Colouring of COVID-19 Affected Region Based on Fuzzy Directed Graphs
Source abstract
Abstract
Graph colouring is the system of assigning a colour to each vertex of a graph. It is done in such a way that adjacent vertices do not have equal colour. It is fundamental in graph theory. It is often used to solve real-world problems like traffic light signalling, map colouring, scheduling, etc. Nowadays, social networks are prevalent systems in our life. Here, the users are considered as vertices, and their connections/interactions are taken as edges. Some users follow other popular users’ profiles in these networks, and some don’t, but those non-followers are connected directly to the popular profiles. That means, along with traditional relationship (information flowing), there is another relation among them. It depends on the domination of the relationship between the nodes. This type of situation can be modelled as a directed fuzzy graph. In the colouring of fuzzy graph theory, edge membership plays a vital role. Edge membership is a representation of flowing information between end nodes of the edge. Apart from the communication relationship, there may be some other factors like domination in relation. This influence of power is captured here. In this article, the colouring of directed fuzzy graphs is defined based on the influence of relationship. Along with this, the chromatic number and strong chromatic number are provided, and related properties are investigated. An application regarding COVID-19 infection is presented using the colouring of directed fuzzy graphs.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Remote-Sensing Image Classification · Image Retrieval and Classification Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (23)
Show source-provided references
1. Kauffman · Introduction a la Theorie des Sous-Emsembles Flous · 1973
2. Rosenfeld · Fuzzy Sets and Their Applications · 1975 · 77
3. Fuzzy planar graphs10.1109/TFUZZ.2014.2387875 ↗
4. Fuzzy k-competition graphs and p-competition fuzzy graphs10.1007/s12543-013-0140-6 ↗
5. Outbreak of pneumonia of unknown etiology in Wuhan, China: The mystery and the miracle10.1002/jmv.25678 ↗
6. Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia10.1056/NEJMoa2001316 ↗
7. The species severe acute respiratory syndrome-related coronavirus: Classifying 2019-nCoV and naming it SARS-CoV-210.1038/s41564-020-0695-z ↗
8. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: A descriptive study10.1016/S0140-6736(20)30211-7 ↗
9. A new general family of mixed graphs10.1016/j.dam.2018.12.016 ↗
10. Colouring of fuzzy graphs10.1007/s13370-015-0317-8 ↗
11. Mixed graph colorings10.1007/BF01194253 ↗
12. Mixed graph coloring for unit-time job-shop scheduling
13. Applications of edge colouring of fuzzy graphs10.15388/20-INFOR403 ↗
14. Radio fuzzy graphs and assignment of frequency in radio stations10.1007/s40314-019-0888-3 ↗
15. RSM index: A new way of link prediction in social networks10.3233/JIFS-181452 ↗
16. Link prediction in social networks by neutrosophic graph10.2991/ijcis.d.201015.002 ↗
17. Hybrid words representation for airlines sentiment analysis
18. Abusive language detection: A comprehensive review
19. Diabetic retinopathy detection using multi-layer neural networks and split attention with focal loss
20. Link-level performance modelling for next-generation uav relay with millimetre-wave simultaneously in access and backhaul
21. Performance evaluation of next-generation wireless (5G) UAV relay10.1007/s11277-020-07261-x ↗
22. UAV-aided 5G Network in Suburban, Urban, Dense Urban, and High-rise Urban Environments
23. Generalized neutrosophic planar graphs and its application10.1007/s12190-020-01411-x ↗