The Measures Relationships Study of Three Soft Rules Based on Granular Computing

2006 ◽  
pp. 371-376 ◽  
Author(s):  
Qiusheng An ◽  
WenXiu Zhang
Keyword(s):  
Granular Computing

Conversion-based aggregation algorithms for linear ordinal rankings combined with granular computing

2021 ◽  
Vol 219 ◽  
pp. 106880
Author(s):  
Nana Liu ◽  
Zeshui Xu ◽  
Hangyao Wu ◽  
Peijia Ren
Keyword(s):  
Granular Computing

Certain models of granular computing based on rough fuzzy approximations

2020 ◽  
Vol 39 (3) ◽  
pp. 2797-2816
Author(s):  
Muhammad Akram ◽  
Anam Luqman ◽  
Ahmad N. Al-Kenani
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Problem Solving ◽  
Granular Computing ◽  
Research Study ◽  
Network Models ◽  
Mathematical Framework ◽  
Human Reasoning ◽  
Accuracy Measures ◽  
Lower And Upper Approximations

An extraction of granular structures using graphs is a powerful mathematical framework in human reasoning and problem solving. The visual representation of a graph and the merits of multilevel or multiview of granular structures suggest the more effective and advantageous techniques of problem solving. In this research study, we apply the combinative theories of rough fuzzy sets and rough fuzzy digraphs to extract granular structures. We discuss the accuracy measures of rough fuzzy approximations and measure the distance between lower and upper approximations. Moreover, we consider the adjacency matrix of a rough fuzzy digraph as an information table and determine certain indiscernible relations. We also discuss some general geometric properties of these indiscernible relations. Further, we discuss the granulation of certain social network models using rough fuzzy digraphs. Finally, we develop and implement some algorithms of our proposed models to granulate these social networks.

scholarly journals Adaptive-surrogate based on a neuro-fuzzy network and granular computing

2014 ◽  
Author(s):  
Israel Cruz-Vega ◽  
Mauricio Garcia-Limon ◽  
Hugo Jair Escalante
Keyword(s):  
Granular Computing ◽  
Neuro Fuzzy ◽  
Fuzzy Network

Information structures in set-valued information systems from granular computing viewpoint

2018 ◽  
Vol 35 (5) ◽  
pp. e12290
Author(s):  
Fei Xia ◽  
Hongxiang Tang
Keyword(s):  
Information Systems ◽  
Granular Computing ◽  
Information Structures

THE INFORMATION ENTROPY, ROUGH ENTROPY AND KNOWLEDGE GRANULATION IN ROUGH SET THEORY

2004 ◽  
Vol 12 (01) ◽  
pp. 37-46 ◽  
Author(s):  
JIYE LIANG ◽  
ZHONGZHI SHI
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Information Entropy ◽  
Granular Computing ◽  
Rough Set Theory ◽  
Computer Applications ◽  
Mathematical Tool

Rough set theory is a relatively new mathematical tool for use in computer applications in circumstances which are characterized by vagueness and uncertainty. In this paper, we introduce the concepts of information entropy, rough entropy and knowledge granulation in rough set theory, and establish the relationships among those concepts. These results will be very helpful for understanding the essence of concept approximation and establishing granular computing in rough set theory.

Research of text clustering based on fuzzy granular computing

2009 ◽  
Author(s):  
Zhang Xia ◽  
Yin Yixin ◽  
Xu Mingzhu ◽  
Zhao Hailong
Keyword(s):  
Granular Computing ◽  
Text Clustering

scholarly journals Mining Knowledge of Respiratory Rate Quantification and Abnormal Pattern Prediction

2021 ◽  
Author(s):  
Piotr Szczuko ◽  
Adam Kurowski ◽  
Piotr Odya ◽  
Andrzej Czyżewski ◽  
Bożena Kostek ◽  
...  
Keyword(s):  
Signal Analysis ◽  
Granular Computing ◽  
Nearest Neighbor ◽  
Prediction Models ◽  
Respiratory Pattern ◽  
Breathing Patterns ◽  
K Nearest Neighbor ◽  
Normal Breathing ◽  
Abnormal Pattern ◽  
Male Sex

AbstractThe described application of granular computing is motivated because cardiovascular disease (CVD) remains a major killer globally. There is increasing evidence that abnormal respiratory patterns might contribute to the development and progression of CVD. Consequently, a method that would support a physician in respiratory pattern evaluation should be developed. Group decision-making, tri-way reasoning, and rough set–based analysis were applied to granular computing. Signal attributes and anthropomorphic parameters were explored to develop prediction models to determine the percentage contribution of periodic-like, intermediate, and normal breathing patterns in the analyzed signals. The proposed methodology was validated employing k-nearest neighbor (k-NN) and UMAP (uniform manifold approximation and projection). The presented approach applied to respiratory pattern evaluation shows that median accuracies in a considerable number of cases exceeded 0.75. Overall, parameters related to signal analysis are indicated as more important than anthropomorphic features. It was also found that obesity characterized by a high WHR (waist-to-hip ratio) and male sex were predisposing factors for the occurrence of periodic-like or intermediate patterns of respiration. It may be among the essential findings derived from this study. Based on classification measures, it may be observed that a physician may use such a methodology as a respiratory pattern evaluation-aided method.

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