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Regeneration of brownfields in the South Bohemian Region
ŠÁDKOVÁ, Pavla
The thesis on the topic of Brownfield Regeneration in the South Bohemian Region deals with a comprehensive approach to the analysis and regeneration of brownfields in the South Bohemian Region. The main objective of the thesis is to analyse and synthesize the ecological, social, and economic impacts of brownfield and greenfield regeneration on selected sites in the South Bohemian Region. A combination of quantitative and qualitative research methods was used to achieve the goals of the thesis. The work is divided into two parts, theoretical and practical. The theoretical part focuses on defining the issues of brownfields, evaluating the development and current state. It includes possible ways of classifying brownfields and methods of financing. In the second, practical part, a comparison of selected brownfields and greenfields in the South Bohemian Region is conducted. The sites are evaluated according to the model of the South Bohemian Region, which is based on a study by the German Ministry of the Environment and is based on the scoring of given criteria and their parameters. The model consists of 3 criteria - potential from the perspective of the municipality, potential from the perspective of the investor, and the change in the value of the site. Based on these, the significance of the sites is evaluated. Through the comparison of the Preference Index, the thesis subsequently presents a selection of sites suitable for implementation, including a proposal for future use. The outputs of the thesis contribute to a deeper understanding of the issues of brownfield regeneration and offer suggestions for the effective use of the potential of these sites to support the local economy and improve the quality of life for residents.
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Encrypted video-stream identification
MACÁK, Tomáš
The aim of this thesis is to create a data set of measured encrypted video streams and subsequently try to discover if it is possible to identify the content of those streams. In the theoretical part the on - demand video streaming is introduced and then suitable machine learning models applicable to solve this problem are presented. The works focused on a similar topic are presented next. In followed practical part the already mentioned data set is created. This set is then analysed and it is determined if there is a way how to represent those measured video streams for later content identification with use of statistical and machine learning models. In the last part of this chapter the machine learning models for classification and similarity detection are implemented and trained. The models are then tested and the results are summarised and compared.
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Automatic diagnosis of the 12-lead ECG using deep learning
Blaude, Ondřej ; Smital, Lukáš (referee) ; Provazník, Valentine (advisor)
The aim of this diploma thesis is to investigate the problematics of automatic ECG diagnostics, namely on twelve-lead recordings. In the first chapter the heart and its electrical activity measurement is described shortly. In addition to that, the abnormalities which are going to be classified in this thesis are also briefly described. In the second chapter, it is described how the ECG was diagnosed earlier, by classical methods that preceded deep learning. Some of the shortcomings that the classical methods have compared to deep learning are also described here. The third part already pays attention to deep learning itself, and its contribution and advantages compared to classical methods. Convolutional neural networks and their individual blocks are also described here, later attention is paid to selected architectures that were used in some studies. The fourth chapter already focuses on the practical part, in which the data used from the PhysioNet database, the proposed algorithm and its implementation are described in more detail. In the fifth chapter the results are discussed and compared to the corresponding publications.
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Health assessment using smart devices
Vargová, Enikö ; Filipenská, Marina (referee) ; Němcová, Andrea (advisor)
This thesis deals with the possibilities of non-invasive determination of blood glucose from photoplethysmographic signals. Elevated blood sugar is often associated with disease called diabetes mellitus. Diabetes is one of the world’s major chronic diseases. Untreated diabetes is often a cause of death. The aim of the work is to propose methods for glycemic classification and prediction. Two datasets have been created by recording the PPG signals using two smart devices (a smart wristband and a smartphone), along with their blood glucose levels measured in an invasive way. The PPG signals were preprocessed, and suitable features were extracted from them. Various machine-learning models for glycemic classification and prediction were created.
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Machine Learning from Intrusion Detection Systems
Dostál, Michal ; Očenášek, Pavel (referee) ; Hranický, Radek (advisor)
The current state of intrusion detection tools is insufficient because they often operate based on static rules and fail to leverage the potential of artificial intelligence. The aim of this work is to enhance the open-source tool Snort with the capability to detect malicious network traffic using machine learning. To achieve a robust classifier, useful features of network traffic were choosed, extracted from the output data of the Snort application. Subsequently, these traffic features were enriched and labeled with corresponding events. Experiments demonstrate excellent results not only in classification accuracy on test data but also in processing speed. The proposed approach and the conducted experiments indicate that this new method could exhibit promising performance even when dealing with real-world data.
