Uncertainty Fuzziness and Knowledge - Impact Factor

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

Impact Factor and Journal Rank of International Journal of Uncertainty Fuzziness and Knowledge-based Systems

About: The International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems provides a platform for researchers, academics, and practitioners to publish original research, review articles, and application papers. By fostering collaboration and knowledge exchange, the journal aims to advance the state-of-the-art in uncertainty modeling, fuzziness, and knowledge-based systems, contributing to various fields of science, engineering, and decision sciences.
Objective:
The primary objective of The International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems is to serve as a leading publication venue for research that addresses uncertainty, fuzziness, and knowledge representation in systems and decision-making processes. The journal covers a wide range of topics including uncertainty modeling, fuzzy logic, soft computing, knowledge-based systems, expert systems, decision support systems, and applications in areas such as engineering, medicine, economics, and environmental sciences. By promoting interdisciplinary research and collaboration, the journal aims to facilitate the development of robust methodologies and innovative solutions that improve decision-making under uncertainty and enhance system performance across diverse domains.
Interdisciplinary Approach:
The International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems adopts an interdisciplinary approach, encouraging contributions that integrate insights from uncertainty modeling, fuzzy logic, knowledge representation, and related disciplines such as artificial intelligence, cognitive science, operations research, and data analytics. The journal covers diverse interdisciplinary topics such as uncertainty quantification in complex systems, fuzzy sets in decision-making processes, knowledge-based reasoning in intelligent systems, and applications of soft computing techniques in real-world problems. By fostering collaboration across disciplines, the journal facilitates the dissemination of cutting-edge research that addresses practical challenges and enhances the effectiveness of decision support systems in complex environments.
Impact:
The impact of The International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems is significant in advancing both theoretical foundations and practical applications in uncertainty modeling and knowledge-based systems. By publishing rigorous research articles and application papers, the journal contributes to the development of new methodologies, algorithms, and technologies that enable more accurate modeling and decision-making under uncertainty. The journals publications inform the development of innovative solutions that address real-world challenges in diverse domains including engineering design optimization, medical diagnosis, financial forecasting, environmental risk assessment, and intelligent automation. The emphasis on high-quality research and practical relevance ensures that the journals contributions support the continuous evolution and adoption of uncertainty modeling and knowledge-based systems worldwide.
Significance:
The International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems holds significant importance for researchers, educators, practitioners, and decision-makers interested in advancing the fields of uncertainty modeling, fuzziness, and knowledge-based systems. The journals contributions include advancing theoretical frameworks, exploring new applications of soft computing techniques, and addressing practical challenges in decision support and intelligent systems design. By providing insights into the latest developments, emerging trends, and best practices in uncertainty modeling and knowledge-based systems, the journal serves as a valuable resource for fostering innovation and promoting interdisciplinary collaboration globally. It supports the continuous advancement of knowledge and technological innovation in the field of uncertainty modeling and knowledge-based systems, facilitating the development of robust solutions that enhance decision-making capabilities and system performance in complex and dynamic environments.

Journal Home: Journal Homepage

Editor-in-Chief: Bernadette Bouchon-Meunier

Scope: The International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems (IJUFKS) is a peer-reviewed journal that focuses on theoretical and practical advancements in the fields of uncertainty, fuzziness, and knowledge-based systems.
Uncertainty Modeling and Analysis:
Methods for modeling and analyzing uncertainty, including probabilistic reasoning, fuzzy set theory, possibility theory, rough sets, interval methods, and other uncertainty modeling approaches.
Fuzzy Systems and Applications:
Development and application of fuzzy logic, fuzzy control systems, fuzzy optimization, fuzzy decision-making, fuzzy clustering, fuzzy pattern recognition, and fuzzy expert systems.
Knowledge-Based Systems:
Design, development, and implementation of knowledge-based systems, expert systems, ontology-based systems, knowledge representation and reasoning, and knowledge discovery.
Soft Computing Techniques:
Integration of fuzzy logic, neural networks, evolutionary algorithms, and other soft computing techniques for solving complex real-world problems.
Decision Support Systems:
Development and application of decision support systems (DSS) using uncertainty modeling and knowledge-based approaches to assist decision-making in various domains.
Machine Learning and Data Mining:
Applications of uncertainty modeling and knowledge-based systems in machine learning, data mining, predictive analytics, and knowledge discovery from data.
Multi-Criteria Decision Analysis:
Methods for handling uncertainty in multi-criteria decision analysis, including fuzzy multi-criteria decision-making, group decision-making, and decision fusion techniques.
Applications in Engineering and Sciences:
Practical applications of uncertainty modeling, fuzziness, and knowledge-based systems in engineering disciplines (such as control systems, robotics, and manufacturing) and scientific domains (such as biology, medicine, environmental sciences, and economics).
Computational Intelligence:
Advancements in computational intelligence techniques involving uncertainty, including evolutionary computation, swarm intelligence, artificial immune systems, and hybrid intelligent systems.
Real-World Case Studies and Applications:
Real-world case studies, applications, and industrial implementations showcasing the effectiveness and practicality of uncertainty modeling and knowledge-based systems.