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Classification based on longitudinal observations
Bandas, Lukáš ; Komárek, Arnošt (advisor) ; Kulich, Michal (referee)
The concern of this thesis is to discuss classification of different objects based on longitudinal observations. In the first instance the reader is introduced to a linear mixed-effects model which is useful for longitudinal data modeling. Description of discriminant analysis methods follows. These methods ares usually used for classification based on longitudinal observations. Individual methods are introduced in the theoretic aspect. Random effects approach is generalized to continuous time. Subsequently the methods and features of the linear mixed-effects model are applied to real data. Finally features of the methods are studied with help of simulations.
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The influence of spectral resolution on land cover classification in Krkonoše Mts. tundra
Palúchová, Miroslava ; Červená, Lucie (advisor) ; Kupková, Lucie (referee)
The influence of spectral resolution on land cover classification in Krkonoše Mts. tundra Abstract The aim of this diploma thesis was to specify the spectral resolution requirements for classification and to identify the most important spectral bands to discriminate classes of the predefined legend. Aerial hyperspectral data acquired by AisaDUAL sensor were used. The method applied for the selection of the important bands was discriminant analysis performed in IBM SPSS Statistics. The most discriminative bands were found in intervals 1500-1750 nm (beginning of SWIR), 1100- 1300 nm (longer wavelengths of NIR), 670-760 (red-edge) and 500-600 nm (green light). The classification of the selected bands was realized in ENVI 5.4 using the Support Vector Machine classifier, achieving overall accuracy of 80,54 %, Kappa coefficient 0,7755. The suitability of available satellite data for the classification of tundra vegetation in Krkonoše mountains based on spectral resolution was evaluated as well. Keywords: tundra, Krkonoše, classification, spectral resolution, class separability, discriminant analysis, hyperspectral data
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Detection of atrial fibrillation in short-term ECG
Ambrožová, Monika ; Janoušek, Oto (referee) ; Ronzhina, Marina (advisor)
Atrial fibrillation is diagnosed in 1-2% of the population, in next decades, it expects a significant increase in the number of patients with this arrhythmia in connection with the aging of the population and the higher incidence of some diseases that are considered as risk factors of atrial fibrillation. The aim of this work is to describe the problem of atrial fibrillation and the methods that allow its detection in the ECG record. In the first part of work there is a theory dealing with cardiac physiology and atrial fibrillation. There is also basic descreption of the detection of atrial fibrillation. In the practical part of work, there is described software for detection of atrial fibrillation, which is provided by BTL company. Furthermore, an atrial fibrillation detector is designed. Several parameters were selected to detect the variation of RR intervals. These are the parameters of the standard deviation, coefficient of skewness and kurtosis, coefficient of variation, root mean square of the successive differences, normalized absolute deviation, normalized absolute difference, median absolute deviation and entropy. Three different classification models were used: support vector machine (SVM), k-nearest neighbor (KNN) and discriminant analysis classification. The SVM classification model achieves the best results. Results of success indicators (sensitivity: 67.1%; specificity: 97.0%; F-measure: 66.8%; accuracy: 92.9%).
