Soft Computing and Uncertainty Learning with Applications
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E2: Control Theory and Mechanics".
Deadline for manuscript submissions: closed (10 January 2025) | Viewed by 21123
Special Issue Editors
Interests: machine learning; soft computing; image analysis; data mining
Interests: granular computing; network representation learning; knowledge graph; social network analysis
Special Issue Information
Dear Colleagues,
Soft computing methodologies, including fuzzy sets, rough sets, evidence theory and other flexible mathematical models, provide an effective theoretical framework to formulate and process uncertain data and knowledge. In recent years, soft computing methods have experienced rapid developments through being combined with machine learning and other artificial intelligence techniques, and have also achieved successful applications in various kinds of intelligent data analysis tasks.
This Special Issue will focus on recent theoretical and computational studies of soft computing models, algorithms, systems and applications. Topics include, but are not limited to:
- Fundamental soft computing models.
- Granular computing methodologies.
- Three-way decision methodologies.
- Uncertain and approximate reasoning.
- Uncertain machine learning with soft computing.
- Machine learning for uncertain data and knowledge.
- Expert systems based on soft computing and machine learning.
- Data applications of social media, business intelligence, medicine and healthcare, bioinformatics manufacturing, cybernetics and robotics, etc.
Crossfield theoretical studies and applications in soft computing and machine learning are particularly welcome in this Special Issue.
Prof. Dr. Xiaodong Yue
Prof. Dr. Shu Zhao
Prof. Dr. Jie Zhou
Guest Editors
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Keywords
- soft computing
- machine learning
- uncertainty theory
- expert systems
- data applications
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