Novel spherical fuzzy distance and similarity measures and their applications to medical diagnosis

Donyatalab, Yaser; Gündoğdu, Fatma Kutlu; Farid, Fariba; Seyfi-Shishavan, Seyed Amin; Farrokhizadeh, Elmira; Kahraman, Cengiz

doi:10.1016/j.eswa.2021.116330

Cited by 34 publications

(18 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The spherical fuzzy set is capable of expressing membership, non-membership and hesitation in decision makers' opinions. Therefore, spherical fuzzy extensions of MCDM methods are introduced more and more in many fields [22][23][24]. In addition, decision makers' psychological behaviors, such as expectation, risk aversion, and regret aversion, are also believed to have a significant impact on decisions.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Composited Regret-Theory-Based Spherical Fuzzy Prioritization Approach for Moving High-Tech Manufacturing in Southeast Asia

2023

View full text Add to dashboard Cite

After the pandemic, global supply chains will be in the process of restructuring. The relocation of production lines among countries is being considered for the purpose of sustainable development. The problem of determining the most suitable destination for manufacturers’ investments will become important, especially in the field of manufacturing high-tech products, which involves many complicated factors such as technological maturity, support policies, political issues, and technology security. In that context, Southeast Asia is seen as one of the regions attracting multinational manufacturers. To address this problem, a novel composited regret-theory-based spherical fuzzy prioritization approach is proposed. On the one hand, the super-efficiency slack-based model (super-SBM) of data envelopment analysis (DEA) is applied to evaluate efficiency, based on indicators. On the other hand, the novel spherical fuzzy regret-theory-based decision-making approach (SfRDMA) is developed and introduced to determine effectiveness, based on criteria. Then, the efficiency and the effectiveness of countries are combined by a composite-score function that is based on a geometric mean and an arithmetic mean. The findings imply that government policy, political stability, and human resources availability are the three most important criteria. Moreover, India, Thailand, Vietnam, Malaysia, and Indonesia are identified as promising destinations for the world’s high-tech production lines.

show abstract

Section: Related Workmentioning

confidence: 99%

“…To aggregate individual matrices, the spherical weight arithmetic mean is used with decision makers' weights (Ψ 𝑘 ), as described in Equation (7). Hence, the SF direct-influence matrix (𝐴 ̃) is established, as represented in Equation (24).…”

Section: Linguisticmentioning

confidence: 99%

A Composited Regret-Theory-Based Spherical Fuzzy Prioritization Approach for Moving High-Tech Manufacturing in Southeast Asia

2023

View full text Add to dashboard Cite

show abstract

“…Definition 2.4. (Donyatalab et al, 2022). The normalized Minkowski distance, also known as the generalized distance, is given as…”

Section: Preliminariesmentioning

confidence: 99%

A Cognitive-Based Similarity Measure for Decision-Making with Spherical Fuzzy Information

Sharaf

2023

JCCE

View full text Add to dashboard Cite

This study aims to develop a new perspective of similarity measures (SMs) for the recently introduced spherical fuzzy sets (SFSs). SFSs have several favorable properties making them superior to other types of fuzzy sets (FSs). As a consequence, SFSs are currently subject to extensive study to establish robust measures. SMs are one of the known measures of FSs. In the spherical fuzzy environment, some of the extant SMs cannot satisfy the axioms of similarity and provide counter-intuitive cases. Moreover, these conventional SMs are generalizations of SMs for intuitionistic and Pythagorean fuzzy information. None of them reflects the cognitive dimension of a SFS. Hence, the concept of the cognitive impact of a SFS is introduced. The cognitive impact is the logical implications for what human perception ought to ensue. Based on this concept, a new SM is introduced. While the conventional SMs are based on the position of the SFSs relative to each other, the novel SM is based on the effect of each evaluation on decision-making. First, an extensive review of the SMs for SFSs is presented. Second, the new concept of cognitive impact is introduced in the spherical fuzzy environment. Then, the novel SM is developed. A comparative analysis between the novel SM and the extant SMs is conducted. Finally, a multi-criteria decision-making problem is solved, namely green supplier selection using the proposed cognitive-based SM to check its applicability and its validity.

