Neurocomputing

Aims & Scope

Neurocomputing welcomes theoretical contributions aimed at winning further understanding of neural networks and learning systems, including, but not restricted to, architectures, learning methods, analysis of network dynamics, theories of learning, self-organization, biological neural network modelling, sensorimotor transformations and interdisciplinary topics with artificial intelligence, artificial life, cognitive science, computational learning theory, fuzzy logic, genetic algorithms, information theory, machine learning, neurobiology and pattern recognition.

Neurocomputing covers practical aspects with contributions on advances in hardware and software development environments for neurocomputing, including, but not restricted to, simulation software environments, emulation hardware architectures, models of concurrent computation, neurocomputers, and neurochips (digital, analog, optical, and biodevices).

Neurocomputing reports on applications in different fields, including, but not restricted to, signal processing, speech processing, image processing, computer vision, control, robotics, optimization, scheduling, resource allocation and financial forecasting.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	6.5
2024	5.50

SJR (SCImago Journal Rank)

Year	Value
2024	1.471

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	216

Journal Rank

Year	Value
2024	2479

Journal Citation Indicator

Year	Value
2024	32873

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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
• Embase

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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.

• Extreme learning machine: Theory and applications

  Citation: 11104

  Authors: Guang-Bin, Qin-Yu, Chee-Kheong

• Time series forecasting using a hybrid ARIMA and neural network model

  Citation: 3127

  Authors: G.Peter

• A review on the attention mechanism of deep learning

  Citation: 2610

  Authors: Zhaoyang, Guoqiang, Hui

• A survey of deep neural network architectures and their applications

  Citation: 2596

  Authors: Weibo, Zidong, Xiaohui, Nianyin, Yurong, Fuad E.

• On hyperparameter optimization of machine learning algorithms: Theory and practice

  Citation: 2414

  Authors: Li, Abdallah

• Deep learning for visual understanding: A review

  Citation: 1976

  Authors: Yanming, Yu, Ard, Songyang, Song, Michael S.

• A comprehensive survey on support vector machine classification: Applications, challenges and trends

  Citation: 1727

  Authors: Jair, Farid, Lisbeth, Asdrubal

• Deep visual domain adaptation: A survey

  Citation: 1677

  Authors: Mei, Weihong

• Feature selection in machine learning: A new perspective

  Citation: 1646

  Authors: Jie, Jiawei, Shulin, Sheng

• GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification

  Citation: 1459

  Authors: Maayan, Idit, Eyal, Michal, Jacob, Hayit

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