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Dr Longzhi Yang

Associate Professor

Department: Computer and Information Sciences

I am an Associated Professor (Reader) at Northumbria University. Prior to joining the University, I worked on an EU FP7 project Integrated In Silico Models for the Prediction of Human Repeated Dose Toxicity of COSMetics to Optimise Safety (COSMOS) (contract no. 266835) at Bradford University. Before that, I obtained my PhD degree in Computer Science from the University of Wales, Aberystwyth (now Aberystwyth University) in 2011.

I am currently the Director of Learning and Teaching at the Department of Computer and Information Sciences. I supervise PhD and MSc students in the research areas of Computational Intelligence, Machine Learning, Computer Graphics/Vision, Planning/Scheduling, Automatic Control, Decision Making Support and their real-world applications especially under uncertain environment. I published research papers in prestigious journal and conferences in my field, such as the IEEE Transactions on Fuzzy Systems, Fuzzy Sets and Systems, and IEEE International Conference on Fuzzy Systems. My research has been founded by Royal Academy of Engineering, Innovate UK, Royal Society, and other funders; I also work with local companies as a technical consultancy.

Longzhi Yang

Qualifications

  • Computing Science PhD September 18 2011
  • HEA Senior Fellow 2018
  • IEEE Senior Member 2016
  • Fellow (FHEA) Higher Education Academy (HEA) 2015

Key Publications

  • Please visit the Pure Research Information Portal for further information
  • Artificial Human Balance Control by Calf Muscle Activation Modelling, Yin, K., Chen, J., Xiang, K., Pang, M., Tang, B., Li, J., Yang, L. 6 May 2020, In: IEEE Access
  • Exploring resilience for effective learning in computer Science Education, Prickett, T., Crick, T., Harvey, M., Walters, J., Yang, L. 1 Apr 2020
  • Resilience and effective learning in first year undergraduate computer science, Prickett, T., Harvey, M., Walters, J., Yang, L., Crick, T. 2 Mar 2020, Proceedings of Trondheim 2020 , Trondheim, ACM
  • Visual-guided Robotic Object Grasping using Dual Neural Network Controllers, Fang, W., Chao, F., Lin, C., Zhou, D., Yang, L., Chang, X., Shen, Q., Shang, C. 18 May 2020, In: IEEE Transactions on Industrial Informatics
  • Adaptive Activation Function Generation for Artificial Neural Networks through Fuzzy Inference with Application in Grooming Text Categorisation, Zuo, Z., Li, J., Wei, B., Yang, L., Chao, F., Naik, N. 7 Mar 2019, 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2019): New Orleans, Louisiana, USA 23 – 26 June 2019, Piscataway, NJ, IEEE
  • A data-driven robotic Chinese calligraphy system using convolutional auto-encoder and differential evolution, Gao, X., Zhou, C., Chao, F., Yang, L., Lin, C., Xu, T., Shang, C., Shen, Q. 15 Oct 2019, In: Knowledge-Based Systems
  • A Deep-Learning-Driven Light-Weight Phishing Detection Sensor, Wei, B., Hamad, R., Yang, L., He, X., Wang, H., Gao, B., Woo, W. 30 Sep 2019, In: Sensors
  • An Early Diagnosis of Oral Cancer based on Three-Dimensional Convolutional Neural Networks, Xu, S., Liu, C., Zong, Y., Chen, S., Lu, Y., Yang, L., Ng, E., Wang, Y., Wang, Y., Liu, Y., Hu, W., Zhang, C. 12 Nov 2019, In: IEEE Access
  • An Improved Fuzzy Brain Emotional Learning Model Network Controller for Humanoid Robots, Fang, W., Chao, F., Lin, C., Yang, L., Shang, C., Zhou, C. 4 Feb 2019, In: Frontiers in Neurorobotics

PGR Supervision

  • Philip Anderson Start: 01/03/2020
  • Mansoureh Pezhman Pour Development of Medical Image/Video Segmentation Via Deep Learning Models Start: 21/02/2020
  • Laura Pearson Start: 01/03/2020
  • Noe Nnko Start: 01/11/2017
  • Yao Tan Curvature-Based Sparse Rule Base Generation for Fuzzy Rule Interpolation Start: 01/02/2015
  • Zheming Zuo Feature Extraction and Encoding for Video Action Recognition Start: 06/06/2016
  • Jie Li Fuzzy Interpolation Systems with Knowledge Extraction and Adaptation Start: 01/10/2014


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