S. Sharief Basha
Bio: S. Sharief Basha is an academic researcher from VIT University. The author has contributed to research in topics: Graph (abstract data type) & Computer science. The author has an hindex of 2, co-authored 13 publications receiving 17 citations.
TL;DR: The Laplacian energy of an intuitionistic fuzzy graph is defined in terms of its adjacency matrix and the lower and upper bound for the energy are derived and verified with suitable intuitionism fuzzy graphs.
Abstract: Background/Objectives: The concept of Laplacian energy of fuzzy graph is extended to the Laplacian energy of an intuitionistic fuzzy graph. Methods/Statistical Analysis: In this paper, we defined the adjacency matrix of an intuitionistic fuzzy graph and the Laplacian energy of an intuitionistic fuzzy graph is defined in terms of its adjacency matrix. Findings: The lower and upper bound for the energy of an intuitionistic fuzzy graph are also derived and verified with suitable intuitionistic fuzzy graphs. Application/Improvements: Laplacian spectra of intutionistic fuzzy graphs may reveal more analogous results in the chemical molecules.
TL;DR: Adjacency matrix of a fuzzy graph is defined and computed minimum dominating energy of fuzzy graph ED(G̃) and also upper and lower bounds for ED (Ǵ) are established.
Abstract: The minimum dominating energy of a graph has been defined and many of its results and properties have been studied. In this paper, the concept of the minimum dominating energy is extended to fuzzy graphs. Adjacency matrix of a fuzzy graph is defined and computed minimum dominating energy of fuzzy graph ED(G) and also upper and lower bounds for ED(G) are established.
TL;DR: The concepts of Laplacian energy (LE) in Hesitancy fuzzy graphs (HFGs), the weight function of LE of HFGs, and the TOPSIS method technique is used to produce the hes itancy fuzzy weighted-average (HFWA).
Abstract: Decision-making (DM) is a process in which several persons concurrently engage, examine the problems, evaluate potential alternatives, and select an appropriate option to the problem. Technique for determining order preference by similarity to the ideal solution (TOPSIS) is an established DM process. The objective of this report happens to broaden the approach of TOPSIS to solve the DM issues designed with Hesitancy fuzzy data, in which evaluation evidence given by the experts on possible solutions is presents as Hesitancy fuzzy decision matrices, each of which is defined by Hesitancy fuzzy numbers. Findings: we represent analytical results, such as designing a satellite communication network and assessing reservoir operation methods, to demonstrate that our suggested thoughts may be used in DM. Aim: We studied a new testing method for the artificial communication system to give proof of the future construction of satellite earth stations. We aim to identify the best one from the different testing places. We are also finding the best operation schemes in the reservoir. In this article, we present the concepts of Laplacian energy (LE) in Hesitancy fuzzy graphs (HFGs), the weight function of LE of HFGs, and the TOPSIS method technique is used to produce the hesitancy fuzzy weighted-average (HFWA). Also, consider practical examples to illustrate the applicability of the finest design of satellite communication systems and also evaluation of reservoir schemes.
TL;DR: Sugeno Adaptive Neuro-Fuzzy Inference System (SANFIS) prediction model for COVID-19 predic-tion in Andhra Pradesh, India has been proposed as mentioned in this paper .
Abstract: The COVID-19 influenza became a curse on the world. It has been around for two years, so no one needs to make a big introduction of it. It has became a significant challenge around the world. Owing to this, we made dy-namic networks using an amalgamating of fuzzy logic and neural networks for the prediction of sufferers of COVID-19. These hybrid networks serve for the assess-ment of the COVID-19 victims and usefully serve for the assessment of the medical resources needed for future victims. This manuscript proposed Sugeno Adaptive Neuro-Fuzzy Inference System (SANFIS) prediction model for COVID-19 predic-tion in Andhra Pradesh, India. We gathered data on positive COVID-19 sufferers in Andhra Pradesh for this purpose. The data can be separated into three categories: training set, testing set and checking set. We have utilized Root Mean Square Devi-ation (RMSD) for prediction precision. If the prediction model has a lower RMSD value, it is regarded as the best forecast. In this study, we concluded that the 3 Tri-angular MFns for each input were excellent with the extreme precision for all of the districts based on our expertise. In the end, we deployed seven SANFIS replicas in Andhra Pradesh, but we discovered that SANFIS6 and SANFIS7 provided excellent COVID-19 prediction results. These findings will assist the government, healthcare agencies, and medical organizations in planning for future COVID-19 victims' med-ical requirements. These sorts of Sugeno Adaptive Neuro-Fuzzy Inference System (SANFIS) prediction models based on Artificial Intelligence (AI) will be beneficial in overcoming the COVID-19.
TL;DR: In this article , a weighted correlation coefficient measure (WCCM) is proposed to quantify the correlation between two hesitant fuzzy preference relations (HFGs) to assess the strength of the association between HFGs.
