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)

This thesis is carried out in collaboration with a private company, DANSiC AB This study is an extension of a research work started by DANSiC AB in 2019 to classify a source. This study is about classifying a source into two classes with the sensitivity of one source higher than the other as one source has greater importance. The data provided for this thesis is based on sensor measurements on different temperature cycles. The data is high-dimensional and is expected to have a drift in measurements. Principal component analysis (PCA) is used for dimensionality reduction. “Differential”, “Relative” and “Fractional” drift compensation techniques are used for compensating the drift in data. A comparative study was performed using three different classification algorithms, which are “Linear Discriminant Analysis (LDA)”, “Naive Bayes classifier (NB)” and “Random forest (RF)”. The highest accuracy achieved is 59%,Random forest is observed to perform better than the other classifiers. / <p>This work is done with DANSiC AB in collaboration with Linkoping University.</p>

Sand and mud traversed by tidal inlets and channels, which split in subtle branches, salt marshes at the coast, the tide, harsh weather conditions and a high diversity of fauna and flora characterize the ecosystem Wadden Sea. No other landscape on the Earth changes in such a dynamic manner. Therefore, land cover classification and monitoring of vulnerable ecosystems is one of the most important approaches in remote sensing and has drawn much attention in recent years. The Wadden Sea in the southeastern part of the North Sea is one such vulnerable ecosystem, which is highly dynamic and diverse. The tidal flats of the Wadden Sea are the zone of interaction between marine and terrestrial environments and are at risk due to climate change, pollution and anthropogenic pressure. Due to that, the European Union has implemented various directives, which formulate objectives such as achieving or maintaining a good environmental status respectively a favourable conservation status within a given time. In this context, a permanent observation for the estimation of the ecological condition is needed. Moreover, changes can be tracked or even foreseen and an appropriate response is possible. Therefore, it is important to distinguish between short-term changes, which are related to the dynamic manner of the ecosystem, and long-term changes, which are the result of extraneous influences. The accessibility both from sea and land is very poor, which makes monitoring and mapping of tidal flat environments from in situ measurements very difficult and cost-intensive. For the monitoring of big areas, time-saving applications are needed. In this context, remote sensing offers great possibilities, due to its provision of a large spatial coverage and non-intrusive measurements of the Earth’s surface. Previous studies in remote sensing have focused on the use of electro-optical and radar sensors for remote sensing of tidal flats, whereas microwave systems using synthetic aperture radar (SAR) can be a complementary tool for tidal flat observation, especially due to their high spatial resolution and all-weather imaging capability. Nevertheless, the repetitive tidal event and dynamic sedimentary processes make an integrated observation of tidal flats from multi-sourced datasets essential for mapping and monitoring. The main challenge for remote sensing of tidal flats is to isolate the sediment, vegetation or shellfish bed features in the spectral signature or backscatter intensity from interference by water, the atmosphere, fauna and flora. In addition, optically active materials, such as plankton, suspended matter and dissolved organics, affect the scattering and absorption of radiation. Tidal flats are spatially complex and temporally quite variable and thus mapping tidal land cover requires satellites or aircraft imagers with high spatial and temporal resolution and, in some cases, hyperspectral data. In this research, a hierarchical knowledge-based decision tree applied to multi-sensor remote sensing data is introduced and the results have been visually and numerically evaluated and subsequently analysed. The multi-sensor approach comprises electro-optical data from RapidEye, SAR data from TerraSAR-X and airborne LiDAR data in a decision tree. Moreover, spectrometric and ground truth data are implemented into the analysis. The aim is to develop an automatic or semi-automatic procedure for estimating the distribution of vegetation, shellfish beds and sediments south of the barrier island Norderney. The multi-sensor approach starts with a semi-automatic pre-processing procedure for the electro-optical data of RapidEye, LiDAR data, spectrometric data and ground truth data. The decision tree classification is based on a set of hierarchically structured algorithms that use object and texture features. In each decision, one satellite dataset is applied to estimate a specific class. This helps to overcome the drawbacks that arise from a combined usage of all remote sensing datasets for one class. This could be shown by the comparison of the decision tree results with a popular state-of-the-art supervised classification approach (random forest). Subsequent to the classification, a discrimination analysis of various sediment spectra, measured with a hyperspectral sensor, has been carried out. In this context, the spectral features of the tidal sediments were analysed and a feature selection method has been developed to estimate suitable wavelengths for discrimination with very high accuracy. The developed feature selection method ‘JMDFS’ (Jeffries-Matusita distance feature selection) is a filter-based supervised band elimination technique and is based on the local Euclidean distance and the Jeffries-Matusita distance. An iterative process is used to subsequently eliminate wavelengths and calculate a separability measure at the end of each iteration. If distinctive thresholds are achieved, the process stops and the remaining wavelengths are applied in the further analysis. The results have been compared with a standard feature selection method (ReliefF). The JMDFS method obtains similar results and runs 216 times faster. Both approaches are quantitatively and qualitatively evaluated using reference data and standard methodologies for comparison. The results show that the proposed approaches are able to estimate the land cover of the tidal flats and to discriminate the tidal sediments with moderate to very high accuracy. The accuracies of each land cover class vary according to the dataset used. Furthermore, it is shown that specific reflection features can be identified that help in discriminating tidal sediments and which should be used in further applications in tidal flats.