麻豆影院

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Professor Yingjie Yang

Job: Professor of Computational Intelligence

Faculty: Computing, Engineering and Media

School/department: School of Computer Science and Informatics

Research group(s): Centre for Computational Intelligence (CCI) and 麻豆影院 Interdisciplinary Group in Intelligent Transport Systems (DIGITS)

Address: 麻豆影院, The Gateway, Leicester, LE1 9BH, United Kingdom

T: +44 (0)116 257 7939

E: yyang@dmu.ac.uk

W: /cci

 

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Personal profile

Dr. Yingjie Yang was awarded his first PhD in Engineering from Northeastern University in 1994, and his second PhD in Computer Science in 2008. He has published more than 100 papers in international journals and conferences. He has been involved in more than 90 international conferences as a member of program committees and organised a number of international conferences and special sessions such as 2015 IEEE International Conference on Grey Systems and Intelligent Service, IEEE SMC 2014 and IEEE WCCI2008. As a senior member of IEEE, Dr. Yang serves as a co-chair of the Technical Committee on Grey Systems, IEEE Systems, Man and Cybernetics Society and the vice chair for the task force for competition in IEEE Fuzzy Systems Technical Committee. He is serving also as an associate editor for 5 international academic journals, including IEEE Transactions on Cybernetics. He had been invited to give plenary speech at a number of international confertences, such as the 2013, 2011 and 2009 IEEE Conferences on Grey Systems and Intelligent Services and the 2001 international conference on Airport Management.

Research group affiliations

Centre for Computational Intelligence (CCI) 

麻豆影院 Interdisciplinary Group in Intelligent Transport Systems (DIGITS)

Publications and outputs


  • dc.title: Combining pathological and cognitive tests scores: A novel data analytics process to improve dementia prediction models dc.contributor.author: Alshehhi, Talib; Ayesh, Aladdin; Yang, Yingjie; Chen, Feng dc.description.abstract: The term 鈥榙ementia鈥� covers a range of progressive brain diseases from which many elderly people suffer. Traditional cognitive and pathological tests are currently used to detect dementia, however, applications using Artificial Intelligence (AI) methods have recently shown improved results from improved detection accuracy and efficiency. This research paper investigates the efficacy of one type of data analytics called supervised learning to detect Alzheimer鈥檚 disease (AD) - a common dementia condition. The aim is to evaluate cognitive tests and common biological markers (biomarkers) such as cerebrospinal fluid (CSF) to develop predictive classification systems for dementia detection. A data analytics process has been proposed, implemented, and tested against real data obtained from the Alzheimer鈥檚 Disease Neuroimaging Initiative (ADNI) repository. The models showed good power in predicting AD levels, notably from specified cognitive tests鈥� scores and tauopathy related features. dc.description: The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.

  • dc.title: Assessing numerical error bound of classic grey prediction model: An application to the transport performance of China鈥檚 civil aviation industry dc.contributor.author: Chong Li; Liu, S. F.; Yang, Yingjie dc.description.abstract: Although grey system models have been developed and applied successfully to various socio-economic and engineering problems for several decades, the algorithm stability problem of these models has never been investigated. This paper introduces a method to estimate the error bounds of algorithms used in the classic grey prediction model. To reduce the complex calculation in finding the model error bounds, equivalent but simple estimation models are presented. An algebraic optimization technique for the solution processes of the proposed mathematic models is then provided. The backward error bound model is then extended to the other two commonly used linear regression forecasting models and the similarities and differences between them are explored. Finally, the proposed method is applied to the prediction of four key transportation performance indicators for China鈥檚 civil aviation industry. The case study considers not only the traditional accuracy criteria, but also the stability of prediction results in model optimization. The robustness of prediction methods with different types of noise interference and weighting preference scenarios are tested. It is found that model solving methods influence the error bounds, but smaller prediction errors do not necessarily guarantee better backward stability or applicability of the prediction model. Methods described in this paper make it possible to measure numerically the accuracy of any alleged solution of the classic grey prediction model and other linear regression models and provide an objective, quantitative approach to evaluating the effectiveness of information processing in different sample disturbances situations. dc.description: The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.