Abstract: The hesitancy fuzzy graphs (HFGs), an extension of fuzzy graphs, are useful tools for dealing with ambiguity and uncertainty in issues involving decision-making (DM). This research implements a correlation coefficient measure (CCM) to assess the strength of the association between HFGs in this article since CCMs have a high capacity to process and interpret data. The CCM that is proposed between the HFGs has better qualities than the existing ones. It lowers restrictions on the hesitant fuzzy elements’ length and may be used to establish whether the HFGs are connected negatively or favorably. Additionally, a CCM-based attribute DM approach is built into a hesitant fuzzy environment. This article suggests the use of weighted correlation coefficient measures (WCCMs) using the CCM concept to quantify the correlation between two HFGs. The decision-making problems of hesitancy fuzzy preference relations (HFPRs) are considered. This research proposes a new technique for assessing the relative weights of experts based on the uncertainty of HFPRs and the correlation coefficient degree of each HFPR. This paper determines the ranking order of all alternatives and the best one by using the CCMs between each option and the ideal choice. In the meantime, the appropriate example is given to demonstrate the viability of the new strategies.
01 Jan 1999
TL;DR: In this paper, the adjacency matrix, a matrix of O's and l's, is used to store a graph or digraph in a computer, and certain matrix operations are seen to correspond to digraph concepts.
Abstract: In order to store a graph or digraph in a computer, we need something other than the diagram or the formal definition. This something is the adjacency matrix, a matrix of O’s and l’s. The l’s correspond to the arcs of the digraph. Certain matrix operations will be seen to correspond to digraph concepts.
10 Aug 2018
TL;DR: This research study derives the lower and upper bounds for the energy and Laplacian energy of Pythagorean fuzzy graphs (PFGs) and Pythagorian fuzzy digraphs (PFDGs).
Abstract: Pythagorean fuzzy sets (PFSs), an extension of intuitionistic fuzzy sets (IFSs), inherit the duality property of IFSs and have a more powerful ability than IFSs to model the obscurity in practical decision-making problems. In this research study, we compute the energy and Laplacian energy of Pythagorean fuzzy graphs (PFGs) and Pythagorean fuzzy digraphs (PFDGs). Moreover, we derive the lower and upper bounds for the energy and Laplacian energy of PFGs. Finally, we present numerical examples, including the design of a satellite communication system and the evaluation of the schemes of reservoir operation to illustrate the applications of our proposed concepts in decision making.
20 Jul 2018
TL;DR: Concepts of energy, Laplacian energy and signless Laplacan energy in single-valued neutrosophic graphs (SVNGs) are presented, some of their properties are described and relationship among them is developed.
Abstract: A single-valued neutrosophic set is an instance of a neutrosophic set, which provides us an additional possibility to represent uncertainty, imprecise, incomplete and inconsistent information existing in real situations. In this research study, we present concepts of energy, Laplacian energy and signless Laplacian energy in single-valued neutrosophic graphs (SVNGs), describe some of their properties and develop relationship among them. We also consider practical examples to illustrate the applicability of the our proposed concepts.
TL;DR: A decision-making numerical example related to the selection of best housing society for investment is solved by calculating the energy and Randić energy of q-ROFHDGs and an algorithm to exhibit the applicability of the presented concepts in decision making is solved.
Abstract: A q-rung orthopair fuzzy set (q-ROFS) is more practical and powerful than intuitionistic fuzzy set (IFS) and Pythagorean fuzzy set (PFS) to model uncertainty in various decision-making problems. In this research article, we introduce the notion of q-rung orthopair fuzzy Hamacher graphs (q-ROFHGs). We utilize the Hamacher operators because they are flexible and parameterized in decision making. We determine the energy of q-ROFHGs as well as the energy of splitting and shadow q-ROFHGs. In addition, we propose the Randić energy of q-ROFHG and its some substantial results. Further, we present the idea of q-rung orthopair fuzzy Hamacher digraphs (q-ROFHDGs). We solve a decision-making numerical example related to the selection of best housing society for investment by calculating the energy and Randić energy of q-ROFHDGs and an algorithm to exhibit the applicability of the presented concepts in decision making. Finally, we present the conclusion.
TL;DR: The new concept is called neutrosophic soft expert graph-based multi-criteria decision making method (NSEGMCDM for short) and a comparison analysis is conducted between the proposed approach and other existing methods, to verify the feasibility and effectiveness of the developed approach.
Abstract: In this paper, we first define the concept of neutrosophic soft expert graph. We have established a link between graphs and neutrosophic soft expert sets. Basic operations of neutrosophic soft expert graphs such as union, intersection and complement are defined here. The concept of neutrosophic soft expert soft graph is also discussed in this paper. The new concept is called neutrosophic soft expert graph-based multi-criteria decision making method (NSEGMCDM for short). Finally, an illustrative example is given and a comparison analysis is conducted between the proposed approach and other existing methods, to verify the feasibility and effectiveness of the developed approach.