Professor Francisco Chiclana

Job: Professor of Computational Intelligence and Decision Making

Faculty: Computing, Engineering and Media

School/department: School of Computer Science and Informatics

Research group(s): Center for Computational Intelligence (CCI)

Address: 制服无码, The Gateway, Leicester, LE1 9BH UK

T: +44 (0)116 207 8413

E: chiclana@dmu.ac.uk

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Personal profile

Professor Francisco Chiclana received the B.Sc. and Ph.D. degrees in Mathematics, both from the University of Granada (Spain) in 1989 and 2000, respectively. He is currently a Professor of Computational Intelligence and Decision Making, and founder of DIGITS - 制服无码 Interdisciplinary Group in Intelligent Transport Systems, Faculty of Technology, 制服无码 (Leicester, UK).

Professor Francisco Chiclana was the Coordinator of DMU submission for REF 2014 UOA 11: Computer Science and Informatics.

Professor Chiclana has been Deputy Course Leader of the MScs in Computing, Information Technology, and Information Systems Management; Programme Tutor Years 1 and 2 of the BSc/HND/FD Business Information Technology. In 2013, Professor Chiclana co-developed the Doctoral Training Programme (DTP) in Intelligent Systems (IS) that he presently co-leads. Currently, he is Course Leader of BSc/MCOMP in Intelligent Systems (IS) and of MSc IS/ IS & Robotics (ISR).

