International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems - World Scientific | 2024 Impact Factor: 1.0 | Cite Score: 2.9 | Q3

About the Journal:
The International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems (IJUFKS) is a leading peer-reviewed journal that publishes high-quality research on the theoretical foundations and practical applications of uncertainty management, fuzzy logic, and knowledge-based systems. Established to advance the understanding of computational intelligence and decision-making under uncertainty, the journal serves as a platform for researchers and practitioners to exchange innovative ideas that bridge the gap between theory and real-world applications in various domains such as artificial intelligence, data science, and engineering systems.
The International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems is a forum for research on various methodologies for the management of imprecise, vague, uncertain or incomplete information. The aim of the journal is to promote theoretical or methodological works dealing with all kinds of methods to represent and manipulate imperfectly described pieces of knowledge, excluding results on pure mathematics or simple applications of existing theoretical results. It is published bimonthly, with worldwide distribution to researchers, engineers, decision-makers, and educators.

Scope of the Journal:
The journal encompasses a wide range of topics related to uncertainty modeling and intelligent systems, including:
- Fuzzy logic, rough sets, and soft computing methods
- Uncertainty reasoning, probabilistic models, and evidence theory
- Knowledge-based and rule-based systems
- Decision support systems and expert systems
- Machine learning and intelligent data analysis under uncertainty
- Information fusion, pattern recognition, and approximate reasoning
- Applications of fuzzy and uncertainty theories in engineering, economics, and social sciences
- Hybrid intelligent systems combining fuzzy logic, neural networks, and evolutionary computation
- Optimization and control under uncertainty
- Knowledge representation and inference mechanisms