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Použití evolučních a genetických algoritmů v ekonomických aplikacích
Popelka, Ondřej
This thesis describes new evolutionary artificial intelligence methods suitable for solving complex tasks. These include planning, optimization, decision, prediction and other problems. All of these are tasks which an intelligent human being can quickly learn to solve, yet they cannot be solved by machines in reasonable time. For this type of problems usually no analytical method or algorithm exists. These challenges represent the domain for artificial intelligence. This work concentrates on evolutionary methods of artificial intelligence based on genetic algorithms. Specifically grammatical evolution and differnetial evolution are described. The first part of this thesis describes the principles of genetic algorithms especially those used in grammatical evolution. Later the grammatical evolution method is described. Grammatical evolution is a genetic algorithm extended with a context-free grammar processor. This enables it to generate structured strings in an arbitrary language defined by a regular or context-free grammar. Second part of this work focuses on description of a generic computational system, which enables user-friendly control of grammatical evolution. The architecture of the system is thoroughly described. It composes of a computation service, database server and completely separated user interface. Also the problems solved using this system are described. These include symbolic regression, classification and generation of combinatorial logic circuits. All of these tasks were solved using the described implementation.
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Automatic recognition of meaning in texts
Jeleček, Jiří ; Dvořák, Pavel (referee) ; Povoda, Lukáš (advisor)
As part of this work it was designed and implemented a system using data mining techniques from the text in order to detect emotions in Czech, English and German language texts. Because the system is built mostly on machine learning techniques, was designed and created training set, which was later used to build the model classifier using the selected algorithms.
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Segmentation of MR images using machine learning algorithms
Dorazil, Jan ; Mikulka, Jan (referee) ; Dvořák, Pavel (advisor)
This thesis concerns with magnetic resonance image segmentation using Random Forests algorithm. Employed technologies accomplishing the specified task include C++ progra- mming language with libraries ITK and OpenCV. This work descibes the technique of processing images from loading through preprocessing to the actual segmentation. The outcome from this work is a programme that automatically segmentates MR images of mouse’s head to the brain and the surroundings.
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Procedural programming in database
Nimrichter, Adam ; Povoda, Lukáš (referee) ; Uher, Václav (advisor)
Thesis deals with verification of concept of performing calculations inside database. Describes PostgreSQL database, its features and procedural language PL/pgSQL. Also focuses on machine learning methods, implementation of forward selection algorithm and verification of his functionality. Frequently used tool is MADlib, which is an open-source library of scalable in-database algorithms for machine learning, statistics and other analytic tasks.
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Feature Selection Based on Combination of Uncorrelated Evaluation Functions
Vaculík, Karel ; Klusáček, Jan (referee) ; Honzík, Petr (advisor)
In order to process large amount of data, it is necessary to use computers. It is possible to use statistical methods or machine learning in some cases. In either case, data can be represented with large number of features. Selection of suitable subset of features can be crucial for efficient processing. This thesis explores a subgroup of feature selection methods which are called filter methods. Comparison of such methods is carried out and the results are used in the design of a new method. This new method uses a combination of existing methods.
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Scalable machine learning using Hadoop and Mahout tools
Kryške, Lukáš ; Atassi, Hicham (referee) ; Burget, Radim (advisor)
This bachelor’s thesis compares several tools for building a scalable, machine learning platform and describes their advantages and disadvantages. It also practically demonstrates functionality of this scalable platform based on the Apache Hadoop and Apache Mahout tools and measures performance of the K-Means algorithm for total of five computing nodes.
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Gender recognition from the text data
Mačát, Jakub ; Burda, Karel (referee) ; Červenec, Radek (advisor)
This bacheor`s work is focused on gender identification from a text just from an e-mail`s form and also contemporary techniques of data mining and text mining. The technique`s advantages and disadvantages and options of use. There was realized a program for recognizing gender in Java. In a program Rapid Miner is demostrated processing various learning methods. By both programs thete are described their basic attributes, used methods and operators used in the implementation. The programs were tested ona real data. Then there are mentioned methods for program`s extends. eventually there are given examples as the programs process stated assignment.
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Methods for fast sequence comparison and identification in metagenomic data
Kupková, Kristýna ; Škutková, Helena (referee) ; Sedlář, Karel (advisor)
Předmětem této práce je vytvoření metody sloužící k identifikaci organismů z metagenomických dat. Doposud k tomuto účelu spolehlivě dostačovaly metody založené na zarovnání sekvencí s referenční databází. Množství dat ovšem s rozvojem sekvenačních technik rapidně roste a tyto metody se tak stávají díky své výpočetní náročnosti nevhodnými. V této diplomové práci je popsán postup nové techniky, která umožňuje klasifikaci metagenomických dat bez nutnosti zarovnání. Metoda spočívá v převedení sekvenovaných úseků na genomické signály ve formě fázových reprezentací, ze kterých jsou následně extrahovány vektory příznaků. Těmito příznaky jsou tři Hjorthovy deskriptory. Ty jsou dále vystaveny metodě maximalizace věrohodnosti směsi Gaussovských rozložení, která umožňuje spolehlivé roztřídění fragmentů podle jejich příslušnosti k organismu.
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Methods of deep learning in image processing tasks
Polášková, Lenka ; Marcoň, Petr (referee) ; Mikulka, Jan (advisor)
The clue of learning to recognize objects using neural network lies in imitation of animal neural network's behavior. In spite the details of how brain works is not known yet, the teams consisting of scientists from various medical or technical professions are trying to search for them. Thanks to giants like Geoffrey Hinton science made a big progress in this domain. The convolutional networks which are based on animal model of optical system can be advantageously used for image segmentation and therefore they ware chosen for segmentation of tumor and edema from images of magnetic resonance. The models of artificial neural networks used in this work had achieved the 41\% of success in edema segmentation and 79\% in segmentation of tumor from brain issue.
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