Shang-Ming Zhou

60 papers B 14C 1Journal 34Unranked 8
YearRankTypeTitle / Venue / Authors
2025 J jnl
AI-driven biomedical perspectives on mental fatigue in the post-COVID-19 Era: trends, research gaps, and future directions.
J. Big Data
Saba Parveen, Md Belal Bin Heyat, Umair Tariq, Faijan Akhtar, Hafiz Muhammad Zeeshan, Seth Christopher Yaw Appiah, Shang-Ming Zhou, Huang Lei
2025 — conf
Advancing AI Literacy in Medical Education: A Medical AI Competency Framework Development.
AIED (5)
Chang Cai, Jamie Andrew Duell, Daisy Minghui Chen, Weng Kin Ho, Bernett Teck Kwong Lee, Fang Li, Siyuan Liu, Olivia Ng, Vidya Sudarshan, Shang-Ming Zhou, Gaoxia Zhu, Xiuyi Fan
2025 — conf
Enhancing Diagnostic Accuracy of Drug-Resistant Tuberculosis on Chest X-Rays Using Data-Efficient Image Transformers.
BIOSTEC (2): HEALTHINF
Joan Jonathan Mnyambo, Amir Aly, Shang-Ming Zhou, Yinghui Wei, Stephen Mullin, Emmanuel C. Ifeachor
2025 J jnl
Unveiling novel bladder cancer associations from multicentred primary and secondary care electronic health records by machine learning: a case-control study.
J. Biomed. Informatics
Xu Wang, Andrea Preston, Jonathan Aning, Shang-Ming Zhou
2025 J jnl
ieGENES: A machine learning method for selecting differentially expressed genes in cancer studies.
J. Biomed. Informatics
Xiao-Lei Xia, Shang-Ming Zhou, Yunguang Liu, Na Lin, Ian M. Overton
2024 J jnl
The role of ontologies and knowledge in Explainable AI.
Semantic Web
Roberto Confalonieri, Oliver Kutz, Diego Calvanese, Jose Maria Alonso-Moral, Shang-Ming Zhou
2023 — conf
Explainable Artificial Intelligence and Mobile Health for Treating Eating Disorders in Young Adults with Autism Spectrum Disorder Based on the Theory of Change: A Mixed Method Protocol.
FICTA (2)
Omobolanle Omisade, Alexander E. Gegov, Shang-Ming Zhou, Alice Good, Catherine Tryfona, Sandeep Singh Sengar, Amie-Louise Prior, Bangli Liu, Taiwo Adedeji, Carrie M. Toptan
2022 J jnl
Automatically Generating Natural Language Descriptions of Images by a Deep Hierarchical Framework.
IEEE Trans. Cybern.
Lin Huo, Lin Bai, Shang-Ming Zhou
2021 — conf
A Comparison of Explanations Given by Explainable Artificial Intelligence Methods on Analysing Electronic Health Records.
BHI
Jamie Andrew Duell, Xiuyi Fan, Bruce Burnett, Gert Aarts, Shang-Ming Zhou
2021 B conf
ExMed: An AI Tool for Experimenting Explainable AI Techniques on Medical Data Analytics.
ICTAI
Marcin Kapcia, Hassan Eshkiki, Jamie Andrew Duell, Xiuyi Fan, Shang-Ming Zhou, Benjamin Mora
2021 J jnl
Towards a Shapley Value Graph Framework for Medical peer-influence.
CoRR
Jamie Andrew Duell, Monika Seisenberger, Gert Aarts, Shang-Ming Zhou, Xiuyi Fan
2021 J jnl
Type-1 OWA Operators in Aggregating Multiple Sources of Uncertain Information: Properties and Real-World Applications in Integrated Diagnosis.
IEEE Trans. Fuzzy Syst.
Shang-Ming Zhou, Francisco Chiclana, Robert I. John, Jonathan M. Garibaldi, Lin Huo
2019 J jnl
Covariate shift estimation based adaptive ensemble learning for handling non-stationarity in motor imagery related EEG-based brain-computer interface.
Neurocomputing
Haider Raza, Dheeraj Rathee, Shang-Ming Zhou, Hubert Cecotti, Girijesh Prasad
2018 J jnl
Covariate Shift Estimation based Adaptive Ensemble Learning for Handling Non-Stationarity in Motor Imagery related EEG-based Brain-Computer Interface.
CoRR
Haider Raza, Dheeraj Rathee, Shang-Ming Zhou, Hubert Cecotti, Girijesh Prasad
2017 J jnl
Light source detection for digital images in noisy scenes: a neural network approach.
Neural Comput. Appl.
David A. Elizondo, Shang-Ming Zhou, Charalambos Chrysostomou
2015 J jnl
Introduction: Advances in IoT research and applications.
Inf. Syst. Frontiers
Pan Wang, Ricardo Valerdi, Shang-Ming Zhou, Ling Li
2014 — conf
Conversational Interaction Recognition Based on Bodily and Facial Movement.
