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相关概念视频

Aggregates Classification01:29

Aggregates Classification

947
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...
947
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,...
2.2K
Classification of Systems-II01:31

Classification of Systems-II

445
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,
445
Classification of Systems-I01:26

Classification of Systems-I

533
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:
533
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

3.4K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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相关实验视频

Updated: Jan 7, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

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检测海关欺诈行为,使用梯度增强方法进行联合分类和风险估计.

Rawabi Alwanin1, Mohamed Maher Ben Ismail2, Ouiem Bchir2

  • 1Department of Computer Science, King Saud University, Riyadh, Saudi Arabia. ralwaneen@ksu.edu.sa.

Scientific reports
|December 25, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种双学习的基于XGBoost的方法 (DXGBA),用于检测海关欺诈. 该方法有效地识别了价值低的进口,并估计了收入损失,优化了检查并收回了大量收入.

相关实验视频

Last Updated: Jan 7, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.6K

科学领域:

  • 数据科学数据科学数据科学
  • 机器学习 机器学习
  • 公共财政是公共财政.

背景情况:

  • 关税收入对政府至关重要,需要先进的分析来检测欺诈行为.
  • 检测海关欺诈的传统方法是劳动密集的,昂贵的,并且严重依赖专家判断.
  • 机器学习提供了一种解决方案,可以有效地识别欺诈行为并最大限度地减少收入损失.

研究的目的:

  • 引入和评估双学习的基于XGBoost的方法 (DXGBA),用于检测海关欺诈.
  • 证明DXGBA在同时分类欺诈性申报和估计相关收入风险方面的能力.
  • 优化海关检查,在有限的资源范围内最大限度地收回收入.

主要方法:

  • 制定价值低的进口作为双重监督学习任务.
  • 应用DXGBA模型在一个单一的推进框架内同时进行分类和回归.
  • 调查重新抽样策略 (SMOTE, RU) 以解决阶级不平衡问题.
  • 使用基准海关数据集进行评估,并与基准模型进行比较.

主要成果:

  • DXGBA成功检测欺诈行为并估计收入影响,根据风险对申报进行排名.
  • 该模型可以回收高达87.98%的收入,而审计仅10%的申报.
  • 使用基于树的嵌入和基于自动编码器的深度特征的增强管道进一步提高了准确性和收入估计.

结论:

  • DXGBA提供了一种高性能解决方案,用于检测海关欺诈和收入风险评估.
  • 双重学习框架使检查能够有效地确定优先级,最大限度地提高收入回收.
  • DXGBA的性能优于现有方法,展示了其提高海关管理效率的潜力.