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Journal Photo for IEEE Transactions on Neural Networks and Learning Systems
Peer reviewed only Open Access

IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS)

Publisher : IEEE Computational Intelligence Society
Computer Science Computer Science Applications Computer Networks
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e-ISSN 2162-2388
p-ISSN 2162-237X
Issue Frequency Monthly
Impact Factor 10.4
Est. Year 1990
Mobile 17325625501
Country United States
Language English
APC YES
Impact Factor Assignee Google Scholar
Email ieeetnnls@gmail.com

Journal Descriptions

IEEE Transactions on Neural Networks and Learning Systems publishes technical articles that deal with the theory, design, and applications of neural networks and related learning systems. The articles in this journal are peer reviewed in accordance with the requirements set forth in the IEEE PSPB Operations Manual (sections 8.2.1.C & 8.2.2.A). Each published article was reviewed by a minimum of two independent reviewers using a single-anonymous peer review process, where the identities of the reviewers are not known to the authors, but the reviewers know the identities of the authors. Articles will be screened for plagiarism before acceptance.

IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS) is :-

  • International, Peer-Reviewed, Open Access, Refereed, Computer Science, Computer Science Applications, Computer Networks, Communications, Software, Artificial Intelligence, Broadcast Technologies , Online or Print , Monthly Journal

  • UGC Approved, ISSN Approved: P-ISSN P-ISSN: 2162-237X, E-ISSN: 2162-2388, Established: 1990, Impact Factor: 10.4
  • Does Not Provide Crossref DOI

  • Indexed in: Scopus, WoS

  • Not indexed in DOAJ, PubMed, UGC CARE

Indexing

Publications of IEEE TNNLS

Guest Editorial: Deep Neural Networks for Graphs: Theory, Models, Algorithms, and Applications
Pietro Liò April, 2024
Deep neural networks for graphs (DNNGs) represent an emerging field that studies how the deep learning method can be generalized to graph-structured data. Since graphs are a powerful and fle...