Recommending Turmeric Variety for Higher Production Using Interval-Valued Fuzzy Soft Set Model and PSO

2021 ◽  
Vol 12 (2) ◽  
pp. 94-110
Author(s):  
R. K. Mohanty ◽  
B. K. Tripathy
Keyword(s):  
Uncertain Data ◽  
Optimization Technique ◽  
Soft Set ◽  
Soft Sets ◽  
Fuzzy Soft Sets ◽  
The Universe ◽  
Interval Valued ◽  
Better Than ◽  
Universe Of Discourse ◽  
Fuzzy Interval

Soft set is one of the latest mathematical models to handle uncertainty. In a soft set, every element of its parameter set is associated with a subset of the universe of discourse under consideration. In recent times, soft set and its hybrid models have been used extensively to handle decision making problems with uncertain data. It's established that an appropriate hybrid model works better than its basic components. In this article, an algorithm is proposed that is used to recommend the best variety of turmeric having a given set of parameters using Interval valued fuzzy soft sets. Most importantly, the priorities of parameters are taken as fuzzy interval values so that higher uncertainty can be handled properly. Reduction of parameters helps in getting down the complexity of the process under consideration. A metaheuristic optimization technique is a better option to handle these kinds of problems. The authors use particle swarm optimization (PSO) to achieve parameter reduction.

scholarly journals Generalised Interval-Valued Fuzzy Soft Set

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Shawkat Alkhazaleh ◽  
Abdul Razak Salleh
Keyword(s):  
Decision Making ◽  
Similarity Measure ◽  
Fuzzy Set ◽  
Medical Diagnosis ◽  
Soft Set ◽  
Fuzzy Soft Set ◽  
Soft Sets ◽  
Decision Making Problem ◽  
Fuzzy Soft Sets ◽  
Interval Valued

We introduce the concept of generalised interval-valued fuzzy soft set and its operations and study some of their properties. We give applications of this theory in solving a decision making problem. We also introduce a similarity measure of two generalised interval-valued fuzzy soft sets and discuss its application in a medical diagnosis problem: fuzzy set; soft set; fuzzy soft set; generalised fuzzy soft set; generalised interval-valued fuzzy soft set; interval-valued fuzzy set; interval-valued fuzzy soft set.

scholarly journals Data Analysis Approach for Incomplete Interval-Valued Intuitionistic Fuzzy Soft Sets

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1061
Author(s):  
Hongwu Qin ◽  
Huifang Li ◽  
Xiuqin Ma ◽  
Zhangyun Gong ◽  
Yuntao Cheng ◽  
...  
Keyword(s):  
Missing Data ◽  
Soft Set ◽  
Fuzzy Soft Set ◽  
Soft Sets ◽  
Intuitionistic Fuzzy ◽  
Degree Of Membership ◽  
Filling Method ◽  
Fuzzy Soft Sets ◽  
Interval Valued ◽  
Intuitionistic Fuzzy Soft Sets

The model of interval-valued intuitionistic fuzzy soft sets is a novel excellent solution which can manage the uncertainty and fuzziness of data. However, when we apply this model into practical applications, it is an indisputable fact that there are some missing data in many cases for a variety of reasons. For the purpose of handling this problem, this paper presents new data processing approaches for an incomplete interval-valued intuitionistic fuzzy soft set. The missing data will be ignored if percentages of missing degree of membership and nonmember ship in total degree of membership and nonmember ship for both the related parameter and object are below the threshold values; otherwise, it will be filled. The proposed filling method fully considers and employs the characteristics of the interval-valued intuitionistic fuzzy soft set itself. A case is shown in order to display the proposed method. From the results of experiments on all thirty randomly generated datasets, we can discover that the overall accuracy rate is up to 80.1% by our filling method. Finally, we give one real-life application to illustrate our proposed method.

scholarly journals MCGDM Based on VIKOR and TOPSIS Methods Using Spherical Interval Valued Fuzzy Soft With Aggregation Operators

