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

5.5K
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...
5.5K
Decision Making01:20

Decision Making

147
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...
147
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
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

4.3K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
4.3K
Heuristics01:21

Heuristics

111
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...
111
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

132
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...
132

You might also read

Related Articles

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

Sort by
Same author

DNRHP: Temporal Network Representation Learning via Hawkes Point Process.

IEEE transactions on neural networks and learning systems·2025
Same author

The impact of volunteer service on moral education performance and mental health of college students.

PloS one·2024
Same author

A multi-view contrastive learning for heterogeneous network embedding.

Scientific reports·2023
Same author

Improving Hybrid CTC/Attention Architecture for Agglutinative Language Speech Recognition.

Sensors (Basel, Switzerland)·2022

Related Experiment Video

Updated: Jul 24, 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

Multi-Attribute Decision Making with Einstein Aggregation Operators in Complex Q-Rung Orthopair Fuzzy Hypersoft

Changyan Ying1,2,3, Wushour Slamu1,2,3, Changtian Ying4

  • 1School of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.

Entropy (Basel, Switzerland)
|July 8, 2023
PubMed
Summary
This summary is machine-generated.

We introduce the complex q-rung orthopair fuzzy hypersoft set (Cq-ROFHSS) to model imprecise human interpretations. This advanced fuzzy set theory offers a flexible tool for decision-making with complex data, outperforming existing methods.

Keywords:
Einstein aggregation operatorcomplex q-rung orthopair fuzzy hypersoft set (Cq-ROFHSS)multi-attribute decision making

More Related Videos

Executing Complexity-Increasing Queries in Relational MySQL and NoSQL MongoDB and EXist Size-Growing ISO/EN 13606 Standardized EHR Databases
07:26

Executing Complexity-Increasing Queries in Relational MySQL and NoSQL MongoDB and EXist Size-Growing ISO/EN 13606 Standardized EHR Databases

Published on: March 19, 2018

9.4K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K

Related Experiment Videos

Last Updated: Jul 24, 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
Executing Complexity-Increasing Queries in Relational MySQL and NoSQL MongoDB and EXist Size-Growing ISO/EN 13606 Standardized EHR Databases
07:26

Executing Complexity-Increasing Queries in Relational MySQL and NoSQL MongoDB and EXist Size-Growing ISO/EN 13606 Standardized EHR Databases

Published on: March 19, 2018

9.4K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K

Area of Science:

  • Fuzzy Set Theory
  • Decision Making
  • Information Granularity

Background:

  • Human interpretations often involve imprecision and ambiguity.
  • Existing fuzzy set theories, such as complex intuitionistic and Pythagorean fuzzy sets, have limitations in capturing complex, multi-dimensional data.
  • There is a need for more generalized mathematical tools to handle such data effectively.

Purpose of the Study:

  • To introduce and formalize the concept of the complex q-rung orthopair fuzzy hypersoft set (Cq-ROFHSS).
  • To extend the capabilities of fuzzy set theory for modeling complex, imprecise, and contradictory two-dimensional data.
  • To develop multi-attribute decision-making algorithms based on the proposed Cq-ROFHSS framework.

Main Methods:

  • Development of the Cq-ROFHSS by combining the parametric structures of complex q-rung orthopair fuzzy sets and hypersoft sets.
  • Establishment of basic set-theoretic operations and properties for Cq-ROFHSS.
  • Introduction of Einstein operations and aggregation operators for Cq-ROFHSS values.
  • Development of two multi-attribute decision-making algorithms using score and accuracy functions.

Main Results:

  • The proposed Cq-ROFHSS framework effectively captures a greater degree of imprecision and ambiguity compared to existing theories.
  • The developed decision-making algorithms successfully prioritize ideal schemes in periodically inconsistent datasets.
  • A case study on distributed control systems demonstrates the feasibility and rationality of the Cq-ROFHSS approach.
  • Comparative analysis confirms the flexibility, validity, and superiority of the proposed model over mainstream technologies.

Conclusions:

  • The Cq-ROFHSS is a powerful and flexible extension of fuzzy set theory for handling complex, multi-parameterized, and imprecise data.
  • The developed decision-making algorithms provide an effective method for analyzing and prioritizing complex decision problems.
  • The research contributes a valuable mathematical tool for applications requiring the modeling of nuanced and contradictory information.