Dr Daqing Chen
| Name | Dr Daqing Chen |
|---|---|
| Job title | Senior Lecturer |
| Organisational Unit | Computer Science and Informatics |
| ORCID | https://orcid.org/0000-0003-0030-1199 |
Research outputs
The Impact of Supervised Manifold Learning on Structure Preserving and Classification Error: A Theoretical Study
Hajderanj, L., Chen, D. and Weheliye, I. (2021). The Impact of Supervised Manifold Learning on Structure Preserving and Classification Error: A Theoretical Study. IEEE Access.Enhancing Transformer-based language models with Commonsense Representations for Knowledge-driven Machine Comprehension
Li, R., Jiang, Z., Wang, L., Lu, X., Zhao, M. and Chen, D. (2021). Enhancing Transformer-based language models with Commonsense Representations for Knowledge-driven Machine Comprehension. Knowledge-Based Systems. https://doi.org/10.1016/j.knosys.2021.106936Maneuvering target tracking of UAV based on MN-DDPG and transfer learning
Li, B., Yang, Z.P., Chen, D.Q., Liang, S.Y. and Ma, H. (2020). Maneuvering target tracking of UAV based on MN-DDPG and transfer learning. Defence Technology. https://doi.org/10.1016/j.dt.2020.11.014Lip Reading Sentences Using Deep Learning with Only Visual Cues
Fanghour, S., Chen, D., Guo, K. and Xiao, P. (2020). Lip Reading Sentences Using Deep Learning with Only Visual Cues. IEEE Access. https://doi.org/10.1109/ACCESS.2020.3040906Deep Learning Causal Attributions of Breast Cancer
Chen, D, Hajderanj, L, Mallet, S, Camenen, P, Li, B, Ren, H and Zhao, E (2020). Deep Learning Causal Attributions of Breast Cancer. Computing 2021. London 15 - 16 Jul 2021 The Science and Information (SAI) Organization.UAV Maneuvering Target Tracking in Uncertain Environments based on Deep Reinforcement Learning and Meta-learning
Li, B., Gan, Z., Chen, D. and Aleksandrovich, D.S. (2020). UAV Maneuvering Target Tracking in Uncertain Environments based on Deep Reinforcement Learning and Meta-learning. Remote Sensing. 12 (22), p. 3789. https://doi.org/10.3390/rs12223789Single- and Multi-Distribution Dimensionality Reduction Approaches for a Better Data Structure Capturing
Hajderanj, L., Chen, D., Grisan, E. and Dudley-McEvoy, S (2020). Single- and Multi-Distribution Dimensionality Reduction Approaches for a Better Data Structure Capturing. IEEE Access. 8, pp. 207141 - 207155. https://doi.org/10.1109/ACCESS.2020.3038460Learning Bayesian Networks based on Order Graph with Ancestral Constraints
Wang, Z., Gao, X., Tian, X., Yang, Y. and Chen, D. (2020). Learning Bayesian Networks based on Order Graph with Ancestral Constraints. Knowledge-Based Systems. https://doi.org/10.1016/j.knosys.2020.106515The Development of a Skin Image Analysis Tool by Using Machine Learning Algorithms
Xiao, P., Zhang, Xu, Pan, Wei, Ou, Xiang, Bontozoglou, C., Chirikhina, E. and Chen, D. (2020). The Development of a Skin Image Analysis Tool by Using Machine Learning Algorithms. Cosmetics. 7 (3), p. e67. https://doi.org/10.3390/cosmetics7030067Skin Capacitive Imaging Analysis Using Deep Learning GoogLeNet
Zhang, X., Pan, W., Bontozoglou, C., Chirikhina, E., Chen, D. and Xiao, P. (2019). Skin Capacitive Imaging Analysis Using Deep Learning GoogLeNet. Computing Conference 2020. London, UK 16 - 17 Jul 2019 Springer.Effectiveness analysis of ship formation air defence based on deep belief network
Li, B., Luo, H., Wang, Y. and Chen, D. (2020). Effectiveness analysis of ship formation air defence based on deep belief network. The Journal of Engineering. 2020 (13), pp. 394-398. https://doi.org/10.1049/joe.2019.1201An adaptive dwell time scheduling model for phased array radar based on three-way decision
