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  2022 (7)
Using Knowledge Graphs to Unlock Practical Collection, Integration, and Audit of AI Accountability Information. Naja, I.; Markovic, M.; Edwards, P.; Pang, W.; Cottrill, C. D.; and Williams, R. IEEE Access, 10: 74383–74411. 2022.
Using Knowledge Graphs to Unlock Practical Collection, Integration, and Audit of AI Accountability Information [link]Paper   doi   link   bibtex  
Multiscale increment entropy: An approach for quantifying the physiological complexity of biomedical time series. Wang, X.; Liu, X.; Pang, W.; and Jiang, A. Inf. Sci., 586: 279–293. 2022.
Multiscale increment entropy: An approach for quantifying the physiological complexity of biomedical time series [link]Paper   doi   link   bibtex  
Restorable-inpainting: A novel deep learning approach for shoeprint restoration. Hassan, M.; Wang, Y.; Wang, D.; Pang, W.; Wang, K.; Li, D.; Zhou, Y.; and Xu, D. Inf. Sci., 600: 22–42. 2022.
Restorable-inpainting: A novel deep learning approach for shoeprint restoration [link]Paper   doi   link   bibtex  
All particles driving particle swarm optimization: Superior particles pulling plus inferior particles pushing. Liu, Q.; Li, J.; Ren, H.; and Pang, W. Knowl. Based Syst., 249: 108849. 2022.
All particles driving particle swarm optimization: Superior particles pulling plus inferior particles pushing [link]Paper   doi   link   bibtex  
An improved density peak clustering algorithm guided by pseudo labels. Wang, Y.; Pang, W.; and Zhou, J. Knowl. Based Syst., 252: 109374. 2022.
An improved density peak clustering algorithm guided by pseudo labels [link]Paper   doi   link   bibtex  
ErythroidCounter: an automatic pipeline for erythroid cell detection, identification and counting based on deep learning. Zhou, Y.; Wang, Y.; Wu, J.; Hassan, M.; Pang, W.; Lv, L.; Wang, L.; and Cui, H. Multim. Tools Appl., 81(18): 25541–25556. 2022.
ErythroidCounter: an automatic pipeline for erythroid cell detection, identification and counting based on deep learning [link]Paper   doi   link   bibtex  
Multicomponent Alkane IR Measurement System Based on Dynamic Adaptive Moving Window PLS. Li, Z.; Pang, W.; Liang, H.; Chen, G.; Zheng, X.; and Ni, P. IEEE Trans. Instrum. Meas., 71: 1–13. 2022.
Multicomponent Alkane IR Measurement System Based on Dynamic Adaptive Moving Window PLS [link]Paper   doi   link   bibtex  
  2021 (16)
A Multi-View Clustering Algorithm for Mixed Numeric and Categorical Data. Ji, J.; Li, R.; Pang, W.; He, F.; Feng, G.; and Zhao, X. IEEE Access, 9: 24913–24924. 2021.
A Multi-View Clustering Algorithm for Mixed Numeric and Categorical Data [link]Paper   doi   link   bibtex  
HFPQ: deep neural network compression by hardware-friendly pruning-quantization. Fan, Y.; Pang, W.; and Lu, S. Appl. Intell., 51(10): 7016–7028. 2021.
HFPQ: deep neural network compression by hardware-friendly pruning-quantization [link]Paper   doi   link   bibtex  
Minimum Distribution Support Vector Clustering. Wang, Y.; Chen, J.; Xie, X.; Yang, S.; Pang, W.; Huang, L.; Zhang, S.; and Zhao, S. Entropy, 23(11): 1473. 2021.
Minimum Distribution Support Vector Clustering [link]Paper   doi   link   bibtex  
In-Line Detection with Microfluidic Bulk Acoustic Wave Resonator Gas Sensor for Gas Chromatography. Hu, J.; Qu, H.; Pang, W.; and Duan, X. Sensors, 21(20): 6800. 2021.
In-Line Detection with Microfluidic Bulk Acoustic Wave Resonator Gas Sensor for Gas Chromatography [link]Paper   doi   link   bibtex  
A Genetic Algorithm with Tree-structured Mutation for Hyperparameter Optimisation of Graph Neural Networks. Yuan, Y.; Wang, W.; and Pang, W. In IEEE Congress on Evolutionary Computation, CEC 2021, Kraków, Poland, June 28 - July 1, 2021, pages 482–489, 2021. IEEE
A Genetic Algorithm with Tree-structured Mutation for Hyperparameter Optimisation of Graph Neural Networks [link]Paper   doi   link   bibtex  
An Immune-Inspired Approach to Macro-Level Neural Ensemble Search. Frachon, L.; Pang, W.; and Coghill, G. M. In IEEE Congress on Evolutionary Computation, CEC 2021, Kraków, Poland, June 28 - July 1, 2021, pages 2491–2498, 2021. IEEE
An Immune-Inspired Approach to Macro-Level Neural Ensemble Search [link]Paper   doi   link   bibtex  
History-Aware Expansion and Fuzzy for Query Reformulation. Pang, W.; and Duan, R. In Fang, L.; Chen, Y.; Zhai, G.; Wang, Z. J.; Wang, R.; and Dong, W., editor(s), Artificial Intelligence - First CAAI International Conference, CICAI 2021, Hangzhou, China, June 5-6, 2021, Proceedings, Part II, volume 13070, of Lecture Notes in Computer Science, pages 227–238, 2021. Springer
History-Aware Expansion and Fuzzy for Query Reformulation [link]Paper   doi   link   bibtex  
A systematic comparison study on hyperparameter optimisation of graph neural networks for molecular property prediction. Yuan, Y.; Wang, W.; and Pang, W. In Chicano, F.; and Krawiec, K., editor(s), GECCO '21: Genetic and Evolutionary Computation Conference, Lille, France, July 10-14, 2021, pages 386–394, 2021. ACM
A systematic comparison study on hyperparameter optimisation of graph neural networks for molecular property prediction [link]Paper   doi   link   bibtex  
Which hyperparameters to optimise?: an investigation of evolutionary hyperparameter optimisation in graph neural network for molecular property prediction. Yuan, Y.; Wang, W.; and Pang, W. In Krawiec, K., editor(s), GECCO '21: Genetic and Evolutionary Computation Conference, Companion Volume, Lille, France, July 10-14, 2021, pages 1403–1404, 2021. ACM
Which hyperparameters to optimise?: an investigation of evolutionary hyperparameter optimisation in graph neural network for molecular property prediction [link]Paper   doi   link   bibtex  
Explainable Artificial Intelligence in Healthcare: Opportunities, Gaps and Challenges and a Novel Way to Look at the Problem Space. Korica, P.; Gayar, N. E.; and Pang, W. In Yin, H.; Camacho, D.; Tiño, P.; Allmendinger, R.; Tallón-Ballesteros, A. J.; Tang, K.; Cho, S.; Novais, P.; and Nascimento, S., editor(s), Intelligent Data Engineering and Automated Learning - IDEAL 2021 - 22nd International Conference, IDEAL 2021, Manchester, UK, November 25-27, 2021, Proceedings, volume 13113, of Lecture Notes in Computer Science, pages 333–342, 2021. Springer
Explainable Artificial Intelligence in Healthcare: Opportunities, Gaps and Challenges and a Novel Way to Look at the Problem Space [link]Paper   doi   link   bibtex  
The Accountability Fabric: A Suite of Semantic Tools For Managing AI System Accountability and Audit. Markovic, M.; Naja, I.; Edwards, P.; and Pang, W. In Seneviratne, O.; Pesquita, C.; Sequeda, J.; and Etcheverry, L., editor(s), Proceedings of the ISWC 2021 Posters, Demos and Industry Tracks: From Novel Ideas to Industrial Practice co-located with 20th International Semantic Web Conference (ISWC 2021), Virtual Conference, October 24-28, 2021, volume 2980, of CEUR Workshop Proceedings, 2021. CEUR-WS.org
The Accountability Fabric: A Suite of Semantic Tools For Managing AI System Accountability and Audit [pdf]Paper   link   bibtex  
A Novel Genetic Algorithm with Hierarchical Evaluation Strategy for Hyperparameter Optimisation of Graph Neural Networks. Yuan, Y.; Wang, W.; Coghill, G. M.; and Pang, W. CoRR, abs/2101.09300. 2021.
