Neurocomputing - Elsevier - Impact Factor

Neurocomputing - Elsevier | 2025 Impact Factor:6.7| Cite Score:10.8 | Q1

Impact Factor and Journal Rank of Neurocomputing

About: Neurocomputing is a peer-reviewed journal published by Elsevier. It focuses on research in the field of neurocomputing, which encompasses artificial intelligence, machine learning, neural networks, and cognitive computing. The journal provides a platform for researchers, academics, and practitioners to publish original research articles, reviews, and case studies that advance the understanding and application of computational models inspired by the brain.
Objective: The primary objective of Neurocomputing is to promote research and innovation in the field of neurocomputing. The journal aims to explore methodologies, algorithms, and applications that enhance the development and performance of intelligent systems. By publishing high-quality research, the journal contributes to the advancement of neurocomputing solutions and their impact on various industries and domains.
Interdisciplinary Focus: Neurocomputing adopts an interdisciplinary approach, welcoming contributions from various fields related to neurocomputing, including but not limited to, Artificial Intelligence, Machine Learning, Neural Networks, Cognitive Computing, Computer Science, Data Science, Robotics, Bioinformatics, Computational Neuroscience, Signal Processing. This interdisciplinary perspective fosters collaboration and innovation, leading to the development of advanced techniques and solutions that address the complex challenges in neurocomputing.
Global Reach and Impact: With a broad international readership and authorship, Neurocomputing has a global reach and impact. Its publications contribute to the dissemination of knowledge and advancements in neurocomputing worldwide. The journal content influences both academic research and practical applications, driving progress in areas such as deep learning, pattern recognition, natural language processing, and intelligent systems.
High Standards and Rigorous Review: Maintaining high academic standards, Neurocomputing conducts a rigorous peer-review process. Each submitted manuscript undergoes thorough evaluation by experts in the field to ensure the quality, originality, and scientific rigor of the research. This stringent review process upholds the integrity and reputation of the journal, ensuring that only high-quality and impactful research is published.
Significance: Neurocomputing plays a significant role in advancing research and practice in neurocomputing. By providing a platform for the publication of cutting-edge research findings, the journal contributes to the growth of knowledge and innovation in neurocomputing principles and applications. It serves as an essential resource for researchers, engineers, and practitioners seeking to develop intelligent systems, improve computational models, and leverage neurocomputing technologies to solve real-world problems across various sectors.

Journal Home: Journal Homepage

Editor-in-Chief: Zidong Wang

Scope: The Neurocomputing Journal, published by Elsevier, is a leading peer-reviewed journal that focuses on the theory, design, implementation, and application of neurocomputing systems and technologies. It provides a platform for researchers, engineers, and practitioners to publish their innovative research and advancements in the field of neural networks and artificial intelligence (AI). Here is an overview of its key focus areas and scope:
1. Neural Network Theory and Models:
Research on the theoretical foundations and mathematical models of neural networks.
Topics covering neural network architectures, learning algorithms, and the analysis of neural network dynamics and behaviors.
2. Machine Learning and Deep Learning:
Advancements in machine learning techniques, including supervised, unsupervised, and reinforcement learning.
Research on deep learning architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs).
3. Cognitive Computing and Neuroscience:
Exploration of cognitive computing models and their connection to neuroscience and biological systems.
Research on brain-inspired computing, neural coding, and the simulation of cognitive processes using neurocomputing techniques.
4. Computational Intelligence and Optimization:
Topics covering computational intelligence approaches, such as fuzzy systems, evolutionary algorithms, and swarm intelligence.
Research on optimization techniques and their application in neurocomputing, including metaheuristics and hybrid algorithms.
5. Neuroinformatics and Bioinformatics:
Advancements in neuroinformatics and bioinformatics, focusing on the application of neurocomputing techniques to biological data analysis and interpretation.
Research on the modeling and simulation of biological systems, gene expression analysis, and protein structure prediction.
6. Pattern Recognition and Signal Processing:
Exploration of pattern recognition techniques and signal processing methods using neural networks.
Research on image processing, speech recognition, natural language processing, and time-series analysis.
7. Robotics and Autonomous Systems:
Advancements in the application of neurocomputing to robotics and autonomous systems.
Research on robotic perception, sensor fusion, path planning, and control strategies for intelligent robots.
8. Neurocomputing Applications:
Topics covering the application of neurocomputing techniques in various domains, including healthcare, finance, engineering, and social sciences.
Research on real-world applications, such as medical diagnosis, financial forecasting, fault detection, and human-computer interaction.
9. Hardware Implementations and Neuromorphic Computing:
Exploration of hardware implementations of neural networks, including digital, analog, and hybrid approaches.
Research on neuromorphic computing, brain-inspired hardware architectures, and the development of efficient and scalable neurocomputing systems.
10. Emerging Trends and Future Directions:
Advancements in emerging trends in neurocomputing, such as quantum neural networks, spiking neural networks, and cross-disciplinary approaches.
Research on the future directions of neurocomputing, including ethical considerations, societal impact, and the integration of neurocomputing with other technologies.