Li, B., Tian, L., Chen, D. and Liang, S. (2020). An adaptive dwell time scheduling model for phased array radar based on three-way decision. Journal of Systems Engineering and Electronics. pp. 500-509. https://doi.org/10.23919/JSEE.2020.000030Three‐way decision of target threat decision making based on adaptive threshold algorithms
Li, B., Tian, Li., Han, Y. and Chen, D. (2020). Three‐way decision of target threat decision making based on adaptive threshold algorithms. The Journal of Engineering. 2020 (13), pp. 293-297. https://doi.org/10.1049/joe.2019.1202An Adaptive Task Scheduling Method for Networked UAV Combat Cloud System Based on Virtual Machine and Task Migration
Li, B., Liang, S., Tian, L., Chen, D. and Zhang, M. (2020). An Adaptive Task Scheduling Method for Networked UAV Combat Cloud System Based on Virtual Machine and Task Migration. Mathematical Problems in Engineering. p. 5391479. https://doi.org/10.1155/2020/5391479A Task Scheduling Algorithm for Phased Array Radar Based on Dynamic Three-way Decision
Li, B., Tian, L., Chen, D. and Han, Y. (2019). A Task Scheduling Algorithm for Phased Array Radar Based on Dynamic Three-way Decision. Sensors. 20 (1). https://doi.org/10.3390/s20010153Intelligent aircraft maneuvering decision based on CNN
Li, B, Liang, S, Tian, L and Chen, D (2019). Intelligent aircraft maneuvering decision based on CNN. the 3rd International Conference on Computer Science and Application Engineering. Sanya, China 22 - 24 Oct 2019 ACM Press. https://doi.org/10.1145/3331453.3362046Predicting Customer Profitability Dynamically over Time: An Experimental Comparative Study
Chen, D., Guo, K. and Li, B. (2019). Predicting Customer Profitability Dynamically over Time: An Experimental Comparative Study. 24th Iberoamerican Congress on Pattern Recognition (CIARP 2019). Havana, Cuba 28 - 31 Oct 2019 https://doi.org/10.1007/978-3-030-33904-3_16Intelligent Attitude Control of Aircraft Based on LSTM
Li, B, Gao, P, Li, X and Chen, D (2019). Intelligent Attitude Control of Aircraft Based on LSTM. 3rd International Conference on Artificial Intelligence Applications and Technologies. Beijing, China 01 - 03 Aug 2019 IOP Publishing. https://doi.org/10.1088/1757-899X/646/1/012013FRS: A Simple Knowledge Graph Embedding Model for Entity Prediction
Wang, L.F., Lu, X., Jiang, Z., Zhang, Z., Li, R., Zhao, M. and Chen, D. (2019). FRS: A Simple Knowledge Graph Embedding Model for Entity Prediction. Mathematical Biosciences and Engineering. 16 (6), pp. 7789-7807. https://doi.org/10.3934/mbe.2019391Design of a voice control 6DoF grasping robotic arm based on ultrasonic sensor, computer vision and Alexa voice assistance
Wang, Z, Chen, D and Xiao, P (2019). Design of a voice control 6DoF grasping robotic arm based on ultrasonic sensor, computer vision and Alexa voice assistance. International Conference on Information Technology in Medicine and Education. Qingdao, China 23 - 25 Aug 2019 IEEE. https://doi.org/10.1109/ITME.2019.00150Intelligent Flight Control of Combat Aircraft Based on Autoencoder
Li, B., Gao, P., Liang, S. and Chen, D. (2019). Intelligent Flight Control of Combat Aircraft Based on Autoencoder. 2019 The 4th International Conference on Robotics, Control and Automation. GuangZhou 26 - 28 Jul 2019 https://doi.org/10.1145/3351180.3351210Towards automated cost analysis, benchmarking and estimating in construction: a machine learning approach