Research group affiliations

CCI -

Publications and outputs

dc.title: Multiobjective Optimization-Based Subjective Probability Distribution Aggregation: A Survey dc.contributor.author: Chen, Zhen-Song; Wang, Han; Ma, Zheng; Yang, Yi; Zhu, Zhengze; Chiclana, Francisco; Pedrycz, Witold; Skibniewski, Miros艂aw J. dc.description.abstract: Subjective probability distribution aggregation (SPDA) refers to the process of combining multiple expert judgments into a single representative distribution and has become essential for decision-making under deep uncertainty. Despite its importance, a systematic review of multiobjective optimization-driven approaches to SPDA remains lacking. To address this gap, we trace the paradigm evolution from classical axiomatic and Bayesian approaches to contemporary optimization-driven frameworks, revealing a progression from single-objective consensus maximization through biobjective consensus-confidence models to comprehensive multiobjective formulations incorporating fairness and social welfare. We conduct a systematic literature review and establish a unified taxonomy that classifies methods by structural objectives and behavioral tradeoffs. Bibliometric analysis reveals a marked shift toward socially informed aggregation, with fairness concern and group dynamics emerging as dominant themes. We systematically examine applications across risk engineering, economics and finance, climate science, and policy planning, demonstrating that method selection must align with domain-specific uncertainty structures. In addition, key research challenges are identified, and a forward-looking roadmap is outlined to guide future investigations. This synthesis consolidates existing advances and provides both theoretical foundations and practical guidance for developing next-generation SPDA methods that balance statistical performance with social acceptability in complex decision environments. 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 multivariate time series forecasting for container reloading operations: an improved adaptive multi-granularity cascade forest model integrating dc.contributor.author: Guo, Jingwei; Wang, Yimin; Chen, Zhen-Song; Chiclana, Francisco; Pedrycz, Witold dc.description.abstract: Despite advancements in multivariate time series forecasting, critical challenges persist in operational applications like railway port container reloading. Key limitations include inadequate raw data utilization and, fundamentally, an inability to model the intrinsic multi-granularity temporal patterns of logistics operations, which span from equipment cycles to weekly planning rhythms. To address these issues, this study proposes a novel Improved Multivariate Adaptive Multi-Granularity Cascade Forest Model (IM_MAGCFM). Its design is theoretically grounded to bridge specific gaps: (1) multi-granularity scanning aligned with operational cycles for meaningful time series decomposition; (2) repurposing Gradient Boosting Decision Trees as a feature derivation engine to capture complex interactions among factors like cargo type and weather; and (3) integrating XGBoost for feature selection to optimize the high-dimensional feature space, ensuring robustness and efficiency. Empirical analysis with Alashankou railway port data shows IM_MAGCFM significantly outperforms baselines, with average reductions of 57.83% in MSE, 36.08% in RMSE, 30.94% in MAE, and 67.86% in MAPE. Validation on an independent Malaszewicze port dataset confirms its generalizability and robustness across operational environments. This research contributes a domain-aware, theoretically coherent framework to multivariate forecasting, demonstrating how logistics insights can drive integrated machine learning design for enhanced predictive accuracy and reliable operational decision-making in international rail logistics. 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: Targeting the right person: An optimization-driven framework for optimal persuader selection in social networks with opinion evolution dc.contributor.author: Liang, Qian; Yu, Wenyu; Zhang, Zhen; Chiclana, Francisco dc.description.abstract: Optimizing influence campaigns in social networks under limited resources constitutes a fundamental managerial challenge. Yet existing opinion dynamics and influence optimization models often oversimplify decision-making by neglecting user indecision and emphasizing internal consensus formation rather than externally guided persuasion. To bridge this gap, this paper develops a comprehensive framework for designing optimal, budget-constrained influence campaigns. We first propose a novel state-dependent opinion evolution (SDOE) model that explicitly captures individual uncertainty through three distinct decision states: 鈥渁cceptance,鈥� 鈥渋ndecision,鈥� and 鈥渞ejection.鈥� This formulation provides a more realistic representation of how opinions translate into observable decisions in social networks. Building on the SODE model, we formulate two optimization problems that strategically select persuaders to maximize either cumulative adoption or cumulative public awareness over a given time horizon. Both problems are initially posed as mixed-integer nonlinear programming (MINLP) models. A key methodological contribution lies in reformulating these intractable MINLPs into equivalent mixed-integer linear programs, enabling the computation of globally optimal solutions using standard solvers. The proposed approach is validated through extensive computational experiments, which compare the SDOE model against the social network DeGroot model and benchmark the resulting persuasion strategies against six widely used seeding heuristics. Our framework offers both theoretical and practical value by equipping decision-makers with a rigorous implementable tool for designing effective influence strategies in complex social network environments. 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: Altruism鈥揊airness Driven Optimization for Group Decision-Making Under Uncertainty dc.contributor.author: Wang, Han; Ma, Zheng; Chen, Zhen-Song; Wang, Zengqiang; Chiclana, Francisco dc.description.abstract: The significance of group decision-making (GDM) has grown substantially in public affairs and resource allocation. However, existing research predominantly emphasizes consensus analysis, often neglecting the social preferences exhibited by decision-makers in real-world scenarios. To bridge this gap, this article introduces a multiobjective optimization-based GDM model that incorporates the social preferences of decision-makers. First, a decision-maker opinion representation model is developed using probability density functions (PDFs), integrating altruism and fairness utility functions to comprehensively capture the preferences of diverse decision-makers. Second, by introducing altruism鈥揻airness utility as a third optimization objective, the traditional bi-objective model is extended to balance consensus, confidence, and altruism鈥揻airness, aiming to achieve more robust decision outcomes. Finally, a case study on the high-speed rail (HSR) line between Chengdu and Chongqing is conducted to preliminarily validate the model鈥檚 effectiveness. Passenger evaluations of the importance of various service attributes were collected and analyzed using the proposed multiobjective optimization-based collective opinion generation model. The results indicate that while the inclusion of the altruism鈥揻airness objective slightly reduces consensus on certain indicators, it significantly enhances overall fairness. This demonstrates that incorporating fairness principles into practical management can lead to more balanced and stable decision-making. This study offers novel insights into integrating altruistic preferences and fairness principles into GDM and lays a foundation for broader applications in future research. 