A Novel Genetic Algorithm with Hierarchical Evaluation Strategy for Hyperparameter Optimisation of Graph Neural Networks [link]Paper   link   bibtex  
A Systematic Comparison Study on Hyperparameter Optimisation of Graph Neural Networks for Molecular Property Prediction. Yuan, Y.; Wang, W.; and Pang, W. CoRR, abs/2102.04283. 2021.
A Systematic Comparison Study on Hyperparameter Optimisation of Graph Neural Networks for Molecular Property Prediction [link]Paper   link   bibtex  
A Genetic Algorithm with Tree-structured Mutation for Hyperparameter Optimisation of Graph Neural Networks. Yuan, Y.; Wang, W.; and Pang, W. CoRR, abs/2102.11995. 2021.
A Genetic Algorithm with Tree-structured Mutation for Hyperparameter Optimisation of Graph Neural Networks [link]Paper   link   bibtex  
A Survey on Physarum Polycephalum Intelligent Foraging Behaviour and Bio-Inspired Applications. Awad, A.; Pang, W.; Lusseau, D.; and Coghill, G. M. CoRR, abs/2103.00172. 2021.
A Survey on Physarum Polycephalum Intelligent Foraging Behaviour and Bio-Inspired Applications [link]Paper   link   bibtex  
Which Hyperparameters to Optimise? An Investigation of Evolutionary Hyperparameter Optimisation in Graph Neural Network For Molecular Property Prediction. Yuan, Y.; Wang, W.; and Pang, W. CoRR, abs/2104.06046. 2021.
Which Hyperparameters to Optimise? An Investigation of Evolutionary Hyperparameter Optimisation in Graph Neural Network For Molecular Property Prediction [link]Paper   link   bibtex  
  2020 (23)
Clustering Mixed Numeric and Categorical Data With Cuckoo Search. Ji, J.; Pang, W.; Li, Z.; He, F.; Feng, G.; and Zhao, X. IEEE Access, 8: 30988–31003. 2020.
Clustering Mixed Numeric and Categorical Data With Cuckoo Search [link]Paper   doi   link   bibtex  
An Efficient v-Minimum Absolute Deviation Distribution Regression Machine. Wang, Y.; Wang, Y.; Song, Y.; Xie, X.; Huang, L.; Pang, W.; and Coghill, G. M. IEEE Access, 8: 85533–85551. 2020.
An Efficient v-Minimum Absolute Deviation Distribution Regression Machine [link]Paper   doi   link   bibtex  
Goods Consumed During Transit in Split Delivery Vehicle Routing Problems: Modeling and Solution. Yang, W.; Wang, D.; Pang, W.; Tan, A.; and Zhou, Y. IEEE Access, 8: 110336–110350. 2020.
Goods Consumed During Transit in Split Delivery Vehicle Routing Problems: Modeling and Solution [link]Paper   doi   link   bibtex  
GANs-Based Data Augmentation for Citrus Disease Severity Detection Using Deep Learning. Zeng, Q.; Ma, X.; Cheng, B.; Zhou, E.; and Pang, W. IEEE Access, 8: 172882–172891. 2020.
GANs-Based Data Augmentation for Citrus Disease Severity Detection Using Deep Learning [link]Paper   doi   link   bibtex  
An Energy-Efficient Implementation of Group Pruned CNNs on FPGA. Pang, W.; Wu, C.; and Lu, S. IEEE Access, 8: 217033–217044. 2020.
An Energy-Efficient Implementation of Group Pruned CNNs on FPGA [link]Paper   doi   link   bibtex  
Self-adaptive parameter and strategy based particle swarm optimization for large-scale feature selection problems with multiple classifiers. Xue, Y.; Tang, T.; Pang, W.; and Liu, A. X. Appl. Soft Comput., 88: 106031. 2020.
Self-adaptive parameter and strategy based particle swarm optimization for large-scale feature selection problems with multiple classifiers [link]Paper   doi   link   bibtex  
Deep Ensemble Learning for Human Action Recognition in Still Images. Yu, X.; Zhang, Z.; Wu, L.; Pang, W.; Chen, H.; Yu, Z.; and Li, B. Complex., 2020: 9428612:1–9428612:23. 2020.
Deep Ensemble Learning for Human Action Recognition in Still Images [link]Paper   doi   link   bibtex  
A systematic density-based clustering method using anchor points. Wang, Y.; Wang, D.; Pang, W.; Miao, C.; Tan, A.; and Zhou, Y. Neurocomputing, 400: 352–370. 2020.
A systematic density-based clustering method using anchor points [link]Paper   doi   link   bibtex  
Inferring structure and parameters of dynamic system models simultaneously using swarm intelligence approaches. Usman, M.; Pang, W.; and Coghill, G. M. Memetic Comput., 12(3): 267–282. 2020.
Inferring structure and parameters of dynamic system models simultaneously using swarm intelligence approaches [link]Paper   doi   link   bibtex  
McDPC: multi-center density peak clustering. Wang, Y.; Wang, D.; Zhang, X.; Pang, W.; Miao, C.; Tan, A.; and Zhou, Y. Neural Comput. Appl., 32(17): 13465–13478. 2020.