Chen, D, Hajderanj, L and Fiske, J (2019). Towards automated cost analysis, benchmarking and estimating in construction: a machine learning approach. 13th Multi Conference on Computer Science and Information Systems (MCCSIS). Porto, Portugal 16 - 18 Jul 2019Improving Prediction Accuracy of Breast Cancer Survivability and Diabetes Diagnosis via RBF Networks trained with EKF models
Adegoke, V, Chen, D and Banissi, E (2019). Improving Prediction Accuracy of Breast Cancer Survivability and Diabetes Diagnosis via RBF Networks trained with EKF models. International Journal of Computer Information Systems and Industrial Management.Distributed deep networks based on Bagging-Down SGD algorithm
Qin, C, Gao, X and Chen, D (2019). Distributed deep networks based on Bagging-Down SGD algorithm. Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics. 41 (5), pp. 1021-1027. https://doi.org/10.3969/j.issn.1001-506X.2019.05.13Enhancing Ensemble Prediction Accuracy of Breast Cancer Survivability and Diabetes Diagnostic using optimized EKF-RBFN trained prototypes, The 10th International Conference on Soft Computing and Pattern Recognition
Adegoke, V, Chen, D, Banissi, E and Barikzai, S (2019). Enhancing Ensemble Prediction Accuracy of Breast Cancer Survivability and Diabetes Diagnostic using optimized EKF-RBFN trained prototypes, The 10th International Conference on Soft Computing and Pattern Recognition. The 10th International Conference on Soft Computing and Pattern Recognition. Porto, Portugal 13 - 15 Dec 2018A New Supervised t-SNE with Dissimilarity Measure for Effective Data Visualization and Classification
Hajderanj, L, Weheliye, I and Chen, D (2019). A New Supervised t-SNE with Dissimilarity Measure for Effective Data Visualization and Classification. 2019 8th International Conference on Software and Information Engineering. Cairo 09 - 12 Apr 2019Recurrent Neural Networks for Decoding Lip Read Speech
Fenghour, S, Chen, D and Xiao, P (2019). Recurrent Neural Networks for Decoding Lip Read Speech. 2019 8th International Conference on Software and Information Engineering (ICSIE 2019). Cairo 09 - 12 Apr 2019Learning Bayesian network parameters via minimax algorithm
Gao, X, Gao, G, Ren, H, Chen, D and He, C (2019). Learning Bayesian network parameters via minimax algorithm. International Journal of Approximate Reasoning. 108, pp. 62-75. https://doi.org/10.1016/j.ijar.2019.03.001Learning Bayesian Networks using the Constrained Maximum a Posteriori Probability Method
Yang, Y, Gao, X, Guo, Z and Chen, D (2019). Learning Bayesian Networks using the Constrained Maximum a Posteriori Probability Method. Pattern Recognition. 91, pp. 123-134. https://doi.org/10.1016/j.patcog.2019.02.006Contour Mapping for Speaker-Independent Lip Reading System
Fenghour, S, Chen, D and Xiao, P (2018). Contour Mapping for Speaker-Independent Lip Reading System. The 11th International Conference on Machine Vision (ICMV 2018). Munich, Germany 01 - 03 Nov 2018Visual analytics in the public sector: An analysis on diversities and similarities of London’s wards
Chen, D, Sanz, BM and Zhao, E (2018). Visual analytics in the public sector: An analysis on diversities and similarities of London’s wards. International Conference on Big Data Analytics, Data Mining and Computational Intelligence 2018 (BigDaCI 2018). Madrid, Spain 18 - 20 Jul 2018 Bigdaci.Predictive Ensemble Modelling: An Experimental Comparison of Boosting Implementation Methods
Adegoke, V, Chen, D, Barikzai, S and Banissi, E (2017). Predictive Ensemble Modelling: An Experimental Comparison of Boosting Implementation Methods. 2017 European Modelling Symposium (EMS). Manchester 20 - 21 Nov 2017Prediction of Breast Cancer Survivability using Ensemble Algorithms
