Neural Computing And Applications Letpub – Free Forever

In modern smart manufacturing environments, the accurate and real-time detection of surface defects remains a critical challenge due to the scarcity of defective samples and the high variability of defect scales. Traditional Convolutional Neural Networks (CNNs) often struggle to extract meaningful features from small or subtle defects in complex industrial backgrounds. This paper proposes a novel hybrid deep learning framework, named the , to address these limitations. The proposed architecture integrates a pre-trained ResNet-50 backbone with a custom-designed Multi-Scale Feature Fusion (MSFF) module and a Convolutional Block Attention Module (CBAM). The MSFF module captures hierarchical contextual information at different resolutions, while the CBAM highlights salient defect regions while suppressing background noise. We evaluated the proposed method on three publicly available benchmark datasets: NEU-DET (steel surfaces), PCB-DAT (printed circuit boards), and MT-DEF (magnetic tile defects). Experimental results demonstrate that AGMS-Net achieves a mean Average Precision (mAP) of 89.4% on the NEU-DET dataset, outperforming state-of-the-art methods such as YOLOv5 and Faster R-CNN by a margin of 3.2% and 4.1%, respectively. Furthermore, the model maintains a competitive inference speed, making it suitable for real-time industrial deployment.

Official Springer pages rarely state acceptance rates. However, synthesized LetPub user data suggests:

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on the title page, specifying funding organizations in full. Define abbreviations at the first mention. Open Access : The journal offers open access options under Creative Commons licenses (CC BY or CC BY-NC-ND). Springer Nature Link Aims and Scope

面对当前的索引状态不确定性，高水平的神经计算及其应用研究团队和个人研究者应该采取**"理性接触、谨慎选择"**的策略。在等待期刊状态修复或转入正轨的过程中，不妨将LetPub当作一个多维度数据决策平台——既看影响因子的高位增长，也看被剔除的红色提醒；既看CiteScore的Q1底气，也看社区里一年等审、失望吐槽的真实声音；既看重Springer品牌对稿件质量的兜底保障，也明确自身考核体系的刚性约束。 neural computing and applications letpub

The journal’s title is Neural Computing . 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.

(www.letpub.com) is an online service founded by ACCDON LLC, a US-based company, designed to assist non-native English-speaking researchers in publishing in high-quality international journals. Over time, it has evolved from a language editing service into a comprehensive ecosystem that includes a powerful scientific journal selector tool and a thriving community of peer reviews. Researchers turn to LetPub to: In modern smart manufacturing environments, the accurate and

Supervised and unsupervised learning, adaptive computing, and pattern recognition.

Delays usually occur if the reviewer database lacks available experts in a highly niche sub-field. Acceptance Rate a US-based company