McDPC: multi-center density peak clustering [link]Paper   doi   link   bibtex  
Visual Dialogue State Tracking for Question Generation. Pang, W.; and Wang, X. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020, pages 11831–11838, 2020. AAAI Press
Visual Dialogue State Tracking for Question Generation [link]Paper   link   bibtex  
A Multi-Modal Deep Learning Approach to the Early Prediction of Mild Cognitive Impairment Conversion to Alzheimer's Disease. Rana, S. S.; Ma, X.; Pang, W.; and Wolverson, E. In 7th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2020, Leicester, United Kingdom, December 7-10, 2020, pages 9–18, 2020. IEEE
A Multi-Modal Deep Learning Approach to the Early Prediction of Mild Cognitive Impairment Conversion to Alzheimer's Disease [link]Paper   doi   link   bibtex  
Evolutionary Learning for Soft Margin Problems: A Case Study on Practical Problems with Kernels. Wang, W.; Pang, W.; Bingham, P. A.; Mania, M.; Chen, T.; and Perry, J. J. In IEEE Congress on Evolutionary Computation, CEC 2020, Glasgow, United Kingdom, July 19-24, 2020, pages 1–7, 2020. IEEE
Evolutionary Learning for Soft Margin Problems: A Case Study on Practical Problems with Kernels [link]Paper   doi   link   bibtex  
Guessing State Tracking for Visual Dialogue. Pang, W.; and Wang, X. In Vedaldi, A.; Bischof, H.; Brox, T.; and Frahm, J., editor(s), Computer Vision - ECCV 2020 - 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XVI, volume 12361, of Lecture Notes in Computer Science, pages 683–698, 2020. Springer
Guessing State Tracking for Visual Dialogue [link]Paper   doi   link   bibtex  
Dual Functions of Ghz Frequency Acoustic Resonator System for Biosamples Capture and Sensing. Liu, C.; Pang, W.; Duan, X.; and Wang, Y. In 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, EMBC 2020, Montreal, QC, Canada, July 20-24, 2020, pages 3994–3997, 2020. IEEE
Dual Functions of Ghz Frequency Acoustic Resonator System for Biosamples Capture and Sensing [link]Paper   doi   link   bibtex  
Smartphone-enabled Dynamic Chemiluminescence Biomarker Quantitation Using Acoustic Tweezers Approach\(^\mbox*\). Chen, X.; Liu, B.; Pang, W.; and Duan, X. In 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, EMBC 2020, Montreal, QC, Canada, July 20-24, 2020, pages 5041–5044, 2020. IEEE
Smartphone-enabled Dynamic Chemiluminescence Biomarker Quantitation Using Acoustic Tweezers Approach\(^\mbox*\) [link]Paper   doi   link   bibtex  
Research on the Design of Intelligent Interactive Toys Based on Marker Education. Lu, Y.; and Pang, W. In Stephanidis, C.; Harris, D.; Li, W.; Schmorrow, D. D.; Fidopiastis, C. M.; Zaphiris, P.; Ioannou, A.; Fang, X.; Sottilare, R. A.; and Schwarz, J., editor(s), HCI International 2020 - Late Breaking Papers: Cognition, Learning and Games - 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19-24, 2020, Proceedings, volume 12425, of Lecture Notes in Computer Science, pages 462–471, 2020. Springer
Research on the Design of Intelligent Interactive Toys Based on Marker Education [link]Paper   doi   link   bibtex  
Acoustofluidic Tweezers for the 3D Manipulation of Microparticles. Guo, X.; Ma, Z.; Goyal, R.; Jeong, M.; Pang, W.; Fischer, P.; Duan, X.; and Qiu, T. In 2020 IEEE International Conference on Robotics and Automation, ICRA 2020, Paris, France, May 31 - August 31, 2020, pages 11392–11397, 2020. IEEE
Acoustofluidic Tweezers for the 3D Manipulation of Microparticles [link]Paper   doi   link   bibtex  
Transfer Learning For Endoscopy Disease Detection & Segmentation With Mask-RCNN Benchmark Architecture. Rezvy, S.; Zebin, T.; Braden, B.; Pang, W.; Taylor, S.; and Gao, X. W. In Ali, S.; Daul, C.; Rittscher, J.; Stoyanov, D.; and Grisan, E., editor(s), Proceedings of the 2nd International Workshop and Challenge on Computer Vision in Endoscopy, EndoCV@ISBI 2020, Iowa City, Iowa, USA, 3rd April 2020, volume 2595, of CEUR Workshop Proceedings, pages 68–72, 2020. CEUR-WS.org
Transfer Learning For Endoscopy Disease Detection & Segmentation With Mask-RCNN Benchmark Architecture [pdf]Paper   link   bibtex  
8-bit Convolutional Neural Network Accelerator for Face Recognition. Pang, W.; Li, Y.; and Lu, S. In 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference, UEMCON 2020, New York City, NY, USA, October 28-31, 2020, pages 35–39, 2020. IEEE
8-bit Convolutional Neural Network Accelerator for Face Recognition [link]Paper   doi   link   bibtex  
Short Text Classification via Term Graph. Pang, W. CoRR, abs/2001.10338. 2020.
Short Text Classification via Term Graph [link]Paper   link   bibtex  
Guessing State Tracking for Visual Dialogue. Pang, W.; and Wang, X. CoRR, abs/2002.10340. 2020.
Guessing State Tracking for Visual Dialogue [link]Paper   link   bibtex  
ImmuNetNAS: An Immune-network approach for searching Convolutional Neural Network Architectures. Chen, K.; and Pang, W. CoRR, abs/2002.12704. 2020.
ImmuNetNAS: An Immune-network approach for searching Convolutional Neural Network Architectures [link]Paper   link   bibtex  
  2019 (18)
FREDPC: A Feasible Residual Error-Based Density Peak Clustering Algorithm With the Fragment Merging Strategy. Parmar, M. D.; Pang, W.; Yu, D.; Jiang, J.; Wang, L.; Wang, L.; and Zhou, Y. IEEE Access, 7: 89789–89804. 2019.
FREDPC: A Feasible Residual Error-Based Density Peak Clustering Algorithm With the Fragment Merging Strategy [link]Paper   doi   link   bibtex  
Smartphone-Enabled Aerosol Particle Analysis Device. Li, Y.; Pang, W.; Sun, C.; Zhou, Q.; Lin, Z.; Chang, Y.; Li, Q.; Zhang, M.; and Duan, X. IEEE Access, 7: 101117–101124. 2019.
Smartphone-Enabled Aerosol Particle Analysis Device [link]Paper   doi   link   bibtex  
Electromagnetic Design and Analysis of Axial Flux Permanent Magnet Generator With Unequal-Width PCB Winding. Wang, X.; Pang, W.; Gao, P.; and Zhao, X. IEEE Access, 7: 164696–164707. 2019.
Electromagnetic Design and Analysis of Axial Flux Permanent Magnet Generator With Unequal-Width PCB Winding [link]Paper   doi   link   bibtex  
Explosion gravitation field algorithm with dust sampling for unconstrained optimization. Hu, X.; Huang, L.; Wang, Y.; and Pang, W. Appl. Soft Comput., 81. 2019.
Explosion gravitation field algorithm with dust sampling for unconstrained optimization [link]Paper   doi   link   bibtex  
LatinPSO: An algorithm for simultaneously inferring structure and parameters of ordinary differential equations models. Tian, X.; Pang, W.; Wang, Y.; Guo, K.; and Zhou, Y. Biosyst., 182: 8–16. 2019.
