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Acoustic analysis of emotionally affected sentences in patients with Parkinson's disease
Gavlasová, Radka ; Kováč, Daniel (referee) ; Mekyska, Jiří (advisor)
This thesis focuses on Parkinson's disease and its effect on emotional expression in speech. The aim was to conduct a literature search on acoustic emotional analysis of PD patients and to implement acoustic parameters to distinguish between healthy and diseased individuals. The database used contained recordings of 100 patients with PD and 52 healthy controls for various speech tasks. For this analysis, 7 emotionally coloured sentences and 11 acoustic parameters were selected and implemented in Python. From the statistical analysis, it was found that the most significant parameters include pauses in speech and intensity variability. The XGBoost algorithm with 10-fold stratified cross-validation was used for classification. A total of 10 models were implemented to analyze all tasks together and each task separately. Optimization was performed using randomized search. For the combination of all tasks, the significant parameter was the variability in intensity or speech rate. For the individual speech tasks, variability in intonation and formant areas was highly significant. The best model achieved a 63% success rate (BACC) and 85% sensitivity. The results suggest that emotional prosody affects classification, confirming previous findings and pointing to the need for further investigation in this area.
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System for tracking and classification of objects in the sky
Franka, Jakub ; Honec, Peter (referee) ; Janáková, Ilona (advisor)
This work deals with the use of computer vision in the field of detection, tracking and classification of flying objects in a real environment. The goal is to create a robust system, capable of working effectively in adverse conditions and accurately identifying different types of objects in the sky. The work progresses from theoretical foundations, choice of methods, to the design and implementation of a computer vision system.
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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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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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Creating an Information Extension for the 3D Model of Tombstone – Finding the Most Effective Way
Adamcová, Alice
This article deals with creating a 3D model of a stone tombstone created with the terrestrial photogrammetry method and creating an information extension of this 3D model. Information extension is understood a 3D model classification considering different criteria, followed by classified 3D model visualisation. The paper mainly deals with testing the usefulness of different types of programs that can be used to create the information extension and based on this testing defines the most effective way of work.
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The MDSR Vegetation Filter
Kučera, Jakub
This paper deals with the software implementation of a vegetation filter using the MDSR algorithm and its subsequent testing on a variety of pre-selected point clouds. The results of each filtering algorithm were compared visually in sections with the results of other conventionally used filtering algorithms. Comparisons were also made by calculating the root mean square deviations of the results of the conventional algorithms from the TIN surface generated from the MDSR filter results.
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