophageal squamous dysplasia and detection in AI-based system. Gut, 70(Suppl 4), poster papers DOI (Impact Factor: 23.059)
  23. Wang, Y., Chen, J, Xie, X., Yang, S., Pang, W., Huang, L., Zhang, S., Zhao, S. Minimum Distribution Support Vector Clustering. Entropy. 2021; 23(11):1473. DOI (Impact Factor: 2.587)
  24. 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)
  25. Ji, J., Li, Z., He, F., Feng, G.,Pang, W., Zhao, X., 2021 A Multi-View Clustering Algorithm for Mixed Numeric and Categorical Data, IEEE Access, vol. 9, pp. 24913-24924, DOI (Impact Factor: 3.745)
  26. Zeng, Q., Ma, X., Cheng, B., Zhou, E., Pang, W., 2020, GANs-Based Data Augmentation for Citrus Disease Severity Detection Using Deep Learning, IEEE Access, vol. 8, pp. 172882-172891, 2020, DOI (Impact Factor: 4.098)
  27. Yu, X., Pang, W., Xu, Q., Liang, M., 2020, Mammographic Image Classification with Deep Fusion Learning, Scientifc Reports, Vol.10, No. 14361 DOI (Impact Factor: 3.998).
  28. Wang, Y., Yang, Y., Guo, J. Xie, X., Liang, S., Zhang, R., Pang, W., Huang, L., 2020, Cancer genotypes prediction and associations analysis from imaging phenotypes: A survey on radiogenomics, Biomarkers in Medicine, vol.14 No. 12. DOI (Impact Factor: 2.479)
  29. Yang, W., Wang, D., Pang, W., Tan A.H., Zhou, Y., 2020, Goods Consumed during Transit in Split Delivery Vehicle Routing Problems: Modeling and Solution, IEEE Access,Vol.8, pp. 110336-110350 DOI. (Impact Factor: 4.098)
  30. 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)
  31. Wang, Y., Wang, Y., Song, Y., Xie, X., Huang, L., Pang, W & Coghill, G. M., 2020 An Efficient v-minimum Absolute Deviation Distribution Regression Machine, IEEE Access, URL.(Impact Factor: 4.098)
  32. 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)
  33. 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)
  34. Ji, J, Pang, W, Li, Z, He, F, Feng G. and Zhao X. 2020 “Clustering Mixed Numeric and Categorical Data with Cuckoo Search,” IEEE Access.DOI (Impact Factor: 4.098)
  35. 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)
  36. Wang, Y, Zhou, Y, Pang, W, Liang, Y &Wang, S, 2020, ‘Clustering Single-cell RNA-sequencing Data based on Matching Clusters Structures’ Tehnički vjesnik – Technical Gazette, Vol. 27, No. 1, DOI(Impact Factor: 0.664).
  37. Yu, X, Zhang, Z, Wu, L, Pang, W, Chen, H, Yu Z & Li, B, 2020, Deep Ensemble Learning for Human Action Recognition in Still Images, Complexity, Vol 2020, Article ID: 9428612 DOI (Impact Factor: 2.591)
  38. 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)
  39. Parmar, MD, Pang, W , Hao, D, Jang, J, Liupu, W & Zhou, Y 2019, 'FREDPC: A Feasible Residual Error-Based Density Peak Clustering Algorithm With the Fragment Merging Strategy' IEEE Access, vol. 7, pp. 89789-89804. DOI (Impact Factor: 4.098)
  40. Wang, X, Pang, W, & Wang, Z, 2019, Meta Struct-CF:A Meta Structure Based Collaborative Filtering Algorithm in Heterogeneous Information Networks, Computer Science (in Chinese), Vol 46, No 6A, pp.397-401. URL
  41. 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)
  42. 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)
  43. 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)
  44. 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. DOI (Impact Factor: 0.924)
  45. 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. DOI (Impact Factor: 1.376)
  46. Wang, Y, Pang, W & Zhou, Y 2018, 'Density propagation based adaptive multi-density clustering algorithm' PloS ONE, vol. 13, no. 7, e0198948, pp. 1-13. DOI (Impact Factor: 2.776)
  47. Yu, X, Yu, Z, Pang, W , Li, M & Wu, L 2018, 'An improved EMD-based dissimilarity Metric for Unsupervised Linear Subspace Learning' Complexity, vol. 2018, 8917393. DOI (Impact Factor: 2.591)
  48. 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. DOI (Impact Factor: 4.098)
