The journal covers the spectrum of neural network research but places a distinct emphasis on application-oriented papers. The scope includes, but is not limited to:
Key Takeaway for Authors: If your paper is purely mathematical without experimental validation or a clear application context, NCA may not be the best fit. The journal prefers papers that demonstrate how a proposed method solves a specific problem.
| Field | Details | |-------|---------| | Full Title | Neural Computing and Applications | | Publisher | Springer (Springer Nature) | | ISSN | 0941-0643 (print) / 1433-3058 (online) | | Frequency | Monthly (some years: 24 issues) | | Open Access | Hybrid (traditional subscription or OA with APC) | | LetPub Journal Rank | Q1 / Q2 (varies by category) |
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Neural Computing and Applications (NCAA): Journal Insights via LetPub
Neural Computing and Applications (NCAA) is a high-profile international journal published by Springer London that focuses on the practical application of neural computing and related techniques. For many researchers, platforms like LetPub are essential for tracking the journal's real-time impact factor, peer-review speed, and submission difficulty based on community feedback. Key Metrics and Rankings (2025-2026)
According to the latest data available on LetPub and other indexing services, NCAA maintains a strong position in the field of Artificial Intelligence: neural computing and applications letpub
Impact Factor: The 2-year impact is approximately 3.986, with real-time estimates for 2026 trending around 4.7.
Journal Quartile: It is consistently ranked as a Q1 journal in both "Artificial Intelligence" and "Software" categories according to Scopus/CiteScore.
H-index: The journal boasts a significant H-index of 146, reflecting its long-term influence in computer science.
CAS Partition: In the Chinese Academy of Sciences (CAS) ranking system, it is often categorized in District 3 (Engineering Technology). Scope and Featured Research
NCAA publishes original research, reviews, and case studies. Its scope is broad, covering everything from theoretical algorithms to hardware implementations. Key topics include:
Machine Learning: Deep learning, supervised/unsupervised learning, and reinforcement learning. The journal covers the spectrum of neural network
Hybrid Systems: Neuro-fuzzy systems, genetic algorithms, and evolutionary computing.
Practical Applications: Intelligent forecasting, image segmentation (medical imaging), emotion recognition, and industrial diagnostics. Submission Experience and Peer Review
Community reports on LetPub suggest that while the journal is highly regarded, authors should prepare for a rigorous and sometimes lengthy process:
Review Time: The average time from submission to a final decision is approximately 9 months.
Acceptance Rate: Community data indicates an acceptance rate of roughly 50%, though this varies significantly by paper quality.
Author Tips: Contributors often mention that the journal is "friendly" to Chinese scholars, who account for over 40% of the published articles. Publication Models and Costs Key Takeaway for Authors: If your paper is
NCAA offers a hybrid publishing model. Authors can choose between:
Subscription Model: Traditional publishing with no Article Processing Charge (APC).
Open Access (OA): Articles are made freely available to the public for an APC of approximately $3,190 USD (£2,290 / €2,590).
For authors looking to improve their chances of acceptance, services like LetPub's English Editing are often used to meet the journal's strict language requirements.
The journal covers theory and practice of neural computing and its applications, including:
"A Hybrid Multi-Scale Deep Learning Framework for Defect Detection in Industrial Manufacturing: Integrating Attention Mechanisms with Transfer Learning"
LetPub allows verified reviewer comments – worth checking recent ones before submitting.
The journal’s title is Neural Computing and Applications. Pure theoretical papers without real or simulated applications are often rejected. Include a section on: case study, benchmark comparison with state-of-the-art, or deployment scenario.