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相关概念视频

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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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
732
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

329
The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and...
329
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

748
The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
748
Heuristics01:21

Heuristics

153
Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
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Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

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A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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使用灰狼优化算法增强基于类比的软件成本估计.

Taghi Javdani Gandomani1, Maedeh Dashti1, Sadegh Ansaripour2

  • 1Department of Computer Science, Faculty of Mathematical Sciences, Shahrekord University, Shahrekord, Chaharmahal and Bakhtiari, Iran.

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PubMed
概括
此摘要是机器生成的。

这项研究通过将灰狼优化 (GWO) 算法与基于类比的估计 (ABE) 集成来增强软件成本估计 (SCE). GWO-ABE方法提高了预测项目成本的准确性和稳定性,为软件开发规划提供了更可靠的解决方案.

关键词:
基于类比的估计估计.灰狼优化算法 灰狼优化算法软件成本估计软件成本估计

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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
  • 人工智能的人工智能

背景情况:

  • 准确的软件成本估计 (SCE) 对项目成功至关重要,但许多项目超过预算.
  • 基于类比的估计 (ABE) 是一种流行的SCE方法,但它的准确性可以得到改进.
  • 优化算法有可能提高ABE在软件大小和成本估计方面的精度.

研究的目的:

  • 通过将灰狼优化 (GWO) 算法与ABE集成,为SCE提出一种创新的方法.
  • 通过使用GWO优化其相似性函数来提高ABE的精度.
  • 评估拟议的基于GWO的ABE (GWO-ABE) 方法用于软件成本预测的有效性.

主要方法:

  • 数学建模并将GWO算法纳入ABE框架.
  • 优化ABE相似性函数使用欧几里德和曼哈顿距离测量,具有特征权重.
  • 使用交叉验证和与其他进化算法进行比较,对多个软件项目数据集进行GWO-ABE的严格评估.

主要成果:

  • 与传统的ABE相比,GWO-ABE方法显示了更高的估计准确性和模型稳定性.
  • 在关键性能指标方面,GWO-ABE取得了显著的改进,包括降低了相对错误的平均幅度 (MMRE) 和相对错误的中位数幅度 (MdMRE).
  • 该方法的结果是更高的预测百分比 (PRED),表明在多个数据集中具有优异的预测性能,特别是在欧几里德距离函数中.

结论:

  • 将GWO集成到ABE中有效地解决了通过优化特征权重来解决传统类比方法的局限性.
  • 拟议的GWO-ABE方法为软件项目成本估计提供了可靠和优质的解决方案.
  • 以GWO为例的Metaheuristic优化在推进软件估计技术,以更好地规划和管理项目方面发挥着重要作用.