  49. Xue, Y, Jiang, J, Ma, T, Liu, J, Geng, H & Pang, W 2018, 'A Self-adaptive Artificial Bee Colony Algorithm with Symmetry Initialization' Journal of Internet Technology, vol. 19, no. 5, pp. 1347-1362. DOI (Impact Factor: 0.715)
  50. Xue, Y, Zhao, B, Ma, T & Pang, W 2018, 'A Self-adaptive Fireworks Algorithm for Classification Problems' IEEE Access, vol. 6, pp. 44406-44416. DOI (Impact Factor: 4.098)
  51. Huang, L, Wang, G, Wang, Y, Pang, W & Ma, Q 2016, '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, 1650167. DOI (Impact Factor: 0.736)
  52. Wang, G, Huang, L, Wang, Y, Pang, W & Ma, Q 2016, 'Link community detection based on line graphs with a novel link similarity measure' International Journal of Modern Physics B, vol. 30, no. 6, 1650023. DOI (Impact Factor: 0.736)
  53. 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. DOI
  54. 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)
  55. 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, vol. 7, no. 6, pp. 667-679. DOI (Impact Factor: 1.933)
  56. 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, vol. 7, no. 6, pp. 652-666. DOI (Impact Factor: 1.933)
  57. 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, vol. 29, no. 7, 1550024. DOI (IMpact Factor: 0.397)
  58. 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)
  59. 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, vol. 83, pp. 51-62. DOI (Impact Factor: 3.782)
  60. 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. DOI (Impact Factor: 3.057)
  61. 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)
  62. 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)
  63. Ji, J, Pang, W , Zheng, Y, Wang, Z, Ma, Z & Zhang, L 2015, 'A novel cluster center initialization for the k-prototypes algorithms using centrality and distance' Applied Mathematics & Information Sciences, vol. 9, no. 6, pp. 2933-2942. DOI
  64. 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, vol. 2015, 628101. DOI (Impact Factor: 0.616)
  65. 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)
  66. 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, vol. 2014, 603025. DOI (Impact Factor: 0.616)
  67. 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, vol. 2014, 898705. DOI
  68. Kaloriti, D, Tillmann, A, Cook, E, Jacobsen, M, You, T, Lenardon, M, Ames, L, Barahona, M, Chandrasekaran, K, Coghill, G, Goodman, D, Gow, NAR, Grebogi, C, Ho, H-L, Ingram, P, McDonagh, A, de Moura, APS, Pang, W , Puttnam, M, Radmaneshfar, E, Romano, MC, Silk, D, Stark, J, Stumpf, M, Thiel, M, Thorne, T, Usher, J, Yin, Z, Haynes, K & Brown, AJP 2012, 'Combinatorial stresses kill pathogenic Candida species' Medical Mycology, vol. 50, no. 7, pp. 699-709. DOI (Impact Factor: 1.979)
  69. 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)
  70. 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)
  71. 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)
  72. 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
  73. 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, vol. 10, no. 4, pp. 1087-1095. DOI (Impact Factor: 2.097)
  74. 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)
  75. 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), vol. 43, no. 4, pp. 477-480.
  76. 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, vol. 26, no. 8, pp. 1331-1334.
  77. 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, vol. 26, no. 2, pp. 214-217.
  78. 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, vol. 4, no. 1, pp. 118-124.
  79. Huang, L, Wang, K, Zhou, C, Pang, W & Dong, L 2003, 'Particle Swarm Optimization for Traveling Salesman Problems' Acta Scientiarium Naturalium Universitatis Jilinensis, vol. 41, no. 4, pp. 477-480.
  80. 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, vol. 40, no. 4, pp. 369-373.