  • dc.title: Single batch-processing machine scheduling problem with interval grey processing time dc.contributor.author: Xie, Naiming; Yihang Qin; Nanlei Chen; Yang, Yingjie dc.description.abstract: This paper investigates a single batch-processing machine scheduling problem with uncertain processing time. The uncertain processing time is characterized by interval grey number. A grey mixed integer linear programming model is established to formulate this uncertain scheduling problem to minimize the makespan. To solve this problem, a genetic algorithm with targeted population generation and neighbourhood search is designed. The results of experiments demonstrate that the proposed algorithm has excellent performance in both efficiency and stability. The resulting scheduling scheme can be shown through the Gantt chart with interval grey processing time, offering a novel approach for visualizing scheduling schemes with uncertain processing time. dc.description: The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.

  • dc.title: Research on tomato disease image recognition method based on DeiT dc.contributor.author: Sun, Changxia; Song, Zhengdao; Li, Yong; Liu, Qian; Si, Haiping; Yang, Yingjie; Cao, Qing dc.description.abstract: Tomatoes, globally cultivated and economically significant, play an essential role in both commerce and diet. However, the frequent occurrence of diseases severely affects both yield and quality, posing substantial challenges to agricultural production worldwide. In China, where tomato cultivation is carried out on a large scale, disease prevention and identification are increasingly critical for enhancing yield, ensuring food safety, and advancing sustainable agricultural practices. As agricultural production scales and the demand for efficient methodologies grows, traditional disease recognition methods no longer meet current needs. The agricultural sector's move towards more modern and scalable production methods necessitates more effective and precise disease recognition technologies to support swift decision-making and timely preventive actions. To address these challenges, this paper proposes a novel tomato disease recognition method that integrates the data-efficient image transformers (DeiT) model with strategies like exponential moving average (EMA) and self-distillation, named EMA-DeiT. By leveraging deep learning technologies, this method significantly improves the accuracy of disease recognition. The enhanced EMA-DeiT model demonstrated exemplary performance, achieving a 99.6鈥�% accuracy rate in identifying ten types of tomato leaf diseases within the PlantVillage public dataset and 98.2鈥�% on the Dataset of Tomato Leaves, which encompasses six disease types. In generalization tests, it achieved 97.1鈥�% accuracy on the PlantDoc dataset and 97.6鈥�% on the Tomato-Village dataset. Utilizing the improved DeiT model, a comprehensive tomato disease recognition system was developed, featuring modules for image collection, disease detection, and information display. This system facilitates an integrated process from image collection to intelligent disease analysis, enabling agricultural workers to promptly understand and respond to disease occurrences. This system holds significant practical value for implementing precision agriculture and enhancing the efficiency of agricultural production. dc.description: The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.

  • dc.title: Enhancing Clinical Trial Outcome Prediction with Artificial Intelligence: A Systematic Review dc.contributor.author: Qian, Long; Lu, Xin; Haris, Parvez; Zhu, Jianyong; Li, Shuo; Yang, Yingjie dc.description.abstract: Clinical trials are pivotal in drug development yet fraught with uncertainties and resource-intensive demands. The application of AI models to forecast trial outcomes could mitigate failures and expedite the drug discovery process. This review synthesizes AI methodologies impacting clinical trial outcomes, focusing on clinical text embedding, trial multimodal learning, and prediction techniques, while addressing practical challenges and opportunities. dc.description: open access article