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: Brain compensation mechanisms of large language models in clinical decision-making in acupuncture: A fusion study using fNIRS and eye-tracking dc.contributor.author: Lin, Wei; Yin, Tao; Chen, Zhen-Song; Zeng, Fang; Chiclana, Francisco; Pedrycz, Witold dc.description.abstract: Reconstructing cognitive processes during medical decision-making tasks from multimodal neuroimaging data is a significant challenge for both artificial intelligence and clinical research. Unlike prior work that relies on sequential pipelines, which are prone to losing domain-specific semantic relationships and thus to lower accuracy, higher cognitive load, and diminished interpretability in specialized domains like acupuncture, this study presents the Knowledge Graph-Enhanced Neurocognitive Framework. Grounded in the integrative decision-making mechanisms of acupuncture, this framework combines Functional Near-Infrared Spectroscopy (fNIRS) and eye-tracking signals with canonical literature and clinical knowledge. This multimodal approach preserves fine-grained semantics while substantially reducing cognitive burden and enhancing model transparency, offering a new technical route for Artificial Intelligence (AI) assisted acupuncture decisions. The framework integrates hierarchical neural features extracted from fNIRS and eye-tracking with treatment patterns from the medical literature using a multi-feature fusion approach. The experiment involved randomly assigning 60 participants to either an AI-assisted or a non-AI-assisted group. The AI group used a Large Language Models (LLMs) to assist with acupoint compatibility prescriptions based on medical records, while the non-AI group performed manual selection without LLMs support. Both groups were assessed using fNIRS and eye-tracking during acupoint compatibility tasks. The framework achieved 72 % accuracy in treatment compatibility tasks, fNIRS data indicated distinct cortical activation patterns, with the AI group demonstrating less activation in regions associated with cognitive load. Eye-tracking data revealed that the AI group exhibited shorter fixation durations and fewer attentional adjustments, suggesting enhanced cognitive efficiency. Our approach aims to enhance the application of brain-inspired AI in clinical settings, specifically targeting the intricate and dynamic nature of acupuncture treatment decisions. 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: Utilizing Hierarchical Efficacy Regions of the Human Brain for Treatment Prediction through Information Fusion dc.contributor.author: Lin, Wei; Yin, Tao; Chen, Zhen-Song; Zeng, Fang; Ding, Weiping; Chiclana, Francisco; Pedrycz, Witold dc.description.abstract: Brain network dysfunction is an important feature of the disease. Acupuncture鈥檚 therapeutic effects are achieved through dynamic interregional brain interactions and the fusion of multisource neural information. In this study, we enrolled 177 patients with Functional Dyspepsia (FD) who received 20 sessions of acupuncture treatment and underwent functional magnetic resonance imaging before and after treatment. Subsequently, we constructed the Hierarchical Efficacy Network (HEffNet) to predict the improvement value of the Nepean Dyspepsia Symptom Index by iteratively identifying the core trunk (efficacy region) from the Brain Tree to form a hierarchical information integration path. The model uses a Kalman filter and an improved Gated Graph Neural Network (GGNN) to capture the topological connectivity of brain regions. Experimental results showed that the features of hierarchical efficacy regions were significantly related to therapeutic efficacy. Patients who experienced significant symptom improvement formed a multi-level stable network across the frontal lobe, limbic lobe, insula, and subcortical nuclei, confirming that acupuncture works by optimizing the information fusion between brain regions. The HEffNet model outperformed baseline models in the prediction task. Ablation experiments verified the effectiveness of the GGNN and residual modules. The therapeutic efficacy of acupuncture for FD stems from the hierarchical information fusion of multiple brain regions. HEffNet quantifies this mechanism to achieve precise efficacy prediction and provides a new paradigm for studying acupuncture鈥檚 central mechanisms. 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: Interpretable Train-Level Entry Flow Prediction Using GMM-Based Features and Ensemble Learning dc.contributor.author: Liu, Hongyi; Shuai, Bin; Chen, Zhen-Song; Wang, Zengqiang; Chiclana, Francisco dc.description.abstract: Despite the rapidly increasing number of railway passengers, the existing station-level entry passenger flow forecasting studies typically capture only the overall trends, hindering an accurate estimation of differences in entry passenger flow between different train services. This limitation in traditional models affects their ability to account for fluctuations in passenger flow caused by passenger heterogeneity, consequently limiting the effectiveness of station management and service design. Therefore, there is an urgent need for a refined method for train-level entry passenger flow (TLPF) forecasting to improve operational efficiency and optimize station management. This study first addresses the shortcomings in the existing TLPF characterization methods and proposes a fitting approach based on the Gaussian mixture model (GMM). Using GMM, the multi-modal and heterogeneous features of the entry passenger flow data can be captured more efficiently, especially in cases where multiple train-service patterns coexist. Based on this fitting method, the study introduces a two-stage forecasting framework, integrating features related to passenger heterogeneity, such as age, travel frequency, and historical waiting time. In the first stage, a machine learning model is used to estimate the waiting time of individual passengers. In the second