Back to Top
Go Back
Journal Photo for ACM Computing Surveys
Peer reviewed only Open Access

ACM Computing Surveys (ACS CS)

Publisher : Association for Computing Machinery
Mathematics Computing Machinery Theoretical Computer Science
Claim Your Journal Get PRC Visit Website
e-ISSN 1557-7341
p-ISSN 0360-0300
Issue Frequency Quarterly
Impact Factor 16.6
Est. Year 1969
Mobile 2128697440
DOI YES
Country United States
Language English
APC YES
Impact Factor Assignee SJR
Email csur-admin@acm.org

Journal Descriptions

ACM is pleased to announce that its Publications Board has approved an updated Peer Review Policy. If you have any questions regarding the update, the associated FAQ addresses topics such as confidentiality, the use of large language models in the peer review process, conflicts of interest, and several other relevant concerns. If there are any issues that are not addressed in the FAQ, please contact ACM’s Director of Publications, Scott Delman.

ACM Computing Surveys (ACS CS) is :-

  • International, Peer-Reviewed, Open Access, Refereed, Mathematics, Computing Machinery, Theoretical Computer Science, Computer Science, General Computer Science , Online or Print , Quarterly Journal

  • UGC Approved, ISSN Approved: P-ISSN P-ISSN: 0360-0300, E-ISSN: 1557-7341, Established: 1969, Impact Factor: 16.6
  • Provides Crossref DOI

  • Indexed in: Scopus

  • Not indexed in WoS, DOAJ, PubMed, UGC CARE

Indexing

Publications of ACS CS

Machine Learning Methods for Data Association in Multi-Object Tracking
Panos M. Pardalos August, 2020
Data association is a key step within the multi-object tracking pipeline that is notoriously challenging due to its combinatorial nature. A popular and general way to formulate data associat...
State of the Art and Potentialities of Graph-level Learning
Pietro Liò October, 2024
Graphs have a superior ability to represent relational data, such as chemical compounds, proteins, and social networks. Hence, graph-level learning, which takes a set of graphs as input, has...
Explaining the Explainers in Graph Neural Networks: a Comparative Study
Pietro Liò August, 2024
Following a fast initial breakthrough in graph based learning, Graph Neural Networks (GNNs) have reached a widespread application in many science and engineering fields, prompting the need f...