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相关概念视频

Inductive Reasoning00:59

Inductive Reasoning

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Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...
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Deductive Reasoning01:16

Deductive Reasoning

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Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
For example, a researcher can deduce specific predictions...
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Reasoning01:30

Reasoning

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Reasoning is the action of thinking about something in a logical, sensible way. It is integral to problem-solving, decision-making, and critical thinking. Reasoning can be inductive or deductive. Reasoning involves transforming information into conclusions, which is essential for problem-solving, decision-making, and critical thinking.
Inductive reasoning involves deriving generalizations from specific observations. This type of reasoning helps form beliefs about the world. For example,...
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Reason and Intuition01:37

Reason and Intuition

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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
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Classification of Systems-I01:26

Classification of Systems-I

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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相关实验视频

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Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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一个动态的模糊的基于规则的推理系统,使用模糊的推理与语义推理.

Nora Shoaip1, Shaker El-Sappagh2,3,4, Tamer Abuhmed5

  • 1Information Systems Department, Faculty of Computers and Information, Damanhour University, 22511, Damanhour, Egypt.

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|February 21, 2024
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概括

这项研究引入了一种新的医学诊断系统,使用语义,模糊逻辑和动态规则来灵活和早期发现疾病. 该系统在诊断阿尔茨海默氏症和相关认知状态方面取得了很高的准确性.

关键词:
阿尔茨海默氏症是阿尔茨海默氏症的一种疾病.临床决策支持系统模糊的基于规则的系统.存在论推理的存在论推理语义上的相似性 语义上的相似性

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相关实验视频

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科学领域:

  • 人工智能在医学中的应用
  • 医疗信息学 医疗信息学
  • 计算语言学 计算语言学

背景情况:

  • 当前的医疗诊断系统往往依赖于静态的,一般的规则,限制了适应新环境的适应能力.
  • 医疗术语互操作性的挑战阻碍了数据交换,分析和解释.
  • 异质和可变的诊断标准,包括有语义和语言变化的症状,使早期检测复杂化.

研究的目的:

  • 开发一个灵活的,标准的和早期的医疗诊断系统.
  • 解决静态诊断规则的局限性,提高医学术语的互操作性.
  • 整合语义推理和模糊推理,以实现动态和智能决策.

主要方法:

  • 开发了一个医疗诊断系统,整合了本体学语义推理和模糊推理.
  • 利用动态决策规则,提高了在评估症状和并发症时的可解释性,活力和智力.
  • 将该系统应用于阿尔茨海默病神经成像倡议 (ADNI) 数据集,以进行现实世界的案例研究.

主要成果:

  • 拟议的系统证明了对阿尔茨海默病 (AD) 和相关疾病的高诊断准确性.
  • 对阿尔茨海默病的诊断准确率达到了97.2%,对于晚期轻度认知障碍 (LMCI) 的诊断准确率达到了95.4%,对于早期轻度认知障碍 (EMCI) 的诊断准确率达到了94.8%,对于显著记忆障碍 (SMC) 的诊断准确率达到了93.1%,对于认知正常 (CN) 的诊断准确率达到了96.3%.

结论:

  • 这种新的系统通过语义推理和模糊逻辑有效地提高了早期医学诊断.
  • 本体学和模糊推理的整合为医疗决策提供了动态和智能的方法.
  • 该系统显示了对像阿尔茨海默氏症这样的神经退行性疾病的准确和早期诊断的巨大潜力.