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相关概念视频

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 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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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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End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
344
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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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
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将ML预测推入到DBMS中

Matteo Paganelli1, Paolo Sottovia2, Kwanghyun Park3

  • 1University of Modena and Reggio Emilia 41121 Modena Italy.

IEEE transactions on knowledge and data engineering
|November 13, 2023
PubMed
概括
此摘要是机器生成的。

本研究探讨了数据库管理系统 (DBMS) 中的机器学习 (ML) 推断. 虽然在DBMS中的ML显示出效率的希望,但它在复杂的任务中扎,例如文本特色化和神经网络.

关键词:
在MLOPs中,MLOPs是最常见的.在这里,我们可以使用SQL SQL.机器学习是机器学习.

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科学领域:

  • 数据库管理系统 数据库管理系统
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 目前关于数据库内机器学习 (ML) 的研究主要集中在模型训练上.
  • 在应用程序中部署ML模型至关重要的ML推断在数据库管理系统 (DBMS) 中基本被忽视.
  • 将ML推断集成到DBMS中,在效率,性能和数据治理方面提供了潜在的好处.

研究的目的:

  • 调查使用DBMS用于ML预测服务的可行性.
  • 开发和评估一种用于将训练有素的ML管道转化为用于DBMS内执行的SQL查询的方法.

主要方法:

  • 开发了一种新的技术来将ML管道,包括特色化器和模型 (线性,基于树的) 翻译成SQL查询.
  • 在数据库中的ML推断性能与Sklearn和ml.net等流行的ML框架进行了基准测试.

主要成果:

  • 数据库中的ML管道在几个场景中实现了与外部ML框架相美的性能.
  • 当在DBMS中执行文本特色化和神经网络模型时,观察到显著的性能限制.

结论:

  • DBMS可以是一个适合于某些类型的模型和数据的ML预测平台.
  • 需要进行进一步的研究,以优化DBMS中复杂的ML任务的性能,特别是文本特色化和神经网络.