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相关概念视频

Aggregates Classification01:29

Aggregates Classification

389
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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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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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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Force Classification01:22

Force Classification

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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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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相关实验视频

Updated: Sep 17, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

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通过集体学习方法增强区块链交易分类.

Amrutanshu Panigrahi1, Abhilash Pati1, Bibhuprasad Sahu2

  • 1Department of CSE, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India.

Scientific reports
|July 2, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一种机器学习模型,以将区块链交易分类为有风险或无风险的. 基于整体的方法实现了高精度,增强了对区块链系统的信任.

关键词:
区块链 区块链 区块链 区块链组合特征选择组合特征选择机器学习 机器学习排名聚合方式 排名聚合方式排名的平均化 排名的平均化

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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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相关实验视频

Last Updated: Sep 17, 2025

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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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科学领域:

  • 计算机科学 计算机科学
  • 数据科学数据科学数据科学
  • 网络安全 网络安全

背景情况:

  • 区块链技术为金融,SCM和物联网提供安全的信息共享.
  • 越来越多的用户数量需要强大的方法来识别恶意区块链交易.
  • 保持对区块链生态系统的信任需要准确的交易风险评估.

研究的目的:

  • 开发和评估一种机器学习 (ML) 模型,用于将区块链交易分类为有风险或无风险.
  • 探索集合特征选择和分类方法对此任务的有效性.

主要方法:

  • 采用了四种特征选择技术:CFS,RFE,RF和IG.
  • 利用集体特征选择 (等级平均和等级聚合) 来结合特征子集.
  • 应用集体分类 (硬投票,软投票,加权平均) 来基于学习者预测.
  • 在三个不同的区块链交易数据集上评估了模型.

主要成果:

  • 排名平均整体特征选择达到99.24%的最大精度.
  • 排名聚合组合特征选择达到98.73%的最大精度.
  • 提出的基于集合的模型在对交易风险进行分类方面表现出了很高的表现.

结论:

  • 合并方法,特别是排名平均值,对于区块链交易风险分类中的特征选择非常有效.
  • 开发的ML模型显著提高了识别风险交易的能力,增强了对区块链网络的信任.
  • 这项研究提供了一个可扩展和准确的解决方案,用于保护区块链生态系统免受恶意活动的影响.