Discrete Dynamics in Nature and Society
Volume 2019 (2019), Article ID 3246439, 16 pages
https://doi.org/10.1155/2019/3246439

Research Article

Similarity Measures between Temporal Complex Intuitionistic Fuzzy Sets and Application in Pattern Recognition and Medical Diagnosis

Mohammed M. Khalaf,¹ Sayer Obaid Alharbi,² and Wathek Chammam2,3

¹Higher Institute of Engineering and Technology King Marriott, P.O. Box 3135, Egypt
²Department of Mathematics, College of Science Al-Zulfi, Al-Majmaah University, P.O. Box 66, Al-Majmaah 11952, Saudi Arabia
³Department of Mathematics, Faculty of Sciences of Gabès, Gabès University, Gabès, Tunisia

Correspondence should be addressed to Mohammed M. Khalaf; khalfmohammed2003@yahoo.com

Received 27 March 2019; Accepted 2 June 2019; Published 1 July 2019

Academic Editor: Francisco R. Villatoro

Copyright © 2019 Mohammed M. Khalaf et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

This work addresses the issue of similarity measures between two temporal complex Atanassov’s intuitionistic fuzzy sets, many measures of similarity between complex Atanassov’s intuitionistic fuzzy sets. What was proposed before did not consider the abstention group influence, which may lead to counterintuitive results in some cases. A new structure of temporal complex Atanassov’s intuitionistic fuzzy sets is obtained. This set is formally generalized from a conventional Atanassov’s intuitionistic complex fuzzy sets. Here we analyze the limitations of the existing similarity measures. Then, a new similarity measure of temporal complex Atanassov’s intuitionistic fuzzy sets is proposed and several numeric examples are given to demonstrate the validity of the proposed measure. Finally, the proposed similarity measure is applied to pattern recognition and medical diagnosis.

1. Introduction

Fuzzy set theory was conferred by Zadeh [1] to solve difficulties in dealing with uncertainties. Since then, the theories of fuzzy sets and fuzzy logic have been examined by many researchers to solve many real life problems involving ambiguous and uncertain environment. By adding a new component the idea of the concept of Atanassov’s intuitionistic fuzzy set (AIFS) was introduced [2]. Applications of these sets have been broadly studied in other aspects such as image processing [3], multicriteria decision making [4], pattern recognition [5], etc. Buckley [6] and Nguyen et al. [7] combined complex numbers with fuzzy sets. On the other hand, the innovative complex fuzzy set is introduced. The complex fuzzy set is characterized by a membership function, , whose range is not limited to but extended to the unit circle in the complex plane. Hence, is a complex-Valued function that assigns a grade of membership of the form , to any element in the universe of discourse. The value of is defined by the two variables, and , both real-valued, with . Complex fuzzy set theory modifies the original concept of fuzzy membership by asserting that, at least in some instances, it is necessary to add a second dimension to the expression of membership. However, this added dimension does not alter the basic concept of fuzziness. Membership in a complex fuzzy set remains “as fuzzy” as membership in a traditional fuzzy set. The fuzziness of membership, i.e., the representation of membership as a value in the range , is retained in complex fuzzy sets through the amplitude of the grade of membership, . The novelty of complex fuzzy sets is manifested in the additional dimension of membership: the phase of the grade of membership, . The properties of membership phase are discussed at length in this section. Ramot et al. [8, 9] extended the range of membership to “unit circle in the complex plane”, unlike others who limited the range to . Omar [10] studied similarity measures between temporal intuitionistic fuzzy sets. As the complex fuzzy membership grade is two-dimensional (amplitude and phase), a complex fuzzy set can be visually represented by a three-dimensional graph where the universe of discourse is the third axis. Figure 1 shows the complex fuzzy set.

Figure 1: Complex fuzzy set defined in [11].

We divide the paper into four main sections. In the first section preliminaries and basic definitions, we provide some details about the complex fuzzy sets. In the second section, detail is given about the complex version of temporal complex intuitionistic fuzzy set, which is an extension of complex intuitionistic fuzzy set by adding the times and studied the correlation coefficient between two temporal complex intuitionistic fuzzy set. In the third section, details is given about similarity measures between other extensions of temporal complex intuitionistic fuzzy set and extend the method proposed by Chaira [12] for intuitionistic fuzzy set based on the Sugeno [13] and Omar [10] intuitionistic fuzzy generator. In the fourth section, we give application in pattern recognition, medical diagnosis, and topology.