stage, the predicted results are combined with other relevant factors to forecast the TLPF. The framework was validated using various machine learning algorithms and ensemble learning strategies. Compared to traditional methods, GMM provided a 10% improvement in fitting and prediction accuracy. Moreover, compared to single forecasting frameworks, the two-stage prediction framework improved the mean square error by 70%, which further improved by 30% with the addition of ensemble learning, Using Shapley additive explanation analysis, the study reveals that passenger waiting behavior is influenced by factors such as age and missed train costs, whereas TLPF is significantly impacted by passenger-group differences and train-departure times. This study not only improves prediction accuracy but also provides an interpretable framework, supporting the construction of intelligent railway stations and the optimization of management strategies. The findings have broad application implications for incorporating passenger heterogeneity into transportation models and open new directions for future entry passenger flow forecasting research. 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: A multi-agent LLM framework for severity classification of complaint events: Probabilistic reasoning with scene uncertainty dc.contributor.author: Yang, Yi; Wang, Dong-Lin; Chiclana, Francisco; Chen, Zhen-Song dc.description.abstract: In response to the growing demand for intelligent severity classification of automotive complaint events, this study proposes a large language models-based multi-agent (LLM-MA) system. First, using recalled text data structured in 鈥渄efect description-possible consequence鈥� pairs, a network community detection algorithm is applied to construct a Chain-of-Thought (CoT) set that comprehensively captures typical automotive components and contextual scenarios. Second, a collaborative multi-agent architecture is designed, consisting of: a Driving Scenario Ambiguity Analysis Agent, which extracts scene-level uncertain features through semantic enhancement; Severity Classification Agents, which employ a dynamic temperature parameter strategy combined with generalized Beta distribution fitting to quantitatively model classification uncertainty; and an Aggregation Agent, which constructs a multi-objective optimization model incorporating three critical dimensions 鈥� CoT consistency, group consensus, and confidence level 鈥� to intelligently integrate classification results. The resulting three-tier discrimination framework encompasses scene perception, probabilistic reasoning, and dynamic aggregation, offering interpretable and robust decision support for the management and early warning of automotive safety complaints. Experimental results show that the proposed LLM-MA achieves a severity classification accuracy exceeding 0.93. This system helps establish a collaborative mechanism that supports consumer protection, corporate quality improvement, and evidence-based regulatory decision-making. 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: Knowledge Graph Clustering and Dual Strategies for Adaptive Mechanism in Large-Scale Group Decision Making dc.contributor.author: Meng, Yanli; Chiclana, Francisco; Gong, Ping; Wang, Sha; Wang, Li; Yang, Haijun dc.description.abstract: Clustering and feedback mechanisms are important methods in the large-scale group consensus framework. This article proposes a novel clustering method rooted in objective information similarity that uses knowledge graph technology along with the development of a dual-strategy-driven comprehensive adaptive feedback mechanism (CAFM). An expert knowledge graph is constructed using objective textual information to extract relationships that are converted into semantic vectors using Knowledge Graph Embedding (KGE). This approach can quantify experts鈥� similarities and their subsequent clustering. A reliability degree index for subgroups is defined to quantify willingness to adjust preferences in conjunction with their sizes and is proposed to derive subgroup weighting values. Building on these foundations, the CAFM is developed to guide opinion interactions. Feedback coefficients are generated using subgroup reliability and similarity values to determine adjustment willingness levels. Two optimization functions are constructed to address diverse strategic needs: a single-objective function that minimizes adjustments and a bi-objective function that simultaneously minimizes adjustments and maximizes satisfaction. Finally, the proposed model鈥檚 applicability and effectiveness are validated via a case study involving the selection of small and medium-sized enterprises (SMEs). Results underscore the potential of the model to improve consensus efficiency and decision making quality in large-scale group decision contexts. 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: A novel consensus feedback mechanism for multi-agents based on cooperation willingness in group decision making dc.contributor.author: Cao, Mingshuo; Cai, Wenjie; Chiclana, Francisco; Wu, Jian dc.description.abstract: In group decision making (GDM) processes, heterogeneous cooperation willingness among agents critically impacts consensus efficiency and associated costs. Usually, it often overlook individual cooperative tendencies for traditional feedback mechanisms, resulting in excessive adjustment costs or risks of collaborative breakdown. To address this limitation, this article introduces a novel consensus feedback mechanism for group decision making anchored by Cooperation Willingness Index (CWI). Firstly, we develop a multidimensional CWI model integrating three critical dimensions: MBTI personality traits, node centrality weights within social networks, and situational penalty coefficients derived from decision-making contexts. Agents are systematically classified into high, medium, and low categories through a hybrid methodology employing Sigmoid-based normalization and K-means clustering. Secondly, differentiated feedback strategies are implemented building on this classification: High CWI agents engage in a multilateral consensus interaction mechanism to accelerate convergence toward group consensus; Medium CWI agents participate in bilateral consensus interaction mechanism with peers demonstrating similar consensus levels to foster incremental consensus; Low CWI agents undergo dynamic weight degradation, progressively transferring decision making influence to mitigate conflict escalation. Finally, we advances a paradigm-shifting framework for dynamic group decision-making that simultaneously preserves individual behavioral characteristics and optimizes collective consensus formation, demonstrating significant potential for complex scenarios requiring urgent coordination, such as emergency response systems and international arbitration proceedings. 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