Conference Papers (70)

  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. Gavriilidis, K., Munafo, A., Pang, W. and Hastie, H. A Surrogate Model Framework for Explainable Autonomous Behaviour. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2023) Workshop on Explainable Robotics pdf. arxiv
  4. Ghadage A., Yi D., Coghill G., & Pang W. (Accepted/In press). Multi-stage Bias Mitigation for Individual Fairness in Algorithmic Decisions. Artificial Neural Networks in Pattern Recognition. ANNPR 2022. Lecture Notes in Computer Science vol 13739. Springer. DOI
  5. Rajan D., Jiang S., Yi D., Pang, W., & Coghill G. (Accepted/In press). Enhanced Affinity Propagation Clustering on Heterogeneous Information Network. Paper presented at 21st UK Workshop on Computational Intelligence, Sheffield , United Kingdom.
  6. Gao X., Taylor S., Pang, W., Lu X., Braden B., Early detection of oesophageal cancer through colour contrast enhancement for data augmentation, SPIE Medical Imaging 2022, 20-24 Feb. 2022, San Diego, USA URL
  7. Korica P., Gayar N.E., Pang, W. (2021) Explainable Artificial Intelligence in Healthcare: Opportunities, Gaps and Challenges and a Novel Way to Look at the Problem Space. In: Yin H. et al. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2021. IDEAL 2021. Lecture Notes in Computer Science, vol 13113. Springer, Cham. DOI
  8. 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
  9. Gavrillidis, K., Carreno, Y., Munafo, A., Pang, W, Petrick, R., and Hastie, H.. Plan Verbalisation for Robots Acting in Dynamic Environments. ICAPS 2021 Workshop on Knowledge Engineering for Planning and Scheduling (KEPS), 2021. URL, VIDEO
  10. Forrest, J., Sripada, S., & Coghill, G., 2021 ‘Are Contrastive Explanations Useful?’, SICSA Workshop on eXplainable Artificial Intelligence 2021. URL, VIDEO
  11. Zainyte, A. & Pang, W, 2021, ‘Challenges and Future Directions for Accountable Machine Learning’, SICSA Workshop on eXplainable Artificial Intelligence 2021. URL, VIDEO
  12. Pang, W, Markovic, M., Naja, I., Fung, C. P. & Edwards, P., 1 Jun 2021, ‘On Evidence Capture for Accountable AI Systems’,SICSA Workshop on eXplainable Artificial Intelligence 2021.URL, VIDEO
  13. Fung, C. P., Pang, W, Naja, I., Markovic, M. & Edwards, P., 1 Jun 2021, ‘Towards Accountability Driven Development for Machine Learning Systems’, SICSA Workshop on eXplainable Artificial Intelligence 2021. URL, VIDEO
  14. Yuan, Y., Wang, W. & Pang, W, 2021 ‘Which Hyperparameters to Optimise? An Investigation of Evolutionary Hyperparameter Optimisation in Graph Neural Network for Molecular Property Prediction’, 11th Workshop on Evolutionary Computation for the Automated Design of Algorithms, GECCO ‘21: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp.1403-1404. DOI, arXiv, Open Access
  15. 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
  16. 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
  17. 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
  18. Rana, S, Ma, X, Wolverson, E, Pang, W 2020, ‘A Multi-Modal Deep Learning Approach to the Early Prediction of Mild Cognitive Impairment Conversion to Alzheimer’s Disease’, The IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (the 7th BDCAT),DOI.
  19. 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.
  20. Rezvy, S, Zebin, T, Braden, B, Pang, W , Taylor, S, Gao, X 2020 ‘Transfer learning for endoscopy disease detection & segmentation with mask-RCNN benchmark architecture’, EndoCV2020 workshop (in conjunction with IEEE International Symposium in Biomedical Imaging 2020), URL.