ICIAR (1)
Jingjing Deng, Xianghua Xie, Shang-Ming Zhou
2014 J jnl
Guest Editorial Special Section on IoT.
IEEE Trans. Ind. Informatics
Zu De Zhou, Ricardo Valerdi, Shang-Ming Zhou, Li Wang
2014 B conf
Mining textual data from primary healthcare records: Automatic identification of patient phenotype cohorts.
IJCNN
Shang-Ming Zhou, Muhammad A. Rahman, Mark D. Atkinson, Sinead Brophy
2014 J jnl
Type-1 OWA methodology to consensus reaching processes in multi-granular linguistic contexts.
Knowl. Based Syst.
Francisco Mata, Luis G. Pérez, Shang-Ming Zhou, Francisco Chiclana
2014 B conf
Using EEG artifacts for BCI applications.
IJCNN
Wanli Ma, Dat Tran, Trung Le, Hong Lin, Shang-Ming Zhou
2013 J jnl
Type-Reduction of General Type-2 Fuzzy Sets: The Type-1 OWA Approach.
Int. J. Intell. Syst.
Francisco Chiclana, Shang-Ming Zhou
2012 J jnl
Guest Editorial Special Section on Enterprise Systems.
IEEE Trans. Ind. Informatics
Zu De Zhou, Ricardo Valerdi, Shang-Ming Zhou
2012 J jnl
Guest EditorialIntegrated Healthcare Information Systems.
IEEE Trans. Inf. Technol. Biomed.
Ling Li, Ri-Li Ge, Shang-Ming Zhou, Ricardo Valerdi
2012 J jnl
Incorporation of expert variability into breast cancer treatment recommendation in designing clinical protocol guided fuzzy rule system models.
J. Biomed. Informatics
Jonathan M. Garibaldi, Shang-Ming Zhou, Xiao-Ying Wang, Robert Ivor John, Ian O. Ellis
2011 J jnl
Alpha-Level Aggregation: A Practical Approach to Type-1 OWA Operation for Aggregating Uncertain Information with Applications to Breast Cancer Treatments.
IEEE Trans. Knowl. Data Eng.
Shang-Ming Zhou, Francisco Chiclana, Robert Ivor John, Jonathan M. Garibaldi
2011 — ch.
Fuzzification of the OWA Operators for Aggregating Uncertain Information with Uncertain Weights.
Recent Developments in the Ordered Weighted Averaging Operators
Shang-Ming Zhou, Francisco Chiclana, Robert Ivor John, Jonathan M. Garibaldi
2011 — conf
The Type-1 OWA Operator and the Centroid of Type-2 Fuzzy Sets.
EUSFLAT Conf.
Francisco Chiclana, Shang-Ming Zhou
2010 J jnl
A novel Bayesian learning method for information aggregation in modular neural networks.
Expert Syst. Appl.
Pan Wang, Lida Xu, Shang-Ming Zhou, Zhun Fan, Youfeng Li, Shan Feng
2010 B conf
Construction of parsimonious Takagi-Sugeno fuzzy rule based model in characterising impacts of child health, socioeconomic deprivations on educational outcomes.
FUZZ-IEEE
Shang-Ming Zhou, Ronan A. Lyons, Mike B. Gravenor
2010 J jnl
On aggregating uncertain information by type-2 OWA operators for soft decision making.
Int. J. Intell. Syst.
Shang-Ming Zhou, Robert Ivor John, Francisco Chiclana, Jonathan M. Garibaldi
2010 B conf
SVM with entropy regularization and particle swarm optimization for identifying children's health and socioeconomic determinants of education attainments using linked datasets.
IJCNN
Shang-Ming Zhou, Ronan A. Lyons, Owen Bodger, Joanne C. Demmler, Mark D. Atkinson
2009 J jnl
Extracting Takagi-Sugeno Fuzzy Rules with Interpretable Submodels via Regularization of Linguistic Modifiers.
IEEE Trans. Knowl. Data Eng.
Shang-Ming Zhou, John Q. Gan
2009 J jnl
Fuzziness index driven fuzzy relaxation algorithm and applications to image processing.
Ann. Oper. Res.
Shang-Ming Zhou, John Q. Gan, Lida Xu, Robert Ivor John
2009 B conf
Methods of interpretation of a non-stationary fuzzy system for the treatment of breast cancer.
FUZZ-IEEE
Xiao-Ying Wang, Jonathan M. Garibaldi, Shang-Ming Zhou, Robert Ivor John
2009 J jnl
On Constructing Parsimonious Type-2 Fuzzy Logic Systems via Influential Rule Selection.
IEEE Trans. Fuzzy Syst.
Shang-Ming Zhou, Jonathan M. Garibaldi, Robert Ivor John, Francisco Chiclana
2009 B conf
Type-1 OWA operator based non-stationary fuzzy decision support systems for breast cancer treatments.
