A concise overview of "Neural Computing and Applications" — publishing with LetPub guidance
The editors are sensitive to overhyped terms like “artificial intelligence” used generally. Be specific: convolutional neural network, long short-term memory, attention mechanism, variational autoencoder, etc. neural computing and applications letpub
Our study explores [insert brief topic, e.g., "new hybrid neuro-fuzzy systems for traffic forecasting"]. We found that [insert one key result, e.g., "our model improves accuracy by 15% over standard LSTM networks"]. A concise overview of "Neural Computing and Applications"
Disclaimer: This article is for informational purposes only. Journal metrics and editorial policies change over time. Authors are advised to consult the official Springer website for the most current instructions for authors. We found that [insert one key result, e
| Journal | Impact Factor (approx) | Review Speed | Acceptance Rate | Publisher | |---------|------------------------|--------------|----------------|-----------| | | 5.6 | 4–6 months | ~25% | Springer | | Neurocomputing | 6.0 | 3–5 months | ~22% | Elsevier | | Neural Networks | 7.8 | 5–7 months | ~18% | Elsevier | | IEEE Trans. on Neural Networks and Learning Systems | 10.4 | 6–9 months | ~12% | IEEE | | Applied Soft Computing | 8.7 | 4–6 months | ~20% | Elsevier |
According to user reports on LetPub, the average review speed was around 9 months . It wasn't the fastest route, but for a piece of work this significant, Elena valued the rigorous peer review over a rushed decision.