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相关概念视频

Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

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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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Classification of Signals01:30

Classification of Signals

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
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Classification of Systems-I01:26

Classification of Systems-I

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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Classification of Systems-II01:31

Classification of Systems-II

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Improving Translational Accuracy02:07

Improving Translational Accuracy

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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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Distribution Reliability and Automation01:25

Distribution Reliability and Automation

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Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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相关实验视频

Updated: Jun 13, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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检测重构类型的软件提交消息基于集体机器学习算法.

Dimah Al-Fraihat1, Yousef Sharrab2, Abdel-Rahman Al-Ghuwairi3

  • 1Department of Software Engineering, Faculty of Information Technology, Isra University, Amman, 11622, Jordan. d.fraihat@iu.edu.jo.

Scientific reports
|September 12, 2024
PubMed
概括

本研究引入了一种先进的机器学习方法,以准确地检测来自提交消息的软件重构类型. 使用XGBoost和TF-IDF的新方法实现了100%的准确性,显著改善了代码质量分析.

关键词:
承诺进行分类.文档 文档 文档 文档整体增强机器学习进行重构,即重构.

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相关实验视频

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科学领域:

  • 软件工程 软件工程 软件工程
  • 机器学习 机器学习
  • 自然语言处理自然语言处理.

背景情况:

  • 重构增强软件设计,而不会改变外部行为.
  • 提交消息对于跟踪代码更改至关重要.
  • 从提交消息中分类重构类型具有挑战性,但对软件质量至关重要.

研究的目的:

  • 为准确的重构类型检测提出一种全新的集体机器学习方法.
  • 改进现有的分类重构文档分类方法.

主要方法:

  • 使用了四个整体机器学习算法.
  • 雇佣文本清理,预处理和功能工程 (TF-IDF,词袋).
  • 应用超参数优化和二进制转换 (一对一,一对其余).

主要成果:

  • TF-IDF特征工程技术的性能优于其他方法.
  • 使用TF-IDF的XGBoost算法在所有指标上实现了100%的准确性.
  • 结果超过了同一个数据集的当前最先进的性能.

结论:

  • 拟议的整体机器学习方法有效地检测重构类型.
  • 这种方法显著提高了软件的内部质量评估.
  • 实现100%的准确性证明了高级机器学习在软件工程中的潜力.