Top PDF N-Valued Interval Neutrosophic Sets and Their Application in Medical Diagnosis

N-Valued Interval Neutrosophic Sets and Their Application in Medical Diagnosis

N-Valued Interval Neutrosophic Sets and Their Application in Medical Diagnosis

In this paper a new concept is called n-valued interval neutrosophic sets is given. The basic operations are introduced on n-valued interval neutrosophic sets such as; union, intersection, addition, multiplication, scalar multiplication, scalar division, truth- favorite and false-favorite. Then, some distances between n-valued interval neutrosophic sets (NVINS) are proposed. Also, we propose an efficient approach for group multi-criteria decision making based on n-valued interval neutrosophic sets. An application of n-valued interval neutrosophic sets in medical diagnosis problem is given.
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Execution of N-Valued interval neutrosophic sets in medical diagnosis

Execution of N-Valued interval neutrosophic sets in medical diagnosis

As medical diagnosis contains lots of uncertainties and increased volume of information available to physicians from new medical technologies, the process of classifying different sets of symptoms under a single name of disease becomes difficult. In some practical situations, there is the possibility of each element having different truth membership, indeterminate and false membership functions. The unique feature of n-valued interval neutrosophic set is that it contains multi truth membership, indeterminate and false membership. By taking one time inspection, there may be error in diagnosis. Hence, multi time inspection, by taking the samples of the same patient at different times gives the best diagnosis. So, n-valued interval neutrosophic sets and their applications play a vital role in medical diagnosis.
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Cosine Similarity Measure Of Rough Neutrosophic Sets And Its Application In Medical Diagnosis

Cosine Similarity Measure Of Rough Neutrosophic Sets And Its Application In Medical Diagnosis

In this paper, we define a rough cosine similarity measure between two rough neutrosophic sets. The notions of rough neutrosophic sets (RNS) will be used as vector representations in 3D-vector space. The rating of all elements in RNS is expressed with the upper and lower approximation operator and the pair of neutrosophic sets which are characterized by truth-membership degree, indeterminacy-membership degree, and falsity-membership degree. A numerical example of the medical diagnosis is provided to show the effectiveness and flexibility of the proposed method.
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New Concepts of Interval-Valued Fuzzy Graphs with Application

New Concepts of Interval-Valued Fuzzy Graphs with Application

approximate reasoning [13,14], Roy and Biswas on medical diagnosis [24] and Mendel on intelligent control [19]. In 1975, Rosenfeld [25] discussed the concept of fuzzy graphs whose basic idea was introduced by Kauffman [18] in 1973. The fuzzy relation between fuzzy sets were also considered by Rosenfeld and he developed the structure of fuzzy graphs, obtain analogs of several graph theoretical concepts. Bhattacharya [4] gave some remarks on fuzzy graphs. Mordeson and Peng [21] introduced some operations on fuzzy graphs. The complement of a fuzzy graph was defined by Mordeson [20]. Bhutani and Rosenfeld introduced the concept of M-strong fuzzy graphs in [5] and studied some of their properties. Shannon and Atanassov [39] introduced the concept of intuitionistic fuzzy relations and intuitionistic fuzzy graphs. Hongmei and Lianhua gave the definition of interval-valued graph in [15]. Recently Akram introduced the concepts of bipolar fuzzy graphs and intervalvalued fuzzy graphs in [1,2,3]. Pal and Rashmanlou [23] studied irregular inteval-valued fuzzy graphs. Also, they defined antipodal interval- valued fuzzy graphs [26], balanced interval-valued fuzzy graphs [27] and a study on bipolar fuzzy graphs [28]. Rashmanlou and Jun investigated complete interval-valued fuzzy graphs [29]. Samanta and Pal defined fuzzy tolerance graphs [32], fuzzy threshold graphs [36], fuzzy planar graphs [38], fuzzy k-competition graphs and p-competition fuzzy graphs [34], irregular bipolar fuzzy graphs [35]. Borzooei and Rashmanlou [6-12] investigated new concepts on vague graphs. In this paper, we present the concepts of neighbourly irregular intervalvalued fuzzy graphs, neighbourly totally irregular intervalvalued fuzzy graphs, highly irregular intervalvalued fuzzy graphs, and highly totally irregular intervalvalued fuzzy graphs are introduced and investigated. A necessary and sufficient condition under which neighbourly irregular and highly irregular intervalvalued fuzzy graphs are equivalent is discussed.
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SIMILARITY MEASURE BETWEEN NEUTROSOPHIC REFINED SETS AND THEIR APPLICATIONS IN MEDICAL DIAGNOSIS