  • dc.title: A Novel Fuzzy Logic Framework for Model Reliability Evaluation in Permeability Prediction using GPR dc.contributor.author: Lawal, Ahmad; Yang, Yingjie; Baisa, Nathanael L.; He, Hongmei dc.description.abstract: Permeability is a critical parameter in reservoir engineering and hydrocarbon extraction, yet its prediction remains challenging due to inherent uncertainties in subsurface data. While Gaussian Process Regression (GPR) has proven effective in predicting permeability with associated uncertainties, it generates multiple metrics that are difficult to interpret, particularly in high-stakes environments. This study proposes a novel approach using fuzzy logic to compute a single, comprehensive metric that accounts for model reliability. Our method incorporates human input and reasoning into the modelling process, enhancing the model鈥檚 interpretability and its ability to handle uncertainty. Additionally, we introduce a new visualization technique to simplify the understanding of fuzzy logic outputs for non-technical stakeholders. The proposed methodology demonstrates that GPR achieves a higher reliability level (0.89) compared to traditional machine learning counterparts, which are typically neutral to uncertainties. By providing a comprehensive, transparent, and easily interpretable measure of model reliability, this approach significantly aids in making more informed and responsible decisions in reservoir management. Our framework represents a crucial step towards improving the practical application of advanced machine learning techniques in the oil and gas industry, potentially extending to other fields where uncertainty quantification is vital.

  • dc.title: A Hybrid Trust Service Architecture for Cloud Computing dc.contributor.author: Yang, Zhongxue; Qin, Xiaolin; Yang, Yingjie; Yagink, Tarjana dc.description.abstract: Trust service is a very important issue in cloud computing, and a cloud user needs a trust mechanism in selecting a reliable cloud service provider. Many trust technologies such as SLA, cloud audit, self-assessment questionnaire, accreditation, and so on, are proposed by some research organizations like CSA. However, all of these just provide a initial trust and have many limitations. A hybrid trust service architecture for cloud computing is proposed in this paper, which primary includes two trust modules named the initial trust module and trust-aided evaluation module. After an initial and a basic trust is established in initial trust module, the trust-aided evaluation module will be used to verify the service provider dependable further. The approaches of D-S evidence theory and Dirichlet distribution PDF are introduced to compute the trust degree value as well. The hybrid service architecture can obtain more effects on selecting the reliable service provider and promote the computing efficiency greatly.

  • dc.title: A Novel Framework for Reservoir Permeability Prediction using GPR with Grey Relational Grades and Uncertainty Quantification dc.contributor.author: Lawal, Ahmad; Yang, Yingjie; Baisa, Nathanael L.; He, Hongmei dc.description.abstract: Reservoir permeability prediction is crucial for hydrocarbon exploration and production. Traditional methods have limitations, and Gaussian Process Regression (GPR) offers a powerful alternative. However, GPR can be sensitive to kernel parameters. This paper proposes a novel framework, GPR with Grey Relational Lengthscale Adaptation (GRLA-GPR), that incorporates Grey Relational Grades (GRG) from NMR log data into GPR lengthscale updates to improve permeability prediction with a focus on uncertainty quantification. The framework utilizes a Radial Basis Function (RBF) and Matern kernels' GPR model and calculates GRG to capture relationships between NMR data sequences. The calculated GRG values are then used to update the GPR lengthscale during training. A validation strategy is employed to evaluate the performance. The effectiveness of the framework is assessed using accuracy metrics (mean absolute error, mean squared error and R2) and uncertainty quantification metrics (variance and prediction interval normalized average width). The results are compared to a baseline GPR model without GRG-based updates. The proposed framework achieved a better performance in terms of accuracy and uncertainty quantification, providing more reliable permeability estimates for informed decision-making in reservoir characterization.

  • dc.title: MFFGD: An adaptive Caputo fractional-order gradient algorithm for DNN dc.contributor.author: Huang, Zhuo; Mao, Shuhua; Yang, Yingjie dc.description.abstract: As a primary optimization method for neural networks, gradient descent algorithm has received significant attention in the recent development of deep neural networks. However, current gradient descent algorithms still suffer from drawbacks such as an excess of hyperparameters, getting stuck in local optima, and poor generalization. This paper introduces a novel Caputo fractional-order gradient descent (MFFGD) algorithm to address these limitations. It provides fractional-order gradient derivation and error analysis for different activation functions and loss functions within the network, simplifying the computation of traditional fractional order gradients. Additionally, by introducing a memory factor to record past gradient variations, MFFGD achieves adaptive adjustment capabilities. Comparative experiments were conducted on multiple sets of datasets with different modalities, and the results, along with theoretical analysis, demonstrate the superiority of MFFGD over other optimizers. dc.description: The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.