F. Chiclana, E. Herrera-Viedma, S. Alonso, F. Herrera: IEEE Transactions on Fuzzy Systems 17 (1), 14-23, February 2009. doi:10.1109/TFUZZ.2008.928597

S. -M. Zhou, F. Chiclana, R. I. John, J. M. Garibaldi: Fuzzy Sets and Systems 159 (24), 3281-3296, December 2008. doi:10.1016/j.fss.2008.06.018

S-M. Zhou, F. Chiclana,R. John, J. M. Garibaldi: IEEE Transactions on Knowledge and Data Engineering 23 (10) 1455-1468, October 2011. doi: 10.1109/TKDE.2010.191

F. Herrera, E. Herrera-Viedma, S. Alonso, F. Chiclana: Fuzzy Optimization and Decision Making 8, 337-364, 2009 (ISSN: 1568-4539). doi: 10.1007/s10700-009-9065-2

Patrizia Pérez-Asurmendi, F. Chiclana: Applied Soft Computing 18, May 2014, Pages 196–208. doi: 10.1016/j.asoc.2014.01.010

F. Chiclana, J. M. Tapia-Garcia, M. J. del Moral, E. Herrera-Viedma: Information Sciences 221, 110-123, February 2013, doi: 10.1016/j.ins.2012.09.014

Jian Wu, F. Chiclana: Knowledge-Based Systems 59, March 2014, Pages 97–107. doi: 10.1016/j.knosys.2014.01.017

S. Greenfield, F. Chiclana, S. Coupland, R. I. John: Information Sciences 179(13), 2055-2069, June 2009. doi: 10.1016/j.ins.2008.07.011

S. Greenfield, F. Chiclana, R. John, S. Coupland: Information Sciences 189, 77-92, April 2012. doi: 10.1016/j.ins.2011.11.042

E. Herrera-Viedma, F. Herrera, F. Chiclana , M. Luque: European Journal of Operational Research 154(1), 98-109, April 2004. doi:10.1016/S0377-2217(02)00725-7

F. Chiclana, F. Herrera, E. Herrera-Viedma: Fuzzy Sets and Systems 97(1), 33-48, July 1998. doi:10.1016/S0165-0114(96)00339-9

Research interests/expertise

Fuzzy preference modelling, decision making problems with heterogeneous fuzzy information, decision support systems, the consensus reaching process, recommender systems, social networks, modelling situations with missing/incomplete information, rationality/consistency, intelligent mobility and aggregation of information.

Areas of teaching

I have a lot of teaching experience that I have acquired over the past 21 years as a secondary school teacher of mathematics (Granada, Montoro-Cordoba, Estepona and Mabella - Malaga) in Spain (September 1990 - July 2003), and at 制服无码 (August 2003 - present) lecturing different modules at undergraduate, postgraduate (MSc) and PhD levels (see list below). Previously, I worked as a temporary lecturer at the Department of Algebra, University of Granada, in Spain (January 1990-March 1990) teaching calculus and financial mathematics to first year students of management studies.

In June 2005, I completed the HEA accredited programme for staff new to teaching in Higher Education which entitled me to registered practitioner status of the Higher Education Academy. Certificate presentation was on 28th September 2005 by the Director of Human Resources. I was glad to have Dr Jenny Carter as my mentor during my first 2 years at DMU. Currently, I am a fellow of the Higher Education Academy.

