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相关概念视频

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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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Machines: Problem Solving I01:22

Machines: Problem Solving I

300
A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
300
Machines: Problem Solving II01:30

Machines: Problem Solving II

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

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Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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相关实验视频

Updated: Jun 7, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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利用机器学习算法来分配分布式敏捷软件开发中的任务.

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

  • 1Department of Software Engineering, Faculty of Information Technology, Isra University, 11622, Amman, Jordan.

Heliyon
|November 18, 2024
PubMed
概括

机器学习在分布式敏捷软件开发 (DASD) 中有效地分配任务. 随机森林实现了96.7%的准确性,提高了效率并防止了项目失败.

关键词:
这就是DASD.分布敏捷软件开发分布式敏捷软件开发机器学习 (ML) 是指机器学习.软件工程 软件工程 软件工程项目管理软件项目管理软件任务分配 任务分配

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科学领域:

  • 计算机科学 计算机科学
  • 软件工程 软件工程 软件工程

背景情况:

  • 分布敏捷软件开发 (DASD) 在全球越来越多地被采用.
  • 有效的任务分配对于减轻项目失败和客户不满等风险至关重要.
  • 由于全球人才采购和降低成本,DASD中出现了协调和沟通方面的挑战.

研究的目的:

  • 在DASD中应用机器学习 (ML) 预测算法,以实现最佳的任务对角色分配.
  • 帮助软件管理人员提高任务分配的效率和有效性.
  • 为应对DASD固有的协调和沟通挑战.

主要方法:

  • 数据集预处理涉及清理,规范化和分成培训,验证和测试集.
  • 评估了四个ML分类器:随机森林,决策树,K-最近邻居 (K-NN) 和AdaBoost.
  • 绩效的评估是基于任务分配的预测准确性.

主要成果:

  • 随机森林在任务分配预测方面表现出卓越的表现,准确率为96.7%.
  • K-最近的邻居 (K-NN) 实现了94.2%的准确性.
  • 决策树和AdaBoost显示了可比的结果,分别准确率为93.5%和93%.

结论:

  • 机器学习模型在解决DASD环境中的任务分配复杂性方面非常有效.
  • 该研究证实了ML在优化分布式软件项目的资源管理方面的潜力.
  • 这些有希望的结果表明,在提高DASD项目成功率方面,ML的应用范围更广.