A study on regular picture fuzzy graph with applications in communication networks

Xiao, Wei; Dey, Arindam; Sơn, Lê Hoàng

doi:10.3233/jifs-191913

Cited by 25 publications

(15 citation statements)

References 40 publications

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“…Graph The joints and links between adjacent joints of the skeleton representation (a human skeletal kinematic tree) naturally remind the researchers of graph [67,66]. Graph ( , ) is a data format and also an encoding mode that can be used to represent social networks [83,74], communication networks [80,98], protein molecular networks [6,76], etc. The nodes in the graph represent individuals in the network, and the linked edges represent connections between individuals.…”

Section: Mathematical Representationmentioning

confidence: 99%

3D Human Motion Prediction: A Survey

Lyu¹,

Chen²,

Liu³

et al. 2022

Preprint

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3D human motion prediction, predicting future poses from a given sequence, is an issue of great significance and challenge in computer vision and machine intelligence, which can help machines in understanding human behaviors. Due to the increasing development and understanding of Deep Neural Networks (DNNs) and the availability of large-scale human motion datasets, the human motion prediction has been remarkably advanced with a surge of interest among academia and industrial community. In this context, a comprehensive survey on 3D human motion prediction is conducted for the purpose of retrospecting and analyzing relevant works from existing released literature. In addition, a pertinent taxonomy is constructed to categorize these existing approaches for 3D human motion prediction. In this survey, relevant methods are categorized into three categories: human pose representation, network structure design, and prediction target. We systematically review all relevant journal and conference papers in the field of human motion prediction since 2015, which are presented in detail based on proposed categorizations in this survey. Furthermore, the outline for the public benchmark datasets, evaluation criteria, and performance comparisons are respectively presented in this paper. The limitations of the state-of-the-art methods are discussed as well, hoping for paving the way for future explorations.

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Section: Mathematical Representationmentioning

confidence: 99%

3D Human Motion Prediction: A Survey

Lyu¹,

Chen²,

Liu³

et al. 2022

Preprint

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“…They have also applied the idea of PF sets to CGs, PF genus graphs [9][10][11]. Xiao et al studied on regular PFG and its application in communication networks in [49].…”

Section: Research Backgroundmentioning

confidence: 99%

Picture fuzzy tolerance graphs with application

Das

Ghorai

Pal

2021

Complex Intell. Syst.

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In this study, the notions of picture fuzzy tolerance graphs, picture fuzzy interval containment graphs and picture fuzzy $$\phi $$ ϕ -tolerance graphs are established. Three special types of picture fuzzy tolerance graphs having bounded representations are introduced and studied corresponding properties of them taking $$\phi $$ ϕ as max, min and sum functions. Also, picture fuzzy proper and unit tolerance graphs are established and some related results are investigated. The class of picture fuzzy $$\phi $$ ϕ -tolerance chaingraphs which is the picture fuzzy $$\phi $$ ϕ -tolerance graphs of a nested family of picture fuzzy intervals are presented. A real-life application in sports competition is modeled using picture fuzzy min-tolerance graph. Also a comparison is given between picture fuzzy tolerance graphs and intuitionistic fuzzy tolerance graphs.

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“…Afterwards, picture fuzzy multi-graph (PFMG) was introduced in [27]. Regular picture fuzzy graphs (RPFGs) along with its applications towards networking communications have been explored in [28]. Recently, Koczy et al [29] more investigated the term PFGs and they added several significant graphical terms for PFGs and demonstrated them with examples.…”

Section: Introductionmentioning

confidence: 99%

Bipolar Picture Fuzzy Graphs with Application

2021

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In this manuscript, we introduce and discuss the term bipolar picture fuzzy graphs along with some of its fundamental characteristics and applications. We also initiate the concepts of complete bipolar picture fuzzy graphs and strong bipolar picture fuzzy graphs. Firstly, we apply different types of operations to bipolar picture fuzzy graphs and then we introduce various products of bipolar picture fuzzy graphs. Several other terms such as order and size, path, neighbourhood degrees, busy values of vertices and edges of bipolar picture fuzzy graphs are also discussed. These terminologies also lay the foundations for the discussion about the regular bipolar picture fuzzy graphs. Moreover, we also discuss isomorphisms, weak and co-weak isomorphisms and automorphisms of bipolar picture fuzzy graphs. Finally, at the base of bipolar picture fuzzy graph we present the construction of a bipolar picture fuzzy acquaintanceship graph, which would be an important tool to measure the symmetry or asymmetry of acquaintanceship levels of social networks, computer networks etc.

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A study on regular picture fuzzy graph with applications in communication networks

Cited by 25 publications

References 40 publications

3D Human Motion Prediction: A Survey

3D Human Motion Prediction: A Survey

Picture fuzzy tolerance graphs with application

Bipolar Picture Fuzzy Graphs with Application

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