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相关概念视频

Force Classification01:22

Force Classification

1.6K
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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Observational Learning01:12

Observational Learning

317
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
317
Methods of Classification and Identification01:28

Methods of Classification and Identification

201
Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
201
Masking and Demasking Agents01:19

Masking and Demasking Agents

2.7K
EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
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Nonconscious Mimicry01:13

Nonconscious Mimicry

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Nonconscious mimicry occurs when individuals alter their mannerisms to match the behaviors and expressions of those nearby, without intention.
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Aggregates Classification01:29

Aggregates Classification

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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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相关实验视频

Updated: Sep 14, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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在Instagram上使用集体学习方法检测假个人资料.

Bharti Goyal1, Nasib Singh Gill1, Preeti Gulia1

  • 1Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak, Haryana, India.

Scientific reports
|July 21, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种机器学习模型来检测假冒的Instagram帐户,大大提高了在线安全和用户信任. 先进的混合系统实现了高精度,有效地减少垃圾邮件和欺骗性内容.

关键词:
伪造的个人资料是假的网格搜索 简历 简历 简历在Instagram上使用Instagram.随机的森林随机的森林在SMOTE中使用.标尺_pos_重量_标尺_权重在XGBOOST中使用XGBOOST.

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科学领域:

  • 计算机科学 计算机科学
  • 社交媒体安全 社交媒体安全
  • 机器学习 机器学习

背景情况:

  • 在Instagram上的假账户有助于垃圾邮件,有害信息和欺骗性内容,侵蚀用户的信任并危及在线安全.
  • 现有的识别虚假个人资料的方法不足,需要先进的解决方案来保持平台的完整性.

研究的目的:

  • 开发和评估一个高度准确的机器学习模型,用于识别假冒的Instagram帐户.
  • 提高在线身份验证系统的有效性和可信度.

主要方法:

  • 实现一个混合系统,将XGBoost,SMOTE用于类平衡,以及GridSearchCV用于超参数调.
  • 使用scale_pos_weight优化和适应性发现用于假冒账户的趋势分析.
  • 利用随机森林超参数微调来提高检测准确度.

主要成果:

  • 拟议模型的F1得分为98%,回忆率为98%,精度为98.3%,准确率为98.24%.
  • 显著减少了假账户,提高了平台的安全性.
  • 建立了在社交媒体环境中保护信任的新标准.

结论:

  • 开发的混合机器学习系统提供了一种最先进的解决方案,用于检测Instagram上的假账户.
  • 这项研究改善了在线身份验证系统,并加强了用户的信任.
  • 这些发现为未来在社交媒体安全和打击欺骗性在线实践方面的进展奠定了基础.