LatinPSO: An algorithm for simultaneously inferring structure and parameters of ordinary differential equations models [link]Paper   doi   link   bibtex  
Two-dimensional composite solitons in Bose-Einstein condensates with spatially confined spin-orbit coupling. Li, Y.; Zhang, X.; Zhong, R.; Luo, Z.; Liu, B.; Huang, C.; Pang, W.; and Malomed, B. A. Commun. Nonlinear Sci. Numer. Simul., 73: 481–489. 2019.
Two-dimensional composite solitons in Bose-Einstein condensates with spatially confined spin-orbit coupling [link]Paper   doi   link   bibtex  
Hybrid matter-wave - microwave solitons on the lattice. Luo, Z.; Luo, W.; Pang, W.; Mai, Z.; Li, Y.; and Malomed, B. A. Commun. Nonlinear Sci. Numer. Simul., 77: 168–180. 2019.
Hybrid matter-wave - microwave solitons on the lattice [link]Paper   doi   link   bibtex  
Data-driven two-layer visual dictionary structure learning. Yu, X.; Yu, Z.; Wu, L.; Pang, W.; and Lin, C. J. Electronic Imaging, 28(2): 023006. 2019.
Data-driven two-layer visual dictionary structure learning [link]Paper   doi   link   bibtex  
˘nicode24322˘nicode26500˘nicode20449˘nicode24687˘nicode32593˘nicode32476˘nicode20013˘nicode22522˘nicode20110˘nicode20803˘nicode32467˘nicode26500˘nicode30340˘nicode21327˘nicode21516˘nicode36807˘nicode28388˘nicode31639˘nicode27861 (MetaStruct-CF: A Meta Structure Based Collaborative Filtering Algorithm in Heterogeneous Information Networks). Wang, X.; Pang, W.; and Wang, Z. ˘nicode35745˘nicode31639˘nicode26426˘nicode31185˘nicode23398, 46(6A): 397–401. 2019.
˘nicode24322˘nicode26500˘nicode20449˘nicode24687˘nicode32593˘nicode32476˘nicode20013˘nicode22522˘nicode20110˘nicode20803˘nicode32467˘nicode26500˘nicode30340˘nicode21327˘nicode21516˘nicode36807˘nicode28388˘nicode31639˘nicode27861 (MetaStruct-CF: A Meta Structure Based Collaborative Filtering Algorithm in Heterogeneous Information Networks) [link]Paper   doi   link   bibtex  
Inferring structure and parameters of dynamic systems using particle swarm optimization. Usman, M.; Awad, A.; Pang, W.; and Coghill, G. M. In López-Ibáñez, M.; Auger, A.; and Stützle, T., editor(s), Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO 2019, Prague, Czech Republic, July 13-17, 2019, pages 101–102, 2019. ACM
Inferring structure and parameters of dynamic systems using particle swarm optimization [link]Paper   doi   link   bibtex  
A physarum-inspired competition algorithm for solving discrete multi-objective optimization problems. Awad, A.; Usman, M.; Lusseau, D.; Coghill, G. M.; and Pang, W. In López-Ibáñez, M.; Auger, A.; and Stützle, T., editor(s), Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO 2019, Prague, Czech Republic, July 13-17, 2019, pages 195–196, 2019. ACM
A physarum-inspired competition algorithm for solving discrete multi-objective optimization problems [link]Paper   doi   link   bibtex  
Towards Real-Time Detection of Squamous Pre-Cancers from Oesophageal Endoscopic Videos. Gao, X. W.; Braden, B.; Taylor, S.; and Pang, W. In Wani, M. A.; Khoshgoftaar, T. M.; Wang, D.; Wang, H.; and Seliya, N., editor(s), 18th IEEE International Conference On Machine Learning And Applications, ICMLA 2019, Boca Raton, FL, USA, December 16-19, 2019, pages 1606–1612, 2019. IEEE
Towards Real-Time Detection of Squamous Pre-Cancers from Oesophageal Endoscopic Videos [link]Paper   doi   link   bibtex  
A Hexagonal Cell Automaton Model to Imitate Physarum Polycephalum Competitive Behaviour. Awad, A.; Pang, W.; Lusseau, D.; and Coghill, G. M. In Fellermann, H.; Bacardit, J.; Moreno, Á. G.; and Füchslin, R. M., editor(s), 2019 Conference on Artificial Life, ALIFE 2019, online, July 29 - August 2, 2019, pages 203–210, 2019. MIT Press
A Hexagonal Cell Automaton Model to Imitate Physarum Polycephalum Competitive Behaviour [link]Paper   doi   link   bibtex  
DeepSwarm: Optimising Convolutional Neural Networks Using Swarm Intelligence. Byla, E.; and Pang, W. In Ju, Z.; Yang, L.; Yang, C.; Gegov, A. E.; and Zhou, D., editor(s), Advances in Computational Intelligence Systems - Contributions Presented at the 19th UK Workshop on Computational Intelligence, September 4-6, 2019, Portsmouth, UK, volume 1043, of Advances in Intelligent Systems and Computing, pages 119–130, 2019. Springer
DeepSwarm: Optimising Convolutional Neural Networks Using Swarm Intelligence [link]Paper   doi   link   bibtex  
Swarm Inspired Approaches for K-prototypes Clustering. Albalawi, H.; Pang, W.; and Coghill, G. M. In Ju, Z.; Yang, L.; Yang, C.; Gegov, A. E.; and Zhou, D., editor(s), Advances in Computational Intelligence Systems - Contributions Presented at the 19th UK Workshop on Computational Intelligence, September 4-6, 2019, Portsmouth, UK, volume 1043, of Advances in Intelligent Systems and Computing, pages 201–209, 2019. Springer
Swarm Inspired Approaches for K-prototypes Clustering [link]Paper   doi   link   bibtex  
DeepSwarm: Optimising Convolutional Neural Networks using Swarm Intelligence. Byla, E.; and Pang, W. CoRR, abs/1905.07350. 2019.
DeepSwarm: Optimising Convolutional Neural Networks using Swarm Intelligence [link]Paper   link   bibtex  
ImmuNeCS: Neural Committee Search by an Artificial Immune System. Frachon, L.; Pang, W.; and Coghill, G. M. CoRR, abs/1911.07729. 2019.
ImmuNeCS: Neural Committee Search by an Artificial Immune System [link]Paper   link   bibtex  
Visual Dialogue State Tracking for Question Generation. Pang, W.; and Wang, X. CoRR, abs/1911.07928. 2019.
Visual Dialogue State Tracking for Question Generation [link]Paper   link   bibtex  
  2018 (12)
A Self-Adaptive Fireworks Algorithm for Classification Problems. Xue, Y.; Zhao, B.; Ma, T.; and Pang, W. IEEE Access, 6: 44406–44416. 2018.
A Self-Adaptive Fireworks Algorithm for Classification Problems [link]Paper   doi   link   bibtex  
A General Framework for Accelerating Swarm Intelligence Algorithms on FPGAs, GPUs and Multi-Core CPUs. Li, D.; Huang, L.; Wang, K.; Pang, W.; Zhou, Y.; and Zhang, R. IEEE Access, 6: 72327–72344. 2018.
