Dr Scott Yang

Lecturer in Computer Science

Computer Science

+44 (0) 1792 987193

su.yang@swansea.ac.uk

Office - 216
Second Floor
Computational Foundry
Bay Campus

Available For Postgraduate Supervision

Research Links

https://orcid.org/0000-0002-6618-7483

ResearchGate

Google Scholar

About

SU YANG received the Ph.D. degree in electronic engineering from the University of Kent, Canterbury, U.K., in 2015. During the Ph.D. studies, he was with the Intelligent Interactions Research Group, School of Engineering and Digital Arts. Later he joined the College of Engineering, Temple University, Philadelphia, PA, USA, as a Postdoctoral Research Associate and project CO-PI, as well as a Senior Research Associate in the Intelligent Systems Research Centre, Ulster University, Londonderry, Northern Ireland, U.K. He is currently a lecturer in the Department of Computer Science, Swansea University.

Areas Of Expertise

Pattern recognition
Machine Learning
Signal processing
Biomedical imaging
Bio-signal analysis
Signal source reconstruction/localization

Career Highlights

Teaching Interests

Artificial Intelligence
Machine Learning
Data Analytics
Biometrics

Research

Publications

Research Highlights

Luo, Y., Zhang, S., Zhang, S., Liu, J., Wang, Y., & Yang, S. (2023). A Secure and Efficient Framework for Outsourcing Large-scale Matrix Determinant and Linear Equations. ACM Transactions on Embedded Computing Systems, 22(5), 1-22.
https://doi.org/10.1145/3611014, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa66056
Liu, X., Yang, S., & Liu, Z. (2021). Predicting Fluid Intelligence via Naturalistic Functional Connectivity Using Weighted Ensemble Model and Network Analysis. NeuroSci, 2(4), 427-442.
https://doi.org/10.3390/neurosci2040032, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa59033
Liu, J., Qin, S., Luo, Y., Wang, Y., & Yang, S. (2022). Intelligent Traffic Light Control by Exploring Strategies in an Optimised Space of Deep Q-Learning. IEEE Transactions on Vehicular Technology, 71(6), 5960-5970.
https://doi.org/10.1109/tvt.2022.3160871, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60406
Luo, Y., Wang, F., Liu, J., Li, L., Yang, S., & Zhang, S. (2022). COVER: A Secure Blind Image Watermarking Scheme. Circuits, Systems, and Signal Processing, 41(12), 6931-6959.
https://doi.org/10.1007/s00034-022-02099-z, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60689
http://dx.doi.org/10.1007/s00034-022-02099-z
Yang, S. & Deravi, F. (2022). Re-Engineered Word Embeddings for Improved Document-Level Sentiment Analysis. Applied Sciences, 12(18), 9287
https://doi.org/10.3390/app12189287, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61289

