IEEE Computational Intelligence Magazine - Impact Factor

IEEE Computational Intelligence Magazine | 2024 Impact Factor:11.2 | Cite Score:15.5 | Q1

IEEE Computational Intelligence Magazine Journal

Impact Factor and Journal Rank of IEEE Computational Intelligence Magazine

About: The IEEE Computational Intelligence Magazine is a highly regarded publication that focuses on the latest advancements, trends, and applications in the field of computational intelligence (CI). As a flagship magazine of the IEEE Computational Intelligence Society, it serves as a comprehensive platform for researchers, practitioners, and enthusiasts to explore the diverse aspects of CI, including its theoretical foundations, practical applications, and societal implications.
Content: In-depth articles that provide comprehensive coverage of specific topics or themes within the field of CI, offering insights into recent research developments, technological advancements, and practical applications. Comprehensive surveys and reviews that summarize and analyze the state-of-the-art in specific areas of CI, highlighting key achievements, emerging trends, and future directions.
High Standards and Impact: The IEEE Computational Intelligence Magazine upholds rigorous standards of quality and relevance through a thorough peer-review process and editorial oversight by experts in the field of CI. It is recognized for its high impact and influence, with articles frequently cited by researchers and practitioners in academia and industry.
Global Reach: The magazine attracts contributions from researchers, practitioners, and thought leaders worldwide, ensuring a diverse range of perspectives, methodologies, and applications in the field of CI.
Significance: It plays a significant role in advancing the field of computational intelligence by providing a comprehensive platform for knowledge dissemination, collaboration, and education. Its broad scope, high standards, and global reach make it an indispensable resource for researchers, practitioners, policymakers, educators, and students interested in exploring the exciting possibilities of computational intelligence.

Journal Home: Journal Homepage

Editor-in-Chief: Chuan-Kang Ting

Scope: The IEEE Computational Intelligence Magazine serves as a platform for disseminating high-quality, peer-reviewed articles, tutorials, and surveys covering a broad range of topics within the field of computational intelligence.
The scope of the magazine includes, but is not limited to, the following areas:
Foundations of Computational Intelligence: Fundamental theories, principles, and methodologies underlying computational intelligence techniques, including neural networks, fuzzy systems, and evolutionary computation.
Machine Learning and Pattern Recognition: Research on algorithms, models, and techniques for machine learning, data mining, and pattern recognition, with a focus on applications in various domains.
Artificial Neural Networks: Development and applications of artificial neural networks (ANNs), including deep learning architectures, training algorithms, and computational models inspired by biological neural networks.
Fuzzy Systems: Theoretical and applied research on fuzzy logic, fuzzy set theory, fuzzy control systems, and fuzzy decision-making, with applications in engineering, robotics, and decision support systems.
Evolutionary Computation: Studies on evolutionary algorithms, genetic algorithms, genetic programming, and other evolutionary optimization techniques, including their applications in optimization, modeling, and design.
Swarm Intelligence: Exploration of swarm-based optimization algorithms inspired by the collective behaviors of natural systems, such as particle swarm optimization (PSO) and ant colony optimization (ACO).
Hybrid and Ensemble Methods: Integration of multiple computational intelligence techniques, such as combining neural networks with evolutionary algorithms or fuzzy logic with swarm intelligence, to improve performance and robustness.
Adaptive and Learning Systems: Development of adaptive and learning systems capable of self-organization, self-regulation, and autonomous decision-making in dynamic and uncertain environments.