Computer Science Review

Aims & Scope

Computer Science Review intends to fulfil a need in the Computer Science community by publishing research surveys and expository overviews in computer science and related fields.

The reviews are aimed at a general computer science audience seeking a full and expert overview of the latest in computer science research.

The journal will publish research surveys and expository overviews in computer science.

Articles from other fields are welcome, as long as their content is relevant to computer science.

Articles should be of sufficient scientific interest and help to advance the fundamental understanding of ongoing research, applied or theoretical, for a general computer science audience.

The treatment of each topic should be more than a catalogue of known results.

Emphasis should be on clarity and originality of presentation and each survey should add insight to the topic under review.

A survey may typically contain the following elements: -Introduction (including motivation and historical remarks) -Outline of the Survey -Basic concepts, examples and results (with sketches of the proofs) -Comments on the relevance of the results, relations to other results and applications -Open problems -Critical review of the relevant literature -Comprehensive bibliography

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	12.7

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	88

Journal Rank

Year	Value
2024	618

Journal Citation Indicator

Year	Value
2024	3874

SJR (SCImago Journal Rank)

Year	Value
2024	3.276

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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 and Mathematics, 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.

• Reservoir computing approaches to recurrent neural network training

  Citation: 2206

  Authors: Mantas, Herbert

• Graph clustering

  Citation: 1441

  Authors: Satu Elisa

• A survey on deep learning and its applications

  Citation: 1130

  Authors: Shi, Ping, Khushnood

• Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE)

  Citation: 621

  Authors: Farzana, Samira, Bassant

• Fault tree analysis: A survey of the state-of-the-art in modeling, analysis and tools

  Citation: 576

  Authors: Enno, Mariëlle

• Traditional and recent approaches in background modeling for foreground detection: An overview

  Citation: 554

  Authors: Thierry

• Agent Based Modelling and Simulation tools: A review of the state-of-art software

  Citation: 524

  Authors: Sameera, Georgios K., Pierre, Gregory M.P.

• A Survey on quantum computing technology

  Citation: 465

  Authors: Laszlo, Sandor

• The evolution of sentiment analysis—A review of research topics, venues, and top cited papers

  Citation: 462

  Authors: Mika V., Daniel, Miikka

• A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability

  Citation: 346

  Authors: Xiaowei, Daniel, Wenjie, James, Youcheng, Emese, Min, Xinping

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