  • dc.title: Forecasting the output of high-tech industry in China: A novel nonlinear grey time-delay multivariable model with variable lag parameters dc.contributor.author: Zhou, Huimin; Yang, Yingjie; Geng, Shuaishuai dc.description.abstract: Under the rapidly developing economy in China, accurate forecasting holds vital significance for policymaking and operational planning within the high-tech industry. However, the influencing factors affecting the output, accompanied by the time-delay effect, could be nonlinear, and uncertain. Thereby, this paper proposes a new nonlinear grey multivariable model with time-varying lag parameters. To be specific, the newly designed time delay function and power exponent are introduced, which can significantly enhance the adaptability and flexibility of the proposed method. The Grey Wolf Optimization algorithm is utilized to calculate the dynamic time lag parameters and power exponent to improve the prediction reliability. Furthermore, this new approach is applied to predict the high-tech industry鈥檚 output in China, Shanghai Municipality, and the Eastern Region, with due consideration given to the time-delay effect between input factors and outputs. To assess its efficacy, some leading models are selected for comparison to the proposed model. Furthermore, the utilization of Monte-Carlo simulation, the Probability Density Analysis, and the simulations are used to demonstrate the robustness and stability of this new method. The findings show that the proposed model is a feasible and applicable approach for prediction, exhibiting outstanding accuracy. dc.description: The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.

Key research outputs

  • R-Fuzzy sets: a novel combination of fuzzy sets with rough sets with capability to represent some situations difficult with other extensions;
  • Grey sets: a formal formulation of the concept of grey sets and its operations;
  • Relative Strength of Effect: a factor analysis method based on trained neural networks;
  • Application of neural networks in overlay operation of GIS
  • Airport noise simulation using neural networks

Research interests/expertise

Dr. Yang’s research interests are mainly with uncertainty models and their applications. His theoretical work involves fuzzy sets, rough sets, grey systems and neural networks. In applications, his interests are transportation planning, environment evaluation and civil engineering simulation and analysis.

Areas of teaching

  • Databases
  • Data Warehousing
  • AI programming

Qualifications

  • PhD in Engineering (1994 from Northeastern University, China)
  • PhD in Computer Science (2008 from Loughborough University, UK)

Courses taught

  • IMAT5167
  • IMAT5118
  • IMAT5103
  • IMAT2427
  • PHAR5350

Honours and awards

Best Paper Award, the 2013 IEEE Conference on Computational Intelligenceand Computing Research.

Membership of external committees

  • Co-chair of the Technical Committee on Grey Systems of IEEE Systems, Man,and Cybernetics Society, 2012 -- present
  • Vice-chair of the Task Force on Competitions for Fuzzy Systems Technical Committeeof IEEE Computational Intelligence Society, 2011 -- present
  • PC members for over 90 international academic conferences

Membership of professional associations and societies

  • Senior Member of IEEE, 2013 -- present
  • Member of IEEE, Mar 2007 -- 2013
  • Member of the Rail Research UK Association, May 2013 -- present

Current research students

First supervisor for:

  • Manal Alghieth
  • Mohammad Al Azawi
  • Arjab Khuman
  • Nguyen Thi Mai Phuong
  • Tarjana Yagnik

Externally funded research grants information

    • "International Network on Grey Systems and its Applications", Leverhulme Trust, PI, £124997, 2015--2018.

    • "Grey Systems and Its Application to Data Mining and Decision Support", EU FP7 Marie Curie International IncomingFellowship, PI, €309235, 2015--2016.

    • "Modeling Conditions, Mechanism and Characters of Grey Prediction Model GM(1,1)", Leverhulme Trust InternationalVisiting Fellowship, PI, £25500, 2013--2014.