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The influence of spectral resolution on land cover classification in Krkonoše Mts. tundra
Palúchová, Miroslava ; Červená, Lucie (advisor) ; Kupková, Lucie (referee)
The influence of spectral resolution on land cover classification in Krkonoše Mts. tundra Abstract The aim of this diploma thesis was to specify the spectral resolution requirements for classification and to identify the most important spectral bands to discriminate classes of the predefined legend. Aerial hyperspectral data acquired by AisaDUAL sensor were used. The method applied for the selection of the important bands was discriminant analysis performed in IBM SPSS Statistics. The most discriminative bands were found in intervals 1500-1750 nm (beginning of SWIR), 1100- 1300 nm (longer wavelengths of NIR), 670-760 (red-edge) and 500-600 nm (green light). The classification of the selected bands was realized in ENVI 5.4 using the Support Vector Machine classifier, achieving overall accuracy of 80,54 %, Kappa coefficient 0,7755. The suitability of available satellite data for the classification of tundra vegetation in Krkonoše mountains based on spectral resolution was evaluated as well. Keywords: tundra, Krkonoše, classification, spectral resolution, class separability, discriminant analysis, hyperspectral data
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Ecology,ethology and variability of european green lizard Lacerta viridis in Natural reservation Tiché údolí
Chmelař, Jan ; Rehák, Ivan (advisor) ; Moravec, Jiří (referee)
The European green lizard, Lacerta viridis, is in the Bohemia region stated as critically endangered species. Populations in this region are located beyond the northern border of continuous range of this species and are closely related to the "riverine phenomenon", and deeply engorged river valleys. The chosen locality in Tiché údolí is a subject to a long-term conservational management aimed to strengthen and maintain abundance of the local population. This management is a direct output of a previous study of this population performed in years 1995-1997. Main goal of the presented study is to compare current population characteristics with the older study. The locality has been visited 119 times in years from 2011 to 2014. The studied population now displays higher abundance and inhabits a larger area. The author also performed a spatial analysis of the places with presence of an observed individual in order to determine and evaluate significance of the chosen abiotic factors for habitat discrimination. The results indicate that positive discrimination is based on the presence of a rock debris and a hiding place. Strongest factors towards negative discrimination were high percentages of grass and high vegetation coverage. This study also contains and discusses ecological, ethological and...
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Bankruptcy prediction modelling in construction business
Burdych, Filip ; Kuběnka,, Michal (referee) ; Karas, Michal (advisor)
This master thesis deals with bankruptcy prediction models for construction companies doing business in Czech Republic. Terms important for understanding the issue are defined in the theoretical part. In analytical part, there are five current bankruptcy prediction models tested on the analysed sample and resulted accuracy compared with original ones. On the basis of knowledges acquired, there is developed a brand-new bankruptcy prediction model.
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Bankruptcy Prediction Modelling in Construction Business
Srbová, Pavla ; Kuběnka,, Michal (referee) ; Karas, Michal (advisor)
The diploma thesis is aimed at creating a bankruptcy model for companies of the construction industry in the Czech Republic by using discriminant analysis. In the theoretical part, the concept of bankruptcy model is defined; this part is focused on the inclusion of bankruptcy models in economics, a look into their history, a description of selected models and a brief characteristic of the construction industry. In the practical part, the reliability of selected bankruptcy models is counted and a new bankruptcy model is built.
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Weighted Data Depth and Depth Based Discrimination
Vencálek, Ondřej ; Hlubinka, Daniel (advisor) ; Anděl, Jiří (referee) ; Malý, Marek (referee)
The concept of data depth provides a powerful nonparametric tool for multivariate data analysis. We propose a generalization of the well-known halfspace depth called weighted data depth. The weighted data depth is not affine invariant in general, but it has some useful properties as possible nonconvex central areas. We further discuss application of data depth methodology to solve discrimination problem. Several classifiers based on data depth are reviewed and one new classifier is proposed. The new classifier is a modification of k-nearest- neighbour classifier. Classifiers are compared in a short simulation study. Advantage gained from use of the weighted data depth for discrimination purposes is shown.
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Role of Behavioral Finance in Portfolio Investment Decisions: Evidence from India
Subash, Rahul ; Báťa, Karel (advisor) ; Jandík, Tomáš (referee)
I Role of Behavioral Finance in Portfolio Investment Decisions: Evidence from India Abstract Extreme volatility has plagued financial markets worldwide since the 2008 Global Crisis. Investor sentiment has been one of the key determinants of market movements. In this context, studying the role played by emotions like fear, greed and anticipation, in shaping up investment decisions seemed important. Behavioral Finance is an evolving field that studies how psychological factors affect decision making under uncertainty. This thesis seeks to find the influence of certain identified behavioral finance concepts (or biases), namely, Overconfidence, Representativeness, Herding, Anchoring, Cognitive Dissonance, Regret Aversion, Gamblers' Fallacy, Mental Accounting, and Hindsight Bias, on the decision making process of individual investors in the Indian Stock Market. Primary data for analysis was gathered by distributing a structured questionnaire among investors who were categorized as (i) young, and (ii) experienced. Results obtained by analyzing a sample of 92 respondents, out of which 53 admitted to having suffered a loss of at least 30% because of the crisis, revealed that the degree of exposure to the biases separated the behavioral pattern of young and experienced investors. Gamblers' Fallacy, Anchoring and...
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