Related Experiment Video
Updated: Jun 11, 2025
08:12
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
Published on: March 1, 2022
2.4K
Interval-valued picture fuzzy decision-making framework with partitioned maclaurin symmetric mean aggregation
Muhammad Azeem1, Jawad Ali2, Jawad Ali3
1Department of Mathematics and Statistics, University of Agriculture Faisalabad, Punjab, 38000, Pakistan.
Scientific Reports
|October 4, 2024
Summary
This study introduces new interval-valued picture fuzzy (IVPF) partitioned Maclaurin symmetric mean operators for decision-making. These operators effectively handle uncertainty and improve multi-criteria decision-making processes.
Area of Science:
- Decision Sciences
- Fuzzy Set Theory
- Information Fusion
Background:
- Interval-valued picture fuzzy (IVPF) sets extend picture fuzzy sets to model complex uncertainty.
- Existing methods lack robust operators for interrelationships among IVPFSs and criteria partitions.
Purpose of the Study:
- To explore interrelationships among multiple IVPFSs and criteria partitions.
- To introduce novel IVPF partitioned Maclaurin symmetric mean operators.
- To develop a multi-criteria decision-making (MCDM) procedure using these operators.
Main Methods:
- Investigation of IVPF partitioned Maclaurin symmetric mean and weighted IVPF partitioned Maclaurin symmetric mean operators.
- Identification of special cases of these operators.
- Development and application of an MCDM procedure.
Main Results:
- The proposed operators exhibit desirable properties for handling uncertainty.
- The developed MCDM procedure is practical and valid, as shown by a numerical example.
- The new approach demonstrates superiority over existing methods.
Conclusions:
- The novel IVPF partitioned operators offer a powerful tool for decision-making under uncertainty.
- The proposed MCDM method enhances the analysis of complex decision problems.
- This research contributes to the advancement of fuzzy set theory applications in decision science.
Related Concept Videos
01:09Decision Making: P-value Method
5.3K
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...
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...
5.3K
01:07Midrange
3.6K
A somewhat easy to compute quantitative estimate of a data set’s central tendency is its midrange, which is defined as the mean of the minimum and maximum values of an ordered data set.
Simply put, the midrange is half of the data set’s range. Similar to the mean, the midrange is sensitive to the extreme values and hence the prospective outliers. However, unlike the mean, the midrange is not sensitive to all the values of the data set that lie in the middle. Thus, it is prone to...
Simply put, the midrange is half of the data set’s range. Similar to the mean, the midrange is sensitive to the extreme values and hence the prospective outliers. However, unlike the mean, the midrange is not sensitive to all the values of the data set that lie in the middle. Thus, it is prone to...
3.6K
00:57Weighted Mean
4.9K
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...
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...
4.9K
01:14Multicompartment Models: Overview
112
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
112
01:12Maximum Size of Aggregate
92
The maximum size of aggregate is defined as the aperture of the sieve retaining 15 percent or more of the particles present in the aggregate sample. The aggregate's maximum size impacts the concrete's water requirement, workability, and strength. Larger aggregates reduce the surface area needing cement paste coverage, which can lower water needs, thereby allowing a decrease in the water-to-cement ratio when the desired workability and richness of the mix are to be maintained, which can...
92
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
45
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
45