Bidirectional approximate reasoning for rule-based systems using interval-valued fuzzy sets

With the increasing risk to human health and environmental issues, the selection of appropriate management and treatment of healthcare waste has become a major problem, especially in developing countries. There are various alternatives to dispose of health care waste. The important is to assess the best alternative among them. The assessment of each alternative should be done based on public health, psychological, economic, environmental, technological, and operational aspect. The selection of the best health care waste treatment (HCWT) alternative is a complicated, multi-criteria decision-making (MCDM) problem involving numerous disparate qualitative and quantitative features. Hence, in this research article, the MCDM method is presented for estimating and choosing the best alternative of HCWT by COPRAS technique in a Pythagorean fuzzy set (PFS). Here, in this paper, first of all, a new entropy measure on PFSs is proposed and its validity is studied. Thereafter, the MCDM technique Complex Proportional Assessment (COPRAS) is discussed in which the criteria weights are assessed by the proposed entropy measure and score function to enhance an efficacy and efficiency of the proposed technique. Furthermore, the above-defined technique is employed to resolve the real-life problem to obtain the best treatment alternative to disposal of the health care waste. Finally, sensitivity analysis is presented to rationale the proposed viewpoint for prioritizing HCWT alternatives.

Section: Introductionmentioning

confidence: 99%

Pythagorean fuzzy entropy measure-based complex proportional assessment technique for solving multi-criteria healthcare waste treatment problem

Chaurasiya

Jain

2022

“…Then, Chen ( 1997a ); Chen et al. ( 1997 ); Chen and Hsiao ( 2000 ) introduced different types of bidirectional ruled based reasoning and applications of interval-valued fuzzy hypergraphs. Mendel et al.…”

Section: Introductionmentioning

confidence: 99%

New concepts of inverse fuzzy mixed graphs and its application

Poulik

Ghorai

2021

Fuzzy mixed graph (FMG) can be used to model some graphical intercommunication network systems if there are many directed and undirected relations between some vertices. In inverse fuzzy graph, the membership value of edges are greater than or equal to the minimum of the membership value of the corresponding vertices. In inverse fuzzy mixed graph (IFMG), directed and undirected relations exist between some vertices and it can be used to analyze many graphical problems of real life such that the membership values of edges are greater than or equal to the minimum of the membership value of the corresponding pair of vertices. In this article, the concept of IFMG is introduced first with some of its properties. Then some isomorphic properties are studied and complement of IFMG is given. Different types of operations like union, intersection, product and join between two IFMGs are defined and investigated some of their related results. An algorithm of the proposed method is executed to identify some vertices. An application is depicted using the concept IFMG to examine the order of vertices in a social network group according to communication gaps.

“…In such real time scenarios, the arc lengths can be expressed as about 30 minute, around 30-90 min, nearly 90 min, between 90 and 110 min, etc. Fuzzy set is one of the most important mathematical tools to handle the uncertainty of the model (Chen 1996;Chen et al 1997Chen et al , 2006Chen and Hsiao 2000;Wang and Chen 2008;Horng et al 2005;Chen and Hong 2014). Most of the researchers have used type-1 fuzzy set to express those uncertain arc weights.…”

Section: Introductionmentioning

confidence: 99%

A new algorithm for finding minimum spanning trees with undirected neutrosophic graphs

Dey¹,

Broumi

Sơn

et al. 2018

In this paper, we discuss the minimum spanning tree (MST) problem of an undirected neutrosophic weighted connected graph in which a single-valued neutrosophic number, instead of a real number/fuzzy number, is assigned to each arc as its arc length.We define this type of MST as neutrosophic minimum spanning tree (NMST). We describe the utility of neutrosophic numbers as arc lengths and its application in different real world MST problems. Here, a new algorithm for designing the MST of a neutrosophic graph is introduced. In the proposed algorithm, we incorporate the uncertainty in Kruskal algorithm for designing MST using neutrosophic number as arc length. A score function is used to compare different NMSTs whose weights are computed using the addition operation of neutrosophic numbers. We compare this weight of the NMST with that of an equivalent classical MST with real numbers as arc lengths. Compared with the existing algorithms for NMST, the proposed algorithm is more efficient due to the fact that the addition operation and the ranking of neutrosophic number can be done in straightforward manners. The proposed algorithm is illustrated by numerical examples.