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相关概念视频

Decision Making01:20

Decision Making

112
Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
112
Decision Making: P-value Method01:09

Decision Making: P-value Method

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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
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Bipolar Disorder01:30

Bipolar Disorder

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Bipolar disorder is a chronic mental health condition marked by significant mood fluctuations, including episodes of mania and depression. Elevated energy levels, heightened mood or irritability, impulsive behavior, reduced sleep needs, rapid speech, racing thoughts, inflated self-esteem, and distractibility characterize mania. Individuals with bipolar disorder often alternate between depressive and manic states, with periods of emotional stability lasting an average of six months to a year.
67
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
4.0K
Heuristics01:21

Heuristics

93
Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
93
Mania and Antimanic Drugs: Overview01:24

Mania and Antimanic Drugs: Overview

184
Mania, a psychological condition characterized by elevated mood, increased energy, and reduced sleep need, is part of the bipolar disorder cycle. The exact cause of mania isn't entirely known, but it is thought to be a combination of genetic, environmental, and neurological factors. Bipolar disorder involves alternating manic and depressive episodes. Mood stabilizers like lithium, antipsychotics, and anticonvulsants help manage these episodes. Lithium carbonate is particularly effective as...
184

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A progressive approach to multi-criteria group decision-making: N-bipolar hypersoft topology perspective.

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N-双极性超软集:提高决策算法

Sagvan Y Musa1

  • 1Department of Mathematics, Faculty of Education, University of Zakho, Zakho, Iraq.

PloS one
|January 16, 2024
PubMed
概括

本研究介绍了N-双极超软 (N-BHS) 集,这是处理混合数据类型的新框架. N-BHS套件提供了增强的多功能性,并解决了目前不确定性管理模型中的局限性.

科学领域:

  • 模糊的集合理论 模糊的集合理论
  • 不确定性定量化 不确定性定量化
  • 决策支持系统是什么?

背景情况:

  • 传统的双极超软 (BHS) 集与混合数据类型作斗争.
  • 现有的N双极软集在处理多参数近似函数方面存在局限性.
  • 需要一个更通用的框架来管理对二进制和非二进制数据的评估.

研究的目的:

  • 介绍N双极超软 (N-BHS) 集作为BHS集的扩展.
  • 开发一个参数化表示,以进行细微的属性感知.
  • 针对多参数函数和不确定性的N双极软集中的地址限制.

主要方法:

  • 对于有限颗粒度的宇宙的定义参数化表示.
  • 分区属性成不连接的子属性值.
  • 概述了代数定义:不完整的,高效的,正常化的N-BHS集合,补充和值衍生的BHS集合.
  • 探索的集合理论运算:相对零/整数,子集,扩展/限制结合和交叉.
  • 建议和比较的决策方法.

主要成果:

  • 证明了N-BHS集在混合数据评估中的增强多功能性.
  • 展示了N-BHS集能够提供细微的属性感知的能力.

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  • 通过代数定义和运算说明了N-BHS集在解决不确定性相关问题的有效性.
  • 提出了比较的决策方法.
  • 结论:

    • N-双极超软 (N-BHS) 套件为管理复杂数据评估提供了一个强大而多功能扩展.
    • 参数化表示和属性分区提高了处理不确定性的精度.
    • N-BHS 套件为决策提供了一个强大的框架,在特定应用中优于现有模型.