A novel approach to concept-cognitive learning in interval-valued formal contexts: a granular computing viewpoint

2021 ◽  
Author(s):  
Meng Hu ◽  
Eric C. C. Tsang ◽  
Yanting Guo ◽  
Qingshuo Zhang ◽  
Degang Chen ◽  
...  
Keyword(s):  
Granular Computing ◽  
Cognitive Learning ◽  
Novel Approach ◽  
Interval Valued

The linguistic modeling of interval-valued time series: A perspective of granular computing

2019 ◽  
Vol 478 ◽  
pp. 476-498 ◽  
Author(s):  
Wei Lu ◽  
Wei Zhou ◽  
Dan Shan ◽  
Liyong Zhang ◽  
Jianhua Yang ◽  
...  
Keyword(s):  
Time Series ◽  
Granular Computing ◽  
Linguistic Modeling ◽  
Interval Valued

scholarly journals A comprehensive survey on formal concept analysis, its research trends and applications

2016 ◽  
Vol 26 (2) ◽  
pp. 495-516 ◽  
Author(s):  
Prem Kumar Singh ◽  
Cherukuri Aswani Kumar ◽  
Abdullah Gani
Keyword(s):  
Incomplete Data ◽  
Granular Computing ◽  
Possibility Theory ◽  
Formal Concept ◽  
Research Papers ◽  
Emerging Trends ◽  
Comprehensive Survey ◽  
Interval Valued ◽  
Fuzzy Setting ◽  
Significant Attention

AbstractIn recent years, FCA has received significant attention from research communities of various fields. Further, the theory of FCA is being extended into different frontiers and augmented with other knowledge representation frameworks. In this backdrop, this paper aims to provide an understanding of the necessary mathematical background for each extension of FCA like FCA with granular computing, a fuzzy setting, interval-valued, possibility theory, triadic, factor concepts and handling incomplete data. Subsequently, the paper illustrates emerging trends for each extension with applications. To this end, we summarize more than 350 recent (published after 2011) research papers indexed in Google Scholar, IEEE Xplore, ScienceDirect, Scopus, SpringerLink, and a few authoritative fundamental papers.

scholarly journals Practical Employment of Granular Computing to Complex Application Layer Cyberattack Detection

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Rafał Kozik ◽  
Marek Pawlicki ◽  
Michał Choraś ◽  
Witold Pedrycz
Keyword(s):  
Machine Learning ◽  
Homeland Security ◽  
Scientific Community ◽  
Granular Computing ◽  
Network Data ◽  
Application Layer ◽  
Practical Application ◽  
Information Granules ◽  
Machine Learning Methods ◽  
Novel Approach

Network and information security are regarded as some of the most pressing problems of contemporary economy, affecting both individual citizens and entire societies, making them a highlight for homeland security. Innovative approaches to handle this challenge are undertaken by the scientific community, proposing the utilization of the emerging, advanced machine learning methods. This very paper puts forward a novel approach to the detection of cyberattacks taking inventory of the practical application of information granules. The feasibility of utilizing Granular Computing (GC) as a solution to the most current challenges in cybersecurity is researched. To the best of our knowledge, granular computing has not yet been widely examined or used for cybersecurity application purposes. The major contribution of this work is a method for constructing information granules from network data. We then report promising results on a benchmark dataset.

A novel approach for ranking in interval-valued information systems

2015 ◽  
Vol 30 (1) ◽  
pp. 523-534 ◽  
Author(s):  
Bin Xie ◽  
Lei-jun Li ◽  
Ju-sheng Mi
Keyword(s):  
Information Systems ◽  
Novel Approach ◽  
Interval Valued

Density aggregation operators for interval-valued q-rung orthopair fuzzy numbers and their application in multiple attribute decision making

2021 ◽  
pp. 1-14
Author(s):  
Huijuan Guo ◽  
Ruipu Yao
Keyword(s):  
Decision Making ◽  
Fuzzy Numbers ◽  
Multiple Attribute Decision Making ◽  
Aggregation Operator ◽  
Aggregation Operators ◽  
Weighted Arithmetic ◽  
Geometric Average ◽  
Novel Approach ◽  
Multiple Attribute ◽  
Interval Valued

The symmetry between fuzzy evaluations and crisp numbers provides an effective solution to multiple attribute decision making (MADM) problems under fuzzy environments. Considering the effect of information distribution on decision making, a novel approach to MADM problems under the interval-valued q-rung orthopair fuzzy (Iq-ROF) environments is put forward. Firstly, the clustering method of interval-valued q-rung orthopair fuzzy numbers (Iq-ROFNs) is defined. Secondly, Iq-ROF density weighted arithmetic (Iq-ROFDWA) intermediate operator and Iq-ROF density weighted geometric average (Iq-ROFDWGA) intermediate operator are developed based on the density weighted intermediate operators for crisp numbers. Thirdly, combining the density weighted intermediate operators with the Iq-ROF weighted aggregation operators, Iq-ROF density aggregation operators including Iq-ROF density weighted arithmetic (Iq-ROFDWAA) aggregation operator and Iq-ROF density weighted geometric (Iq-ROFDWGG) aggregation operator are proposed. Finally, effectiveness of the proposed method is verified through a numerical example.

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