|
|
Toolbox for automatic EEG data quality assessment
Meloun, Jan ; Gajdoš, Martin (referee) ; Lamoš, Martin (advisor)
This thesis deals with the design of a tool for the automatic evaluation of EEG data quality. The theoretical part of the thesis contains a description of the formation and propagation of the action potential through the nervous system. Furthermore, a theoretical description of the EEG recording and its artifacts. The following is a description of the methods used to detect artifacts. In the practical part of the thesis, there is a description of the design of the tool for automatic EEG quality assessment, including a discussion of the results based on the provided data.
|
|
|
Detekce kategorie obsahu webové stránky prostřednictvím metod strojového učení.
DOHNAL, Patrik
This bachelor thesis is focused on design and the implementation of the algorithm for classifying the websites into a several categories. The implementation of this software is written in Python. For classifying purposes I use machine learning models such as Naive Bayes classifier, K-Nearest neighbors and Support Vector Machines. Within the process it is assumed to collect my own dataset, wich will be used for training and testing purposes. Thesis also includes detailed description of the methods I uesd.
|
|
|
Spatial-temporal analysis of HD-EEG data in pacients with nerodegenerative disease
Jordánek, Tomáš ; Kozumplík, Jiří (referee) ; Lamoš, Martin (advisor)
This master’s thesis deals with diagnostics of prodromal stage of Lewy body disease using microstate analysis. First part of the thesis includes theoretical background which is needed for understanding discussed topics and presented results. This part consists of description of the disease, diagnostic options, electroencephalography, pre-processing of the EEG record and the microstate analysis process. Theoretical background is followed by a practical part of the thesis. In the beginning, there is a chapter about a dataset, used EEG device, and own solution of the pre-processing. Microstate analysis is discussed next, its output parameters were compared between groups with statistical methods. Comparison of the subjects in prodromal stage of Lewy body disease and healthy controls brought significant differences in three parameters of microstates, in rate of unlabelled time frames and also for some counts of transitions between each map or unlabelled sections. Comparison of the subjects in prodromal stage of Lewy body disease and healthy controls brought significant differences in three parameters of microstates, in rate of unlabelled time frames and also for some counts of transitions between each map or unlabelled sections.
|
|
|
Classification of Music Files Using Machine Learning
Sládek, Matyáš ; Smrčka, Aleš (referee) ; Janoušek, Vladimír (advisor)
This thesis is focused on classification of music files using machine learning algorithms. Seven classifiers were compared in this thesis, based on classification accuracy and speed. Two feature extraction methods, two feature selection methods and two parameter optimization methods were used. The best classifier proved to be XGBClassifier, which had reached accuracy of 87.56 % on dataset Extended Ballroom Dataset, 64.56 % on dataset FMA: A Dataset For Music Analysis and 83.50 % on dataset GTZAN. This model could be used for playlist creation or music database categorization.
|
|
|
Analysis of Mobile Devices Network Communication Data
Abraham, Lukáš ; Bartík, Vladimír (referee) ; Burgetová, Ivana (advisor)
At the beginning, the work describes DNS and SSL/TLS protocols, it mainly deals with communication between devices using these protocols. Then we'll talk about data preprocessing and data cleaning. Furthermore, the thesis deals with basic data mining techniques such as data classification, association rules, information retrieval, regression analysis and cluster analysis. The next chapter we can read something about how to identify mobile devices on the network. We will evaluate data sets that contain collected data from communication between the above mentioned protocols, which will be used in the practical part. After that, we finally get to the design of a system for analyzing network communication data. We will describe the libraries, which we used and the entire system implementation. We will perform a large number of experiments, which we will finally evaluate.
|
|
|
Data Mining Case Study in Python
Stoika, Anastasiia ; Burgetová, Ivana (referee) ; Zendulka, Jaroslav (advisor)
This thesis focuses on basic concepts and techniques of the process known as knowledge discovery from data. The goal is to demonstrate available resources in Python, which enable to perform the steps of this process. The thesis addresses several methods and techniques focused on detection of unusual observations, based on clustering and classification. It discusses data mining task for data with the limited amount of inspection resources. This inspection activity should be used to detect unusual transactions of sales of some company that may indicate fraud attempts by some of its salespeople.
|
|
|
Data Mining with Python
Šenovský, Jakub ; Bartík, Vladimír (referee) ; Zendulka, Jaroslav (advisor)
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.
|
|
|
Visualization of data mining results through BI tools
Fiřtík, Zdeněk ; Chudán, David (advisor) ; Rauch, Jan (referee)
The topis of this thesis is an attempt to visualize the data of the minig data through the tools of business intelligence. The paper moves from a detailed characterization of basic concepts, methodologies and procedures, which are an integral part of the data mining process and subsequent visualization. The thesis is meant for those who are interested in data mining and BI tools, and can offer motives to consider whether these tools will be suitable for their use.
|
|
|
Social networks and data mining
Zvirinský, Peter ; Mrázová, Iveta (advisor) ; Neruda, Roman (referee)
Recent data mining methods represent modern approaches capable of analyzing large amounts of data and extracting meaningful and potentially useful information from it. In this work, we discuss all the essential steps of the data mining process - including data preparation, storage, cleaning, data analysis as well as visualization of the obtained results. In particular, this work is focused on the data available publicly from the Insolvency Register of the Czech Republic, that comprises all insolvency proceedings commenced after 1. January 2008 in the Czech Republic. With regard to the considered type of data, several data mining methods have been discussed, implemented, tested and evaluated. Among others, the studied techniques include Market Basket Analysis, Bayesian networks and social network analysis. The obtained results reveal several social patterns common in the current Czech society.
|
|
|
Sentiment Analysis with Use of Data Mining
Sychra, Martin ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
The theme of the work is sentiment analysis, especially in terms of informatics (marginally from a linguistic point of view). The linguistic part discusses the term sentiment and language methods for its analysis, e.g. lemmatization, POS tagging, using the list of stopwords etc. More attention is paid to the structure of the sentiment analyzer which is based on some of the machine learning methods (support vector machines, Naive Bayes and maximum entropy classification). On the basis of the theoretical background, a functional analyzer is projected and implemented. The experiments are focused mainly on comparing the classification methods and on the benefits of using the individual preprocessing methods. The success rate of the constructed classifier reaches up to 84 % in the cross-validation.
|
guest ::
