Computational Intelligence Methods for Data Analysis and Mining of eLearning Activities

2010 ◽  
pp. 195-224 ◽  
Author(s):  
Pavla Dráždilová ◽  
Gamila Obadi ◽  
Kateřina Slaninová ◽  
Shawki Al-Dubaee ◽  
Jan Martinovič ◽  
...  
Keyword(s):  
Data Analysis ◽  
Computational Intelligence ◽  
Computational Intelligence Methods

Visual data analysis with computational intelligence methods

2010 ◽  
Vol 58 (3) ◽  
pp. 393-401 ◽  
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.

ADAPTAÇÃO E APLICAÇÃO DE MÉTODOS DE INTELIGÊNCIA COMPUTACIONAL PARA A ESTIMAÇÃO DE UMA FONTE DE POLUENTES EM ESTUÁRIOS

Engevista ◽  
2014 ◽  
Vol 17 (2) ◽  
pp. 152
Author(s):  
Radael De Souza Parolin ◽  
Pedro Paulo Gomes Watts Rodrigues ◽  
Antônio J. Silva Neto
Keyword(s):  
Computational Intelligence ◽  
Computational Models ◽  
Source Position ◽  
Iterative Search ◽  
Contaminant Source ◽  
Resources Management ◽  
Search Spaces ◽  
Grid Points ◽  
Computational Intelligence Methods

The quality of a given water body can be assessed through the analysis of a number of indicators. Mathematical and computational models can be built to simulate the behavior of these indicators (observable variables), in such a way that different scenarios can be generated, supporting decisions regarding water resources management. In this study, the transport of a conservative contaminant in an estuarine environment is simulated in order to identify the position and intensity of the contaminant source. For this, it was formulated an inverse problem, which was solved through computational intelligence methods. This approach required adaptations to these methods, which had to be modified to relate the source position to the discrete mesh points of the domain. In this context, two adaptive techniques were developed. In one, the estimated points are projected to the grid points, and in the other, points are randomly selected in the iterative search spaces of the methods. The results showed that the methodology here developed has a strong potential in water bodies’ management and simulation.

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