SIMILARITY MEASURE BETWEEN NEUTROSOPHIC REFINED SETS AND THEIR APPLICATIONS IN MEDICAL DIAGNOSIS

Neutrosopohic refined set is an important extension of neutrosophic set. In this paper, we focus on introducing similarity measure between neutrosophic refined sets based on the exponential operation. The proposed similarity measure provides a new way to handle the indeterminate and inconsistent information. Also we have examined some relevant properties of similarity measure between neutrosophic refined sets based on exponential operation. Finally, an application of neutrosophic refined set is given in medical diagnosis problems to illustrate the benefit of the proposed approach.
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More on Intuitionistic Neutrosophic Soft Sets

More on Intuitionistic Neutrosophic Soft Sets

The theory of neutrosophic set (NS), which is the generalization of the classical sets, conventional fuzzy set [1], intuitionistic fuzzy set [2]and interval valued fuzzy set [3],was introduced by Samarandache [4]. This concept has been applied in many fields such as Databases [5, 6], Medical diagnosis problem [7], Decision making problem [8],Topology [9],control theory [10] and so on. The concept of neutrosophic set handle indeterminate data whereas fuzzy set theory, and intuitionstic fuzzy set theory failed when the relation are indeterminate.
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Neutrosophic Refined Sets in Medical Diagnosis

Neutrosophic Refined Sets in Medical Diagnosis

In 1965, fuzzy set theory was firstly given by Zadeh which is applied in many real applications to handle uncertainty. Then interval valued fuzzy set, intuitionistic fuzzy set theory and interval valued intuitionistic fuzzy set were introduced by Turksen, Atanassov and Atanassov and Gargov respectively. These theories can only handle incomplete information not the indeterminate information and inconsistent information which exists commonly in belief systems. So, Neutrosophic set (generalization of fuzzy sets, intuitionistic fuzzy sets and so on) defined by Smarandache [1] has capability to deal with uncertainty, imprecise, incomplete and inconsistent information which exists in real world from philosophical point of view. Ye [4] proposed the vector similarity measures of simplified neutrosophic sets.
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SIMILARITY MEASURE OF INTERVAL
VALUED VAGUE SETS TO MULTIPLE
ATTRIBUTE DECISION MAKING

SIMILARITY MEASURE OF INTERVAL VALUED VAGUE SETS TO MULTIPLE ATTRIBUTE DECISION MAKING

In real life, a person may observe that an object belongs and not belongs to a set to certain degree, but it is possible that he is not sure about it. In other words, there may be some hesitation or uncertainty about the membership and non-membership degree of an object belonging to a set. In fuzzy set theory there is no means to incorporate that hesitation in membership degree. A possible solution is to use vague sets and the concept of vague set was proposed by Gau and Buehrer [1993]. Distance measure between vague sets is one of the most important technologies in various application fields of vague sets. But these methods are unsuitable to deal with the similarity measures of IFSs. In this paper we have extended the work of Zeshui Xu [2007] and also proposed a method to develop some similarity measure of interval valued vague sets and define the positive and negative ideal of interval valued vague sets, and apply the similarity measures to multiple attribute decision making based on vague information. A numerical example is also given to elaborate our technique.
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Reliability Analysis of a Powerloom Plant Using Interval Valued Intuitionistic  Fuzzy Sets