2. Preliminaries and Basic Definitions

Definition 1 (see [8]). A complex fuzzy set (CFS) defined on a universe is an object of the form defined on a universe of discourse which is an object of the form

where , , and

Definition 2 (see [2]). A complex intuitionistic fuzzy set (CIFS) defined on a universe of discourse is an object of the form

where , , , and +

Definition 3 (see [14]). Let and be two CIFSs in , where

Then, is given as

where

Definition 4 (see [15]). Let and be two CIF-sets in , where

Then, for all ,
(1) if and only if ; For amplitude terms and , for phase terms.
(2) if and only if ; For amplitude terms and , for phase terms.

Definition 5 (see [16]). Let and be two CIFSs in , where

Then, for all ,
(1)
where

(2) The complex fuzzy complement of , denoted by , is specified by a function

(3) and .

Example 6. Consider . Let be a CIF-subset of , as given by

Then

And and .

3. Temporal Complex Intuitionistic Fuzzy Set

Definition 7. Let be a universe, be a nonempty set of time moments, and A temporal complex intuitionistic fuzzy set (TCIFS) defined on a universe of discourse is an object of the form

where and such that +, , and being the degrees of membership and nonmembership, respectively, of the element at the moment . And at the moment , where .
The hesitation degree of a TCIFS is defined by such that for each For brevity we will write instead of when this does not cause confusions.

Example 8. Suppose that is a universal, with respect to the time set , and . Then, TCIFSs are defined by

Example 9. Suppose with respect to the time set . Then, the details of a TCIFS are explained in Tables 1, 2, and 3

Table 1: TIFS .

Table 2: .

Table 3: .

Definition 10. Let and be two TCIFSs. Then

where

Definition 11. We define the following two operators over a TCIFS :

Theorem 12. and are TCIFSs.

Proof. Suppose that

and

Therefore,

Then is TCIFSs. Also, by the same fashion are TCIFSs.

Theorem 13. For every TCIFS ,

(1);(2);(3);(4)

Proof. The proof is obvious.

Theorem 14. For every TCIFS ,

(1);(2)

Proof. (1)

(2) By the same fashion, one has the following.

Definition 15. Let and be two TCIFSs defined on the universe of discourse and the time moments . The correlation coefficient of and is given by

where

is the correlation of two TCIFSs and , and

are the information temporal complex intuitionistic energies of and , respectively.

Example 16. Suppose that with respect to the time set . The details of a are explained in Table 4, Table 5 explained , and Table 6 explained the correlation coefficient between TCIFS and TCIFS .

Table 4: TCIFS .

Table 5: TCIFS .

Table 6: Then .

.3041

.6648

.5000
.2544

.4301
.6403
.6802

.5508
.4204

.5581

Proposition 17. Let and be two TCIFS. Then

(1);(2)If and , then, ;(3)

Proof. Let From Definition 10, , , , , and then
(1)

(2) If and

(3)

Theorem 18. Suppose that and are TCIFSs in the universal with respect to the time set . Then

(1)if and , then ;(2);(3)

Proof. (1) Let and be two TCIFSs defined on the universe of discourse and the time moments . The correlation coefficient of and is given by

If , then Then from Proposition 17 (2), one has the following.
If and , then ; then

(2) From Proposition 17 (3), Then

(3) We will prove that such that it is evident , so suppose that

Then

Then

Then

Then

But

Hence , and then

Definition 19. Let be a function, and let TCIFSs in the universal with respect to the time set . Then is said to be the similarity degree between and , satisfying the following statements:

(1).(2) if .(3).(4)If . Then

Now we can have the following degrees of the similarity between and satisfy the conditions from to . Let

where ; . Then

From a comparison between similarity measures , we give the following example.

Example 20. Suppose that is TCIFSs defined on with respect to the time set The details of a are explained in Table 7, Table 8 explained , and Table 9 explained a comparison between similarity measures , , and .

Table 7: TCIFS .

Table 8: TCIFS .

Table 9: A comparison between similarity measures .

i

9980

9986

9916

9941

9930

9705

9986

9941

9986

9941

9929
9968

9699
9868

4. Similarity Measures between Other Extensions of Temporal Complex Intuitionistic Fuzzy Set

The following definition extend the method proposed by Chaira [12] for intuitionistic fuzzy set based on the Sugeno [13] and Omar [10] intuitionistic fuzzy generator.

Definition 21. If is the degrees of membership function of the element at the moment , then nonmembership function , where

And , , and by help of the Sugeno [6] intuitionistic fuzzy generator, TCIFS is given by

The hesitation degree of a TCIFS is

Example 22. Suppose that is TCIFS defined on with respect to the time set . The details of a are explained in Tables 10, 11, and 12, Table 13 explained when , and Table 14 explained the hesitation degree of a TCIFS .
If , then one has the following (see Tables 13 and 14).

Table 10: TIFS .

Table 11: .

Table 12: TCIFS .

Table 13: TCIFS .

Table 14: The hesitation degree of a TCIFS .