    • "Grey Systems and Computational Intelligence", Royal Society, PI, £12000, 2011-- 2013.

    • "ITRAQ: Integrated Traffic Management and Air Quality Control Using Space Services", Europe Space Agency, CI, €97834, 2011--2012.

    • "Conference grant", Royal Academy of Engineering, PI, £500, Oct 2007.

Internally funded research project information

  • "Project application on Grey Systems and Uncertainty", 麻豆影院 Research Leave scheme, PI, £7104, 2012--2013.

  • "Initial preparation for EU research network on grey systems", 麻豆影院 RIF Fund, PI, £7000, 2011--2012.

  • "Emerging uncertainty models and their applications", 麻豆影院 PhD scholarship, PI, £55080, 2012--2016.

  • "Conference grant", 麻豆影院 RITI Fund, PI, £1500, Jun 2009.

  • "Conference grant", 麻豆影院 RITI Fund, PI, £1500, Jun 2008.

Professional esteem indicators

Editorial board:

  • Associate Editor of IEEE Transaction on Cybernetics (Institute of Electrical and Electronics Engineers) ISSN: 1083-4419
  • Associate Editor of Scientific World Journal (Hindawi Publishing Corporation) ISSN: 2356-6140
  • Associate Editor of Journal of Intelligent and Fuzzy Systems (IOS Press) ISSN: 1064-1246
  • Assocaite Editor of Journal of Grey Systems (Research Information Ltd) ISSN: 0957-3720
  • Associated Editor of Grey Systems: Theory and Applications (Emerald) ISSN: 2043-9377

Plenary talks and academic seminars

  • Keynote speaker at the 2013 IEEE International Conference on Grey Systems and Intelligent Services, Macau, 2013
  • Seminar on grey numbers at Nanjing University of Aeronautics and Astronautics, Nanjing, 2012
  • Keynote speaker at the 2011 IEEE International Conference on Grey Systems and Intelligent Services, Nanjing,2011
  • Seminar on grey numbers at Nanjing University of Aeronautics and Astronautics, Nanjing, 2011
  • Seminar series on computational intelligence at Nanjing University of Aeronautics and Astronautics, full financialsupport from Nanjing University of Aeronautics and Astronautics, Nanjing, 2010
  • Keynote speaker at the 2009 IEEE International Conference on Grey Systems and Intelligent Services, Nanjing,2009
  • Seminar on grey systems at University of Hull, 2008
  • Keynote speaker at the Airport Environmental Management Workshop in Singapore, full financial support fromSingapore Aviation Academy (organisor), Singapore, 2001

Conference management

  • Chair of the Program Committee for the 2015 IEEE International Conference on Grey Systems and Intelligent Services,Leicester, 2015
  • Chair of the Program Committee for the 2015 International Conference on Advanced Computational Intelligence,Wuyi, 2015
  • Chair of the Program Committee for the 2013 IEEE International Conference on Grey Systems and Intelligent Services,Macau, 2013
  • Co-chair of the special session on grey systems at the 2014 IEEE International Conference on Systems, Man and Cybernetics, San Diego, 2014
  • Co-chair of the special session on grey systems at the 2012 IEEE International Conference on Systems, Man and Cybernetics, Seoul, 2012
  • Co-chair of the special session on grey systems at the 2011 IEEE International Conference on Systems, Man and Cybernetics, Anchorage, 2011
  • Co-chair of the Program Committee for the 2011 IEEE International Conference on Grey Systems and IntelligentServices, Nanjing, 2011
  • Co-chair of the Program Committee for the 2009 IEEE International Conference on Grey Systems and Intelligent Services, Nanjing, 2009
  • Session chair for 3 regular sessions at the 2008 IEEE World Congress of Computational Intelligence, Hong Kong,2008
  • Co-chair of the special session on grey systems at the 2008 IEEE World Congress of Computational Intelligence,Hong Kong, 2008
  • Member of the organising committee of the 2007 IEEE International Conference on Grey Systems and Intelligent Services, Nanjing, 2007