Reliability Analysis of a Powerloom Plant Using Interval Valued Intuitionistic Fuzzy Sets

In this paper, we investigate the reliability analysis of a powerloom plant by using interval valued intuitionistic fuzzy sets (IVIFS). Herein, we modeled a powerloom plant as a gracefully degradable system having two units A(n) and B(m) connected in series. The reliability of n components of unit A and m components of unit B is assumed to be an IVIFS defined over the universe of discourse [0, 1]. Thus, the reliability of the system obtained is an IVIFS that covers the inherited uncertainty in data collection and reliability evaluation of a powerloom plant.
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Interval–valued Fuzzy Bridges and Interval–valued Fuzzy Cutnodes

Interval–valued Fuzzy Bridges and Interval–valued Fuzzy Cutnodes

IVFGs and investigated some properties. They also introduced the notion of interval- valued fuzzy complete graphs and presented some properties of self complementary and self weak complementary interval-valued fuzzy complete graphs. Akram also introduced intervalvalued fuzzy line graphs [2] and bipolar fuzzy graphs [1]. Talebi and H. Rashmanlou [47] studied on isomorphism of IVFGs. Rashmanlou and Jun [29] defined the three new operations, direct product, semi strong product and strong product of IVFGs and discussed its properties on complete IVFGs. Debnath [28] introduced domination in IVFGs. Rashmanlou and Pal defined Irregular IVFG [26], Balanced IVFG [30] and Antipodal IVFG [31] and studied its properties. Also, they studied on the properties of highly irregular IVFG [33] and defined isometry on IVFG [32]. Akram, Alshehri and Dudek [4] introduced certain types of IVFG such as balanced IVFGs, neighbourly irregular IVFGs, neighbourly total irregular IVFGs, highly irregular IVFGs, highly total irregular IVFGs. Again Akram, Yousaf and Dudek [7] studied on the properties of self centered IVFGs. Pal, Samanta and Rashmanlou [27] defined the degree and total degree of an edge in the Cartesian product and composition of two IVFG and obtained some results. Mohideen [8] studied on strong and regular IVFGs. Narayanan and Maheswari [34] introduced strongly edge irregular and strongly edge totally irregular IVFG and made a comparative study between the two. Talebi, Rashmanlou and Ameri [48] studied on product IVFGs. Total regularity of the join of two IVFGs was discussed in [40]. Again regular and edge regular IVFGs were studied in [41].
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Multiattribute decision making models and methods using interval valued fuzzy sets

Multiattribute decision making models and methods using interval valued fuzzy sets

The theory of fuzzy sets proposed by Zadeh [1] has attracted wide attentions in various fields, especially where conventional mathematical techniques are of limited effectiveness, including biological and social sciences, linguistic, psychology, economics, and more generally soft sciences. In such fields, variables are difficult to quantify and dependencies among variables are so ill-defined that precise characterization in terms of algebraic, difference or differential equations becomes almost impossible. Even in fields where dependencies between variables are well defined, it might be necessary or advantageous to employ fuzzy rather than crisp algorithms to arrive at a solution. Out of several higher-order fuzzy sets,interval-valued fuzzy sets introduced by Zadeh [2-3]and intuitionistic fuzzy sets introduced by Atanassov [4-5]have been found to be well suited to dealing with vagueness. The concept of an interval-valued fuzzy set can be viewed as an alternative approach to define a fuzzy set in cases where available information is not sufficient for the definition of an imprecise concept by means of a conventional fuzzy set. In general, the theory of interval-valued fuzzy sets is the generalization of fuzzy sets. Therefore, it is expected that interval-valued fuzzy sets could be used to simulate human decision-making processes and any activities requiring human expertise and knowledge, which are inevitably imprecise or not totally reliable[6-8].
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From interval-valued data to general type-2 fuzzy sets

