IEEE Intelligent Systems

Aims & Scope

IEEE Intelligent Systems serves users, managers, developers, researchers, and purchasers who are interested in intelligent systems and artificial intelligence, with particular emphasis on applications.

Typically they are degreed professionals, with backgrounds in engineering, hard science, or business.

The publication emphasizes current practice and experience, together with promising new ideas that are likely to be used in the near future.

Sample topic areas for feature articles include knowledge-based systems, intelligent software agents, natural-language processing, technologies for knowledge management, machine learning, data mining, adaptive and intelligent robotics, knowledge-intensive processing on the Web, and social issues relevant to intelligent systems.

Also encouraged are application features, covering practice at one or more companies or laboratories; full-length product stories (which require refereeing by at least three reviewers); tutorials; surveys; and case studies.

Often issues are theme-based and collect articles around a contemporary topic under the auspices of a Guest Editor working with the EIC.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	6.1

SJR (SCImago Journal Rank)

Year	Value
2024	1.326

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	145

Journal Rank

Year	Value
2024	2985

Journal Citation Indicator

Year	Value
2024	1406

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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 Computer Science, 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.

• The Unreasonable Effectiveness of Data

  Citation: 1150

  Authors: Alon, Peter, Fernando

• Semantic Web services

  Citation: 1060

  Authors: S.A., T.C.

• The Semantic Web Revisited

  Citation: 1014

  Authors: N., T., W.

• Affective Computing and Sentiment Analysis

  Citation: 986

  Authors: Erik

• New Avenues in Opinion Mining and Sentiment Analysis

  Citation: 907

  Authors: Erik, Bjorn, Yunqing, Catherine

• Ontology learning for the Semantic Web

  Citation: 882

  Authors: A., S.

• FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare

  Citation: 740

  Authors: Yiqiang, Xin, Jindong, Chaohui, Wen

• Agents and the Semantic Web

  Citation: 580

  Authors: J.

• Creating Semantic Web contents with Protege-2000

  Citation: 559

  Authors: N.F., M., S., M., R.W., M.A.

• Harnessing the Crowdsourcing Power of Social Media for Disaster Relief

  Citation: 537

  Authors: Huiji, Geoffrey, Rebecca

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