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Comparison of road tax regulations in the Czech Republic and neighboring countries
PRÁŠKOVÁ, Vendula
The work focuses on comparing the adjustment of road and similar taxes in the Czech Republic and neighbouring states respectively. It describes the road tax treatment in the Czech Republic, the reasons for its introduction and the development of this tax since 1992, through relevant tax laws and regulations. Furthermore, adjustments to similar taxes on Germany, Poland, Austria and Slovakia are described in detail here. In the next part of the thesis, the criteria for cluster analysis are discussed, namely the share of road tax in the tax mix, the share of road tax in GDP and the tax burden of road tax per capita, for lower risk, all these data are taken from EUROSTAT and OECD In US dollars (USD), data on the number of motor vehicles in the region is added to expand the issue. A clustered analysis was carried out on the basis of the values of these indicators for 2012, 2021 and 2022. The result of this analysis is three dendrograms, each representing one of the years studied. Dendrograms provide a visual representation of the cluster structure and allow for the interpretation of relationships.
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ECG Cluster Analysis
Pospíšil, David ; Kozumplík, Jiří (referee) ; Klimek, Martin (advisor)
This diploma thesis deals with the use of some methods of cluster analysis on the ECG signal in order to sort QRS complexes according to their morphology to normal and abnormal. It is used agglomerative hierarchical clustering and non-hierarchical method K – Means for which an application in Mathworks MATLAB programming equipment was developed. The first part deals with the theory of the ECG signal and cluster analysis, and then the second is the design, implementation and evaluation of the results of the usage of developed software on the ECG signal for the automatic division of QRS complexes into clusters.
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Analysis of AVG signals
Musil, Václav ; Sekora, Jiří (referee) ; Rozman, Jiří (advisor)
The presented thesis discusses the basic analysis methods of arteriovelocitograms. The core of this work rests in classification of signals and contribution to possibilities of noninvasive diagnostic methods for evaluation patients with peripheral ischemic occlusive arterial disease. The classification employs multivariate statistical methods and principles of neural networks. The data processing works with an angiographic verified set of arteriovelocitogram dates. The digital subtraction angiography classified them into 3 separable classes in dependence on degree of vascular stenosis. Classification AVG signals are represented in the program by the 6 parameters that are measured on 3 different places on each patient’s leg. Evaluation of disease appeared to be a comprehensive approach at signals acquired from whole patient’s leg. The sensitivity of clustering method compared with angiography is between 82.75 % and 90.90 %, specificity between 80.66 % and 88.88 %. Using neural networks sensitivity is in range of 79.06 % and 96.87 %, specificity is in range of 73.07 % and 91.30 %.
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Analysis of AVG signal
Matušek, Adam ; Kozumplík, Jiří (referee) ; Rozman, Jiří (advisor)
Bachelor´s thesis deals with analysis of AVG signal (arteriovelocitogram). Measured data predicate about speed and caracteristic of blood flow in human arteria. Signal is taken by ultrasound mesure. We use reflection of mechanic wave from moving objets followed by a chase of frequence. This fenomen is called Doppler effect. When we know analysis results we can diagnostic the existence and phase of ischemic desease. The clasification of the dates was made by methods of cluster analysis. Statistic algorithm was realised in programing world MATLAB.
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Methods for Clustering Data
Pohlídal, Antonín ; Burgetová, Ivana (referee) ; Bartík, Vladimír (advisor)
This bachelor's thesis deals with hierarchical clustering methods with a focus on implementation of agglomerative hierarchical clustering method and its comparison with the DENCLUE method. First of all, various methods are described with emphasis on hierarchical clustering methods. Further, there is an implementation of the selected method, using the Java programming language and MySQL database. The last part contains a comparison with the implementation of DENCLUE method, implemented by Mr. Bc. Radim Kapavík.
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Data Mining on Oracle Database Server and MS SQL Server
Opršal, Martin ; Chmelař, Petr (referee) ; Stryka, Lukáš (advisor)
This bachelor's thesis deals with issue of knowledge discovery in databases. This document is focused in getting rules from relation databases based on Microsoft SQL server or Oracle Data mining server. The practical part of this document is about design applications that run on both servers. These applications are programmed in asp.NET, C# for Microsoft SQL server and Java for Oracle server.
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Knowledge Discovery in Multimedia Databases
Málik, Peter ; Bartík, Vladimír (referee) ; Chmelař, Petr (advisor)
This master"s thesis deals with the knowledge discovery in multimedia databases. It contains general principles of knowledge discovery in databases, especially methods of cluster analysis used for data mining in large and multidimensional databases are described here. The next chapter contains introduction to multimedia databases, focusing on the extraction of low level features from images and video data. The practical part is then an implementation of the methods BIRCH, DBSCAN and k-means for cluster analysis. Final part is dedicated to experiments above TRECVid 2008 dataset and description of achievements.
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Default Risk Modeling in Chemistry Industry
Jedlička, Jaromír ; Czekus, Robert (referee) ; Režňáková, Mária (advisor)
My thesis is focused on the presentation of a scoring model for companies in chemical industry with use of cluster analysis methods. There is a description of financial risks, financial analysis indicators and models which are used to evaluate financial risks of a company. There is also a mathematical description of hierarchical cluster methods.
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Statistical Classification Methods
Barvenčík, Oldřich ; Žák, Libor (referee) ; Michálek, Jaroslav (advisor)
The thesis deals with selected classification methods. The thesis describes the basis of cluster analysis, discriminant analysis and theory of classification trees. The usage is demonstrated by classification of simulated data, the calculation is made in the program STATISTICA. In practical part of the thesis there is the comparison of the methods for classification of real data files of various extent. Classification methods are used for solving of the real task – prediction of air pollution based of the weather forecast.
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