Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Decision Making: P-value Method01:09

Decision Making: P-value Method

5.6K
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...
5.6K
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

4.1K
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...
4.1K
Decision Making01:20

Decision Making

216
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...
216
Weighted Mean00:57

Weighted Mean

5.3K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
5.3K
Gauss's Law: Problem-Solving01:10

Gauss's Law: Problem-Solving

2.1K
Gauss's law helps determine electric fields even though the law is not directly about electric fields but electric flux. In situations with certain symmetries (spherical, cylindrical, or planar) in the charge distribution, the electric field can be deduced based on the knowledge of the electric flux. In these systems, we can find a Gaussian surface S over which the electric field has a constant magnitude. Furthermore, suppose the electric field is parallel (or antiparallel) to the area...
2.1K
Hückel's Rule Diagram of π MOs: Frost Circle01:08

Hückel's Rule Diagram of π MOs: Frost Circle

4.6K
The Frost circle or the inscribed polygon method is a graphical method for determining the relative energies of π molecular orbitals (MOs) for planar, fully conjugated, and monocyclic compounds. This method was first described by A. A. Frost and Boris Musulin in 1953.
A Frost circle is constructed by drawing a polygon whose number of edges is equal to the number of carbons of the given cyclic system, with one of the vertices pointing down. Then, a circle is drawn enclosing the polygon so...
4.6K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Making Group Decisions within the Framework of a Probabilistic Hesitant Fuzzy Linear Regression Model.

Sensors (Basel, Switzerland)·2022
Same author

Hesitant 2-tuple fuzzy linguistic multi-criteria decision-making method based on correlation measures.

PloS one·2022
Same author

New Pythagorean Entropy Measure with Application in Multi-Criteria Decision Analysis.

Entropy (Basel, Switzerland)·2021
Same author

Targeting oxidative stress through antioxidants in diabetes mellitus.

Journal of drug targeting·2017
Same author

Use of Topical Rapamycin in Facial Angiofibromas in Indian Skin Type.

Indian journal of dermatology·2016
Same author

A Study of Hair Follicular Transplantation as a Treatment Option for Vitiligo.

Journal of cutaneous and aesthetic surgery·2016

Related Experiment Video

Updated: Sep 5, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.6K

The Group Decision-Making Using Pythagorean Fuzzy Entropy and the Complex Proportional Assessment.

Parul Thakur1, Bartłomiej Kizielewicz2, Neeraj Gandotra1

  • 1Yogananda School of AI, Computer and Data Sciences, Faculty of Engineering and Technology, Shoolini University, Solan 173229, Himachal Pradesh, India.

Sensors (Basel, Switzerland)
|July 9, 2022
PubMed
Summary

This study introduces a novel Pythagorean entropy for Multi-Criteria Decision-Analysis (MCDA) to manage uncertainty. The new entropy, combined with the COmplex PRoportional ASsessment (COPRAS) method, enhances decision-making with complex, opposing criteria.

Keywords:
complex proportional assessmentdecision-makingentropymultiple criteria decision analysispythagorean fuzzy sets

More Related Videos

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.5K
A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

13.8K

Related Experiment Videos

Last Updated: Sep 5, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.6K
The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.5K
A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

13.8K

Area of Science:

  • Decision Sciences
  • Information Theory
  • Fuzzy Mathematics

Background:

  • Pythagorean fuzzy sets are advanced tools for handling uncertainty and ambiguity, extending intuitionistic fuzzy sets.
  • These sets are crucial for modeling hesitant information in uncertain environments across various scientific disciplines.
  • Existing methods struggle with problems involving multiple, conflicting criteria, necessitating new approaches.

Purpose of the Study:

  • To propose a novel Pythagorean entropy measure specifically designed for Multi-Criteria Decision-Analysis (MCDA).
  • To develop a robust decision-making framework that effectively manages uncertainty and ambiguity in complex MCDA problems.
  • To integrate the new Pythagorean entropy with the COmplex PRoportional ASsessment (COPRAS) method for enhanced decision support.

Main Methods:

  • Development of a new Pythagorean entropy measure to quantify fuzziness in fuzzy sets.
  • Application of the COmplex PRoportional ASsessment (COPRAS) method to handle MCDA problems with conflicting criteria.
  • Integration of Pythagorean fuzzy sets, the new entropy measure, and the COPRAS method to solve decision problems.

Main Results:

  • The proposed Pythagorean entropy effectively measures the fuzziness of fuzzy sets, reducing ambiguity.
  • The combined approach using Pythagorean sets, new entropy, and COPRAS provides a robust solution for MCDA problems.
  • The method successfully addresses decision-making scenarios with multiple and opposing criteria.

Conclusions:

  • The novel Pythagorean entropy offers a significant advancement in quantifying uncertainty within fuzzy set theory.
  • The integration with COPRAS provides an effective framework for complex MCDA, improving decision accuracy.
  • This research contributes a valuable tool for tackling real-world problems characterized by ambiguity and conflicting objectives.