Fuzzy Soft Matrix Research Articles

Colonoscopy contrast-enhanced by intuitionistic fuzzy soft sets for polyp cancer localization

Medical images often suffer from low contrast, irregular gray-level spacing and contain a lot of uncertainties due to constraints of imaging devices and environment (various lighting conditions) when capturing images. In order to achieve any clinical-diagnosis method for medical imaging with better comprehensibility, image contrast enhancement algorithms would be appropriate to improve the visual quality of medical images. In this paper, an automated image enhancement method is presented for colonoscopy images based on the intuitionistic fuzzy soft set. The fuzzy soft set is used to model the intuitionistic fuzzy soft image matrix based on a set of soft features of the colonoscopy images. The technique decomposes the fuzzy image into multiple blocks and estimates a soft-score based on an adaptive soft parametric hesitancy map by using the hesitant entropy for each block to quantify the uncertainties. In the processing stage, an adaptive intensity modification process is done for each block according to its soft-score. These scores are accurately addressed the gray-level ambiguities in colonoscopy images that lead to better results. Finally, the enhanced image achieved by performing a defuzzification together with all unprocessed blocks. Qualitative and quantitative assessments demonstrate that the proposed method improves image contrast and region-of-interest of polyps in colonogram. Experimental results on enhancing a large CVC-Clinic-DB and ASU-Mayo clinic colonoscopy benchmark datasets show that the proposed method outperforms the state-of-the-art medical image enhancement methods.

A Minimum Trust Discount Coefficient Model for Incomplete Information in Group Decision Making with Intuitionistic Fuzzy Soft Set

This article proposes a framework to deal with incomplete information in multiple criteria group decision making with intuitionistic fuzzy soft set. To do that, the weighted sum method for choice values and simple mathematical expectation method are extended to the case of obtained fuzzy soft set, and they are proved to have the same result in estimating the incomplete information. In order to reduce the system error in the estimating process cause by multiple decision information, a minimum trust discount coefficient model is established according to the relevant methods of evidence theory. Then, a new definition of entropy for intuitionistic fuzzy sets is introduced to determine the weights of group experts. Therefore, individual decision-making matrices are integrated into a comprehensive decision-making matrix by the integration operation formula of intuitionistic fuzzy soft matrix. The decision making is realized according to the difference of the score values of objects. Finally, the steps of this method are concluded, and one example is given to explain the application of this method.

Using Fuzzy Soft Matrix in Medical Analysis for Human’s Illness

Nowadays the human being of in this world with faced many troubles that can manage by theory of soft set. We can develop the analysis of medical field using fuzzy soft matrices (FSM). And apply IFSM. Here we establish the characterization of intersection and union of IIVFSM with examples. And the end, we can study about how to develop medical analysis using IIVFSM.

Application of Repeated Average Method on Generalized Fuzzy Soft Matrix for Decision Making

Molodtsov introduced the theory of soft sets, which can be seen as a new mathematical approach to vagueness. Maji and his associates have further initiated several basic notions of soft set theory. They have also introduced the concept of fuzzy soft set, a more generalized concept, which is a combination of fuzzy set and soft set. In this paper, Repeated Average Method has been used in Generalized Fuzzy Soft Matrix for prompt decision making.
 Asiat. Soc. Bangladesh, Sci. 45(2): 249-259, December 2019

An Arithmetic Mean of FSM in Making Decision

This paper is to forward the notion of FSM. We use fuzzy soft matrix as to take decision in the rainfall level for five years. So many functions are expanded in the fuzzy soft matrices. Here we conclude the arithmetic mean of fuzzy soft matrices in decision making.

A Study on Matrices Using Interval Valued Intuitionistic Fuzzy Soft Set and Its Application in Predicting Election Results in India

Nowadays the concept of matrix is used widely in different fields such as engineering, medical, economics, game theory, geology, computer science etc. Matrices are also used in representing the real world data like the population of people, infant mortality rate etc. In economics very large matrices are used for optimization of problems. Matrices play an important role to represent different types of soft set in concise form by which we can easily perform algebraic operations on them. Classical matrices can’t represent all types of uncertainties present in daily life problems. To tackle those problems related to uncertainties fuzzy matrix is introduced in which every member belongs to the unit interval [0, 1]. By combining soft set and fuzzy matrix a new concept fuzzy soft matrix is introduced. Later it has been extended to intuitionistic fuzzy soft matrix, interval-valued fuzzy soft matrix, interval-valued intuitionistic fuzzy soft matrix etc. In this paper we give a brief discussion on different types of interval valued intuitionistic fuzzy soft matrices and apply some new matrix operations on them. Moreover a new methodology has been developed to solve interval valued intuitionistic fuzzy soft set based real life decision making problems which may contain more than one decision maker and put an effort to apply it to a more relevant way in predicting election results in India by using the concept of choice matrix.

Diagnostic methods and risk analysis based on fuzzy soft information

In our daily life problem we face uncertainties in making right decisions. In this study, we propose two different decision-making problems in medical field. The first problem is fever diagnosing and second problem is mouth cancer risk analysis. In the first problem, we use fuzzy soft similarity measures and fuzzy soft matrix operations to diagnose the type of fever. We consider a hypothetical case study and manipulate similarity measures on it. Our work diagnoses different patients having similar symptoms. We also develop a small application using JAVA. In the second problem, we perform risk analysis of mouth cancer. The proposed fuzzy soft expert system takes two biochemical parameters as inputs that is, serum total malondialdehyde (MDA), and serum proton donors capacity (donors_protons) and determines the risk of mouth cancer. Our study facilitates doctors by diagnosing mouth cancer at its earlier stages. There are four main components of our fuzzy soft expert system. The first component is named as fuzzification which converts crisp input into linguistic variables and formulates fuzzy sets. The second component transforms fuzzy sets into their respective fuzzy soft sets. The third component determines indispensable parameters and performs parameter reduction. The fourth component performs risk analysis by using algorithm. We use exemplary dataset and run all the components of fuzzy soft expert system to compute cancer risk.