All Research

Journal Articles

Liu, J., Wang, Y., Luo, Y., Zhang, S., Jiang, D., Hua, Y., Qin, S., & Yang, S. (2023). Hardware Spiking Neural Networks with Pair-Based STDP Using Stochastic Computing. Neural Processing Letters, 55(6), 7155-7173.
https://doi.org/10.1007/s11063-023-11255-8, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63218
Liu, J., Qin, S., Su, M., Luo, Y., Wang, Y., & Yang, S. (2023). Multiple intersections traffic signal control based on cooperative multi-agent reinforcement learning. Information Sciences, 647, 119484
https://doi.org/10.1016/j.ins.2023.119484, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa64123
http://dx.doi.org/10.1016/j.ins.2023.119484
Liu, J., Qin, S., Su, M., Luo, Y., Zhang, S., Wang, Y., & Yang, S. (2023). Traffic signal control using reinforcement learning based on the teacher-student framework. Expert Systems with Applications, 228, 120458
https://doi.org/10.1016/j.eswa.2023.120458, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63462
Luo, Y., Zhang, S., Zhang, S., Liu, J., Wang, Y., & Yang, S. (2023). A Secure and Efficient Framework for Outsourcing Large-scale Matrix Determinant and Linear Equations. ACM Transactions on Embedded Computing Systems, 22(5), 1-22.
https://doi.org/10.1145/3611014, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa66056
Huang, Z., Jiang, X., Huang, S., Qin, S., & Yang, S. (2023). An efficient convolutional neural network-based diagnosis system for citrus fruit diseases. Frontiers in Genetics, 14
https://doi.org/10.3389/fgene.2023.1253934, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa66057
Luo, Y., Wang, F., Liu, J., Li, L., Yang, S., & Zhang, S. (2022). COVER: A Secure Blind Image Watermarking Scheme. Circuits, Systems, and Signal Processing, 41(12), 6931-6959.
https://doi.org/10.1007/s00034-022-02099-z, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60689
http://dx.doi.org/10.1007/s00034-022-02099-z
Liu, J., Liang, X., Yang, R., Luo, Y., Lu, H., Li, L., Zhang, S., & Yang, S. (2022). Federated learning-based vertebral body segmentation. Engineering Applications of Artificial Intelligence, 116, 105451
https://doi.org/10.1016/j.engappai.2022.105451, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61423
Yang, S. & Deravi, F. (2022). Re-Engineered Word Embeddings for Improved Document-Level Sentiment Analysis. Applied Sciences, 12(18), 9287
https://doi.org/10.3390/app12189287, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61289
Liu, J., Qin, S., Luo, Y., Wang, Y., & Yang, S. (2022). Intelligent Traffic Light Control by Exploring Strategies in an Optimised Space of Deep Q-Learning. IEEE Transactions on Vehicular Technology, 71(6), 5960-5970.
https://doi.org/10.1109/tvt.2022.3160871, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60406
Liu, X., Yang, S., & Liu, Z. (2021). Predicting Fluid Intelligence via Naturalistic Functional Connectivity Using Weighted Ensemble Model and Network Analysis. NeuroSci, 2(4), 427-442.
https://doi.org/10.3390/neurosci2040032, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa59033
Yang, S., Bornot, J., Fernandez, R., Deravi, F., Wong-Lin, K., & Prasad, G. (2021). Integrated space–frequency–time domain feature extraction for MEG-based Alzheimer’s disease classification. Brain Informatics, 8(1), 24
https://doi.org/10.1186/s40708-021-00145-1, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa58561
Yang, S., Bornot, J., Fernandez, R., Deravi, F., Hoque, S., Wong-Lin, K., & Prasad, G. (2021). Detection of Mild Cognitive Impairment with MEG Functional Connectivity Using Wavelet-Based Neuromarkers. Sensors, 21(18), 6210
https://doi.org/10.3390/s21186210, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa58109
Liu, J., Lu, H., Luo, Y., & Yang, S. (2021). Spiking neural network-based multi-task autonomous learning for mobile robots. Engineering Applications of Artificial Intelligence, 104, 104362
https://doi.org/10.1016/j.engappai.2021.104362, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa58937
Liu, J., Li, M., Luo, Y., Yang, S., Li, W., & Bi, Y. (2021). Alzheimer's disease detection using depthwise separable convolutional neural networks. Computer Methods and Programs in Biomedicine, 203, 106032
https://doi.org/10.1016/j.cmpb.2021.106032, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa58938
Zanin, M., Aitya, N., Basilio, J., Baumbach, J., Benis, A., Behera, C., Bucholc, M., Castiglione, F., Chouvarda, I., Comte, B., Dao, T., Ding, X., Pujos-Guillot, E., Filipovic, N., Finn, D., Glass, D., Harel, N., Iesmantas, T., Ivanoska, I...., & Schmidt, H. (2021). An Early Stage Researcher's Primer on Systems Medicine Terminology. Network and Systems Medicine, 4(1), 2-50.
https://doi.org/10.1089/nsm.2020.0003, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa58942
Sanchez-Bornot, J., Lopez, M., Bruña, R., Maestu, F., Youssofzadeh, V., Yang, S., Finn, D., Todd, S., McLean, P., Prasad, G., & Wong-Lin, K. (2021). High-dimensional brain-wide functional connectivity mapping in magnetoencephalography. Journal of Neuroscience Methods, 348, 108991
https://doi.org/10.1016/j.jneumeth.2020.108991, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa58943
Liu, J., Liang, Z., Luo, Y., Cao, L., Zhang, S., Wang, Y., & Yang, S. (2020). A Hardware Pseudo-Random Number Generator Using Stochastic Computing and Logistic Map. Micromachines, 12(1), 31
https://doi.org/10.3390/mi12010031, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa58944
Liu, J., Wu, G., Luo, Y., Qiu, S., Yang, S., Li, W., & Bi, Y. (2020). EEG-Based Emotion Classification Using a Deep Neural Network and Sparse Autoencoder. Frontiers in Systems Neuroscience, 14
https://doi.org/10.3389/fnsys.2020.00043, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa58945
Yang, S., Bornot, J., Wong-Lin, K., & Prasad, G. (2019). M/EEG-Based Bio-Markers to Predict the MCI and Alzheimer's Disease: A Review From the ML Perspective. IEEE Transactions on Biomedical Engineering, 66(10), 2924-2935.
https://doi.org/10.1109/tbme.2019.2898871, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa58934
Yang, S., Hoque, S., & Deravi, F. (2019). Improved Time-Frequency Features and Electrode Placement for EEG-Based Biometric Person Recognition. IEEE Access, 7, 49604-49613.
https://doi.org/10.1109/access.2019.2910752, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa58933
Yang, S., Deravi, F., & Hoque, S. (2018). Task sensitivity in EEG biometric recognition. Pattern Analysis and Applications, 21(1), 105-117.
https://doi.org/10.1007/s10044-016-0569-4, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa58935
Yang, S. & Deravi, F. (2017). On the Usability of Electroencephalographic Signals for Biometric Recognition: A Survey. IEEE Transactions on Human-Machine Systems, 47(6), 958-969.
https://doi.org/10.1109/thms.2017.2682115, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa58936

