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How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.
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When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
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Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
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Deep Neural Networks for Image-Based Dietary Assessment
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适应性动态超图学习,用于成分意识的食品推.

Yazeed Alkhrijah1, Abbas N Talib2, Narinderjit Singh Sawaran Singh3

  • 1Department of Electrical Engineering, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Kingdom of Saudi Arabia.

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|December 5, 2025
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概括

这项研究引入了一种新的食品推系统FRMADHG,该系统使用动态超图来更好地了解用户偏好和成分关系. 它显著提高了推的准确性,并提供了透明的,成分级的解释.

关键词:
适应性注意力 适应性注意力可解释的人工智能改善食物营养 改善食物营养食品建议书 食品建议书多目标学习多目标学习三方超图是指三方的超图.

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科学领域:

  • 人工智能的人工智能
  • 计算机科学 计算机科学
  • 数据科学数据科学数据科学

背景情况:

  • 传统的食品推系统与复杂的用户-食品-成分相互作用作斗争.
  • 像协作过和图形神经网络这样的现有方法过于简化了关系.
  • 当前的超图形方法缺乏适应动态用户偏好和成分语义的能力.

研究的目的:

  • 提出FRMADHG (具有多目标自适应动态超图的食品建议),为增强食品建议提供一个新的框架.
  • 为了捕捉更高层次的交互,使用连接用户,食物和成分的三方超图.
  • 为透明的饮食决策提供多粒度的解释性.

主要方法:

  • 开发了一种复杂性意识的适应性注意力机制,用于动态加权.
  • 实施了针对不同语义角色的特定类型的嵌入传播规则.
  • 利用了在训练过程中演变的动态拉普拉斯建筑.
  • 采用多目标学习策略,结合三重排名,对比学习和规范化.

主要成果:

  • FRMADHG取得了显著的改进:精度@10增长了19.8%,回忆@10增长了18.7%,相比于最先进的方法.
  • 废除研究证实了动态超图构造 (12.4%),适应性注意 (11.8%) 和对比性学习 (9.7%) 的影响.
  • 用户研究证实了成分级解释在建立信任和满意度方面的有效性.

结论:

  • 通过其自适应动态超图方法,FRMADHG有效地模拟复杂的互动.
  • 与现有方法相比,该框架提供了优越的推性能和可解释性.
  • 在成分层面的解释提高了用户对食品推系统的信任和满意度.