A General Framework for Accelerating Swarm Intelligence Algorithms on FPGAs, GPUs and Multi-Core CPUs [link]Paper   doi   link   bibtex  
An Improved EMD-Based Dissimilarity Metric for Unsupervised Linear Subspace Learning. Yu, X.; Yu, Z.; Pang, W.; Li, M.; and Wu, L. Complex., 2018: 8917393:1–8917393:24. 2018.
An Improved EMD-Based Dissimilarity Metric for Unsupervised Linear Subspace Learning [link]Paper   doi   link   bibtex  
An Evolutionary Computation Based Feature Selection Method for Intrusion Detection. Xue, Y.; Jia, W.; Zhao, X.; and Pang, W. Secur. Commun. Networks, 2018: 2492956:1–2492956:10. 2018.
An Evolutionary Computation Based Feature Selection Method for Intrusion Detection [link]Paper   doi   link   bibtex  
Dual-Mode Gas Sensor Composed of a Silicon Nanoribbon Field Effect Transistor and a Bulk Acoustic Wave Resonator: A Case Study in Freons. Chang, Y.; Hui, Z.; Wang, X.; Qu, H.; Pang, W.; and Duan, X. Sensors, 18(2): 343. 2018.
Dual-Mode Gas Sensor Composed of a Silicon Nanoribbon Field Effect Transistor and a Bulk Acoustic Wave Resonator: A Case Study in Freons [link]Paper   doi   link   bibtex  
Modeling and Simulation of Ship Swaying Attitude Based on Linear System Theory. Sun, H.; Wang, C.; and Pang, W. In Barolli, L.; Javaid, N.; Ikeda, M.; and Takizawa, M., editor(s), Complex, Intelligent, and Software Intensive Systems - Proceedings of the 12th International Conference on Complex, Intelligent, and Software Intensive Systems, CISIS-2018, Matsue, Japan, 4-6 July 2018, volume 772, of Advances in Intelligent Systems and Computing, pages 695–703, 2018. Springer
Modeling and Simulation of Ship Swaying Attitude Based on Linear System Theory [link]Paper   doi   link   bibtex  
Network System Integration Technique Research Based on DDS. Sun, H.; Xie, X.; and Pang, W. In Barolli, L.; Javaid, N.; Ikeda, M.; and Takizawa, M., editor(s), Complex, Intelligent, and Software Intensive Systems - Proceedings of the 12th International Conference on Complex, Intelligent, and Software Intensive Systems, CISIS-2018, Matsue, Japan, 4-6 July 2018, volume 772, of Advances in Intelligent Systems and Computing, pages 815–823, 2018. Springer
Network System Integration Technique Research Based on DDS [link]Paper   doi   link   bibtex  
Towards making NLG a voice for interpretable Machine Learning. Forrest, J.; Sripada, S.; Pang, W.; and Coghill, G. M. In Krahmer, E.; Gatt, A.; and Goudbeek, M., editor(s), Proceedings of the 11th International Conference on Natural Language Generation, Tilburg University, The Netherlands, November 5-8, 2018, pages 177–182, 2018. Association for Computational Linguistics
Towards making NLG a voice for interpretable Machine Learning [link]Paper   doi   link   bibtex  
\(ε\)-Distance Weighted Support Vector Regression. Ou, G.; Wang, Y.; Huang, L.; Pang, W.; and Coghill, G. M. In Phung, D. Q.; Tseng, V. S.; Webb, G. I.; Ho, B.; Ganji, M.; and Rashidi, L., editor(s), Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3-6, 2018, Proceedings, Part I, volume 10937, of Lecture Notes in Computer Science, pages 209–220, 2018. Springer
\(ε\)-Distance Weighted Support Vector Regression [link]Paper   doi   link   bibtex  
CLEMI-Imputation Evaluation. Chapman, A.; Pang, W.; and Coghill, G. M. In 12th IEEE International Symposium on Applied Computational Intelligence and Informatics, SACI 2018, Timisoara, Romania, May 17-19, 2018, pages 373–378, 2018. IEEE
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A Conceptual Framework of Starlings Swarm Intelligence Intrusion Prevention for Software Defined Networks. Tanimu, K. M.; Pang, W.; and Coghill, G. M. In Martin, K.; Wiratunga, N.; and Smith, L. S., editor(s), Proceedings of the SICSA Workshop on Reasoning, Learning and Explainability, Aberdeen, Scotland, UK, June 27, 2018, volume 2151, of CEUR Workshop Proceedings, 2018. CEUR-WS.org
A Conceptual Framework of Starlings Swarm Intelligence Intrusion Prevention for Software Defined Networks [pdf]Paper   link   bibtex  
Physarum Inspired Connectivity and Restoration for Wireless Sensor and Actor Networks. Awad, A.; Pang, W.; and Coghill, G. M. In Lotfi, A.; Bouchachia, H.; Gegov, A. E.; Langensiepen, C. S.; and McGinnity, T. M., editor(s), Advances in Computational Intelligence Systems - Contributions Presented at the 18th UK Workshop on Computational Intelligence, September 5-7, 2018, Nottingham, UK, volume 840, of Advances in Intelligent Systems and Computing, pages 327–338, 2018. Springer
Physarum Inspired Connectivity and Restoration for Wireless Sensor and Actor Networks [link]Paper   doi   link   bibtex  
  2017 (5)
Wireless Controlled Local Heating and Mixing Multiple Droplets Using Micro-Fabricated Resonator Array for Micro-Reactor Applications. Wang, Z.; Zhang, H.; Yang, Y.; Qu, H.; Han, Z.; Pang, W.; and Duan, X. IEEE Access, 5: 25987–25992. 2017.
Wireless Controlled Local Heating and Mixing Multiple Droplets Using Micro-Fabricated Resonator Array for Micro-Reactor Applications [link]Paper   doi   link   bibtex  
Novel Gas Sensor Arrays Based on High-Q SAM-Modified Piezotransduced Single-Crystal Silicon Bulk Acoustic Resonators. Zhao, Y.; Yang, Q.; Chang, Y.; Pang, W.; Zhang, H.; and Duan, X. Sensors, 17(7): 1507. 2017.
Novel Gas Sensor Arrays Based on High-Q SAM-Modified Piezotransduced Single-Crystal Silicon Bulk Acoustic Resonators [link]Paper   doi   link   bibtex  
Gravitation field algorithm with optimal detection for unconstrained optimization. Huang, L.; Hu, X.; Wang, Y.; Zhang, F.; Liu, Z.; and Pang, W. In 4th International Conference on Systems and Informatics, ICSAI 2017, Hangzhou, China, November 11-13, 2017, pages 1411–1416, 2017. IEEE
Gravitation field algorithm with optimal detection for unconstrained optimization [link]Paper   doi   link   bibtex  
Large margin distribution machine recursive feature elimination. Ou, G.; Wang, Y.; Pang, W.; and Coghill, G. M. In 4th International Conference on Systems and Informatics, ICSAI 2017, Hangzhou, China, November 11-13, 2017, pages 1518–1523, 2017. IEEE
Large margin distribution machine recursive feature elimination [link]Paper   doi   link   bibtex  
A Novel Diversity Measure for Understanding Movie Ranks in Movie Collaboration Networks. Ma, M.; Pang, W.; Huang, L.; and Wang, Z. In Kim, J.; Shim, K.; Cao, L.; Lee, J.; Lin, X.; and Moon, Y., editor(s), Advances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Jeju, South Korea, May 23-26, 2017, Proceedings, Part I, volume 10234, of Lecture Notes in Computer Science, pages 750–761, 2017.