  21. Gao, X, Braden, B, Zhang, L, Taylor, S, Pang, W & Pettdis, M 2019, ‘Case-based reasoning of a deep learning network for prediction of early stage of oesophageal cancer’. in Proceedings of the 25th UK Symposium on Case-Based Reasoning. Cambridge, United Kingdom, URL
  22. Gao, X, Braden, B, Taylor, S & Pang, W 2019, ‘Towards Real-Time Detection of Squamous Pre-cancers from Oesophageal Endoscopic Videos’, Paper presented at The Eighteenth International Conference on Machine Learning and Applications, Boca Raton, United States, 16/12/19 - 19/12/19, DOI
  23. Xu, X, Gao, X, Xu, Z, Zhao, X, Pang, W & Zhou, H 2019, TCPModel: A Short-Term Traffic Congestion Prediction Model Based on Deep Learning. in K Knight, C Zhang, G Holmes & M-L Zhang (eds), Artificial Intelligence: Second CCF International Conference, ICAI 2019, Xuzhou, China, August 22-23, 2019, Proceedings. Communications in Computer and Information Science, vol. 1001, Springer , Singapore, pp. 66-79, The 2nd CCF International Conference on Artificial Intelligence (CCF-ICAI 2019), Xuzhou, China, 22/08/19. [ONLINE] DOI
  24. 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
  25. Albalawi, H, Pang, W & Coghill, GM 2019, 'Swarm Inspired Approaches for K-prototypes clustering' Paper presented at 19th Annual UK Workshop on Computational Intelligence, Portsmouth, United Kingdom, 4/09/19-6/09/19. DOI
  26. Awad, A, Pang, W , Lusseau, D & Coghill, GM 2019, A Hexagonal Cell Automaton Model to Imitate Physarum Polycephalum Competitive Behaviour. in Proceedings of the 2019 Conference on Artificial Life. The 2019 Conference on Artificial Life, Newcastle, United Kingdom, 29/07/19. DOI
  27. 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
  28. 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
  29. Forrest, J, Sripada, S, Pang, W & Coghill, G 2018, Towards making NLG a voice for interpretable Machine Learning. in E Krahmer, A Gatt & M Goudbeek (eds), Proceedings of The 11th International Natural Language Generation Conference., W18-6522, Association for Computational Linguistics (ACL), pp. 177-182, 11th International Conference on Natural Language Generation (INLG 2018) , Tilburg, Netherlands, 5/11/18. URL
  30. Awad, A, Pang, W & Coghill, GM 2018, Physarum Inspired Connectivity and Restoration for Wireless Sensor and Actor Networks. in A Lotfi, H Bouchachia, A Gegov, C Langensiepen & M McGinnity (eds), Advances in Computational Intelligence Systems: Contributions Presented at the 18th UK Workshop on Computational Intelligence, September 5-7, 2018, Nottingham, UK. Advances in Intelligent Systems and Computing (AISC), vol. 840, Springer , pp. 327-338, 18TH ANNUAL UK WORKSHOP ON COMPUTATIONAL INTELLIGENCE, Nottingham, United Kingdom, 5/09/18. DOI
  31. Chapman, A, Pang, W & Coghill, G 2018, CLEMI-imputation evaluation. in SACI 2018 - IEEE 12th International Symposium on Applied Computational Intelligence and Informatics, Proceedings., 8440981, Institute of Electrical and Electronics Engineers Inc., pp. 373-378, 12th IEEE International Symposium on Applied Computational Intelligence and Informatics, SACI 2018, Timisoara, Romania, 17/05/18. DOI
  32. Ou, G, Wang, Y, Huang, L, Pang, W & Coghill, GM 2018, ε-Distance Weighted Support Vector Regression. in D Phung, VS Tseng, PGI Webb, B Ho, M Ganji & L Rashidi (eds), Advances in Knowledge Discovery and Data Mining: 22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3-6, 2018, Proceedings, Part I., 17, Lecture Notes in Artificial Intelligence, Springer International Publishing, PAKDD 2018, Melbourne, Australia, 3/06/18. DOI
  33. 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, The Seventh International Conference on Intelligent Systems and Applications, Venice, Italy, 24/06/18. URL
  34. Karatu, MT, Pang, W & Coghill, GM 2018, A Conceptual Framework of Starlings Swarm Intelligence Intrusion Prevention for Software Defined Networks. in K Martin, N Wiratunga & LS Smith (eds), ReaLX'18: Reasoning, Learning & Explainability in AI. vol. 2151, CEUR Workshop Proceedings, CEUR-WS, ReaLX'18: Reasoning, Learning & Explainability in AI, Aberdeen, United Kingdom, 27/06/18.URL
  35. 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
  36. Awad, A, Pang, W & Coghill, G 2018, Physarum Inspired Model for Mobile Sensor Nodes Deployment in the Presence of Obstacles. in MH Miraz, P Excell, A Ware, S Soomro & M Ali (eds), International Conference for Emerging Technologies in Computing iCETiC 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST), vol. 200, Springer , pp. 153-160, International Conference on Emerging Technologies in Computing 2018 (iCETiC '18) , London, United Kingdom, 23/08/18. DOI