2021 ◽  
Author(s):  
ARULMOZHI K ◽  
Palanikumar M
Keyword(s):  
Score Function ◽  
Soft Set ◽  
Point Of View ◽  
Ideal Solution ◽  
Fuzzy Soft Set ◽  
Decision Matrix ◽  
Soft Sets ◽  
Strong Point ◽  
Fuzzy Soft Sets ◽  
Interval Valued

Abstract Spherical interval valued fuzzy soft set (SIVFS set) has much stronger ability than Pythagorean interval valued fuzzy soft set and interval valued intuitionistic fuzzy soft set. Now, we talk about aggregated operation for aggregating SIVFS decision matrix. TOPSIS and VIKOR methods are strong point of view for multi criteria group decision making (MCGDM), which is a various extensions of interval valued fuzzy soft sets. We talk through a score function based on aggregating TOPSIS and VIKOR method to the SIVFS-positive ideal solution and the SIVFSnegative ideal solution. Also TOPSIS and VIKOR methods are provides the weights of decision makings. To nd out the optimal alternative under closeness is introduced.

scholarly journals A Decision Making Method using Fuzzy Soft Sets

2014 ◽  
Vol 9 (2) ◽  
Author(s):  
Samsiah Abdul Razak ◽  
Daud Mohamad
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Uncertain Data ◽  
Soft Set ◽  
Decision Makers ◽  
Fuzzy Soft Set ◽  
Soft Sets ◽  
Analytic Hierarchy ◽  
Fuzzy Soft Sets

The introduction of soft set theory by Molodstov has gained attention by many as it is useful in dealing with uncertain data. It is advantageous to use due to its parameterization form of data. This concept has been used in solving many decision making problems and has been generalized in various aspects in particular to fuzzy soft set (FSS) theory. In decision making using FSS, the objective is to select an object from a set of objects with respect to a set of choice parameter using fuzzy values. Although FSS theory has been extensively used in many applications, the importance of weight of parameters has not been highlighted and thus is not incorporated in the calculation. As it depends on one’s perception or opinion, the importance of the parameters may differ from one decision maker to another. Besides, existing methods in FSS only consider one or two decision makers to select the alternatives. In reality, group decision making normally involves more than two decision makers. In this paper we present a method for solving group decision making problems that involves more than two decision makers based on fuzzy soft set by taking into consideration the weight of parameters. The method of lambda – max which frequently utilize in fuzzy analytic hierarchy process (FAHP) has been applied to determine the weight of parameters and an algorithm for solving decision making problems is presented. Finally we illustrate the effectiveness of our method with a numerical example.

Neutrosophic Soft Sets and Their Properties

2016 ◽  
pp. 280-307 ◽  
Author(s):  
Mumtaz Ali ◽  
Florentin Smarandache
Keyword(s):  
Hybrid Structure ◽  
Soft Set ◽  
The Other ◽  
Neutrosophic Set ◽  
Main Theme ◽  
Soft Sets ◽  
Neutrosophic Sets ◽  
Parameterized Family ◽  
The Universe ◽  
Universe Of Discourse

Soft set plays an important role in the theory of approximations, as parameterized family of subsets in the universe of discourse. On the other hand, neutrosophic set is based on the neutrosophic philosophy, which states that: Every idea A has an opposite anti(A) and its neutral neut(A). This is the main theme of neutrosophic sets and logics. This chapter is about the hybrid structure called neutrosophic soft set, i.e. a soft set defined over a neutrosophic set. This chapter begins with the introduction of soft sets and neutrosophic sets. The notions of neutrosophic soft sets are defined and their properties studied. Then the algebraic structures associated with neutrosophic soft sets are debated. After that, the mappings on soft classes are studied with some of their properties. Finally, the notion of intuitionistic neutrosophic soft sets is taken into consideration.

scholarly journals On Interval-Valued Hesitant Fuzzy Soft Sets

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Haidong Zhang ◽  
Lianglin Xiong ◽  
Weiyuan Ma
Keyword(s):  
Decision Making ◽  
Fuzzy Set ◽  
Soft Set ◽  
Hesitant Fuzzy Set ◽  
Fuzzy Soft Set ◽  
Soft Sets ◽  
Numerical Examples ◽  
Decision Making Problem ◽  
Fuzzy Soft Sets ◽  
Interval Valued

