Markov chains and rough sets

We define $$2n+1$$ 2 n + 1 and 2n fuzzy numbers, which generalize triangular and trapezoidal fuzzy numbers, respectively. Then, we extend the fuzzy preference relation and relative preference relation to rank $$2n+1$$ 2 n + 1 and 2n fuzzy numbers. When the data is representable in terms of $$2n+1$$ 2 n + 1 fuzzy number, we generalize the FMCDM (fuzzy multi-criteria decision making) model constructed with TOPSIS and relative preference relation. Lastly, we give an example from telecommunications to present the proposed FMCDM model and validate the results obtained.

show abstract

“…For the examples on decision-making problems, we refer (Koppula et al 2019(Koppula et al , 2020Riaz et al 2020;Chen and Huang 2021).…”

Section: Definitions and Preliminariesmentioning

confidence: 99%

Generalization and ranking of fuzzy numbers by relative preference relation

2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…For recent developments in nearrings and Boolean nearrings, we refer Kuncham, Kedukodi, Panackal and Bhavanari [20], Nayak, Kuncham and Kedukodi [23], Koppula, Kedukodi and Kuncham [17] and Kedukodi, Kuncham and Bhavanari [14,15]. We refer Bhavanari and Kuncham [3], Kuncham [18], Pilz [24], Bhavanari [4], Lafuerza-Guillén and Panackal [21], Panackal, Lafuerza-Guillén and Ravindran [25] and Jagadeesha, Kuncham and Kedukodi [8] for basic definitions.…”

Section: Introductionmentioning

confidence: 99%

Extensions of Boolean Rings and Nearrings

Nayak¹,

Kuncham

Kedukodi

2019

J. Sib. Fed. Univ., Math. phys.

View full text Add to dashboard Cite

In this paper, we introduce the notions of left (right) Boolean rings and nearrings. We give examples to show that left (right) Boolean rings are not commutative in general. We obtain interrelations among these algebraic structures and get conditions under which the structures are commutative. Finally, we study the concept of derivations on left (right) Boolean rings and nearrings and obtain commutativity results.

show abstract

Markov frameworks and stock market decision making

2020

View full text Add to dashboard Cite

In this paper, we present applications of Markov rough approximation framework (MRAF). The concept of MRAF is defined based on rough sets and Markov chains. MRAF is used to obtain the probability distribution function of various reference points in a rough approximation framework. We consider a set to be approximated together with its dynamacity and the effect of dynamacity on rough approximations is stated with the help of Markov chains. An extension to Pawlak’s decision algorithm is presented, and it is used for predictions in a stock market environment. In addition, suitability of the algorithm is illustrated in a multi-criteria medical diagnosis problem. Finally, the definition of fuzzy tolerance relation is extended to higher dimensions using reference points and basic results are established.

show abstract

Markov chains and rough sets

Cited by 4 publications

References 37 publications

Generalization and ranking of fuzzy numbers by relative preference relation

Generalization and ranking of fuzzy numbers by relative preference relation

Extensions of Boolean Rings and Nearrings

Markov frameworks and stock market decision making

Contact Info

Product

Resources

About