A Novel Diversity Measure for Understanding Movie Ranks in Movie Collaboration Networks [link]Paper   doi   link   bibtex  
  2016 (7)
A Microfluidic-Based Fabry-Pérot Gas Sensor. Tao, J.; Zhang, Q.; Xiao, Y.; Li, X.; Yao, P.; Pang, W.; Zhang, H.; Duan, X.; Zhang, D.; and Liu, J. Micromachines, 7(3): 36. 2016.
A Microfluidic-Based Fabry-Pérot Gas Sensor [link]Paper   doi   link   bibtex  
A Modified Lattice Configuration Design for Compact Wideband Bulk Acoustic Wave Filter Applications. Yang, Q.; Pang, W.; Zhang, D.; and Zhang, H. Micromachines, 7(8): 133. 2016.
A Modified Lattice Configuration Design for Compact Wideband Bulk Acoustic Wave Filter Applications [link]Paper   doi   link   bibtex  
PUEPro: A Computational Pipeline for Prediction of Urine Excretory Proteins. Wang, Y.; Du, W.; Liang, Y.; Chen, X.; Zhang, C.; Pang, W.; and Xu, Y. In Li, J.; Li, X.; Wang, S.; Li, J.; and Sheng, Q. Z., editor(s), Advanced Data Mining and Applications - 12th International Conference, ADMA 2016, Gold Coast, QLD, Australia, December 12-15, 2016, Proceedings, volume 10086, of Lecture Notes in Computer Science, pages 714–725, 2016.
PUEPro: A Computational Pipeline for Prediction of Urine Excretory Proteins [link]Paper   doi   link   bibtex  
Partitioning Clustering Based on Support Vector Ranking. Peng, Q.; Wang, Y.; Ou, G.; Tian, Y.; Huang, L.; and Pang, W. In Li, J.; Li, X.; Wang, S.; Li, J.; and Sheng, Q. Z., editor(s), Advanced Data Mining and Applications - 12th International Conference, ADMA 2016, Gold Coast, QLD, Australia, December 12-15, 2016, Proceedings, volume 10086, of Lecture Notes in Computer Science, pages 726–737, 2016.
Partitioning Clustering Based on Support Vector Ranking [link]Paper   doi   link   bibtex  
FdDCA: A Novel Fuzzy Deterministic Dendritic Cell Algorithm. Mukhtar, N.; Coghill, G. M.; and Pang, W. In Friedrich, T.; Neumann, F.; and Sutton, A. M., editor(s), Genetic and Evolutionary Computation Conference, GECCO 2016, Denver, CO, USA, July 20-24, 2016, Companion Material Proceedings, pages 1007–1010, 2016. ACM
FdDCA: A Novel Fuzzy Deterministic Dendritic Cell Algorithm [link]Paper   doi   link   bibtex  
A Novel Spatio-Temporal Data Storage and Index Method for ARM-Based Hadoop Server. Han, L.; Huang, L.; Yang, X.; Pang, W.; and Wang, K. In Sun, X.; Liu, A. X.; Chao, H.; and Bertino, E., editor(s), Cloud Computing and Security - Second International Conference, ICCCS 2016, Nanjing, China, July 29-31, 2016, Revised Selected Papers, Part I, volume 10039, of Lecture Notes in Computer Science, pages 206–216, 2016.
A Novel Spatio-Temporal Data Storage and Index Method for ARM-Based Hadoop Server [link]Paper   doi   link   bibtex  
Low-power and real-time computer vision on-chip. Pang, W.; Huang, H.; An, F.; and Yu, H. In International SoC Design Conference, ISOCC 2016, Jeju, South Korea, October 23-26, 2016, pages 43–44, 2016. IEEE
Low-power and real-time computer vision on-chip [link]Paper   doi   link   bibtex  
  2015 (11)
QML-AiNet: An immune network approach to learning qualitative differential equation models. Pang, W.; and Coghill, G. M. Appl. Soft Comput., 27: 148–157. 2015.
QML-AiNet: An immune network approach to learning qualitative differential equation models [link]Paper   doi   link   bibtex  
Qualitative, semi-quantitative, and quantitative simulation of the osmoregulation system in yeast. Pang, W.; and Coghill, G. M. Biosyst., 131: 40–50. 2015.
Qualitative, semi-quantitative, and quantitative simulation of the osmoregulation system in yeast [link]Paper   doi   link   bibtex  
An Integrated Qualitative and Quantitative Biochemical Model Learning Framework Using Evolutionary Strategy and Simulated Annealing. Wu, Z.; Pang, W.; and Coghill, G. M. Cogn. Comput., 7(6): 637–651. 2015.
An Integrated Qualitative and Quantitative Biochemical Model Learning Framework Using Evolutionary Strategy and Simulated Annealing [link]Paper   doi   link   bibtex  
Specific Biomarkers: Detection of Cancer Biomarkers Through High-Throughput Transcriptomics Data. Du, W.; Cao, Z.; Wang, Y.; Zhou, F.; Pang, W.; Chen, X.; Tian, Y.; and Liang, Y. Cogn. Comput., 7(6): 652–666. 2015.
Specific Biomarkers: Detection of Cancer Biomarkers Through High-Throughput Transcriptomics Data [link]Paper   doi   link   bibtex  
Sherlock: A Semi-automatic Framework for Quiz Generation Using a Hybrid Semantic Similarity Measure. Lin, C.; Liu, D.; Pang, W.; and Wang, Z. Cogn. Comput., 7(6): 667–679. 2015.
Sherlock: A Semi-automatic Framework for Quiz Generation Using a Hybrid Semantic Similarity Measure [link]Paper   doi   link   bibtex  
An Initialization Method for Clustering Mixed Numeric and Categorical Data Based on the Density and Distance. Ji, J.; Pang, W.; Zheng, Y.; Wang, Z.; and Ma, Z. Int. J. Pattern Recognit. Artif. Intell., 29(7): 1550024:1–1550024:16. 2015.
An Initialization Method for Clustering Mixed Numeric and Categorical Data Based on the Density and Distance [link]Paper   doi   link   bibtex  
Dynamics of Electrowetting Droplet Motion in Digital Microfluidics Systems: From Dynamic Saturation to Device Physics. Cui, W.; Zhang, M.; Duan, X.; Pang, W.; Zhang, D.; and Zhang, H. Micromachines, 6(6): 778–789. 2015.
Dynamics of Electrowetting Droplet Motion in Digital Microfluidics Systems: From Dynamic Saturation to Device Physics [link]Paper   doi   link   bibtex  
A Novel Bulk Acoustic Wave Resonator for Filters and Sensors Applications. Zhang, Z.; Liang, J.; Zhang, D.; Pang, W.; and Zhang, H. Micromachines, 6(9): 1306–1316. 2015.