show abstract

Section: Applicationsmentioning

confidence: 99%

“…These type of study are evident in Szmidt and Kacprzyk, 40,41 Luo and Zhao, 42 Cheng et al, 43 De at. al., 44 Wei et al, 45 Mondal and Pramanik, 46 Mahanta and Panda, 47 Singh and Ganie, 48 He and Xiao, 35 Zhou et al, 49 Wei and Wei, 12 Ontiveros et al, 50 and Xiao and Ding, 51 Donyatalab et al, 52 Khan et al, 53 and so forth. Furthermore, medical diagnosis problems in modeling and forecasting the spread of Covid-19 under a fuzzy environment are studied by Boccaletti et al 54 and Melin et al 55,56 Consider a set of patients P A A A A = { , , , } = {(0.9, 0.2), (0.7, 0.2), (0.3, 0.9), (0.7, 0.2), (0.2, 0.7)} = {(0.1, 0.9), (0.5, 0.5), (0.7, 0.2), (0.2, 0.8), (0.2, 0.9)} = {(0.9, 0.2), (0.9, 0.2), (0.1, 0.7), (0.3, 0.8), (0.1, 0.6)} = {(0.7, 0.2), (0.6, 0.5), (0.4, 0.5), (0.8, 0.3), (0.4, 0.5)}.…”

Section: Applicationsmentioning

confidence: 99%

Discrete similarity measures on Pythagorean fuzzy sets and its applications to medical diagnosis and clustering problems

Gohain

Chutia

Dutta

2022

Int J of Intelligent Sys

View full text Add to dashboard Cite

Pythagorean fuzzy sets are an extension of intuitionistic fuzzy sets and are more efficient from an application perspective. Though the Pythagorean fuzzy sets are more informative, not much work on similarity measures is available in the literature. Furthermore, existing similarity measures are not efficient. Also, the containment property in Pythagorean fuzzy units is not correctly defined or ineffective. As a result, the existing similarity measures do not reflect appropriate information between the Pythagorean fuzzy sets. The scalar function of the Yager is mainly used for verifying the validity of similarity measures. Most of the existing similarity measures do not conform to the Yager scalar function. Hence, the existing similarity measures exhibit some discrepancies. Furthermore, the existing similarity measures are inconsistent in determining the similarity in intuitionsitic and Pythagorean fuzzy sets. In some real‐world modeling issues, past, present, and cross‐time information are essential. However, such information is missing in the existing similarity measures. Therefore, in this paper, two new measures of similarity are being developed based on the deviation of the parameters: membership degree, nonmembership degree, strength of commitment, direction of commitment, and cross‐time evaluation factors. Under this construction, the proposed similarity measures effectively measure the similarity between the Pythagorean fuzzy sets. Furthermore, the newly defined containment property is also reflected in the proposed similarity measures, which were a limitation in most cases. Moreover, Yager's scalar function is also reflected by the proposed similarity measures. The complement of given information is also essential in some real‐world problems. However, such information is incomplete in the theory of Pythagorean fuzzy sets. Hence, the complement of the Pythagorean fuzzy set is being redefined, and a few related results on similarity measures are proposed. Finally, the proposed similarity measures are tested for applicability to medical diagnosis and clustering problems through some hypothetical case studies.

show abstract

Novel spherical fuzzy distance and similarity measures and their applications to medical diagnosis

Cited by 34 publications

References 36 publications

A Composited Regret-Theory-Based Spherical Fuzzy Prioritization Approach for Moving High-Tech Manufacturing in Southeast Asia

A Composited Regret-Theory-Based Spherical Fuzzy Prioritization Approach for Moving High-Tech Manufacturing in Southeast Asia

A Cognitive-Based Similarity Measure for Decision-Making with Spherical Fuzzy Information

Discrete similarity measures on Pythagorean fuzzy sets and its applications to medical diagnosis and clustering problems

Contact Info

Product

Resources

About