About this Research Topic
In line with this, AI must represent the way that humans deal with issues, including which actions to choose, which to prioritize, which to discard, and which to put off given the many uncertainties involved. Here, developing genetic algorithms, neural networks, etc., and newer AI trends, such as agent-based approaches, can be helpful. Moreover, representing uncertain linguistic expressions plays a significant role in mimicking human thinking in AI algorithms. The goal of this Research Topic is to understand recent advances in fuzzy set theory and its extensions, such as neutrosophic or plithogenic sets. A suitable combination of these tools can lead us to deal with the complexity arising from variables, interrelationships, etc., and the uncertainty that arises from human thinking.
Topics of interest include but are not limited to:
• New modeling in FSE
• Fuzzy preference modelling
• Aggregation operators
• Models and tools for intelligent information processing
• FSE in big data
• FSE in deep learning and smart computing
• FSE in Data-driven distributed optimization
• FSE in multiple criteria decision making
• FSE in complex processes
• FSE in scientific computing
• FSE in recommender systems
• FSE in pattern recognition
• FSE in personalization
• FSE in computer vision
• FSE in reliability analysis
• FSE in health informatics
• FSE in smart city
• FSE in user modeling
• FSE in organizations and complex systems
• FSE in managerial marketing and accounting
Keywords: Fuzzy sets, Fuzzy set extensions, Artificial intelligence, Data mining, Decision-making, Data-driven based model, Recommendation systems
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.