Knowledge-Based Systems

Aims & Scope

Knowledge-Based Systems is an international, interdisciplinary and applications-oriented journal.

This journal focuses on systems that use knowledge-based (KB) techniques to support human decision-making, learning and action; emphases the practical significance of such KB-systems; its computer development and usage; covers the implementation of such KB-systems: design process, models and methods, software tools, decision-support mechanisms, user interactions, organizational issues, knowledge acquisition and representation, and system architectures.

This journal's current leading topics are but not limited to: • Big data techniques and methodologies, data-driven information systems, and knowledge acquisition • Cognitive interaction and intelligent human interfaces • Recommender systems and E-service personalization • Intelligent decision support systems, prediction systems and warning systems • Computational and artificial intelligence based systems and uncertain information processes • Swarm intelligence and evolutionary computing • Knowledge engineering, machine learning-based systems and web semantics The journal also welcomes papers describing novel applications of knowledge based systems in any human endeavor: ranging from financial technology to engineering to health science or any other domain impacted by Artificial Intelligence technologies and its associated techniques and systems.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	7.6

SJR (SCImago Journal Rank)

Year	Value
2024	1.934

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	188

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Business, Management and Accounting, Computer Science and Decision Sciences, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• SCA: A Sine Cosine Algorithm for solving optimization problems

  Citation: 4802

  Authors: Seyedali

• Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm

  Citation: 3998

  Authors: Seyedali

• Recommender systems survey

  Citation: 2403

  Authors: J., F., A., A.

• Equilibrium optimizer: A novel optimization algorithm

  Citation: 1937

  Authors: Afshin, Mohammad, Brent, Seyedali

• Graph embedding techniques, applications, and performance: A survey

  Citation: 1331

  Authors: Palash, Emilio

• A survey on federated learning

  Citation: 1330

  Authors: Chen, Yu, Hang, Bin, Weihong, Yuan

• A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example

  Citation: 1322

  Authors: Wen-Tsao

• AutoML: A survey of the state-of-the-art

  Citation: 1121

  Authors: Xin, Kaiyong, Xiaowen

• Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems

  Citation: 1036

  Authors: Gaurav, Vijay

• A survey on opinion mining and sentiment analysis: Tasks, approaches and applications

  Citation: 959

  Authors: Kumar, Vadlamani

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