|
|
Criminality Analysis in the Czech Republic using Self-Organizing Maps
Mikulíková, Pavla ; Cahlík, Tomáš (advisor) ; Palanská, Tereza (referee)
Crime represents one of the most persistent social problems all around the world. To understand the motivation for criminal behaviour, a thorough analysis of its plausible determinants is necessary. This bachelor thesis aims at exploring whether the method of self-organizing maps, a data mining tool, can help in the investigation of the Czech criminal phenomena. To date, no academic study has tried to uncover potential pat- terns in the Czech crime data employing this type of artificial neural network. It is a visualisation method which maps observations based on their multi-dimensional features into a two-dimensional grid, and at the same time, the similarity between observations is preserved by locating similar observations close to each other. For the analysis, the dataset consisting of 75 Czech districts and 18 variables was used. However, the optimal choice of parameters of the model can be seen as a possible limitation of this method. The final outcome of the model consists of six clusters of districts with various levels of crime rates and other characteristics. Our results showed that self-organizing maps can provide an interesting insight into the crime problem, and social sciences can benefit from its application in many research areas. 1
|
|
|
Neural Network Library and Editor
Rouček, Martin ; Ježek, Pavel (advisor) ; Pešková, Klára (referee)
Neural network models are more often used in desktop applications given the increasing speed of computers. A very widespread platform for writing desktop applicatons is .NET Framework. Nevertheless, there is no neural networks library for the .NET Framework platform with a simple API and the possibility to work with library objects in a graphical interface. The author decided to create such a library. The main part of the thesis is a neural networks library GNNL that is initially limited to implementing two frequently used neural networks models which are a multilayer perceptron and self- organizing map together with learning algorithms of backpropagation and competitive learning. Graphical support of the library GNNL consists of a library GNNLV and neural network editor. The Library GNNLV contains the controls that allow working with GNNL library objects and a programmer can use them in his or hers application. The Neural network editor enables the programmer to create a neural network in a graphical interface, train it, analyze it, save it, and later use it in different applications. Text of the thesis focuses on analyzing and describing the implementation of the library with its graphical support. A major component of the text is a summary of neural networks theory for laics or programmers using library...
|
guest ::
