Knowledge discovery from databases is a complex issue involving integration, data preparation, data mining using machine learning methods and visualization of results. The thesis deals with the whole process of knowledge discovery, especially with the issue of data warehousing, where it offers the design and implementation of a specific data warehouse for the company ROI Hunter, a.s. In the field of data mining, the work focuses on the classification and forecasting of the advertising data available from the prepared data warehouse and, in particular, on the decision tree classification. When predicting the development of new ads, emphasis is put on the rationale for the prediction as well as the proposal to adjust the ad settings so that the prediction ends positively and, with a certain likelihood, the ads actually get better results.

This thesis deals with creation of a mobile library for preprocessing of images with text which represents a part of a system for text recognition. The library is realized with emphasis on generality of use, efficiency and portability. The library providing a set of algorithms primarily for image quality assessment and text detection was created in this thesis. These algorithms enable a substantial decrease in volume of transmitted data and speed up and refinement of the recognition process. An example application for the Android platform able to analyze composition of foods stated on their wrappings was created as well. Overall, the library (system) simplifies development of mobile applications with focus on text extraction and analysis. The mobile application then provides a comfortable way of food harmfulness verification. The thesis offers a reader an overview of current solutions and tools available in this field, it provides a breakdown of important image preprocessing algorithms and guides him through the construction of the library and the application for mobile devices.

The main goal of this thesis was to get acquainted with the phases of data mining, with the support of the programming languages Python and R in the field of data mining and demonstration of their use in two case studies. The comparison of these languages in the field of data mining is also included. The data preprocessing phase and the mining algorithms for classification, prediction and clustering are described here. There are illustrated the most significant libraries for Python and R. In the first case study, work with time series was demonstrated using the ARIMA model and Neural Networks with precision verification using a Mean Square Error. In the second case study, the results of football matches are classificated using the K - Nearest Neighbors, Bayes Classifier, Random Forest and Logical Regression. The precision of the classification is displayed using Accuracy Score and Confusion Matrix. The work is concluded with the evaluation of the achived results and suggestions for the future improvement of the individual models.

Very often, people buy goods on the Internet that they can not see and try. They therefore rely on reviews of other customers. However, there may be too many reviews for a human to handle them quickly and comfortably. The aim of this work is to offer an application that can recognize in Czech reviews what features of a product are most commented and whether the commentary is positive or negative. The results can save a lot of time for e-shop customers and provide interesting feedback to the manufacturers of the products.

This paper describes the way to realization such an application, where a user chooses a photo database to working with and enters a photo into the system. The system using a visual vocabulary finds the most similar photos from the database and offers tags of the searched photo with a suitable form based on the tag statistical analysis of this photo.

This master’s thesis deals with the issue of scanning human fingerprints, which is currently very topical and represents the most widespread biometric technology. The theoretical part of the work acquaints the reader with basics of dactyloscopy and biometrics and concerns technologies used for fingerprinting, image preprocessing methods and commercially available contactless optical scanners. The practical part is a realisation of a contactless optical scanner based on a Raspberry Pi minicomputer, implementation of preprocessing algorithms in Python and testing of the device from the perspective of image quality.

This work deal with determination topohraphic parameters of a randomly rough surface by the help of method of shearing interferometry. It is a optical method for determination surface roughness. The basic idea is based of on deformation interference strips which are made by interference of the same mutually translated monochrome luminous wavefronts. The wavefront is created after transit or reflection monochrome lights from the surface of a studied sample. The wavefronts creates picture with deformed interference strips , which carries information about character of the surface. This information can be profited by algorithms of image processing from the picture . The thesis was developed in research project MSM 0021630529 Intelligent Systems in Automation.

The aim of this work is the genetic design of neural networks, which are able to classify within various classification tasks. In order to create these neural networks, algorithm called NeuroEvolution of Augmenting Topologies (also known as NEAT) is used. Also the idea of preprocessing, which is included in implemented result, is proposed. The goal of preprocessing is to reduce the computational requirements for processing of benchmark datasets for classification accuracy. The result of this work is a set of experiments conducted over a data set for cancer cells detection and a database of handwritten digits MNIST. Classifiers generated for the cancer cells exhibits over 99 % accuracy and in experiment MNIST reduces computational requirements more than 10 % with bringing negligible error of size 0.17 %.

