The current age is characterised by unprecedented information growth, whether it is by amount or complexity. Most of it is available in digital form so we can analyze it using cluster analysis. We have tried to classify the documents from 20 Newsgroups collection in terms of their content only. The aim was to asses available clustering methods in a variety of applications. After the transformation into binary vector representation we performed several experiments and measured the values of entropy, purity and time of execution in application CLUTO. For a small number of clusters the best results offered the direct method (generally hierarchical method), but for more it was the repeated bisection (divisive). Agglomerative method proved not to be suitable. Using simulation we estimated the optimal number of clusters to be 10. For this solution we described in detail features of each cluster using repeated bisection method and i2 criterion function. In the future focus should be set on realisation of binary clustering with advantage of programming languages like Perl or C++. Results of this work might be of interest to web search engine developers and electronic catalogue administrators.

This thesis describes a methodology for expressing the consumption of drugs, a system of ATC/DDD, ATC classification and DDD recommended daily dose assigment. It also describes the data source which are relevant for this expression. In this thesis I will focuse on combined oral contraception and its consumption which I will express in DDD/1000inhab/day, number of packages and in financial expression in czech krones. At the end of this thesis I will evaluate the results of the consumption according State institute for drug control and my own survey.

The master thesis deals with the monetization of big-budget video game titles. For this purpose the attention is paid to the three basic areas, namely business models, specific video game marketing and piracy. The theoretical part of the thesis analyzes and describes these aspects and looks for their strengths and weaknesses. It creates a possible ways to monetize the big-budget video game titles as well as the theoretical framework which is necessary for the research. In our research the attention is paid to the results of the questionnaire investigation, which is focused on gamers opinions about the aspects of monetization described in theoretical part. Thanks to the research and theoretical part of the thesis the optimal way of the monetization of the big-budget video game title was created

This study sought to analyze and compare financial statements prepared in accordance with IFRSs and Czech accounting principles of the company ČD Cargo focusing on the impact of the results of the financial analysis. Basic characteristics of IFRSs and Czech accounting principles are defined and described in the thesis. Emphasis is placed on the most contrasting rules of the systems. Different methods of financial analysis, their use, and informative value are characterized in the next section. Consequently, the methods have been applied and the results are shown with differences based on specific accounting harmonization. The aim is not only to show different results of the financial analysis, but to also explain the reasons behind the differences, based on the applied rules.

The aim of this study is to evaluate feasibility of using Bayes classifiers for predicting corporate bankruptcies. The results obtain show that Bayes classifiers do reach comparable results to then more commonly used methods such the logistic regression and the decision trees. The comparison has been carried out based on Czech and Polish data sets. The overall accuracy rate of these so called naive Bayes classifiers, using entropic discretization along with the hybrid pre-selection of the explanatory attributes, reaches 77.19 % for the Czech dataset and 79.76 % for the Polish set respectively. The AUC values for these data sets are 0.81 and 0.87. The results obtained for the Polish data set have been compared to the already published articles by Tsai (2009) and Wang et al. (2014) who applied different classification algorithms. The method proposed in my study, when compared to the above earlier works, comes out as quite successful. The thesis also includes comparing various approaches as regards the discretisation of numerical attributes and selecting the relevant explanatory attributes. These are the key issues for increasing performance of the naive Bayes classifiers

Tato diplomová práce se zabývá využitím algoritmů strojového učení pro konstrukci hlídacích obvodů. Práce obsahuje popis principů hlídacích obvodů, jejich existující implementace a ostatní teoretické znalosti vztahující se k systémům odolným proti poruchám. Práce je zaměřena na aplikaci hlídacích obvodů na hardware komponentech se sekvenční logikou. Algoritmy strojového učení jsou trénovány pomocí datových množin, které se skládají ze vstup-výstup sekvencí hardwarových komponentů a ukládají se jako časové řady. Cílem práce je určení vhodnosti jednotlivých algoritmů pro jejich aplikaci v hlídacích obvodech. Pro dosažení tohoto cíle, bylo provedeno srovnání vybraných algoritmů strojového učení. Součástí práce je popis parametrů algoritmů a generování datových sad. Práce taktéž zahrnuje experimenty provedeny na dolnopropustném FIR filtru a jejich vyhodnocení. Podle výsledků experimentů je diskutováno, které algoritmy jsou použitelné v hlídacích obvodech.

Práce se zabývá interpretací kvantitativního fázového zobrazení pomocí techniky koherencí řízené holografické mikroskopie. Vzhledem k tomu, že tato technika generuje velké množství kvantitativních fázových obrazů o nezanedbatelné velikosti, manuální analýza by byla časově náročná a neefektivní Za účelem urychlení analýzy obrazů získaných pomocí koherencí řízené holografické mikroskopie je v této práci navržena metodika automatizované interpretace kvantitativních fázových obrazů pomocí strojového učení s učitelem. Kvantitativní fázové obrazy umožňují extrakci parametrů charakterizujících distribuci suché hmoty v buňce a poskytují tak cennou informaci o buněčném chování. Cílem této práce je navrhnout metodologii pro automatizovanou klasifikaci buněk při využití této kvantitativní informace jak ze statických, tak z časosběrných kvantitativních fázových obrazů. Navržená metodika byla testována v experimentech s živými buňkami, jimiž byla vyhodnocena výkonnost klasifikace a významnost parametrů získaných z kvantitativních fázových obrazů.

Thesis deals with the proposal of the neural networks for classification of positive and negative texts. Development took place in the Python programming language. Design of deep neural network models was performed using the Keras high-level API and the TensorFlow numerical computation library. The computations were performed using GPU with support of the CUDA architecture. The final outcome of the thesis is linguistically independent neural network model for classifying texts at character level reaching up to 93,64% accuracy. Training and testing data were provided by multilingual and Yelp databases. The simulations were performed on 1200000 English, 12000 Czech, German and Spanish texts.

The aim of this semestral work is to summarize the existing knowledge from the area of comparison of musical recordings and to implement an evaluation system for determining the period of creation using the music signal parameterization. In the first part of this work are describe representations which can music take. Next, there is a cross-section of parameters that can be extracted from music recordings provides information on the dynamics, tempo, color, or time development of the music’s recording. In the second part is described evaluation system and its individual sub-blocks. The input data for this evaluation system is a database of 56 sound recordings of the first movement of Beethoven’s 5th Symphony. The last chapter is dedicated to a summary of the achieved results.

This diploma thesis deals with the analysis, description and usage of sensor data from an intelligent home system. This term also describes the intelligent system BeeeOn, which provides a sensor data and the possibility of extending this system to automation tasks. This is followed by the analysis of all the measured physical quantities, their properties and their influence on humans. The results from the measured data were used to create a classifier based on deep neural networks to detect current events at home. Detected events can be used for the following automation system to help improve living conditions. At the end of this thesis are discussed the results and options to continue with this project.