Definition 23. Suppose that is TCIFSs in the universal with respect to the time set . Then a cosine similarity measure between is proposed as follows:

where ;

Theorem 24. Suppose that and are TCIFSs in the universal with respect to the time set . Then

(1) if and ,(2),(3),(4)if , then

Proof. (1) Let and be two TCIFSs defined on the universe of discourse and the time moments . The cosine similarity measure between is given by

If and , then

(2)

(3) By the same way in (3) in Theorem 2.1, one has the following.
(4) If , then

Definition 25. Suppose that is TCIFSs in the universal with respect to the time set . Then, the distance measure of the angle is proposed as follows:

Theorem 26. Suppose that and are TCIFSs in the universal with respect to the time set . Then

(1) and ; then ,(2), then ,(3)if, then ,(4)if , then

Proof. (1), (2), and (3) are simple proof.
(4) Let , TCIFSs in the universal with respect to the time set . Then, the distance measure of the angle is proposed as follows:

where , , and

If , for each , , then

Definition 27. Let and be two TCIFSs defined on the universe of discourse and the time moments . Suppose that is correlation coefficient of and . Then a weight similarity measure between TCIFSs is proposed as follows:

And we have the following properties:

(1) then (2)(3)

Remark 28. if

From a comparison between similarity measures , we give the following example (the same data in Example 16).

Example 29. Suppose that is TCIFSs defined on with respect to the time set . The details of a are explained in Table 15, Table 16 explained , and Table 17 explained a comparison between similarity measures between , .

Table 15: TCIFS .

Table 16: TCIFS .

Table 17: Then the similarity measures and .

006

4.1. Application in Pattern Recognition and Medical Diagnosis

Let be the set of symptoms of the diseases with respect to the time set and be the set of diagnoses. By using the similarity measures we try to discover that the patient may suffer from one from diseases which have symptoms at the time , and we let be standard case symptoms of one of diseases (Table 18) and be any case (Table 19); Table 20 explained the similarity measures between a standard case and any case .

Table 18: Standard case symptoms of one of diseases .

Table 19: The symptoms of case .

Table 20: Similarity measures .

006

And we define the symptoms of case by Table 19.

Then Table 20 explained the similarity measures between a standard case and any case .

When the similarity measures are small, then probability that the patient is suffering from the disease at the time is big and the conversely is true.

4.2. Complex Intuitionistic Fuzzy Topology

Definition 30. An intuitionistic complex fuzzy topology on is a family of -sets in which satisfies the following properties:

(1), ,(2)if , then ,(3)if for each , then .

Then is called complex intuitionistic fuzzy topological space. The elements of are called -sets and the complement of the -sets is called -sets.

Example 31. Consider . Let be a -subset of , as given by

Then is an complex intuitionistic fuzzy topology on .

Definition 32. If is called complex intuitionistic fuzzy topological space, , then the interior of is defined as the union of all -subsets of and it is denoted by . That is, is the largest -subset of . The closure of is defined as the intersection of all sets containing and it is denoted by . That is, is the smallest -set containing .

Example 33. Consider . Let be a -subset of , as given by

Then is an intuitionistic complex fuzzy topology on , and .

Definition 34. An intuitionistic complex fuzzy topological space is said to be extremely disconnected, if the closure of each -set is -set.

Definition 35. Let be an intuitionistic complex fuzzy topological space. A subset of is said to be semiopen set (by short -set) (resp., CIF preopen (by short -set), α-open (by short -set), -open (by short -), and b-open (by short -set). If (resp., , , , and ), the family of all -set (resp., -set, -set, -, and -set) in is denoted by (resp., (), (), and ).

The implications between these concepts in the following diagram and the converse are not true in general,

5. Conclusion

In this paper we introduced and studied a temporal complex intuitionistic fuzzy sets as generalization of complex Atanassov’s intuitionistic fuzzy sets by taking the time in the moving of the point; a correlation between two temporal complex intuitionistic fuzzy sets is discussed. A similarity between temporal complex intuitionistic fuzzy sets is main points in the paper as a generalization of the similarity introduced by Omar [10] and Sugeno [13]. We calculate the results by the program Maple 7. Finally we give an applications to know if the patient is suffering from the diseases or not and introduce the main building in a topology by using the same the set. In future research, similarity measures between temporal complex multifuzzy soft and applications in engineering, medical, physics, and automobiles will be studied.

Data Availability

No data were used to support this study.

Conflicts of Interest

The author declares no conflicts of interest.

Authors’ Contributions

All authors contributed equally.

Acknowledgments

Sayer Obaid Alharbi thanks Deanship of Scientific Research (DSR) for providing excellent research facilities. The publication costs of this article were partially covered by the Estonian Academy of Sciences.

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