Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Stereotype Content Model02:16

Stereotype Content Model

The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence categorization, a person will feel...

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

The effectiveness of surgical management in knee flexion contracture in primary total knee arthroplasty.

Pakistan journal of medical sciences·2026
Same author

Comparative Fatigue Analysis of CF-PLA Metamaterial Bone Plates for Orthopaedic Fixation.

Polymers·2026
Same author

Clinical and radiological outcomes of posterolateral fusion alone versus interbody fusion in single-level degenerative spondylolisthesis: A retrospective comparative study.

Medicine·2026
Same author

Sunlight-driven fast photo-degradation of Eriochrome Black T dye using highly efficient La-doped Ag<sub>3</sub>PO<sub>4</sub> decorated with ZnS QDs.

RSC advances·2026
Same author

Facile synthesis and synergistic cytotoxic effect of Ag/Co-ZnO nanoparticles in epithelial breast cancer cells.

Scientific reports·2026
Same author

Evaluating Lifestyle and Educational Factors Influencing Public Knowledge, Attitudes, and Practices Toward <i>Helicobacter pylori</i>-Induced Gastric Ulcer: A Cross-Sectional Study.

Health science reports·2026
Same journal

Thymidylate synthase inhibitory drugs induce p53-dependent pathways differently.

PloS one·2026
Same journal

Top-down and bottom-up attention for joint pattern classification and reconstruction.

PloS one·2026
Same journal

Short- and long-term scaling behavior of blood pressure and pulse arrival time during sleep in healthy controls and patients with obstructive sleep apnea.

PloS one·2026
Same journal

Double DQN-based secrecy energy efficiency and fairness performance in IRS-assisted NOMA systems with friendly jamming.

PloS one·2026
Same journal

10 recommendations for strengthening citizen science for improved societal and ecological outcomes: A co-produced analysis of challenges and opportunities in the 21st century.

PloS one·2026
Same journal

Paying in public: Peer effects, impression management, and willingness to pay on digital payment platforms.

PloS one·2026
查看所有相关文章

相关实验视频

Updated: Jun 9, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K

AMDDL模型:使用深度学习模型检测安卓智能手机恶意软件.

Muhammad Aamir1, Muhammad Waseem Iqbal2, Mariam Nosheen3

  • 1Department of Computer Science, COMSATS University Islamabad, Sahiwal Campus, Sahiwal, Pakistan.

PloS one
|January 19, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了AMDDL模型,这是一种使用卷积神经网络进行Android恶意软件检测的深度学习方法. 该模型达到99.92%的准确性,大大提高了针对不断变化的威胁的移动安全性.

更多相关视频

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

774
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K

相关实验视频

Last Updated: Jun 9, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K
DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

774
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K

科学领域:

  • 计算机科学 计算机科学
  • 网络安全 网络安全
  • 人工智能的人工智能

背景情况:

  • 安卓的广泛使用使其生态系统成为恶意软件的目标.
  • 恶意软件的安装通过API调用和许可授权等各种媒介发生,从而损害了用户隐私和系统安全.
  • 现有的Android恶意软件检测和分类方法需要改进,以对抗复杂的威胁.

研究的目的:

  • 开发和评估AMDDLmodel,这是一种用于准确检测和分类Android恶意软件的新型深度学习技术.
  • 为了提高安卓设备的安全性,并保护用户隐私免受恶意应用程序的侵害.
  • 为了证明深度学习的有效性,特别是卷积神经网络,在识别安卓恶意软件.

主要方法:

  • 实施AMDDL模型,这是一个使用卷积神经网络 (CNN) 的深度学习模型.
  • 该模型的性能通过各种参数进行调整,包括过器大小,时代,学习率和网络层.
  • 使用Drebin数据集评估模型,该数据集包括215个不同的特征.

主要成果:

  • 在检测和分类Android恶意软件方面,AMDDL模型实现了99.92%的高精度.
  • 该模型展示了强大的性能指标,包括精度,回忆和F1分数.
  • 对比分析显示,AMDDL模型在安卓恶意软件检测的准确性方面超过了现有的技术.

结论:

  • AMDDLmodel代表了用于Android恶意软件检测的创新深度学习解决方案.
  • 该模型通过先进的功能工程显著提高了检测准确性和用户安全性.
  • 这些发现突显了深度学习在强大的移动安全和隐私保护方面的潜力.