Rough sets and neural networks both offer good theoretical background for data processing and analysis. However, both of them suffer from few issues. This thesis will investigate methods by which these two concepts are merged, and few such solutions will be implemented and compared with conventional algorithm to study the benefits of this approach.

Sowohl in der Klinischen Labormedizin, der Klinischen Mikrobiologie als auch in der Pathologie ist die Massenspektrometrie (MS) ein bedeutender Bestandteil der Diagnostik geworden. Der Fortschritt in der Gerätetechnik ermöglicht in kurzer Zeit viele, hochaufgelöste Spektren zu generieren. Diese Informationsvielfalt macht die manuelle Auswertung durch den Anwender sehr kompliziert bis unmöglich. Aus diesem Grund ist die Unterstützung durch bioinformatische Programme notwendig. Für die Reproduzierbarkeit der Ergebnisse und die Qualitätskontrolle ist es essentiell, dass die verwendeten Algorithmen transparent und die Programme als Open Source Software (OSS) frei verfügbar sind (Aebersold and Mann, 2003). Das Ziel dieser Arbeit war die Entwicklung von MALDIquant, einer unter der GNU General Public License (GPL) stehenden, flexiblen OSS, die für die o.g. Anwendungsbereiche modernste Algorithmen für die komplette Analyse bietet und in der freien Programmiersprache R (R Core Team, 2014) geschrieben ist. Im Zusammenspiel mit dem dazugehörigen Paket MALDIquantForeign ist MALDIquant in der Lage die üblichen Dateiformate der verschiedenen MS-Geräte zu verarbeiten. Dadurch ist MALDIquant hersteller- und geräteunabhängig und eignet sich nicht nur für MALDI/TOF, sondern für alle zweidimensionalen MS-Daten. Angefangen vom Datenimport über die Prozessierung bis hin zur Analyse der Spektren bietet MALDIquant eine komplette Analyse-Pipeline und implementiert state-of-the-art Methoden. Neben weit verbreiteten Verfahren zur Baseline Correction und Peak Detection zeichnet sich MALDIquant besonders durch ein hervorragendes Peak Alignment aus. Dieses ist sehr genau und aufgrund des Fokus auf die Peaks schneller als die meisten anderen Verfahren und weitestgehend unabhängig von der Qualität der Intensitätenkalibrierung. Eine weitere Stärke von MALDIquant ist die Möglichkeit, eigene Algorithmen zu integrieren, sowie den Ablauf der Analyse den individuellen Bedürfnissen anzupassen. In der beispielhaften Analyse der Daten von Fiedler et al. (2009) konnten durch MALDIquant Peaks gefunden werden, die Patienten mit Pankreaskarzinom von nicht erkrankten Probanden unterscheiden. Einige dieser Peaks wurden bereits in anderen Publikationen beschrieben. Neben diesem Beispiel hat MALDIquant seine Nützlichkeit bereits in verschiedenen Anwendungsbereichen und Publikationen bewiesen, wie etwa in Ouedraogo et al. (2013) oder Jung et al. (2014).:Bibliographische Beschreibung (III) Abbildungsverzeichnis (V) Tabellenverzeichnis (VII) Abkürzungsverzeichnis (IX) 1 Einleitung (1) 1.1 Intention (1) 1.2 Eigene Beiträge (2) 1.3 Übersicht (3) 2 Hintergrund (5) 2.1 Proteomik (5) 2.2 Massenspektrometrie (6) 2.3 Bioinformatik (7) 3 Methoden (9) 3.1 Überblick (9) 3.2 Import der Rohdaten (9) 3.3 Transformation der Intensitäten (11) 3.4 Korrektur der Grundlinie (11) 3.5 Kalibrierung der Intensitäten (13) 3.6 Identifizierung von Merkmalen (15) 3.7 Kalibrierung der m/z-Werte (17) 3.8 Nachbearbeitung (19) 4 Ergebnisse (23) 4.1 Implementierung (23) 4.2 Anwendungsbeispiel Fiedler et al. 2009 (23) 4.3 Vorbehandlung der Daten aus Fiedler et al. 2009 mit MALDIquant (24) 4.4 Multivariate Analyse (24) 4.5 Mögliche Biomarker (26) 5 Diskussion (29) 6 Zusammenfassung (31) 7 Literaturverzeichnis (35) A Publikation (45) B Übersicht Codeumfang (49) C Analyse Fiedler et al. 2009 (51) D Erklärung über die eigenständige Abfassung der Arbeit (69) E Lebenslauf (71) F Danksagung (75)