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相关概念视频

Levels of Use of a GIS01:29

Levels of Use of a GIS

72
Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
72
Bar Graph01:07

Bar Graph

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A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
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Multiple Bar Graph01:07

Multiple Bar Graph

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As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
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Manipulation and Analysis01:21

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GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
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Guidelines and Strategies for Safe Computer Charting01:18

Guidelines and Strategies for Safe Computer Charting

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The guidelines and strategies provided by the American Nurses Association (ANA) and the Canadian Nurses Association (CNA) offer essential principles for ensuring safe and secure computer charting systems in healthcare settings. Let's break down each recommendation:
Maintain Confidentiality and Security:
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Genome-wide Association Studies-GWAS01:11

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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相关实验视频

Updated: Jul 20, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

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一个加权的GraphSAGE基于大数据访问控制上下文意识的方法.

Dibin Shan1, Xuehui Du1, Wenjuan Wang1

  • 1Department of Information Systems Security, PLA Information Engineering University, Zhengzhou, China.

Big data
|August 1, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的加权 GraphSAGE 方法来控制大数据的访问,从而实现自动上下文意识和关系推理. 该方法通过有效建模上下文关系来增强动态访问控制决策.

关键词:
访问控制 访问控制 访问控制大数据就是大数据.环境意识 背景意识图表神经网络的神经网络

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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科学领域:

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

背景情况:

  • 现有的上下文感知访问控制 (CAAC) 方法缺乏自动上下文感知和对上下文关系进行建模和推理的能力.
  • 大数据的动态访问控制在很大程度上依赖于上下文信息,目前的方法无法自动充分利用这些信息.

研究的目的:

  • 为大数据访问控制提出基于GraphSAGE的加权方法,该方法可实现自动上下文意识和推理.
  • 解决现有的CAAC方法在自动建模和理解上下文关系方面的局限性.

主要方法:

  • 访问记录数据的图形建模,将上下文意识转化为图形神经网络 (GNN) 节点学习问题.
  • 开发一个GNN模型,WGraphSAGE,结合加权邻近采样和聚合算法.
  • 通过GNN节点学习和关系强度建模,自动生成CAAC规则.

主要成果:

  • 拟议的WGraphSAGE模型在上下文意识和关系推理方面表现优异,与类似的GNN模型相比.
  • 实验结果显示,与现有的CAAC模型相比,动态访问控制决策具有显著的优势.
  • 该方法有效地实现了节点关系及其优势的自动建模和推理.

结论:

  • 权重的GraphSAGE方法为大数据访问控制中的自动上下文意识提供了一个强大的解决方案.
  • 这种方法显著提高了动态访问控制决策的准确性和效率.
  • 拟议的技术为理解和利用大数据环境中的复杂上下文关系提供了一个强大的工具.