Neural Computing And Applications Letpub ((full)) Link

Neural Computing and Applications (NCAA), published by Springer, is a Q1-ranked journal focusing on practical neural computing applications with a reported 2025 impact factor of approximately 4.7 . According to

, the journal has an average review speed of roughly 9 months and is widely indexed . For more details, visit LetPub.

Neural Computing and Applications (NCAA) is a high-profile international journal published by Springer. It is highly regarded for its focus on the practical application of neural computing and related intelligent systems.

According to the latest data from LetPub and other academic databases: Key Metrics & Ranking

Ranking: Consistently ranked in Q1 for Artificial Intelligence and Software according to Scopus and SJR.

Impact Score: The latest impact score is approximately 4.5 to 6.0, showing a general upward trend over the last few years.

CiteScore: Approximately 11.7 (2025 data), placing it in the top 10%–13% of journals in its field. neural computing and applications letpub

CAS Partition: Generally falls into Zone 3 (中科院3区) in the Chinese Academy of Sciences (CAS) ranking system. Submission & Review Experience

Acceptance Rate: Users on LetPub report an average acceptance rate of around 50%, though internal editorial standards remain rigorous.

Review Speed: The process is known to be relatively thorough, with an average review cycle of approximately 9 months.

Publication Model: Offers a hybrid model. Authors can choose traditional publishing (no fee) or Open Access for a fee (APC approximately $2,780). Scope & Reputation

The journal is particularly popular among scholars from China, India, and Iran. It covers a wide array of topics, including: Adaptive computing and machine learning algorithms. Fuzzy logic, genetic algorithms, and neuro-fuzzy systems.

Real-world applications in forecasting, diagnostics, and intelligent control. Key Metrics (from LetPub) | Metric | Value

Editorial Note: In August 2024, the journal was temporarily flagged as being "dropped from the SCIE list" by some trackers, but most current indices (including Scopus) still list it as active and reputable.

Here is the detailed information regarding Neural Computing and Applications from LetPub (a popular journal evaluation and submission system resource in China).

Key Metrics (from LetPub)

| Metric | Value | | :--- | :--- | | Latest Impact Factor (JCR 2023) | 4.5 | | JCR Category Quartile | Q2 (Computer Science, Artificial Intelligence) | | 5-Year Impact Factor | 5.0 | | CiteScore | 8.4 | | SNIP | 1.342 | | H-Index | 87 |

LetPub User Pros & Cons (from real researcher reviews)

Pros:

• Fast turnaround (often 2–3 months from submission to acceptance).
• Increasing impact factor (from ~2.0 to 4.5 in 5 years).
• No strong bias against purely theoretical work if application is shown.
• Good reputation in China for AI/ML researchers aiming for SCI publication.

Cons:

• Reviewer quality can be inconsistent (some reports are superficial).
• Page charges for color figures (if printed in traditional subscription mode).
• High volume of submissions leads to desk rejection if novelty is low.

5. Submission & Publication Fees

| Model | Cost | |-------|------| | Traditional subscription | No fee for authors (free to publish) | | Open Access (OA) | ~$2790–3290 USD (APC, may vary) | LetPub User Pros & Cons (from real researcher

Check Springer’s latest APC – LetPub usually mirrors this.

Conclusion

The phrase “neural computing and applications letpub” represents more than a search query—it reflects a researcher’s desire for transparency, efficiency, and strategic alignment. By leveraging LetPub’s user-generated intelligence, you can navigate NCAA’s review process with confidence. Remember to focus on reproducible, application-driven neural methods, adhere strictly to Springer formatting, and always cross-check the latest LetPub comments before submission.

Last updated: April 2025. Impact factors and APCs are subject to change. Always verify directly with Springer and LetPub before final submission.

7. Submission Strategy and Reviewer Response Tips

• Choose target journal aligned with scope and novelty level; tailor presentation to readership (applied vs theoretical focus).
• Anticipate reviewer concerns: robustness, reproducibility, baseline strength, novelty; address them proactively in the manuscript.
• In rebuttal: respond point-by-point, supply additional experiments if needed, and highlight edits with clear markers.

LetPub’s Verdict (paraphrased)

Neural Computing and Applications is a solid Q2 journal for neural network and application-oriented AI research. It is not as selective as Pattern Recognition or Neurocomputing, but it is easier than IEEE TNNLS or Neural Networks. Suitable for PhD graduates and early-career researchers needing SCI publications with reasonable speed.

2. Reviewer Quality is High

Unlike some predatory or borderline journals, NCAA reviewers are generally experts in neural networks. Comments are detailed and constructive. One LetPub user wrote: “Two reviewers, each gave 10+ specific comments on methodology and experiments. Improved my paper significantly.”

3. Introduction

• Background: Industry 4.0 demands automated quality control systems to replace manual inspection, which is subjective and labor-intensive.
• Problem Statement: While CNNs have achieved success in general object detection, industrial defect detection presents unique hurdles:
  1. Data Imbalance: Defective samples are rare compared to normal samples.
  2. Scale Variation: Defects can range from micro-cracks to large scratches.
  3. Background Noise: Industrial textures often resemble defects, leading to high false positive rates.
• Contribution: This study introduces a lightweight yet robust hybrid architecture specifically optimized for small dataset scenarios and multi-scale feature extraction.