From interval-valued data to general type-2 fuzzy sets

Abstract —In this paper a new approach is presented to model interval-based data using Fuzzy Sets (FSs). Specifically, we show how both crisp and uncertain intervals (where there is uncertainty about the endpoints of intervals) collected from individual or multiple survey participants over single or repeated surveys can be modelled using type-1, interval type-2, or general type-2 FSs based on zSlices. The proposed approach is designed to minimise any loss of information when transferring the interval- based data into FS models, and to avoid, as much as possible assumptions about the distribution of the data. Furthermore, our approach does not rely on data pre-processing or outlier removal which can lead to the elimination of important information. Different types of uncertainty contained within the data, namely intra- and inter-source uncertainty, are identified and modelled using the different degrees of freedom of type-2 FSs, thus provid- ing a clear representation and separation of these individual types of uncertainty present in the data. We provide full details of the proposed approach, as well as a series of detailed examples based on both real-world and synthetic data. We perform comparisons with analogue techniques to derive fuzzy sets from intervals, namely the Interval Approach (IA) and the Enhanced Interval Approach (EIA) and highlight the practical applicability of the proposed approach.
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Developing Fuzzy TOPSIS Method based on Interval valued Fuzzy Sets

Developing Fuzzy TOPSIS Method based on Interval valued Fuzzy Sets

Ranking competing alternatives in terms of their overall performance with respect to some criterions in fuzzy environment is possible by the use of fuzzy TOPSIS methodology using interval-valued fuzzy-sets concepts. This author presents an effective fuzzy multi-criteria method based upon the fuzzy model and the concepts of positive ideal and negative ideal solution points for prioritizing alternatives using inputs from a team of decision makers. The fuzzy sets concepts are used to evaluate the performance of alternatives and the importance of criteria. Fuzzy TOPSIS based on the interval-valued fuzzy-sets is fully described and a case study on RFID comprised of four main criteria and five alternatives is constructed and solved by the proposed extended TOPSIS method. The TOPSIS methodology used in this article is able to grasp the ambiguity exists in the utilized information and the fuzziness appears in the human judgments and preferences. TOPSIS technique can easily produce satisfactory results, and hence stimulates creativity and the invention for developing new methods and alternative approaches. This article is a very useful source of information for Fuzzy TOPSIS based on the interval-valued fuzzy sets and extends the area of application of RFID technology in general. Due to the fact that a better management of a system is related to the full understanding of the technologies implemented and the system under consideration, sufficient background on the methodologies are provided and a case study is developed and solved by the proposed method.
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Neutrosophic Soft Expert Sets

Neutrosophic Soft Expert Sets

In this paper we introduce the concept of neutrosophic soft expert set (NSES). We also define its basic operations, namely complement, union, intersection, AND and OR, and study some of their properties. We give examples for these concepts. We give an application of this concept in a deci- sion-making problem.

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SINGLE-VALUED NEUTROSOPHIC LINE GRAPHS

SINGLE-VALUED NEUTROSOPHIC LINE GRAPHS

The paper is structured as follows: Section 2 contains a brief background about SVNSs and SVNGs. Section 3 introduces the concept of SVNLG of a SVNG and, investigates their properties. In Section 4, the notion of SVNC consistent with single-valued neutrosophic cycles in SVNGs is proposed and a complete characterization of the structure of the SVNC is presented, and finally we draw conclusions in Section 5.

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Research Article A Selection Method Based on MAGDM with Interval-Valued Intuitionistic Fuzzy Sets

Research Article A Selection Method Based on MAGDM with Interval-Valued Intuitionistic Fuzzy Sets