Adegoke, V, Chen, D, Banissi, E and Barikzai, S (2017). Prediction of Breast Cancer Survivability using Ensemble Algorithms. International Conference on Smart System and Technologies 2017 (SST 2017),. Osijek, Croatia 18 - 20 Oct 2017Feature Extraction and Labelling Large Data Sets Using Deep Learning
Chen, D (2017). Feature Extraction and Labelling Large Data Sets Using Deep Learning. RESEARCHER LINK: Smart Technology for Fighting Virus Epidemics & Bioinformatics. Recife, Pernambuco, Brazil 10 - 13 Sep 2017Learning Bayesian Network Parameters from a Small Data Set: A Further Constrained Qualitatively Maximum a Posteriori Method
Guo, Zhi-gao, Gao, Xiao-guang, Hao, Ren, Yang, Yu, Di, Ruo-hai and Chen, D (2017). Learning Bayesian Network Parameters from a Small Data Set: A Further Constrained Qualitatively Maximum a Posteriori Method. International Journal of Approximate Reasoning. 91 (Dec), pp. 22-35. https://doi.org/10.1016/j.ijar.2017.08.009A Bayesian Approach to Learn Bayesian Networks Using Data and Constraints
Gao, X, Yu, Y, Zhi-gao, G and Chen, D (2016). A Bayesian Approach to Learn Bayesian Networks Using Data and Constraints. 23rd International Conference on Pattern Recognition (ICPR 2016). Cancún, México 04 - 08 Dec 2016 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICPR.2016.7900204On Distributed Deep Network for Processing Large-Scale Sets of Complex Data
Qin, C, Gao, X and Chen, D (2016). On Distributed Deep Network for Processing Large-Scale Sets of Complex Data. 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC). Hangzhou, China. 27 - 28 Aug 2016 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/IHMSC.2016.55On Distributed Deep Network for Processing Large-Scale Sets of Complex Data
Chen, D (2016). On Distributed Deep Network for Processing Large-Scale Sets of Complex Data. 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC). Hangzhou, China 27 - 28 Aug 2016Big Data Analytics In The Public Sector: A Case Study Of NEET Analysis For The London Boroughs
Chen, D, Asaolu, B and Qin, C (2016). Big Data Analytics In The Public Sector: A Case Study Of NEET Analysis For The London Boroughs. International Conference on Big Data Analytics, Data Mining and Computational Intelligence. Funchal, Madeira, Portugal 02 - 04 Jul 2016Making Better Use of Big Data
Chen, D (2016). Making Better Use of Big Data. LSBU Enterprise Count Event, March 2016. London Southbank University 18 - 18 Mar 2016 London South Bank University.Big Data Analytics System for Fact/Data-driven Decision Making
Chen, D (2015). Big Data Analytics System for Fact/Data-driven Decision Making. The Royal Statistical Society, Business and Industry Section. London, UK 18 Nov 2015 Royal Statistical Society .Determining Key (Predictor) Modules for Early Identification of Students At-Risk
Chen, D and Elliott, G (2013). Determining Key (Predictor) Modules for Early Identification of Students At-Risk. International Conference on Advanced Information Engineering and Education Science (ICAIEES 2013). Beijing, China 19 - 20 Dec 2013 Atlantis Press. https://doi.org/10.2991/icaiees-13.2013.22Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining
Chen, D (2012). Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining. Journal of Database Marketing and Customer Strategy Management. 19 (3), pp. 197-208. https://doi.org/10.1057/dbm.2012.174253
total views of outputs2838
total downloads of outputs101
views of outputs this month79
downloads of outputs this month