Cite Score and Journal Rank of Fuzzy Information and Engineering

About:

The Fuzzy Information and Engineering (FIE) journal is a peer-reviewed publication that focuses on the development and application of fuzzy logic, fuzzy systems, and related methodologies in various fields of engineering and information sciences. The journal aims to advance knowledge in the theory and practice of fuzzy systems, offering insights into how fuzzy logic can be applied to solve complex problems in engineering, computer science, and information technology.

Objective
The primary objective of Fuzzy Information and Engineering is to promote the research and development of fuzzy logic and its applications across different domains. The journal seeks to provide a platform for researchers and practitioners to share their findings on theoretical advancements, practical implementations, and novel applications of fuzzy systems. FIE aims to bridge the gap between theoretical research and practical engineering solutions, contributing to the advancement of fuzzy information and engineering technologies.

Topics Covered
The journal covers a wide range of topics related to fuzzy logic and its applications, including but not limited to: Fuzzy Logic Theory and Methods Fuzzy Control Systems Fuzzy Decision Making and Optimization Fuzzy Neural Networks Fuzzy System Modeling and Simulation Fuzzy Data Analysis and Mining Fuzzy Information Retrieval Applications of Fuzzy Logic in Engineering Fuzzy Systems in Artificial Intelligence Hybrid Systems Integrating Fuzzy Logic and Other Techniques

Interdisciplinary Approach
Fuzzy Information and Engineering adopts an interdisciplinary approach, welcoming contributions from various fields such as computer science, electrical engineering, mechanical engineering, and information technology. The journal encourages research that integrates fuzzy logic with other methodologies and technologies to address complex engineering and information problems. By fostering collaboration across disciplines, FIE aims to advance the understanding and application of fuzzy systems in diverse contexts.

Impact and Significance
The Fuzzy Information and Engineering journal is recognized for its contributions to the field of fuzzy logic and engineering through its rigorous peer-review process and commitment to publishing high-quality research. The journals focus on both theoretical advancements and practical applications ensures that its publications are relevant and impactful for researchers, engineers, and practitioners. FIE provides valuable insights and solutions that support the advancement of fuzzy logic technologies and their applications in various domains.

Journal Home:  Journal Homepage

Editor-in-Chief:  Bing-yuan Cao

scope: Fuzzy Information and Engineering is a peer-reviewed journal focused on the development and application of fuzzy logic and its integration into various engineering fields. The journal addresses theoretical advancements and practical implementations involving fuzzy systems, offering a platform for researchers and practitioners to share their work.

Fuzzy Logic and Systems:

Fuzzy Logic Theories: Research on the development of new fuzzy logic theories, including fuzzy sets, fuzzy rules, and fuzzy inference systems.

Fuzzy Control Systems: Studies on the design, analysis, and application of fuzzy control systems in various engineering domains.

Fuzzy Decision Making: Exploration of decision-making processes utilizing fuzzy logic, including multi-criteria decision analysis and decision support systems.

Applications of Fuzzy Systems:

Industrial Automation: Research on the use of fuzzy logic in industrial automation and process control, including robotics and manufacturing systems.

Transportation Systems: Studies on applying fuzzy logic to traffic management, transportation planning, and intelligent transportation systems.

Healthcare and Medicine: Exploration of fuzzy logic applications in medical diagnosis, patient monitoring, and health management systems.

Fuzzy Optimization and Modeling:

Fuzzy Optimization Techniques: Research on optimization methods incorporating fuzzy logic, including linear and nonlinear fuzzy optimization.

Fuzzy Modeling: Studies on developing fuzzy models for complex systems and processes, including system identification and simulation.

Hybrid Fuzzy Systems: Exploration of hybrid systems combining fuzzy logic with other computational intelligence techniques, such as neural networks and genetic algorithms.

Fuzzy Data Analysis and Mining:

Fuzzy Data Mining: Research on techniques for mining and analyzing fuzzy data, including pattern recognition and knowledge discovery.

Fuzzy Statistical Methods: Studies on statistical methods and tools adapted for handling fuzzy data and uncertainty.

Fuzzy Information Retrieval: Exploration of information retrieval systems using fuzzy logic to enhance search and retrieval processes.

Computational Intelligence:

Fuzzy Neural Networks: Research on integrating fuzzy logic with neural networks to develop advanced computational intelligence systems.

Fuzzy Evolutionary Algorithms: Studies on evolutionary algorithms incorporating fuzzy logic for optimization and problem-solving.

Fuzzy Cognitive Maps: Exploration of fuzzy cognitive maps for modeling complex systems and dynamic behaviors.

Fuzzy System Applications in Engineering:

Electrical and Electronics Engineering: Research on the application of fuzzy logic in electrical and electronics engineering, including circuit design and signal processing.

Mechanical Engineering: Studies on using fuzzy systems in mechanical engineering applications, such as system dynamics and control.

Civil and Structural Engineering: Exploration of fuzzy logic applications in civil and structural engineering, including construction management and structural analysis.

Emerging Trends and Technologies:

Fuzzy Systems in Smart Technologies: Research on the role of fuzzy logic in smart technologies, including smart grids, smart cities, and smart homes.

Fuzzy Systems in Data Science: Studies on the integration of fuzzy logic with data science techniques for handling uncertainty and improving data analysis.

Fuzzy Systems and Internet of Things (IoT): Exploration of fuzzy logic applications in IoT systems, including sensor networks and intelligent systems.

Print ISSN:  16168658,

Electronic ISSN:  16168666

Abstracting and Indexing:  SCOPUS

Imapct Factor :  

Subject Area and Category:   Computer Science, Artificial Intelligence, Information Systems, Decision Sciences, Management Science and Operations Research, Engineering, Control and Systems Engineering, Industrial and Manufacturing Engineering, Mathematics, Applied Mathematics, Logic, Theoretical Computer Science

Publication Frequency:  

H Index:  25

Best Quartile:

Q1:  

Q2:  

Q3:  Applied Mathematics

Q4:  

Cite Score:  2.3

SNIP:  0.917

Journal Rank(SJR):  0.304

Latest Articles:   Latest Articles in Fuzzy Information and Engineering

Guidelines for Authors: Fuzzy Information and Engineering Author Guidelines

Paper Submissions: Paper Submissions in Fuzzy Information and Engineering

Publisher:  Taylor & Francis

Country:  United Kingdom