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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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Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
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Force Classification01:22

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
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Introduction to Learning01:18

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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
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EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
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Proactive detection of anomalous behavior in Ethereum accounts using XAI-enabled ensemble stacking with Bayesian optimization.

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通过动态优化和字符级深度学习在云环境中增强网络鱼检测.

Vishnukumar Ravula1, Mangayarkarasi Ramaiah1

  • 1School of Computer Science Engineering and Information Systems (SCORE), Vellore Institute of Technology University, Vellore, Tamil Nadu, India.

PeerJ. Computer science
|June 26, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种新的动态算术优化算法,使用深度学习驱动的网络鱼URL分类 (DAOA-DLPC) 模型来检测云支持的车辆互联网 (IoV) 环境中的网络鱼URL. 该DAOA-DLPC模型在识别安全和网络鱼URL方面实现了高精度,提高了安全性.

关键词:
算术优化算法算术优化算法云计算是一种云计算.网络安全 网络安全深度学习是一种深度学习.网络鱼攻击的攻击.

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科学领域:

  • 网络安全 网络安全
  • 云计算 云计算 云计算 云计算
  • 汽车的互联网 (IoV)

背景情况:

  • 云计算和IoV环境面临着越来越多的网络鱼URL威胁.
  • 传统的机器学习方法与动态的网络鱼格局作斗争.
  • 由于网络鱼攻击,IoV中的服务可靠性受到威胁.

研究的目的:

  • 提出一种新的动态算术优化算法,使用深度学习驱动的网络鱼URL分类 (DAOA-DLPC) 模型.
  • 为了提高网络鱼URL检测在云支持的IoV基础设施.
  • 为了提高识别恶意URL的准确性和效率.

主要方法:

  • 利用字符级嵌入式进行有效的URL模式捕获.
  • 集成嵌入式与多头注意力和双向门式循环单元 (MHA-BiGRU) 深度学习模型.
  • 使用动态算术优化算法 (DAOA) 进行超参数调整.

主要成果:

  • DAOA-DLPC模型实现了98.85%的准确性,98.49%的回忆率和98.38%的F1分数.
  • 与动态环境中的传统模型相比,在动态环境中表现出优越的性能.
  • 通过注意力机制和动态优化展示了计算效率.

结论:

  • DAOA-DLPC模型为IoV中的网络鱼URL检测提供了可行和有效的解决方案.
  • 该模型可以实时学习新的网络鱼攻击形式,并减少假阳性.
  • 拟议的方法显著改善了安全和不安全的URL之间的区别.