I was nominated by students for a Vice-Chancellor's Distinguished Teaching Award in 2009. The students think highly of me and my contribution to the student experience is valued as the following quotation testifies:

"He willingly devotes time to listen to any student and has helped me to achieve good mark. He is consistently excellent communicator, stimulating and informative..."

Areas of Teaching:

Mathematics for Computing
Financial Mathematics
Statistics
Research Methods
Fuzzy Logic

Qualifications

Certificate Successful completion of HEA accredited pathway for staff new to teaching in Higher Education, 制服无码, Leicester, UK (September 2005)
Outstanding Award for a PhD in Mathematics for the academic year 1999/2000, University of Granada, Spain (27 November 2002)
PhD in Mathematics (Distinction Cum Laude), Department of Computer Science and Artificial Intelligence, University of Granada, Spain (24 March 2000)
Public examination to become part of government civil service as a secondary school teacher, Ministry of Education and Science, Spanish Government (July 1990)
Degree in Mathematics (Statistics & Operational Research), University of Granada, Spain (1984-1989)
Certificate of Pedagogic Aptitude, Institute of Educational Science, University of Granada, Spain (1989).

制服无码 taught

Undergraduate

CSCI1004 - Mathematics for Computing (2003-2004)
MGSC1102 - Modelling for Management Decisions 1 (2003-2004)
INFO1007 - Introduction to Business Computing (2003-2004)
INFO1407 - Introduction to Business Computing (2004-2007)
MATH2211 - Information Systems (2003-2005)
COMP2006 - Research in Computing (2004-2008)
CSCI1412 - Computer Technology (2007-2010)
Industrial Placement Visit Tutor (2003-2012)
IMAT1901: Quantitative Methods (2010-2012)
IMAT2701: HND BIT Project (2009-2012)
IMAT3451 - Final Year Project Supervisor (2003-2012)

Postgraduate

IMAT5119 - Fuzzy Logic (2004-2012)
IMAT5120 - Research Methods (2004-2012)
IMAT5314 - MSc Project (2010-2012)

PhD Level

PhD Course: Typesetting Documents with LaTeX (2004-2012)

Honours and awards

Outstanding Award for a PhD in Mathematics for the academic year 1999/2000, University of Granada, Spain (27 November 2002).

Third prize in DMU’s Creative Thinking Awards 2010, for the Greenfield-Chiclana Collapsing Defuzzifier.

Finalist for 1st DMU - THE OSCAR AWARDS in category: Outstanding Contribution to Research Excellence (2012).

Membership of external committees

Fellow of the Higher Education Academy, UK

Member of the European Society for Fuzzy Logic and Technology (EUSFLAT)

Current research students

Current:

Maria Raquel Ureña Perez, University of Granada (Spain)- Department of Computer Science and Artificial Intelligence (DECSAI), University of Granada. January 2012. Co-supervisor: Prof. Enrique Herrera-Viedma
Manal Alghieth (DMU) - Second supervisor. First supervisor: Dr Yingjie Yang (CCI). Mode of study: Full -time on site (01/04/2012)
Simon Witheridge (DMU) - Intelligent Transport Systems: Integrated Traffic Management Control. First supervisor. Second supervisors: Dr Benjamin Passow (DIGITS) and Dr David Elizondo (DIGITS). Mode of study: Full -time on site (01/10/2012). Change to second supervisor and Ben Passow first supervisor from 01-October-2013.
Eseosa Oshodin (DMU) - Decentralised Mechanism for REcommender/Reputation System: A Case Study on Trust. First supervisor. Second supervisors: Dr Samad Ahmadi (VirAL/CCI). Mode of study: Full -time on site (01/10/2013).
Salim Hasshu (DMU) - Smart, Green and Integrated Transport - Personalised traffic health planner. First supervisor. Second supervisors: Dr Benjamin Passow (DIGITS) and Dr David Elizondo (DIGITS). Mode of study: Full -time on site (01/10/2013).

