Neural Processing Letters is an international journal that promotes fast exchange of the current state-of-the art contributions among the artificial neural network community of researchers and users. The Journal publishes technical articles on various aspects of artificial neural networks and machine learning systems. Coverage includes novel architectures, supervised and unsupervised learning algorithms, deep nets, learning theory, network dynamics, self-organization, optimization, biological neural network modelling, and hybrid neural/fuzzy logic/genetic systems. The Journal publishes articles on methodological innovations for the applications of the aforementioned systems in classification, pattern recognition, signal processing, image and video processing, robotics, control, autonomous vehicles, financial forecasting, big data analytics, and other multidisciplinary applications.

Journal highlights:

  • An international journal publishing state-of-the-art research results and innovative ideas on all aspects of artificial neural networks and machine learning
  • Coverage includes theoretical developments, biological models, learning theory, applications, hardware implementation, and more
  • Promotes timely exchange of information in the community of neural network researchers and users
  • High degree of author satisfaction: 91% of authors who answered a survey reported that they would definitely publish or probably publish in the journal again

Journal information

Editors-in-Chief
  • Michel Verleysen,
  • Mohamad H. Hassoun
Publishing model
Hybrid (Transformative Journal). How to publish with us, including Open Access

Journal metrics

2.565 (2021)
Impact factor
2.501 (2021)
Five year impact factor
69 days
Submission to first decision (Median)
175,905 (2021)
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Latest issue

Volume 54

Issue 4, August 2022

Special Issue: Secured Big Data Pre-processing (SBDP) (pp. 2533−2830) / Regular Papers (pp. 2831− 3527)

Latest articles

This journal has 56 open access articles

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About this journal

Electronic ISSN
1573-773X
Print ISSN
1370-4621
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  27. SCImago
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  32. Wanfang
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