On Pythagorean fuzzy soft matrices, operations and their applications in decision making and medical diagnosis

In the present communication, we introduce Pythagorean fuzzy soft matrix and its various possible types. Some binary operations and various properties over the matrices are also being defined with their proof of validity. Further, the Pythagorean fuzzy soft matrices have been taken into account for proposing a new algorithm for decision making by using choice matrix and weighted choice matrix. In addition to this, an algorithm for medical diagnosis problem by making use of score matrix and utility matrix has also been proposed. Numerical examples for each of the applications have been successfully illustrated. A comparative analysis with other existing methods has also been carried out.

An Application of Intuitionistic Fuzzy Soft Matrix in Medical Diagnosis

An Application of Intuitionistic Fuzzy Soft Matrix in Medical Diagnosis

English

English

Fuzzy Soft Min-Max Decision Making and its Applications

In this paper, we study fuzzy soft matrix and fuzzy soft max-min decision making method. Then we introduce a fuzzy soft min-max decision making method based on a respective decision making function and two more examples are given using the AND and OR products of fuzzy soft matrices.

A Group decision making problem using hierarchical based fuzzy soft matrix approach

A Group decision making problem using hierarchical based fuzzy soft matrix approach

Human attitude analysis based on fuzzy soft differential equations with Bonferroni mean

In this paper, we model a system of fuzzy soft differential equations to analyze the behavior over the time of an individual depending on their companion’s actions under any particular situation against some decision. The Bonferroni mean (BM) is a very useful tool for group decision-making problems when arguments are interrelated to each other as Bonferroni mean can capture the interrelationship of the individual arguments. Using this ability of BM, we define Bonferroni fuzzy soft matrix (BFSM) and weighted Bonferroni fuzzy soft matrix (WBFSM) for data representation. WBFSM is a decision matrix and provide the optimum fuzzy soft constant (OFSC) which is the key element of fuzzy soft differential equation. By utilizing the OFSC, we develop a system of fuzzy soft differential equations to study a dynamical process with nonlinear uncertain and vague data. Second, we present a novel efficient technique for analyzing the future attitude of people due to their present decisions. To illustrate the practicality and feasibility of proposed technique an illustrative example is also discussed with the help of phase portrait and line graphs.

On the λ-Robustness of Fuzzy Neutrosophic Soft Matrix

Let( , ) N ≤ be a non-empty, bounded, linearly ordered set a a b max a b ⊕ = { , } , a b min a b ⊗ = { , } for a b, . ∈ N A fuzzy neutrosophic soft vector (FNSV) , , T I F 〈 〉 x x x is said to be a λ -fuzzy neutrosophic soft eigenvector (FNSEv) of a square fuzzy neutrosophic soft matrix (FNSM) A if A x x ⊗ = ⊗λ for some λ ∈ N . A given FNSM A is called (strongly) λ -robust if for every x the FNSV k A x ⊗ is a (greatest ) FNSEv of A for some natural number k. We present a characterization of λ -robust and strongly λ -robust FNSMs. Building on this, an efficient algorithm for checking the λ - robustness and strong λ -robustness of a given FNSM is introduced.

Some Results of Intuitionistic Fuzzy Soft Matrix

The purpose of this article is to consider the notions of intuitionistic fuzzy soft matrices and some basic results. This work deals particularly with the definit ion of transpose of intuitionistic fu zzy soft matrices and then some properties of transpose of intuit ionistic fuzzy soft matrices are studied. After that symmet ric intuitionistic fu zzy matrices are also defined and some properties are discussed. Nu merical examp les are provided to make the concept clear. Index Terms—Fu zzy sets, intuitionistic fuzzy sets, intuitionistic fuzzy soft matrices .

Study of Some Properties of Intuitionistic Fuzzy Soft Matrix

The purpose of this article is to consider the notions of intuitionistic fuzzy soft matrices(IFSM) and to study some basic results associated with such matrices. This work deals particularly with the definition and some properties of transpose of intuitionistic fuzzy soft matrices. In the process, symmetric intuitionistic fuzzy matrix is also defined and some properties are discussed. Numerical examples are provided to make the concept clear.

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English

Intuitionistic Fuzzy Soft Matrix Theory and its Application in Human Life

Intuitionistic Fuzzy Soft Matrix Theory and its Application in Human Life

Group decision making in medical system: An intuitionistic fuzzy soft set approach

In medical system, there may be many critical diseases, where experts do not have sufficient knowledge to handle those problems. For these cases, experts may provide their opinion only about certain aspects of the disease and remain silent for those unknown features. Feeling the need of prioritizing different experts based on their given information, this article uses a novel concept for assigning confident weights to different experts which are mainly based on their provided information. Experts provide their opinions about various symptoms using intuitionistic fuzzy soft matrix (IFSM). In this article, we propose an algorithmic approach based on intuitionistic fuzzy soft set (IFSS) which explores a particular disease reflecting the agreement of all experts. This approach is guided by the group decision making (GDM) model and uses cardinals of IFSS as novel concept. We have used choice matrix (CM) as an important parameter which is based on choice parameters of individual expert. This article has also validated the proposed approach using distance measurements and consents of the majority of experts. The effectiveness of the proposed approach is demonstrated using a suitable case study.

English

English