FUZZ-IEEE
Shang-Ming Zhou, Jonathan M. Garibaldi, Francisco Chiclana, Robert Ivor John, Xiao-Ying Wang
2008 J jnl
Classifying mental tasks based on features of higher-order statistics from EEG signals in brain-computer interface.
Inf. Sci.
Shang-Ming Zhou, John Q. Gan, Francisco Sepulveda
2008 B conf
Compact fuzzy rules induction and feature extraction using SVM with particle swarms for breast cancer treatments.
IEEE Congress on Evolutionary Computation
Shang-Ming Zhou, Robert Ivor John, Xiao-Ying Wang, Jonathan M. Garibaldi, Ian O. Ellis
2008 B conf
Inducing linguistic weights for type-1 OWA operators in soft decision making.
FUZZ-IEEE
Shang-Ming Zhou, Francisco Chiclana
2008 J jnl
Low-level interpretability and high-level interpretability: a unified view of data-driven interpretable fuzzy system modelling.
Fuzzy Sets Syst.
Shang-Ming Zhou, John Q. Gan
2008 — conf
Surface Reconstruction Techniques Using Neural Networks to Recover Noisy 3D Scenes.
ICANN (1)
David A. Elizondo, Shang-Ming Zhou, Charalambos Chrysostomou
2008 J jnl
Type-1 OWA operators for aggregating uncertain information with uncertain weights induced by type-2 linguistic quantifiers.
Fuzzy Sets Syst.
Shang-Ming Zhou, Francisco Chiclana, Robert Ivor John, Jonathan M. Garibaldi
2008 B conf
Type-2 OWA operators - aggregating type-2 fuzzy sets in soft decision making.
FUZZ-IEEE
Shang-Ming Zhou, Francisco Chiclana, Robert Ivor John, Jonathan M. Garibaldi
2007 B conf
A Novel Approach to Type-2 Fuzzy Addition.
FUZZ-IEEE
William Blewitt, Shang-Ming Zhou, Simon Coupland
2007 J jnl
Constructing L2-SVM-Based Fuzzy Classifiers in High-Dimensional Space With Automatic Model Selection and Fuzzy Rule Ranking.
IEEE Trans. Fuzzy Syst.
Shang-Ming Zhou, John Q. Gan
2007 J jnl
Interactive image enhancement by fuzzy relaxation.
Int. J. Autom. Comput.
Shang-Ming Zhou, John Q. Gan, Lida Xu, Robert I. John
2007 B conf
New Type-2 Rule Ranking Indices for Designing Parsimonious Interval Type-2 Fuzzy Logic Systems.
FUZZ-IEEE
Shang-Ming Zhou, Robert I. John, Francisco Chiclana, Jonathan M. Garibaldi
2006 — conf
A New Fuzzy Membership Function with Applications in Interpretability Improvement of Neurofuzzy Models.
ICIC (2)
John Q. Gan, Shang-Ming Zhou
2006 J jnl
A new fuzzy relaxation algorithm for image enhancement.
Int. J. Knowl. Based Intell. Eng. Syst.
Shang-Ming Zhou, John Q. Gan
2006 J jnl
Constructing accurate and parsimonious fuzzy models with distinguishable fuzzy sets based on an entropy measure.
Fuzzy Sets Syst.
Shang-Ming Zhou, John Q. Gan
2006 — —
Machine learning and intelligent data analysis for interpretable neuro-fuzzy system modelling.
Shang-Ming Zhou
2006 — ch.
Multiple Objective Learning for Constructing Interpretable Takagi-Sugeno Fuzzy Model.
Multi-Objective Machine Learning
Shang-Ming Zhou, John Q. Gan
2004 J jnl
An Unsupervised Kernel Based Fuzzy C-Means Clustering Algorithm With Kernel Normalisation.
Int. J. Comput. Intell. Appl.
Shang-Ming Zhou, John Q. Gan
2004 B conf
Improving the interpretability of Takagi-Sugeno fuzzy model by using linguistic modifiers and a multiple objective learning scheme.
IJCNN
Shang-Ming Zhou, John Q. Gan
2004 B conf
Interpretability improvement of input space partitioning by merging fuzzy sets based on an entropy measure.
FUZZ-IEEE
Shang-Ming Zhou, John Q. Gan
2004 C conf
Mercer Kernel, Fuzzy C-Means Algorithm, and Prototypes of Clusters.
IDEAL
Shang-Ming Zhou, John Q. Gan
2003 J jnl
A variational approach to intensity approximation for remote sensing images using dynamic neural networks.
Expert Syst. J. Knowl. Eng.
Shang-Ming Zhou, Hong-Xing Li, Lida Xu
2001 J jnl
A new type of recurrent fuzzy neural network for modeling dynamic systems.
Knowl. Based Syst.
Shang-Ming Zhou, Li Da Xu
1999 J jnl
Dynamic recurrent neural networks for a hybrid intelligent decision support system for the metallurgical industry.
Expert Syst. J. Knowl. Eng.
Shang-Ming Zhou, Li Da Xu