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相关概念视频

Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
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Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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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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Steps in Outbreak Investigation

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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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Statistical Software for Data Analysis and Clinical Trials01:12

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Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
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SAS, short for Statistical Analysis System, is a powerful data analysis, management, and visualization tool. Developed by the SAS Institute in the early 1970s, SAS has evolved into a comprehensive software suite used across various industries for statistical analysis, business intelligence, and predictive modeling.
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相关实验视频

Updated: May 6, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction
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A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

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在敏捷软件开发中用于预测分析的智能技术.

Sahana P Shankar1,2, Shilpa Shashikant Chaudhari3, Vinaytosh Mishra4,5

  • 1Department of Computer Science and Engineering, M.S. Ramaiah Institute of Technology (Affiliated to Visvesvaraya Technological University, Belgaum), Bengaluru, Karnataka, 560054, India. sahanaprabhushankar@gmail.com.

Scientific reports
|February 25, 2026
PubMed
概括
此摘要是机器生成的。

机器学习模型使用敏捷努力估计软件数据集预测软件问题解决时间. XGBoost在各种错误指标上表现出卓越的性能,提高了项目管理和资源配置.

关键词:
在Ages数据库中,Ages数据集是指Ages的数据集.敏捷的敏捷是一种敏捷的行为.深度学习 (Deep Learning) 是一种深度学习.努力估计的努力估计.在 GitHub 上,我们可以找到 GitHub.机器学习 机器学习

相关实验视频

Last Updated: May 6, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

70.1K

科学领域:

  • 软件工程 软件工程 软件工程
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 软件开发的复杂性需要先进的项目管理工具.
  • 预测分析用于解决问题的时间估计,改善决策和资源分配.
  • 来自GitHub的敏捷努力估计软件 (AgES) 数据集为分析提供了丰富的功能.

研究的目的:

  • 分析机器学习模型以预测软件问题解决时间.
  • 用MAE,MSE,RMSE和MdAE等指标来评估模型性能.
  • 确定解决问题的时间预测最有效的方法.

主要方法:

  • 应用传统和先进的机器学习模型 (神经网络,随机森林,线性回归).
  • 使用了AgES数据集,包括贡献者专业知识,问题类别和组件等功能.
  • 使用平均绝对误差 (MAE),平均平方误差 (MSE),根平均平方误差 (RMSE) 和中位数绝对误差 (MdAE) 评估模型.

主要成果:

  • XGBoost算法在被认为的错误指标中通常表现最好.
  • 对比分析包括AgES数据集与现有的敏捷数据集 (TAWOS,Choet等. ) 的情况.
  • 模型评估强调了对现实世界软件项目管理的实际影响.

结论:

  • 机器学习为软件项目管理提供了强大的预测工具.
  • 准确的问题解决时间预测可以实现更好的规划和资源管理.
  • 该研究详细介绍了模型培训,特征的重要性以及ML在软件开发中的变革潜力.