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Related Concept Videos

Understanding Deception01:14

Understanding Deception

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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...
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Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

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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...
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Unrealistic Optimism Bias01:30

Unrealistic Optimism Bias

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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...
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Optimization Problems01:26

Optimization Problems

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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...
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Updated: Jan 17, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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Innovative multi objective optimization based automatic fake news detection.

Cebrail Barut1, Suna Yildirim2, Bilal Alatas3

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

Peerj. Computer Science
|September 24, 2025
PubMed
Summary
This summary is machine-generated.

Detecting fake news is crucial. This study introduces a novel metaheuristic approach for faster, more accurate fake news detection, especially effective on smaller datasets.

Area of Science:

  • Computer Science
  • Information Science
  • Artificial Intelligence

Background:

Keywords:
Fake news detectionMetaheuristic algorithmsMulti objective optimization

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  • The digital age provides unprecedented access to information via the internet and social media.
  • Rapid information dissemination on social media lacks robust mechanisms for accuracy verification, increasing the spread of fake news.
  • Effective fake news detection is critical to mitigate societal misinformation.