Symmetry and Advances in Fuzzy Sets and Fuzzy Optimization in Artificial Intelligence

Dear Colleagues, 

Uncertainty and imprecision are fundamental characteristics of real-world systems and data, presenting substantial challenges to conventional deterministic and exact mathematical modeling techniques. In such a scenario, fuzzy sets and fuzzy optimization, when complemented with symmetry, have shown great potential in addressing these issues across various fields including artificial intelligence, neural networks, deep learning, and control systems. Nevertheless, despite the significant advancements in these areas, ongoing challenges necessitate more refined and innovative approaches. Decision making based on online data classification, and the interpretability of AI in the domain of symmetric fuzzy sets and fuzzy optimization, remain largely uncharted territories.

Fuzzy sets provide a progressive transition from membership to non-membership, encapsulating the essence of vagueness and uncertainty seen in numerous real-world scenarios. Fuzzy optimization addresses mathematical optimization problems where the objective function or constraints involve vague quantities. Combined with symmetry, these fields offer immense versatility. However, the exploration of decision making based on online data classification and AI interpretability within them is an exciting frontier yet to be fully explored.

This Special Issue seeks to tackle these challenges, showcasing the most recent advancements, methodologies, and applications in symmetric fuzzy sets and fuzzy optimization, focusing on the classification and interpretability facets of AI. Through this investigation, we aim to emphasize how these methodologies can be integrated into more robust, reliable, and nuanced AI systems.

This Special Issue, entitled "Symmetry and Advances in Fuzzy Sets and Fuzzy Optimization in Artificial Intelligence", serves as a platform for researchers, practitioners, and scholars to exhibit their original research findings, innovative applications, and the latest advancements that tackle challenges and opportunities within the scope of symmetric fuzzy sets, fuzzy optimization, and their integration into artificial intelligence (AI), neural networks, deep learning, and machine learning.

We especially encourage contributions that advance the current understanding in decision-making methodologies, particularly those that incorporate principles of online data classification and enhance the interpretability of AI within the realm of symmetric fuzzy sets and fuzzy optimization. This includes, but is not limited to, symmetric fuzzy multi-attribute decision-making methods, consensus group decision making in social networks, Bayesian decision methods, and machine learning-based decision-making approaches employing symmetric fuzzy sets and fuzzy optimization.

Possible topics for this Special Issue include, but are not limited to, the following:

• Theoretical foundations of symmetric fuzzy sets and fuzzy optimization;
• Methods, applications, and various uses of symmetric fuzzy membership functions and relations;
• Developing and applying optimization algorithms and multi-objective optimization approaches using symmetric fuzzy set theory and fuzzy logic;
• Decision-making methodologies in the context of symmetric fuzzy sets and fuzzy optimization;
• Development, use, and control of systems based on symmetric fuzzy sets;
• Techniques for symmetric fuzzy clustering and classification;
• Integration of symmetric fuzzy sets into AI techniques, including methods to enhance the interpretability of AI;
• Exploration of machine learning-based decision-making methods incorporating symmetric fuzzy sets and fuzzy optimization, particularly focusing on online data classification decision making;
• Case studies and real-world applications demonstrating the utility and advantages of symmetric fuzzy sets and fuzzy optimization.

Through these pursuits, this Special Issue aims to deepen our understanding of the intricate relationship between symmetric fuzzy sets, fuzzy optimization, and AI, shedding more light on the opportunities they offer in managing the inherent uncertainty and imprecision in real-world systems.

Dr. Jian Liu
Prof. Dr. Peng Li
Guest Editors

Manuscript Submission Information

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