A Novel Bulk Acoustic Wave Resonator for Filters and Sensors Applications [link]Paper   doi   link   bibtex  
An integrative top-down and bottom-up qualitative model construction framework for exploration of biochemical systems. Wu, Z.; Pang, W.; and Coghill, G. M. Soft Comput., 19(6): 1595–1610. 2015.
An integrative top-down and bottom-up qualitative model construction framework for exploration of biochemical systems [link]Paper   doi   link   bibtex  
ADOVA: Anomaly Detection in Online and Virtual spAces. Emele, C. D.; Spakov, V.; Pang, W.; Bone, J.; and Coghill, G. M. In Alqithami, S.; and Hexmoor, H., editor(s), Proceedings of the 3rd International Workshop on Collaborative Online Organizations, COOS 2016, co-located with the 14th International Conference on Autonomous Agents and Multi-Agent Systems, AAMAS 2015, Istanbul, Turkey, May 4, 2015, volume 1569, of CEUR Workshop Proceedings, pages 38–41, 2015. CEUR-WS.org
ADOVA: Anomaly Detection in Online and Virtual spAces [pdf]Paper   link   bibtex  
Automatically Predicting Quiz Difficulty Level Using Similarity Measures. Lin, C.; Liu, D.; Pang, W.; and Apeh, E. In Barker, K.; and Gómez-Pérez, J. M., editor(s), Proceedings of the 8th International Conference on Knowledge Capture, K-CAP 2015, Palisades, NY, USA, October 7-10, 2015, pages 1:1–1:8, 2015. ACM
Automatically Predicting Quiz Difficulty Level Using Similarity Measures [link]Paper   doi   link   bibtex  
  2014 (9)
Building Recognition on Subregion's Multiscale Gist Feature Extraction and Corresponding Columns Information Based Dimensionality Reduction. Li, B.; Pang, W.; Liu, Y.; Yu, X.; Du, A.; Zhang, Y.; and Yu, Z. J. Appl. Math., 2014: 898705:1–898705:10. 2014.
Building Recognition on Subregion's Multiscale Gist Feature Extraction and Corresponding Columns Information Based Dimensionality Reduction [link]Paper   doi   link   bibtex  
QML-Morven: A novel framework for learning qualitative differential equation models using both symbolic and evolutionary approaches. Pang, W.; and Coghill, G. M. J. Comput. Sci., 5(5): 795–808. 2014.
QML-Morven: A novel framework for learning qualitative differential equation models using both symbolic and evolutionary approaches [link]Paper   doi   link   bibtex  
Essential protein identification based on essential protein-protein interaction prediction by integrated edge weights. Jiang, Y.; Wang, Y.; Pang, W.; Chen, L.; Sun, H.; Liang, Y.; and Blanzieri, E. In Zheng, H. J.; Dubitzky, W.; Hu, X.; Hao, J.; Berrar, D. P.; Cho, K.; Wang, Y.; and Gilbert, D. R., editor(s), 2014 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2014, Belfast, United Kingdom, November 2-5, 2014, pages 480–483, 2014. IEEE Computer Society
Essential protein identification based on essential protein-protein interaction prediction by integrated edge weights [link]Paper   doi   link   bibtex  
An immune network approach to learning qualitative models of biological pathways. Pang, W.; and Coghill, G. M. In Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2014, Beijing, China, July 6-11, 2014, pages 1030–1037, 2014. IEEE
An immune network approach to learning qualitative models of biological pathways [link]Paper   doi   link   bibtex  
Mode-Driven Volume Analysis Based on Correlation of Time Series. Jia, C.; Pang, W.; and Fu, Y. R. In Agapito, L.; Bronstein, M. M.; and Rother, C., editor(s), Computer Vision - ECCV 2014 Workshops - Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part I, volume 8925, of Lecture Notes in Computer Science, pages 818–833, 2014. Springer
Mode-Driven Volume Analysis Based on Correlation of Time Series [link]Paper   doi   link   bibtex  
Fuzzy qualitative simulation with multivariate constraints. Pang, W.; and Coghill, G. M. In IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014, Beijing, China, July 6-11, 2014, pages 575–582, 2014. IEEE
Fuzzy qualitative simulation with multivariate constraints [link]Paper   doi   link   bibtex  
Hete-CF: Social-Based Collaborative Filtering Recommendation Using Heterogeneous Relations. Luo, C.; Pang, W.; Wang, Z.; and Lin, C. In Kumar, R.; Toivonen, H.; Pei, J.; Huang, J. Z.; and Wu, X., editor(s), 2014 IEEE International Conference on Data Mining, ICDM 2014, Shenzhen, China, December 14-17, 2014, pages 917–922, 2014. IEEE Computer Society
Hete-CF: Social-Based Collaborative Filtering Recommendation Using Heterogeneous Relations [link]Paper   doi   link   bibtex  
Semi-supervised Clustering on Heterogeneous Information Networks. Luo, C.; Pang, W.; and Wang, Z. In Tseng, V. S.; Ho, T. B.; Zhou, Z.; Chen, A. L. P.; and Kao, H., editor(s), Advances in Knowledge Discovery and Data Mining - 18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, May 13-16, 2014. Proceedings, Part II, volume 8444, of Lecture Notes in Computer Science, pages 548–559, 2014. Springer
Semi-supervised Clustering on Heterogeneous Information Networks [link]Paper   doi   link   bibtex  
Hete-CF: Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations. Luo, C.; Pang, W.; and Wang, Z. CoRR, abs/1412.7610. 2014.
Hete-CF: Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations [link]Paper   link   bibtex  
  2013 (1)
Stepwise modelling of biochemical pathways based on qualitative model learning. Wu, Z.; Pang, W.; and Coghill, G. M. In 13th UK Workshop on Computational Intelligence, UKCI 2013, Guildford, United Kingdom, September 9-11, 2013, pages 31–37, 2013. IEEE
Stepwise modelling of biochemical pathways based on qualitative model learning [link]Paper   doi   link   bibtex  
  2012 (5)
Incremental multi-linear discriminant analysis using canonical correlations for action recognition. Jia, C.; Wang, S.; Peng, X.; Pang, W.; Zhang, C.; Zhou, C.; and Yu, Z. Neurocomputing, 83: 56–63. 2012.
Incremental multi-linear discriminant analysis using canonical correlations for action recognition [link]Paper   doi   link   bibtex  
A fuzzy k-prototype clustering algorithm for mixed numeric and categorical data. Ji, J.; Pang, W.; Zhou, C.; Han, X.; and Wang, Z. Knowl. Based Syst., 30: 129–135. 2012.
A fuzzy k-prototype clustering algorithm for mixed numeric and categorical data [link]Paper   doi   link   bibtex  
Corrigendum to 'A fuzzy k-prototype clustering algorithm for mixed numeric and categorical data' [Knowledge-Based Systems, 30 (2012) 129-135]. Ji, J.; Pang, W.; Zhou, C.; Han, X.; and Wang, Z. Knowl. Based Syst., 36: 363. 2012.
