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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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相关实验视频

Updated: Jan 9, 2026

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一个混合CNN转换器框架,由灰狼算法优化,用于准确的手语识别.

Abdirahman Osman Hashi1,2, Siti Zaiton Mohd Hashim3, Seyedali Mirjalili4,5

  • 1Department of Computer Science, Faculty of Computing, SIMAD University, Mogadishu, Somalia.

Scientific reports
|December 10, 2025
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概括

这项研究提出了一种新的深度学习模型,用于识别美国手语 (ASL) 的手势. 灰狼优化卷积变压器网络在动态手势识别中实现了高精度和效率.

关键词:
卷积神经网络是一种卷积神经网络.灰狼优化 灰狼优化手的手势识别手势识别超参数优化超参数优化标志语言识别功能 标志语言识别功能

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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 精确地识别动态的手势,特别是美国手语 (ASL),对于辅助通信至关重要.
  • 现有的模型在有效捕捉复杂的手语识别的空间和时间特征方面面临挑战.

研究的目的:

  • 引入和评估灰狼优化卷积变压器网络 (GWO-CTransNet) 以提高动态手势识别.
  • 为了利用混合深度学习方法,将CNN,变压器和GWO结合起来,以获得卓越的性能.

主要方法:

  • 开发了一种混合深度学习框架,将卷积神经网络 (CNN) 集成用于空间特征提取和变压器用于时间序列建模.
  • 采用灰狼优化 (GWO) 进行高效的超参数调整和模型配置.
  • 验证了用于静态和动态符号分类的基准数据集 (ASL Alphabet,ASL MNIST) 的模型.

主要成果:

  • 实现了最先进的性能,精度为99.40%,F1分数为99.31%,MCC为0.988和AUC为0.992.
  • 超过了现有的模型,包括PCA-IGWO,KPCA-IGWO,GWO-CNN和AEGWO-NET.
  • 在各种环境条件下,在实时手势检测方面表现出稳健性.

结论:

  • GWO-CTransNet提供了一种强大且可扩展的解决方案,用于基于视觉的手语识别.
  • 该模型提供了高精度,快速推断和适应现实世界辅助通信技术的适应性.
  • 集成GWO显著提高了融合速度和模型通用性.