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相关概念视频

Understanding Deception01:14

Understanding Deception

152
Deception is a pervasive aspect of human communication. Empirical studies have shown that most individuals engage in some form of deceit on a daily basis, with approximately 20% of social exchanges involving deceptive elements. Lying follows a developmental trajectory, peaking during adolescence and declining with age, possibly due to the maturation of cognitive control and social accountability.Cognitive and Social Factors in Deception DetectionDespite its prevalence, accurately detecting...
152
Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

10.0K
Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
10.0K
Unrealistic Optimism Bias01:30

Unrealistic Optimism Bias

211
Unrealistic optimism bias is the tendency to overestimate the likelihood of positive outcomes. This cognitive bias makes individuals believe they are less likely to experience failures, setbacks, or risks and more likely to succeed than others. For example, people may assume they are less prone to health issues, accidents, or financial struggles than their peers, even when they share similar risk factors.One key component of this bias is the above-average effect, where individuals perceive...
211
Optimization Problems01:26

Optimization Problems

20
Optimization problems often involve identifying maximum or minimum values under specific constraints. A well-known example is determining the longest horizontal pipe that can be moved around a right-angled corner, where a 3-meter-wide hallway meets a 2-meter-wide hallway. This scenario, common in architectural design and industrial transport, can be understood conceptually through geometric and trigonometric reasoning.To visualize the problem, consider the pipe as a straight line that touches...
20

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相关实验视频

Updated: Jan 17, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

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基于创新的多目标优化,自动检测假新闻.

Cebrail Barut1, Suna Yildirim2, Bilal Alatas3

  • 1Department of Continuing Education Center, Firat (Euphrates) University, Elazig, Turkey.

PeerJ. Computer science
|September 24, 2025
PubMed
概括
此摘要是机器生成的。

检测假新闻至关重要. 这项研究引入了一种新的元启发方法,用于更快,更准确地检测假新闻,特别是在较小的数据集上更有效.

关键词:
假新闻检测 假新闻检测超启发式算法 (Metaheuristic Algorithms) 是一种算法,可以通过多目标优化优化多目标优化

相关实验视频

Last Updated: Jan 17, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.3K

科学领域:

  • 计算机科学 计算机科学
  • 信息科学 信息科学 信息科学
  • 人工智能的人工智能

背景情况:

  • 数字时代通过互联网和社交媒体提供了前所未有的信息获取机会.
  • 社交媒体上的快速信息传播缺乏可靠的准确性验证机制,增加了虚假新闻的传播.
  • 有效的假新闻检测对于减轻社会错误信息至关重要.

研究的目的:

  • 开发一种有效的检测假新闻的方法.
  • 解决现有的假新闻检测方法的局限性,特别是他们专注于单个标准优化.
  • 提出一种新的方法,用于在假新闻检测中同时优化精度和回忆.

主要方法:

  • 这项研究提出了一种新的元启发方法来检测假新闻.
  • 它在非主导排序遗传算法2 (NSGA-2) 中引入了拥挤距离水平方法的创新应用.
  • 这种方法可以同时优化两个关键标准:精度和回忆.

主要成果:

  • 拟议的方法在检测假新闻方面表现出高的成功率.
  • 在小型数据集上,性能特别显著,超过了传统的人工智能和机器学习方法.
  • 验证使用各种数据集进行,包括Covid-19新闻,叙利亚战争报道和FakeNewsNet (Gossipcop).

结论:

  • 开发的metaheuristic方法在假新闻检测方面取得了重大进展.
  • 同时优化精度和回忆被证明是有效的,特别是在挑战小型数据集时.
  • 这种方法提供了一个更强大的解决方案,用于打击在线虚假信息的传播.