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Suffix Trees Capabilities
Chlubna, Pavel ; Hynek, Jiří (referee) ; Burgetová, Ivana (advisor)
This thesis discusses structure of suffix trees, their implementation and problematics, we can solved with suffix trees. The thesis also focuses on construction of suffix trees, with usage of Ukkonen's algorithm. Apart of implementation of this structure this thesis deals with comparing other commonly used algorithms with suffix trees in term of time complexity. A result of this thesis is application with graphical interface, that shows usage of suffix trees in solving various problematics and shows comparison of this structure to other algorithms.
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Regional Information System of a Non-Profit Organization
Brnka, Radim ; Burgetová, Ivana (referee) ; Burget, Radek (advisor)
The objective of this thesis is to create an information system for a non-profit organization of regional scope, that allows in particular the management of a database of places, news, events, simple advertising and other components. Information system is built on PHP Nette framework, using architecture of Model-View-Presenter. Furthermore, the technologies used are MySQL, XHTML, CSS and JavaScript, presentation layer of the map is based on the application of the technology Google Maps in Adobe Flash.
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Gene Detection in DNA Sequences
Višňovský, Marek ; Burgetová, Ivana (referee) ; Martínek, Tomáš (advisor)
The main goal of this work is to create a method for gene prediction in prokaryotic genomes, which will be later demonstrated and tested on Escherichia coli. The first chapter contains a short introduc-tion into molecular biology. In the second one, we take a closer look on current methods of gene de-tection and prediction and the work ends with an application of acquired knowledge on gene predic-tion mostly using position weight matrices.
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Classification Methods for Micriarrays Data
Hudec, Vladimír ; Bartík, Vladimír (referee) ; Burgetová, Ivana (advisor)
This paper discusses about the data obtained from gene chips and methods of their analysis. Analyzes some methods for analyzing these data and focus on the method of "Random Forests". Shows dataset that is used for specific experiments. Methods are realized in R language environment. Than they are tested, and the results are presented and compared. Results with method "Random Forests" are compared with other experiments on same dataset.
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Network Communication Analysis for Mobile Application Profiling
Meluš, Matej ; Matoušek, Petr (referee) ; Burgetová, Ivana (advisor)
Goal of this thesis was to create profiles of mobile applications based on their network communication, which is stored in created data sets. For this purpose, a tool that can extract profiles from data sets was created. Profiles contain JA3 fingerprints, IP addresses and hostnames, that are associated with specific application based on number of occurrences in data sets. Unmodified profiles describe importance of individual identifiers used by application in network communication. Modified profiles can be used to identify application in network traffic.
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Cluster Analysis Module of a Data Mining System
Riedl, Pavel ; Burgetová, Ivana (referee) ; Zendulka, Jaroslav (advisor)
This master's thesis deals with development of a module for a data mining system, which is being developed on FIT. The first part describes the general knowledge discovery process and cluster analysis including cluster validation; it also describes Oracle Data Mining including algorithms, which it uses for clustering. At the end it deals with the system and the technologies it uses, such as NetBeans Platform and DMSL. The second part describes design of a clustering module and a module used to compare its results. It also deals with visualization of cluster analysis results and shows the achievements.
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Systems of Translation Models and Their Applications
Žitňanská, Lívia ; Burgetová, Ivana (referee) ; Meduna, Alexandr (advisor)
The goal of this thesis is to propose and formally define a system of transducers, specify its characteristics, and compare it to already existing systems. Thus, the System of pushdown transducers (SPT) was introduced. This system consists of modified pushdown transducers whose components communicate via query symbols introduced in Parallel communicating grammar systems (PCGS) and can work in three different modes. Besides theoretical definitions, this thesis also describes the implementation of the proposed system as a transducer from DNA to RNA and amino acids using the SPT of two elements.
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Classification with Use of Neural Networks in the Keras Environment
Pyšík, Michal ; Burgetová, Ivana (referee) ; Bartík, Vladimír (advisor)
Tato práce zkoumá problematiku klasifikace pomocí umělých neuronových sítí s využitím knihovny Keras, poskytující vysokoúrovňové rozhraní pro práci s umělými neuronovými sítěmi v programovacím jazyce Python. Cílem práce je prozkoumat rozsáhlé možnosti této knihovny v oblasti klasifikace a porovnat různé typy a topologie umělých neuronových sítí formou experimentů na vybraných datasetech, což je doplněno jednoduchou experimentální aplikací sloužící především jako rozhraní pro tyto experimenty.
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Knowledge Discovery from Spatio-Temporal Data
Liptáková, Daša ; Burgetová, Ivana (referee) ; Bartík, Vladimír (advisor)
This thesis deals with knowledge discovery from spatio-temporal data. Firstly, it describes the general principles of knowledge discovery and then knowledge discovery from spatio-temporal data, where it mainly focuses on methods for detecting outlying trajectories of moving objects. In the next section, the thesis describes the design and implementation of the mining task and demonstration application. Finally, several experiments are performed over three different datasets.
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K-Nearest Neighbour Search Methods
Cigánik, Marek ; Bartík, Vladimír (referee) ; Burgetová, Ivana (advisor)
The thesis describes the basic concept of the K-nearest neighbors algorithm and its connection with the human concept of object similarity. Concepts and key ideas such as the distance function or the curse of dimensionality are elaborated. The work includes a detailed description of the methods KD-Tree, Spherical Tree, Locality-Sensitive Hashing, Random Projection Tree and families of algorithms based on the nearest neighbor graph. An explanation of the idea with visualizations, pseudocodes and asymptotic complexities is provided for each method. The methods were subjected to experiments and both basic and more advanced metrics were measured and appropriate use cases for individual methods were evaluated.
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