  37. 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
  38. Huang, L, Hu, X, Wang, Y, Zhang, F, Liu , Z & Pang, W 2017, Gravitation Field Algorithm with Optimal Detection for Unconstrained Optimization. in The 2017 4th International Conference on Systems and Informatics (ICSAI 2017). IEEE Press, pp. 1328-1333, The 2017 4th International Conference on Systems and Informatics, Hangzhou Zhejiang, China, 11/11/17.URL
  39. Ou, G, Wang, Y, Pang, W & Coghill, GM 2017, Large Margin Distribution Machine Recursive Feature Elimination. in The 2017 4th International Conference on Systems and Informatics (ICSAI 2017) . IEEE Press, pp. 1427-1432, The 2017 4th International Conference on Systems and Informatics , Hangzhou Zhejiang, China, 11/11/17. URL
  40. 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
  41. Han, L, Huang, L, Yang, X, Pang, W & Wang, K 2016, A Novel Spatio-Temporal Data Storage and Index Method for ARM-Based Hadoop Server. in X Sun, A Liu, H-C Chao & E Bertino (eds), Cloud Computing and Security: Second International Conference, ICCCS 2016. Lecture Notes in Computer Science. Information Systems and Applications, incl. Internet/Web, and HCI (Lecture Notes in Computer Science), vol. 10039, Springer , pp. 206-216, Second International Conference, ICCCS 2016, Nanjing, China, 29/07/16. DOI
  42. 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
  43. Peng, Q, Wang, Y, Ou, G, Huang, L & Pang, W 2016, Partitioning Clustering Based on Support Vector Ranking. in J Li, X Li, S Wang, J Li & QZ Sheng (eds), Advanced Data Mining and Applications: 12th International Conference, ADMA 2016, Gold Coast, QLD, Australia, December 12-15, 2016, Proceedings. vol. 10086, Lecture Notes in Artificial Intelligence, Springer International Publishing, pp. 726-737, ADMA 2016, Gold Coast, Australia, 12/12/16. DOI
  44. Emele, CD, Spakov, V, Pang, W , Bone, JD & Coghill, GM 2015, ADOVA: Anomaly Detection in Online and Virtual spAces. in S Alqithami & H Hexmoor (eds), Proceedings of the 3rd International Workshop on Collaborative Online Organizations: co-located with the 14th International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2015). vol. May 4 2015, Istanbul, Turkey, pp. 38-41, COOS@AAMAS 2015, Istanbul, Turkey, 4/05/15. URL
  45. 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
  46. 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
  47. 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.
  48. 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`
  49. 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
  50. 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
  51. 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
  52. 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
  53. 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.
  54. 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. DOI
  55. Pang, W & Coghill, GM 2012, Extended Kernel Subsets Analysis for Qualitative Model Learning. in P De Wilde, GM Coghill & AV Kononova (eds), 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, 5/09/12. DOI
  56. Pang, W & Coghill, GM 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, 11th UK Workshop on Computational Intelligence, Manchester, United Kingdom, 7/09/11.
  57. Pang, W & Coghill, GM 2010, Learning Qualitative Metabolic Models Using Evolutionary Methods. in 2010 Fifth International Conference on Frontier of Computer Science and Technology. IEEE Computer Society, Changchun, Jilin Province , pp. 436-441, Fifth International Conference on Frontier of Computer Science and Technology (FCST), 2010 , Changchun, Jilin Province , China, 18/08/10. DOI
  58. Pang, W & Coghill, GM 2010, QML-AiNet: An Immune-inspired Network Approach to Qualitative Model Learning. in E Hart, C McEwan, J Timmis & A Hone (eds), of 8th International Conference on Artificial Immune Systems (ICARIS 2010). Lecture Notes in Computer Science, vol. 6209, Springer-Verlag, Berlin Heidelberg, pp. 223-236. DOI
  59. Pang, W & Coghill, GM 2009, An Immune-Inspired Approach to Qualitative System Identification of the Detoxification Pathway of Methylglyoxal. in Lecture Notes in Computer Science. vol. 5666, Lecture Notes in Computer Science, vol. 5666, Springer , pp. 151-164. DOI
  60. 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). Lecture Notes in Artificial Intelligence, no. 5137, IEEE Explore, pp. 287-292. DOI
  61. Pang, W & Coghill, GM 2008, Learning qualitative models of the detoxification pathway of methylglyoxal. in the 8th annual UK Workshop on Computational Intelligence. De Montford University, UK, pp. CD.
