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相关概念视频

Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or playing an...
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A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze each...

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相关实验视频

Updated: Jun 29, 2026

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model
08:20

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

Published on: October 27, 2023

一种混合的长期短期记忆 - 卷积神经网络多流深度学习模型,其中包含卷积区注意模块,用于天花检测.

Benjamin Appiah Yeboah1, Kojo Sam Micah1, Isaac Acquah1

  • 1Biomedical Engineering Program, Department of Computer Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.

Science progress
|March 28, 2025
PubMed
概括

结合LSTM,CNN和CBAM的新深度学习模型有效地检测到mpox. 这种人工智能工具在早期的mopox诊断中显示出高准确度,有助于公共卫生工作.

关键词:
卷积式注意力阻断机制这是LSTM的LSTM.的水是一种天花.深度学习模型深度学习模型模型可解释性模型可解释性模型的解释性可解释性一个MPOX的MPOX.

相关实验视频

Last Updated: Jun 29, 2026

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model
08:20

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

Published on: October 27, 2023

科学领域:

  • 医疗信息学 医疗信息学
  • 人工智能在医学中的应用
  • 传染病诊断 传染病诊断 传染病诊断

背景情况:

  • 麻疹 (Mombox) 是一种致动物性病毒性疾病,引起疼痛的病变,发烧和疲劳.
  • 全球爆发,包括非特有地区,需要改进早期检测方法.
  • 深度学习有望提高像mpox.com这样的传染病的诊断能力.

研究的目的:

  • 开发一种混合深度学习模型,用于早期mopox检测.
  • 整合长期短期记忆 (LSTM),卷积神经网络 (CNN) 和卷积区注意模块 (CBAM) 以提高诊断准确度.

主要方法:

  • 与CBAM一起开发了一个多流LSTM-CNN模型,并在Mpox皮肤损伤数据集v2.0.0.上进行训练.
  • LSTM和CNN层分别用于序列和空间特征提取.
  • 使用CBAM进行特征调节,使用LIME和Grad-CAM进行模型解释性.

主要成果:

  • 混合型号实现了高性能,精度为94%,F1得分为94%,AUC为95.04%.
  • 该模型与现有的最先进的方法相比,显示出具有竞争力的结果.
  • 可解释性技术为模型的诊断推理提供了洞察力.

结论:

  • 开发的LSTM-CNN-CBAM模型是早期mopox检测的可靠工具.
  • 该模型的性能支持其在网络和移动平台上的潜在集成,以实现可访问的诊断.
  • 这种人工智能驱动的方法为管理mopox疫情提供了有希望的解决方案.