Completed:

Dr Sergio Alonso Burgos, University of Granada (Spain)- Group Decision Making With Incomplete Fuzzy Preference Relations. Department of Computer Science and Artificial Intelligence (DECSAI), University of Granada. May 2006. Co-supervisors: Prof. Enrique Herrera-Viedma, Prof. Francisco Herrera.
Dr Fahad Alshathry (DMU) - Building a Decision Support System to integrate digital evidence with interview investigation. Second supervisor. August 2011. First supervisor: Dr Giampaolo Bella (STRL).
- Type-2 Fuzzy Logic: Circumventing the Defuzzification Bottleneck. First supervisor. Second supervisors: Prof. Robert I. John and Dr Simon Coupland (CCI). May 2012.
Tamas Galli (MPhil DMU) - Fuzzy Logic Based Software Product Quality Models by Execution Tracing. First supervisor. Second supervisor: Dr Jenny Carter (CCI). Technical adviser: Helge Janicke (STRL). Mode of study: Part -time distance International PhD Programme (01/03/2011). February 2014.

Externally funded research grants information

Awarded Campus de Excelencia GENIL-BioTIC-UGR Research Visit Grant (1 week) by the University of Granada (Spain). Principal Investigator. €1000. Period: February 2014.
Awarded Campus de Excelencia GENIL-BioTIC-UGR Research Visit Grant (1 week) by the University of Granada (Spain). Principal Investigator. €1200. Period: June 2012.
Awarded University of Granada Research Visit Grant by the Regional Government of Andalucia (Spain). Principal Investigator. €3184. Period: June 2009 - August 2009.
Awarded research funding from the EPSRC for a 3 year projet, which extends my previous work investigating the role of fuzzy logic in aggregation and consensus modelling. Towards a Framework for Modelling Variation, EPSRC, UK, Co-investigator. £145K. Period: 2006 - 2009.
Awarded a Royal Academy of Engineering grant support towards my research networking visit to Spain. 2009.
Awarded 2 Conference Grants (Royal Society and Royal Academy of Engineering) to disseminate my research findings at IPMU 2006 and FUZZ-IEEE 2008.
External research collaborator in Spanish Government Research Projects lead by my collaborators.
Linguistic Information in Decision Making Analysis Processes. Preference Modelling and Applications. Spanish Department for Education and Culture, Co-investigator. €91K. Period: 01/01/2010 to 31/12/2012.
Project of Excellence: Developing the Fuzzy Linguistic Model and its Use in WEB Applications. Regional Government of Andalucia (Spain), Co-investigator. €187K. Period: 01/01/2009 to 31/12/2013.
Decision Models with Uncertainty in Heterogeneous Contexts. Application to Evaluation Processes in On-line Environments. Spanish Department for Education and Culture, Co-investigator. €50K. Period: 01/01/2007 to 31/12/2009.
Project of Excellence: Development of WEB Information Access Systems Based on Artificial Intelligence Techniques (SAINFOWEB). Regional Government of Andalucia (Spain), Co-investigator. €50K. Period: 01/01/2005 to 31/12/2008.
An Information System for the Quality of Aerial Transportation Based on Artificial Intelligence Techniques and Oriented Towards the Citizen. Spanish Department of Transport, Co-investigator. €96K. Period: 01/01/2005 to 31/12/2008.
Flexible Preference Modelling in Decision Making. Applications in online recommender systems (I) and (II). Spanish Department for Education and Culture, Co-investigator. €33K. Period: 01/01/2003 to 31/12/2006.
Spanish National Network in Decision Making, Preference Modelling and Aggregation (I) and (II). Spanish Department for Education and Culture, Co-investigator. €30K. Period: 01/01/2004 to 31/12/2006.
Similarities Between Physics and Mathematics in Secondary Education. Regional Government of Andalucia (Spain), Principal Investigator. €700. Period: 01/09/2002 to 30/06/2003.

Internally funded research project information

Awarded DMU Research Scholarship 2013-14 scheme for 3 years starting from October 2013. This scheme provides for funding to cover both fees and stipend equivalent to the RCUK standard rate (£13,770 for 2012-13) to support one research student from UK or EU. (Principal Investigator with Dr David Elizondo and Dr Benjamin Passow - DIGITS).
Awarded DMU Research Scholarship 2012-13 scheme for 3 years starting from October 2012. This scheme provides for funding to cover both fees and stipend equivalent to the RCUK standard rate (£13,770 for 2012-13) to support one research student from UK or EU. (PI with Dr David Elizondo and Dr Benjamin Passow - DIGITS).
Awarded DMU Revolving Investment Fund (RIF) for Research for the project DIGITS: De Montfort Interest Group In Transport Systems. Principal Investigator. £10K. Period: January 2012 - July 2012.
Awarded £4K under the Faculty of Computing Sciences and Engineering (DMU) Pump Priming initiative to promote external collaborations at the University of Granada and the University of Jaen in Spain. 2005.

