Knowledge-Based Systems - Elsevier - Impact Factor

Knowledge-Based Systems - Elsevier | 2024 Impact Factor:7.6 | Cite Score:15.0 | Q1

Impact Factor and Journal Rank of Knowledge-Based Systems

About: Knowledge-Based Systems is a prominent, peer-reviewed journal that focuses on the design, development, and application of knowledge-based systems, intelligent systems, and artificial intelligence (AI) technologies. Published by Elsevier, this journal serves as an essential platform for researchers, practitioners, and academics who are involved in the creation and utilization of systems that emulate human expertise and decision-making capabilities.
Content Types: Original research presenting new theories, methodologies, and empirical findings in the field of knowledge-based systems. Comprehensive reviews that summarize current advancements, highlight significant trends, and identify future research directions in knowledge-based and intelligent systems. Brief reports on innovative techniques, tools, and findings relevant to knowledge-based systems.
High Standards and Impact: The journal employs a rigorous peer-review process to ensure the publication of high-quality, accurate, and impactful research. Articles published in Knowledge-Based Systems are frequently cited, indicating the journal significant influence and relevance in the field. Known for its high standards and contributions to the field, the journal is widely respected and trusted by the scientific and technological communities.
Global Reach: The journal attracts contributions from researchers and professionals worldwide, providing a diverse and global perspective on knowledge-based systems. Ensures that research findings are accessible to a wide audience through various academic and research databases, libraries, and online platforms.
Significance: Knowledge-Based Systems is a leading journal that significantly contributes to the advancement of research and practice in the field of intelligent systems and artificial intelligence. Its comprehensive scope, rigorous standards, and international reach make it an invaluable resource for researchers, practitioners, and academics dedicated to the study and development of knowledge-based technologies.

Journal Home: Journal Homepage

Editor-in-Chief: Professor Jie Lu

Scope: The journal publishes high-quality research articles, reviews, and case studies that cover a wide range of topics related to knowledge-based systems and their applications across various domains. The scope of the journal includes both theoretical advancements and practical implementations.
Key areas covered by the journal include, but are not limited to:
Knowledge Engineering: Research on the methodologies and tools for the acquisition, representation, and management of knowledge within intelligent systems.
Expert Systems: Studies on systems that mimic the decision-making abilities of a human expert, including rule-based, case-based, and model-based reasoning systems.
Machine Learning: Exploration of algorithms and models that enable systems to learn from data and improve their performance over time.
Fuzzy Systems: Research on systems that use fuzzy logic to handle uncertain or imprecise information in decision-making processes.
Neural Networks: Studies on artificial neural networks and their applications in pattern recognition, classification, and predictive modeling.
Evolutionary Computation: Research on algorithms inspired by natural evolution, such as genetic algorithms, for solving complex optimization problems.
Data Mining: Techniques and applications for extracting meaningful patterns and knowledge from large datasets.
Natural Language Processing (NLP): Studies on systems that can understand, interpret, and generate human language.
Multi-Agent Systems: Research on systems composed of multiple interacting agents that can work collaboratively or competitively to achieve individual or collective goals.
Decision Support Systems: Development of systems that assist in decision-making processes by providing relevant information and analysis.