Neural Computing And Applications Letpub

is a premier, peer-reviewed international journal published by Springer Nature that isolates the bridge between theoretical neural network algorithms and real-world industrial engineering. For global researchers navigating complex submission timelines, the LetPub Journal Indexing and Analytics Platform serves as an essential repository for tracking its real-time metrics, peer review speeds, and acceptance behaviors. This comprehensive guide provides a deep-dive analysis of NCAA’s core academic profile, key metrics pulled from LetPub analytics, its multi-disciplinary scope, and an actionable roadmap to maximize your acceptance probability. Core Journal Metrics at a Glance

Journal Quartile The Neural Computing And Applications is ranked in Q1. Journal Seeker

This makes NCAA more selective than lower-tier IEEE Access but slightly less competitive than Neurocomputing or IEEE TNNLS . neural computing and applications letpub

Published by , Neural Computing and Applications bridges the gap between theoretical neural network models and real-world engineering solutions.

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| Metric | Value | |--------|-------| | | Springer | | ISSN | 0941-0643 (Print), 1433-3058 (Online) | | Latest Impact Factor | 4.5 – 5.5 (varies by year; ~4.9 as of 2023–24) | | 5-Year Impact Factor | ~4.7 | | CiteScore | ~8.1 | | JCR Category | Computer Science, Artificial Intelligence (Q2) | | Eigenfactor | ~0.002 |

Neural computing, also known as neural networks, is a subfield of artificial intelligence that mimics the structure and function of the human brain. It involves the use of artificial neural networks (ANNs) to analyze data, recognize patterns, and make decisions. ANNs are composed of interconnected nodes or "neurons" that process and transmit information, enabling the network to learn and adapt. Core Journal Metrics at a Glance Journal Quartile

The LetPub page provides direct links, but here is the process:

| Metric | Value | |--------|-------| | | ~5.0–6.0 (check current on LetPub) | | 5-Year IF | ~5.2 | | CiteScore | ~8.0–9.0 | | Scimago SJR | Q1 (Computer Science Applications, Artificial Intelligence) | | Eigenfactor | ~0.008 | | H-Index | ~100+ | ： | Metric | Value | |--------|-------| |

Most contributors report . This is slower than high-volume venues like IEEE Access but faster than many traditional journals. One reviewer noted: “First round took 4 months. Minor revisions took 1 month. Accept after two rounds – total 6 months.”

: Video-based traffic sign recognition frameworks executed under real-world constraints.