Professional esteem indicators

Associate Editor and Editorial Board

International journals in

Associate Editor of (from April 2014).
Associate Editor of (from April 2014) (Member of the Editorial Board from October 2011 to March 2014).
Member of the Editorial Board of (from February 2014).
Member of the Advisory Board of (from December 2013).
Member of the Editorial Board of Journal of Multiple-Valued Logic and Soft Computing (Old City Publishing) ISSN: 1542-3980 (from August 2011).
Associate Editor of (from September 2012).
Member of the Editorial Board of (from July 2012).
Member of the Editorial Board of (February 2013).

International journals not in ISI Web of Knowledge

Associate Editor of Journal of Signal Processing Theory and Applications (Columbia International Publishing) ISSN: 2163-2278 (from December 2011).
Member of the Editorial Board of (from October 2007).

Guest Editor for international journals in ISI

in the International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems (IJUFKS), Volume 16, Issue 2 Supp. August 2008. F. Chiclana, E. Herrera-Viedma, S. Alonso, F. Herrera (Eds.).
"COMPUTING WITH WORDS IN DECISION MAKING" in the International Journal of Fuzzy Optimization and Decision Making Journal, Volume 8, Number 4 / December 2009. F. Herrera, E. Herrera-Viedma, S. Alonso and F. Chiclana (Eds.).

Research Council Reviewer and External Examiner

EPSRC
The Royal Society

International

The Research Foundation - Flanders (Belgium) (Fonds Wetenschappelijk Onderzoek - Vlaanderen, FWO)
The Romanian National Council for Development and Innovation
Portuguese Foundation for Science and Technology (FCT)
Austrian Science Fund (FWF)
Netherlands Organisation for Scientific Research, Division of Social Sciences.

PhD external examiner

Univ. Granada (Granada, Spain)
Univ. Jaén (Jaén, Spain)
Ulster University (Belfast, UK)
University of Valladolid (Valladolid, Spain)
École Supérieure d'Électricité (SUPÉLEC, Paris, France).

Conference Organisation, Plenary Talks and Invited Lectures

Co-chair of

Organised and Chaired special sessions in the following international conferences:

in FUZZ-IEEE 2014 - Beijing (China) that will be held as part of the from 6-11 July 2014.
in the that will be held in Moscow - Russia from 3-5 June 2014.
Focus Session on Consensus and Decision Making Under Uncertainty in the 2013 IFSA World Congress NAFIPS Annual Meeting Edmonton, Canada June 24-28, 2013.
Special Session on Fuzzy Preference Modelling, Decision Making and Consensus in first International Conference of Information Technology and Quantitative Management (ITQM 2013), May 16-18, 2013 at Suzhou, China.
Special Session on "Fuzzy Approaches in Preference Modelling, Decision Making and Applications" for the IEEE International Conference on Fuzzy Systems (FUZZ_IEEE), London, UK (2007).
Special Session on "Soft Decision Making - Theory and Applications" for the IEEE International Conference on Fuzzy Systems (FUZZ_IEEE), Hong-Kong, China (2008).
Special Session on "Fuzzy Decision Making Issues: Preference Modelling and Aggregation" for the 8th International FLINS Conference on Computational Intelligence in Decision and Control, Madrid, Spain (2008).
Organised Workshop on "Type-2 Fuzzy Logic and the Modelling of Uncertainty" at the AI-2007 Twenty-seventh SGAI International Conference on Artificial Intelligence, Cambridge, UK.
Plenary talk at the 2009 EUROFUSE Workshop on Preference Modelling and Decision Analysis.
Invited Lectures at the University of Granada, the University of Pamplona, the University of Valladolid, the University of Castilla-La Mancha in Albacete, the University of Jaen (Spain), Ghent University (Belgium) and University of Portsmouth (UK).
Programme committee member of more than 50 international conferences.