By combining the interval-valued hesitant fuzzy set and soft set models, the purpose of this paper is to introduce the concept of interval-valued hesitant fuzzy soft sets. Further, some operations on the interval-valued hesitant fuzzy soft sets are investigated, such as complement, “AND,” “OR,” ring sum, and ring product operations. Then, by means of reduct interval-valued fuzzy soft sets and level hesitant fuzzy soft sets, we present an adjustable approach to interval-valued hesitant fuzzy soft sets based on decision making and some numerical examples are provided to illustrate the developed approach. Finally, the weighted interval-valued hesitant fuzzy soft set is also introduced and its application in decision making problem is shown.

scholarly journals On Generalised Interval-Valued Fuzzy Soft Sets

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Xiaoqiang Zhou ◽  
Qingguo Li ◽  
Lankun Guo
Keyword(s):  
Decision Making ◽  
Set Theory ◽  
Soft Set ◽  
Mathematical Tool ◽  
Lattice Structures ◽  
Fuzzy Soft Set ◽  
Soft Sets ◽  
New Approach ◽  
Fuzzy Soft Sets ◽  
Interval Valued

Soft set theory, initiated by Molodtsov, can be used as a new mathematical tool for dealing with imprecise, vague, and uncertain problems. In this paper, the concepts of two types of generalised interval-valued fuzzy soft set are proposed and their basic properties are studied. The lattice structures of generalised interval-valued fuzzy soft set are also discussed. Furthermore, an application of the new approach in decision making based on generalised interval-valued fuzzy soft set is developed.

Group Decision Making Through Interval Valued Intuitionistic Fuzzy Soft Sets

2018 ◽  
Vol 7 (3) ◽  
pp. 99-117 ◽  
Author(s):  
B. K. Tripathy ◽  
T. R. Sooraj ◽  
R. K. Mohanty ◽  
Abhilash Panigrahi
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Hybrid Models ◽  
Soft Set ◽  
Fuzzy Soft Set ◽  
Soft Sets ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Soft Sets ◽  
Interval Valued

This article describes how the lack of adequate parametrization in some of the earlier uncertainty based models like fuzzy sets, rough sets motivated Molodtsov to introduce a new model in soft set. A suitable combination of individual models leads to hybrid models, which are more efficient than their individual components. So, the authors find the introduction of many hybrid models of soft sets, like the fuzzy soft set (FSS), intuitionistic fuzzy soft sets (IFSS), interval valued fuzzy soft set (IVFSS) and the interval valued intuitionistic fuzzy soft set (IVIFSS). Following the characteristic function approach to define soft sets introduced by Tripathy et al., they re-define IVIFSS in this article. One of the most attractive applications of soft set theory and its hybrid models has been decision making in the form of individual decision making or group decision making. Here, the authors propose a group decision making algorithm using IVIFSS, which generalises many of our earlier algorithms. They compute its complexity and establish the computation experimentally with graphical illustrations.

scholarly journals Interval Valued Fuzzy Soft Sets and Fuzzy Connectives

2019 ◽  
Vol 8 (4) ◽  
pp. 20-23
Keyword(s):  
Normal Form ◽  
Conjunctive Normal Form ◽  
Disjunctive Normal Form ◽  
Soft Set ◽  
Fuzzy Subset ◽  
Fuzzy Soft Set ◽  
Soft Sets ◽  
Fuzzy Soft Sets ◽  
Interval Valued ◽  
Fuzzy Connectives

The Article, We Learning A Few Operations Of Interval Valued Fuzzy Soft Sets Of Connectives And Give Elementary Properties Of Interval Valued Fuzzy Soft Sets Of Principal Disjunctive Normal Form And Principal Conjunctive Normal Form. 2000 Ams Subject Classification: 03f55, 08a72, 20n25. Keywords: Interval Valued Fuzzy Subset, Interval Valued Fuzzy Soft Set, And Principal Conjunctive Normal Form And Principal Disjunctive Normal Form Interval Valued Fuzzy Soft Set ‘’∧ ‘’ Operator And ‘’∨’’ Operator.

Sign in / Sign up

Export Citation Format

Share Document