Book Chapters

Yang, S., Prasad, G., Wong-Lin, K., & Sanchez-Bornot, J. (2021). Machine Learning for E/MEG-Based Identification of Alzheimer's Disease. In Ayman El-Baz and Jasjit S. Suri (Ed.), Machine Learning in Medicine Chapman and Hall/CRC
SU Repository: https://cronfa.swan.ac.uk/Record/cronfa58931
https://www.taylorfrancis.com/chapters/edit/10.1201/9781315101323-8/machine-learning-meg-based-identification-alzheimer-disease-su-yang-girijesh-prasad-kongfatt-wong-lin-jose-sanchez-bornot?context=ubx&refId=0d46572e-9aae-4e70-b417-04ab0a4009e3

Conference Contributions

Luo, Y., Zhang, S., Zhang, S., Liu, J., Liang, C., & Yang, S. (2022). Efficient PCA-based image compression via secure outsourcing edge cloud. In 2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys)IEEE.
https://doi.org/10.1109/hpcc-dss-smartcity-dependsys57074.2022.00242, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63210
http://dx.doi.org/10.1109/hpcc-dss-smartcity-dependsys57074.2022.00242
Yang, S., Lopez, S., Golmohammadi, M., Obeid, I., & Picone, J. (2017). Semi-automated annotation of signal events in clinical EEG data. In 2016 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)IEEE.
https://doi.org/10.1109/spmb.2016.7846855, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa58946

Other Research Outputs

Yang, S. & Arunkumar, S. (n.d.) Feature selection algorithm under conditions of noisy data and limited recording
SU Repository: https://cronfa.swan.ac.uk/Record/cronfa58932

Supervision

Deep Learning Methods for Language Generation and Its Applications in Economy and Healthcare (current)

PhD

Other supervisor: Prof Xianghua Xie
Text-Based Person Re-identification for Robustness Research (current)

PhD

Other supervisor: Prof Mark Jones

Other supervisor: Prof Xianghua Xie
Ear Biometrics (current)

PhD

Other supervisor: Prof Xianghua Xie
Developing methodologies for the efficient creation of data labels, tools to reduce human effort, and aid perception and understanding of big datasets in an underwater context, supporting rapid development and explainability of machine learning models. (current)

PhD

Other supervisor: Prof Mark Jones
N/A. (current)

PhD

Other supervisor: Prof Xianghua Xie
Enhancing the diagnostic performance of a bowel cancer blood test using advanced machine learning algorithms and the incorporation of information from the patient's medical record (current)

PhD

Other supervisor: Prof Gert Aarts

Taught Modules

CSCM35 Big Data and Data Mining

This module introduces students to the fundamental topics of data mining, including data pre-processing techniques, applied probability and statistics, data mining algorithms (incl. associate rule, classification, clustering, outlier detection and probabilistic graphical model), and big data frameworks.
CSCM53 Computer System Concepts

This module gives an overview of some of the main principles underlying computers and computing from both a theoretical and an applied point of view. It includes a brief history of computers and software, an introduction to the representation of data and the basic components of a computer, the basic features of operating systems, file systems, computer networks, the world wide web, and some basic issues of computer security. A brief discussion on the use of formal methods in specifying computer systems is also given.

Key Grants and Projects

Forbidden Pi: TPM 2.0 Secure Computing Extension 2020

Dr Scott Yang

Telephone number

Email address

LinkedIn Account

Research Links

About

Areas Of Expertise

Career Highlights

Research Highlights

All Research

Deep Learning Methods for Language Generation and Its Applications in Economy and Healthcare (current)

Text-Based Person Re-identification for Robustness Research (current)

Ear Biometrics (current)

Developing methodologies for the efficient creation of data labels, tools to reduce human effort, and aid perception and understanding of big datasets in an underwater context, supporting rapid development and explainability of machine learning models. (current)

N/A. (current)

Enhancing the diagnostic performance of a bowel cancer blood test using advanced machine learning algorithms and the incorporation of information from the patient's medical record (current)

CSCM35 Big Data and Data Mining

CSCM53 Computer System Concepts

Forbidden Pi: TPM 2.0 Secure Computing Extension 2020