Neural Processing Letters

Aims & Scope

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 afore mentioned systems in classification, pattern recognition, signal processing, image and video processing, robotics, control, autonomous vehicles, financial forecasting, big data analytics, and other multidisciplinary applications.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	2.8
2024	2.60

Journal Rank

Year	Value
2024	8518

Journal Citation Indicator

Year	Value
2024	3943

SJR (SCImago Journal Rank)

Year	Value
2024	0.672

Quartile

Year	Value
2024	Q2

h-index

Year	Value
2024	72

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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 Neuroscience, 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.

• Least Squares Support Vector Machine Classifiers

  Citation: 7951

  Authors: J.A.K., J.

• Differential Evolution Training Algorithm for Feed-Forward Neural Networks

  Citation: 503

  Authors: Jarmo, Joni-Kristian, Jouni

• Accelerating a Recurrent Neural Network to Finite-Time Convergence for Solving Time-Varying Sylvester Equation by Using a Sign-Bi-power Activation Function

  Citation: 340

  Authors: Shuai, Sanfeng, Bo

• Deep CNN for Brain Tumor Classification

  Citation: 326

  Authors: Wadhah, Wajdi, Imen, Mohamed

• Image Inpainting: A Review

  Citation: 293

  Authors: Omar, Noor, Somaya, Younes

• Clustering Incomplete Data Using Kernel-Based Fuzzy C-means Algorithm

  Citation: 265

  Authors: Dao-Qiang, Song-Can

• Self-Adaptive Evolutionary Extreme Learning Machine

  Citation: 261

  Authors: Jiuwen, Zhiping, Guang-Bin

• Growing Grid — a self-organizing network with constant neighborhood range and adaptation strength

  Citation: 225

  Authors: Bernd

• Daily Activity Feature Selection in Smart Homes Based on Pearson Correlation Coefficient

  Citation: 219

  Authors: Yaqing, Yong, Keyu, Yiming, Jinghuan

• A Greedy EM Algorithm for Gaussian Mixture Learning

  Citation: 210

  Authors: Nikos, Aristidis

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