Dear Colleagues,

This Special Issue presents the extended version of selected papers presented at the MMCITARE 2021 congress. All the accepted papers will follow a strict peer-review process to ensure they reach the quality standards of an international journal.

The objective of the conference is to exchange new knowledge and recent developments in all aspects of computational techniques, mathematical modelling, energy systems, applications of fuzzy sets, and many more. The 3rd International Conference on “Mathematical Modeling, Computational Intelligence Techniques and Renewable Energy” is organized by the University of Technology, Sydney, Australia in association with Forum for Interdisciplinary Mathematics (FIM), Gujarat Chapter; UABC, Mexico; and Pandit Deendayal Energy University, Gandhinagar, India. The conference will provide an opportunity for researchers, academicians, and students to share their knowledge and discuss the latest developments in their field of expertise. This conference provides a platform for young researchers to interact with eminent scientists and researchers across the globe. In this conference, an equal opportunity is provided to all participants to share their ideas in the areas of interdisciplinary mathematics, statistics, computational intelligence, and renewable energy.

The main themes of the Special Issue are focused on innovation because knowledge-based organizations are needed in order to have a knowledge-driven economy (Yildirmaz et al., 2018; Merigo et al. 2016). a variety of disciplines including engineering, management, economics, business, and many more are applied and combined in order to generate different types of innovation (Hashimoto, 2012). A common characteristic in the innovation process is the necessity of reducing uncertainties and risks (Koschatzky, 1998).

The other main topic of the conference is the use of fuzzy methodologies (Merigo et al. 2015; Zadeh, 1965) that can improve decision making in organizations within different strategic areas (Blanco-Mesa et al., 2017; Gil-Lafuente, 2005). One of the fuzzy methodologies that helps to incorporate the expertise, knowledge and expectations of the decision maker is the ordered weighted average (OWA) operator developed by Yager (1988), of which several extensions have been developed (He et al. 2017; Yager et al., 2011). 

(1) Conference name

• 3rd International Conference on Mathematical Modeling, Computational Intelligence Techniques and Renewable Energy (MMCITRE-2022)

(2) Conference venue and dates:

• 4–6 March 2022
• University of Technology Sydney

(3) Key topics of the conference:

• Fuzzy Set Theory and Its Generalizations;
• Computational Intelligence Techniques;
• Information and Coding Theory;
• Decision Making and Expert systems;
• Fuzzy Logic;
• Boundary Element Method;
• Dual Reciprocity Boundary Element Method (DRBEM);
• Numerics of PDEs;
• Continuum Mechanics;
• Number Theory;
• Internet of Things (IoT);
• Computational Biology;
• Hydrodynamics;
• Computer Science;
• Artificial Intelligence;
• Machine Learning;
• Differential Equations and its Applications;
• Energy;
• Fractional Differential Equation;
• Optimization;
• Computational Fluid Dynamics;
• Cloud Computing.

References

Blanco-Mesa, F., Merigó, J.M., & Gil-Lafuente, A.M. (2017). Fuzzy decision making: A bibliometric-based review. Journal of Intelligent & Fuzzy Systems, 32, 2033–2050.

Gil-Lafuente, A.M. (2005). Fuzzy logic in financial analysis. Springer, Berlin.

Hashimoto, M., Kajikawa, Y., Sakata, I., Takeda, Y., & Matsushima, K. (2012). Academic landscape of innovation research and National Innovation System policy reformation in Japan and the United States. International Journal of Innovation and Technology Management, 9(06), 1250044.

He, X. R., Wu, Y. Y., Yu, D., & Merigó, J. M. (2017). Exploring the ordered weighted averaging operator knowledge domain: A bibliometric analysis, International Journal of Intelligent Systems, 32, 1151–1166.

Koschatzky, K. (1998). Firm innovation and region: the role of space in innovation processes. International Journal of Innovation Management, 2(04), 383-408.

Merigó, J. M., Cancino, C., Coronado, F., Urbano, D. (2016). Academic research in innovation: A country analysis. Scientometrics, 108, 559–593.

Merigó, J. M., Gil-Lafuente, A. M., & Yager, R. R. (2015). An overview of fuzzy research with bibliometric indicators. Applied Soft Computing, 27, 420–433.

Yager, R.R. (1988). On ordered weighted averaging aggregation operators in multi-criteria decision making. IEEE Transactions on Systems, Man and Cybernetics, B 18, 183-190.

Yager, R.R., Kacprzyk, J., & Beliakov, G. (2011). Recent developments in the ordered weighted averaging operators: Theory and practice. Springer-Verlag, Berlin.

Yildirmaz, H., Öner, M. A., & Herrmann, N. (2018). Impact of Knowledge Management Capabilities on New Product Development and Company Performance. International Journal of Innovation and Technology Management, 1850030.

Zadeh, L.A. (1965). Fuzzy sets. Information and Control, 8, 338-353. 

Prof. Dr. Ernesto León-Castro
Dr. Manoj Sahni
Dr. Walayat Hussain
Dr. Rajkumar Verma
Prof. Dr. Jose María Merigo
Guest Editors

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• fuzzy set theory and its generalizations
• computational intelligence techniques
• information and coding theory
• decision making and expert systems
• fuzzy logic
• boundary element method
• Dual Reciprocity Boundary Element Method (DRBEM)
• Numerics of PDEs
• continuum mechanics
• number theory
• Internet of Things (IoT)
• computational biology
• hydrodynamics
• computer science
• artificial intelligence
• machine learning
• differential equations and its applications
• energy
• fractional differential equation
• optimization
• computational fluid dynamics
• cloud computing