Data Analysis of Wireless Networks Using Computational Intelligence

In this chapter we review the history of mathematics-based approaches to problem solving. The authors suggest that while the ability of analysts to deal with the extremes of data now available is leading to a new leap in the handling of data analysis, information processing, and control systems, that ability remains grounded in the work of early pioneers of statistical thought. Beginning with pre-history, the paper briefly traces developments in analytical thought to the present day, identifying milestones in this development. The techniques developed in studies of computational intelligence, the applications of which are presented in this volume, form the basis for the next great development in analytical thought.

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Visual data analysis with computational intelligence methods

Bulletin of the Polish Academy of Sciences Technical Sciences ◽

10.2478/v10175-010-0037-z ◽

2010 ◽

Vol 58 (3) ◽

pp. 393-401 ◽

Cited By ~ 2

Author(s):

R. Kruse ◽

M. Steinbrecher

Keyword(s):

Data Analysis ◽

Association Rules ◽

Computational Intelligence ◽

Graphical Model ◽

Visual Analysis ◽

Real Life ◽

Model Parameters ◽

Visual Data ◽

Visual Approach ◽

Computational Intelligence Methods

Visual data analysis with computational intelligence methodsVisual data analysis is an appealing and increasing field of application. We present two related visual analysis approaches that allow for the visualization of graphical model parameters and time-dependent association rules. When the graphical model is defined over purely nominal attributes, its local structure can be interpreted as an association rule. Such association rules comprise one of the most prominent and wide-spread analysis techniques for pattern detection, however, there are only few visualization methods. We introduce an alternative visual representation that also incorporates time since patterns are likely to change over time when the underlying data was collected from real-world processes. We apply the technique to both an artificial and a complex real-life dataset and show that the combined automatic and visual approach gives more and faster insight into the data than a fully-automatic approach only. Thus, our proposed method is capable of reducing considerably the analysis time.

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Computational Intelligence for Big Data Analysis

10.1007/978-3-319-16598-1 ◽

2015 ◽

Cited By ~ 9

Keyword(s):

Big Data ◽

Data Analysis ◽

Computational Intelligence ◽

Big Data Analysis

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Fuzzy rule based data analysis using methods of computational intelligence

Proceedings of 3rd International Symposium on Uncertainty Modeling and Analysis and Annual Conference of the North American Fuzzy Information Processing Society ◽

10.1109/isuma.1995.527672 ◽

2002 ◽

Author(s):

S.K. Halgamuge ◽

H. Genther ◽

M. Glesner

Keyword(s):

Data Analysis ◽

Computational Intelligence ◽

Fuzzy Rule ◽

Rule Based

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Computational Intelligence in Remote Sensing: An Editorial

Sensors ◽

10.3390/s20030633 ◽

2020 ◽

Vol 20 (3) ◽

pp. 633

Author(s):

Manuel Graña ◽

Michal Wozniak ◽

Sebastian Rios ◽

Javier de Lope

Keyword(s):

Artificial Intelligence ◽

Remote Sensing ◽

Data Analysis ◽

Computational Intelligence ◽

Wide Spectrum ◽

Remote Sensing Data ◽

Research Development ◽

Special Issue ◽

Computational Tools ◽

Fruitful Research

Computational intelligence is a very active and fruitful research of artificial intelligence with a broad spectrum of applications. Remote sensing data has been a salient field of application of computational intelligence algorithms, both for the exploitation of the data and for the research/ development of new data analysis tools. In this editorial paper we provide the setting of the special issue “Computational Intelligence in Remote Sensing” and an overview of the published papers. The 11 accepted and published papers cover a wide spectrum of applications and computational tools that we try to summarize and put in perspective in this editorial paper.

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