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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
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Cognitive development continues throughout adulthood, undergoing significant shifts across early, middle, and late stages. Individual transition occurs from adolescent idealism to pragmatic and adaptable thinking in early adulthood. During this period, individuals learn to integrate personal beliefs with the recognition that other perspectives are equally valid. Exposure to the complexities of modern society, diverse experiences, and higher education contribute to this adaptive thought process,...
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联合学习用于使用语音数据检测认知障碍.

Josep Blazquez-Folch1, María Limones Andrade2, Berta Calm1

  • 1Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya, Barcelona, Spain.

Frontiers in artificial intelligence
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概括
此摘要是机器生成的。

联合学习 (FL) 有效地使用语音数据来检测认知障碍,即使数据分布不同. 这种保护隐私的方法增强了阿尔茨海默病的协作研究.

关键词:
阿尔茨海默氏症是阿尔茨海默氏症的一种疾病.认知障碍 认知障碍是一种认知障碍.深度学习是一种深度学习.联合学习的联合学习语音声学 语音声学 语音声学

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科学领域:

  • 神经科学是一个神经科学.
  • 计算机科学 计算机科学
  • 生物医学工程 生物医学工程

背景情况:

  • 阿尔茨海默病 (AD) 研究需要多样化的数据,但隐私问题阻碍了共享.
  • 联合学习 (FL) 为协作数据分析提供了一个保护隐私的解决方案.

研究的目的:

  • 评估数据异质性对FL模型性能的影响,用于使用语音生物标志物检测认知障碍.
  • 在各种数据分布场景下,将FL性能与本地模型进行比较.

主要方法:

  • 分析了2,239名参与者 (认知正常和认知受损) 数字语音录音的声学特征.
  • 联邦平均化 (FedAvg) 和代数据聚合 (IDA) 用于聚合使用MLP前神经网络训练的本地模型.
  • 模型的性能被评估在不同的场景中,具有相同/不平等的贡献和不平衡的类比.

主要成果:

  • 在具有平等贡献和不平衡类的场景中,FL显示了适度的准确性和AUC增长.
  • 在贡献不平等的场景中,FL在较小的数据集上显著提高了性能 (平衡精度从0.51到0.80),同时在较大的数据集上保持了高精度.
  • 在异构的机构数据中,FL展示了可扩展性和有效性.

结论:

  • 联合学习使得基于语音的生物标志物的协作开发能够用于认知障碍的检测.
  • FL是一种可扩展的,保护隐私的方法,适用于神经退行性疾病的数字健康研究,解决数据不平衡和机构差异.