Corrigendum to 'A fuzzy k-prototype clustering algorithm for mixed numeric and categorical data' [Knowledge-Based Systems, 30 (2012) 129-135] [link]Paper   doi   link   bibtex  
Tensor Discriminant Analysis With Multiscale Features for Action Modeling and Categorization. Yu, Z.; Jia, C.; Pang, W.; Zhang, C.; and Zhong, L. IEEE Signal Process. Lett., 19(2): 95–98. 2012.
Tensor Discriminant Analysis With Multiscale Features for Action Modeling and Categorization [link]Paper   doi   link   bibtex  
Extended kernel subset analysis for qualitative model learning. Pang, W.; and Coghill, G. M. In 12th UK Workshop on Computational Intelligence, UKCI 2012, Edinburgh, United Kingdom, September 5-7, 2012, pages 1–7, 2012. IEEE
Extended kernel subset analysis for qualitative model learning [link]Paper   doi   link   bibtex  
  2011 (2)
An immune-inspired approach to qualitative system identification of biological pathways. Pang, W.; and Coghill, G. M. Nat. Comput., 10(1): 189–207. 2011.
An immune-inspired approach to qualitative system identification of biological pathways [link]Paper   doi   link   bibtex  
Research of multi-modal function optimization based on multi-agent immune genetic algorithm. Liu, S.; Meng, X.; Pang, W.; and Wang, H. In International Conference on Electronic and Mechanical Engineering and Information Technology, EMEIT 2011, Harbin, Heilongjiang, China, 12-14 August, 2011, pages 3170–3173, 2011. IEEE
Research of multi-modal function optimization based on multi-agent immune genetic algorithm [link]Paper   doi   link   bibtex  
  2010 (4)
A decision support system using soft computing for modern international container transportation services. Liu, Y.; Zhou, C.; Guo, D.; Wang, K.; Pang, W.; and Zhai, Y. Appl. Soft Comput., 10(4): 1087–1095. 2010.
A decision support system using soft computing for modern international container transportation services [link]Paper   doi   link   bibtex  
Learning Qualitative Differential Equation models: a survey of algorithms and applications. Pang, W.; and Coghill, G. M. Knowl. Eng. Rev., 25(1): 69–107. 2010.
Learning Qualitative Differential Equation models: a survey of algorithms and applications [link]Paper   doi   link   bibtex  
Learning Qualitative Metabolic Models Using Evolutionary Methods. Pang, W.; and Coghill, G. M. In Stojmenovic, I.; Farin, G. E.; Guo, M.; Jin, H.; Li, K.; Hu, L.; Wei, X.; and Che, X., editor(s), Fifth International Conference on Frontier of Computer Science and Technology, FCST 2010, Changchun, Jilin Province, China, August 18-22, 2010, pages 436–441, 2010. IEEE Computer Society
Learning Qualitative Metabolic Models Using Evolutionary Methods [link]Paper   doi   link   bibtex  
QML-AiNet: An Immune-Inspired Network Approach to Qualitative Model Learning. Pang, W.; and Coghill, G. M. In Hart, E.; McEwan, C.; Timmis, J.; and Hone, A., editor(s), Artificial Immune Systems, 9th International Conference, ICARIS 2010, Edinburgh, UK, July 26-29, 2010. Proceedings, volume 6209, of Lecture Notes in Computer Science, pages 223–236, 2010. Springer
QML-AiNet: An Immune-Inspired Network Approach to Qualitative Model Learning [link]Paper   doi   link   bibtex  
  2009 (2)
QML-Morven : a framework for learning qualitative models. Pang, W. Ph.D. Thesis, University of Aberdeen, UK, 2009.
QML-Morven : a framework for learning qualitative models [link]Paper   link   bibtex  
An Immune-Inspired Approach to Qualitative System Identification of the Detoxification Pathway of Methylglyoxal. Pang, W.; and Coghill, G. M. In Andrews, P. S.; Timmis, J.; Owens, N. D. L.; Aickelin, U.; Hart, E.; Hone, A.; and Tyrrell, A. M., editor(s), Artificial Immune Systems, 8th International Conference, ICARIS 2009, York, UK, August 9-12, 2009. Proceedings, volume 5666, of Lecture Notes in Computer Science, pages 151–164, 2009. Springer
An Immune-Inspired Approach to Qualitative System Identification of the Detoxification Pathway of Methylglyoxal [link]Paper   doi   link   bibtex  
  2007 (1)
Modified clonal selection algorithm for learning qualitative compartmental models of metabolic systems. Pang, W.; and Coghill, G. M. In Thierens, D., editor(s), Genetic and Evolutionary Computation Conference, GECCO 2007, Proceedings, London, England, UK, July 7-11, 2007, Companion Material, pages 2887–2894, 2007. ACM
Modified clonal selection algorithm for learning qualitative compartmental models of metabolic systems [link]Paper   doi   link   bibtex  
  2006 (2)
An Evolution Computation Based Approach to Synthesize Video Texture. Meng, Y.; Li, W.; Wang, Y.; Guo, W.; and Pang, W. In Alexandrov, V. N.; van Albada, G. D.; Sloot, P. M. A.; and Dongarra, J. J., editor(s), Computational Science - ICCS 2006, 6th International Conference, Reading, UK, May 28-31, 2006, Proceedings, Part II, volume 3992, of Lecture Notes in Computer Science, pages 223–230, 2006. Springer
An Evolution Computation Based Approach to Synthesize Video Texture [link]Paper   doi   link   bibtex  
Some Improvements in Phrase-Based Statistical Machine Translation. Yang, Z.; Pang, W.; Du, J.; Wei, W.; and Xu, B. In Huo, Q.; Ma, B.; Siong, C. E.; and Li, H., editor(s), Chinese Spoken Language Processing, 5th International Symposium, ISCSLP 2006, Singapore, December 13-16, 2006, Proceedings, volume 4274, of Lecture Notes in Computer Science, pages 704–711, 2006. Springer
Some Improvements in Phrase-Based Statistical Machine Translation [link]Paper   doi   link   bibtex  
  2005 (1)
The CASIA phrase-based machine translation system. Pang, W.; Yang, Z.; Chen, Z.; Wei, W.; Xu, B.; and Zong, C. In 2005 International Workshop on Spoken Language Translation, IWSLT 2005, Pittsburgh, PA, USA, October 24-25, 2005, pages 104–111, 2005. ISCA
The CASIA phrase-based machine translation system [link]Paper   link   bibtex  
  2004 (1)
Fuzzy Discrete Particle Swarm Optimization for Solving Traveling Salesman Problem. Pang, W.; Wang, K.; Zhou, C.; and Dong, L. In 2004 International Conference on Computer and Information Technology (CIT 2004), 14-16 September 2004, Wuhan, China, pages 796–800, 2004. IEEE Computer Society
Fuzzy Discrete Particle Swarm Optimization for Solving Traveling Salesman Problem [link]Paper   doi   link   bibtex