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Metasearch for Reviews on the Czech Web
Šmahel, Michal ; Doležal, Jan (referee) ; Smrž, Pavel (advisor)
The main purpose of this work is to create a metasearch engine for review articles with built-in sentiment analysis. In addition, a complex survey of main text extraction tools and web browser automation tools for web crawling has been carried out to achieve of the best possible results. The resulting metasearch engine provides a web interface for searching relevant review articles, thus saving time spent on manual searching. Thanks to multi-level transformer-based filtering, it can return 10—15 relevant review articles on frequently reviewed topics in about 4 minutes with no effort, just by clicking on a button.
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Classification of global environmental systems according to the level of anthropogenic transformation
Hrdina, Aleš
The topic of the doctoral thesis is the development of a comprehensive classification of global environmental systems based on a geographical synthesis of abiotic, biotic and anthropogenic factors. The dramatic changes in the Earth's natural environment, the noticeable loss of biodiversity and the increasing impact of human activity in many different aspects raise the need for a comprehensive classification that provides an appropriate spatial framework for assessing the impacts of these changes. Several global classifications have been developed in the past, but most of them only work with various natural environmental gradients (especially climate or relief). However, most regions of the world have been so fundamentally affected or even completely transformed by human activity that the omission of anthropogenic factors in comprehensive environmental classifications may lead to erroneous conclusions. For this reason, new global environmental classifications have recently begun to emerge abroad that attempt to deal with anthropogenic changes to the natural environment and include them in a comprehensive assessment. The proposal of a methodology and the actual creation of the classification of global environmental systems based on abiotic gradients, biodiversity distribution and spatial...
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Classification of global environmental systems according to the level of anthropogenic transformation
Hrdina, Aleš ; Romportl, Dušan (advisor) ; Boltižiar, Martin (referee) ; Václavík, Tomáš (referee)
The topic of the doctoral thesis is the development of a comprehensive classification of global environmental systems based on a geographical synthesis of abiotic, biotic and anthropogenic factors. The dramatic changes in the Earth's natural environment, the noticeable loss of biodiversity and the increasing impact of human activity in many different aspects raise the need for a comprehensive classification that provides an appropriate spatial framework for assessing the impacts of these changes. Several global classifications have been developed in the past, but most of them only work with various natural environmental gradients (especially climate or relief). However, most regions of the world have been so fundamentally affected or even completely transformed by human activity that the omission of anthropogenic factors in comprehensive environmental classifications may lead to erroneous conclusions. For this reason, new global environmental classifications have recently begun to emerge abroad that attempt to deal with anthropogenic changes to the natural environment and include them in a comprehensive assessment. The proposal of a methodology and the actual creation of the classification of global environmental systems based on abiotic gradients, biodiversity distribution and spatial...
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Emotion Recognition from Acted and Spontaneous Speech
Atassi, Hicham ; Přibil, Jiří (referee) ; Zahradník, Pavel (referee) ; Smékal, Zdeněk (advisor)
Dizertační práce se zabývá rozpoznáním emočního stavu mluvčích z řečového signálu. Práce je rozdělena do dvou hlavních častí, první část popisuju navržené metody pro rozpoznání emočního stavu z hraných databází. V rámci této části jsou představeny výsledky rozpoznání použitím dvou různých databází s různými jazyky. Hlavními přínosy této části je detailní analýza rozsáhlé škály různých příznaků získaných z řečového signálu, návrh nových klasifikačních architektur jako je například „emoční párování“ a návrh nové metody pro mapování diskrétních emočních stavů do dvou dimenzionálního prostoru. Druhá část se zabývá rozpoznáním emočních stavů z databáze spontánní řeči, která byla získána ze záznamů hovorů z reálných call center. Poznatky z analýzy a návrhu metod rozpoznání z hrané řeči byly využity pro návrh nového systému pro rozpoznání sedmi spontánních emočních stavů. Jádrem navrženého přístupu je komplexní klasifikační architektura založena na fúzi různých systémů. Práce se dále zabývá vlivem emočního stavu mluvčího na úspěšnosti rozpoznání pohlaví a návrhem systému pro automatickou detekci úspěšných hovorů v call centrech na základě analýzy parametrů dialogu mezi účastníky telefonních hovorů.
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Automatic detection of ischemia in ECG
Noremberczyk, Adam ; Potočňák, Tomáš (referee) ; Ronzhina, Marina (advisor)
This thesis discusses the utilization of the artificial neural networks (ANN) for detection of coronary artery disease (CAD) in frequency area. The first part of this thesis is orientated towards the theoretical knowledge. Describes the issue of ECG pathological changes. ECQ are converted to frequency area. Described statistical methods and methods for automatic detection of CAD and MI. Explained the issue of the perceptron and ANN. The second deals with use of Neural Network Toolbox MATLAB®. This part focuses on counting and finding suitable parameters and making connection of band. At the end of the thesis UNS is used to detect ischemic parameters and the results are discussed. Average values for the best settings are 100% accuracy.
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