F 𝑘 = ( ̃ 𝑓 𝑖𝑗 𝑘 ) 𝑚×𝑛 , where 𝑓 ̃ 𝑖𝑗 𝑘 = ([𝑎 𝑖𝑗 𝑘 , 𝑏 𝑖𝑗 𝑘 ], [𝑐 𝑖𝑗 𝑘 , 𝑑 𝑘 𝑖𝑗 ]) is an IVIFN for the alternative 𝐴 𝑖 with respect to attribute 𝑢 𝑗 . In this paper, [𝑎 𝑘 𝑖𝑗 , 𝑏 𝑖𝑗 𝑘 ] and [𝑐 𝑖𝑗 𝑘 , 𝑑 𝑘 𝑖𝑗 ] provided by the expert 𝑒 𝑘 are, respectively, the satisfaction (agreeing) degree interval and dissatisfaction (disagreeing) degree interval of the 𝑖th cloud service 𝐴 𝑖 with respect to the 𝑗th attribute (indicator) 𝑢 𝑗 . 3.1. Determine the Weights of Experts by the Extended GRA Method. Due to the fact that each expert is skilled in some fields rather than all fields, it is more reasonable that the weights of each expert with respect to different attributes should be assigned different values. However, the weights of each expert obtained with the existing methods [34–37] are the same.
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On modular inequalities of interval valued fuzzy soft sets characterized by soft J inclusions

On modular inequalities of interval valued fuzzy soft sets characterized by soft J inclusions

Some researchers endeavored to enrich soft sets by combining them with other soft computing models such as rough sets and fuzzy sets. Using soft sets as the granulation structures, Feng et al. [] initiated soft approximation spaces and soft rough sets, which generalize Pawlak’s rough sets based on soft sets. On the other hand, Maji et al. [] ini- tiated the study on hybrid structures involving both fuzzy sets and soft sets. They in- troduced the notion of fuzzy soft sets, which can be seen as a fuzzy generalization of Molodtsov’s soft sets. Furthermore, Yang et al. [] introduced interval-valued fuzzy soft sets which realize a common extension of both Molodtsov’s soft sets and interval-valued fuzzy sets. It should be noted that there are several different kinds of soft inclusions (also known as soft subsets) in the literature [, , , ]. Feng and Li [] investigated different types of soft subsets and the related soft equal relations in a systematic way.
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Modal Type Operators over Interval Valued Intuitionistic Fuzzy Sets of Second Type

Modal Type Operators over Interval Valued Intuitionistic Fuzzy Sets of Second Type

[3] further introduced the concepts of interval valued intuitionistic fuzzy set. The present authors further introduced the new extension of IVIFS namely interval valued intuitionistic fuzzy sets of second type (IVIFSST) and established some of their properties [4]. The rest of the paper is designed as follows: In Section 2, we give some basic definitions. In Section 3, we introduce modal type operators over interval valued intuitionistic fuzzy sets of second type and establish some of their properties. This paper is concluded in section 4.
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SPECIAL TYPES OF SINGLE VALUED NEUTROSOPHIC GRAPHS

SPECIAL TYPES OF SINGLE VALUED NEUTROSOPHIC GRAPHS

Abstract. Neutrosophic theory has many applications in graph theory, single valued neutrosophic graph (SVNG) is the generalization of fuzzy graph and intuitionistic fuzzy graph. In this paper, we introduced some types of SVNGs, which are subdivision SVNGs, middle SVNGs, total SVNGs and single valued neutrosophic line graphs (SVNLGs), also discussed the isomorphism, co weak isomorphism and weak isomorphism properties of subdivision SVNGs, middle SVNGs, total SVNGs and SVNLGs.

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New Entropy-Based Similarity Measure between Interval-Valued Intuitionstic Fuzzy Sets

New Entropy-Based Similarity Measure between Interval-Valued Intuitionstic Fuzzy Sets

Step 1 : Find out the positive-ideal solution M + and negative-ideal solution M − : M + = {< [µ − 1+ , µ + 1+ ] , [ v − 1+ , v + 1+ ] , ..., [µ − n+ , µ + n+ ] , [ v − n+ , v + n+ ] >} , M − = {< [µ − 1− , µ + 1− ] , [ v 1− − , v 1− + ] , ..., [µ − n− , µ + n− ] , [ v − n− , v + n− ] >} , where, for each j = 1, 2, ..., n,

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