  62. Pang, W & Coghill, GM 2007, Advanced experiments for learning qualitative compartment models. in Proceeding of the 21st International Workshop on Qualitative Reasoning. Aberystwyth, UK, pp. 109-117, Proceeding of the 21st International Workshop on Qualitative Reasoning, Aberystwyth, United Kingdom, 26/06/07. URL
  63. Pang, W 2007, Clonal selection algorithm for learning qualitative compartmental models of metabolic systems. in 7th annual UK Workshop on Computational Intelligence. London, pp. CD.
  64. Pang, W & Coghill, GM 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. ACM Press, pp. 2887-2894. DOI
  65. Meng, Y, Li, W, Wang, Y, Guo, W & Pang, W 2006, An Evolution Computation Based Approach to Synthesize Video Texture. in VN Alexandrov, GD van Albada, PMA Sloot & J Dongarra (eds), Computational Science – ICCS 2006: Proceedings of the 6th International Conference, Part II. vol. 3992, Lecture Notes in Computer Science - Computer Science and General Issues, vol. 3992, Springer , Berlin, Germany, pp. 223-230, 6th International Conference on Computational Science (ICCS 2006), Reading, United Kingdom, 28/05/06. DOI
  66. Pang, W & Coghill, GM 2006, EQML- An Evolutionary Qualitative Model Learning Framework. in 2nd European Symposium on Nature-inspired Smart Information Systems. Puerto de la Cruz, Tenerife, Spain, pp. 1-7. URL
  67. Pang, W & Coghill, GM 2006, Evolutionary approaches for learning qualitative compartment metabolic models. in _Proceeding of the 6th annual UK Workshop on Computational Intelligence._Leeds, UK, pp. 11-16.
  68. 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). IEEE Press, Los Alamos, CA, USA, pp. 796-800, 4th International Conference on Computer and Information Technology (CIT2004), Wuhan, China, 14/09/04. DOI
  69. 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, IEEE Press, New York, NY, USA, pp. 2342-2346. DOI
  70. 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, IEEE Press, pp. 1583-1585. DOI

Book Chapters (2)

  1. Jia, C, Pang, W & Fu, Y 2016, Multimodal Action Recognition. in Y Fu (ed.), Human Activity Recognition and Prediction Springer, Switzerland, pp. 71-85. DOI
  2. Liu, M, Pang, W , Wang, KP & Zhou, CG 2006, Improved Immune Genetic Algorithm For Solving Flow Shop Scheduling Problem. in GR Liu, VBC Tan & X Han (eds), Computational methods. Springer, Dordrecht, Netherlands, pp. 1057-1062. DOI

Abstracts (1)

  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 2012, 'Impact of combinatorial stresses upon Candida albicans' Mycoses, vol. 55, no. Suppl. 4, pp. 15. DOI

Technical Reports (2)

  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. URL
  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. URL

ArXiv Papers (5)

  1. Chen K., Pang, W. (2020) “ImmuNetNAS: An Immune-network approach for searching Convolutional Neural Network Architectures”, URL CODE
  2. Frachon L., Pang, W., & Coghill, G. (2019) “ImmuNeCS: Neural Committee Search by an Artificial Immune System” URL
  3. Byla E, Pang, W (2019), 'DeepSwarm: Optimising Convolutional Neural Networks using Swarm Intelligence', URL, CODE
  4. Wang, Y, Ou G., Pang, W., Huang L., Coghill G.M. (2016), 'e-Distance Weighted Support Vector Regressin', URL
  5. Luo, C, Pang, W., & Wang, Z. (2014). 'Hete-CF: Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations'. URL