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Detection of paroxysmal atrial fibrillation and atrial flutter
Krmela, Jan ; Němcová, Andrea (referee) ; Smíšek, Radovan (advisor)
This bachelor thesis deals with the problem of atrial fibrillation and flutter, the pathophysiology of these arrhythmias and their automatic detection. It includes a theoretical introduction necessary to understand the basal anatomy of the heart, its function, the origin and description of the electrocardiogram and a chapter on cardiac arrhythmias. It also includes a review of automatic detection of atrial fibrillation. The databases used in the practical part of the thesis are also described. The implementation of heart rhythm classification and automatic detection of the beginning and end of paroxysmal episodes is performed in MATLAB environment, the proposed algorithm is tested on the described databases and the results are evaluated.
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Analysis of Surveillance Camera Recordings
Ščavnická, Šárka ; Švec, Tomáš (referee) ; Smrž, Pavel (advisor)
This thesis deals with the systems for analyzing records from security cameras. It aims to create a functional solution that analyzes records and answers questions from the user. The created system combines the YOLO algorithm for object detection and DeepSORT for their subsequent tracking. It contains five models that detect specific situations. Individual models achieved varying degrees of success during testing, with the lowest success rate being 58 % for the getting out of car situation. The highest success rate, 83 %, was obtained by a model for detecting a meeting between two people.
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Utilization of artificial intelligence in vibrodiagnostics
Dočekalová, Petra ; Huzlík, Rostislav (referee) ; Zuth, Daniel (advisor)
The diploma thesis deals with machine learning, expert systems, fuzzy logic, genetic algorithms, neural networks and chaos theory, which fall into the category of artificial intelligence. The aim of this work is to describe and implement three different classification methods, according to which the data set will be processed. The GNU Octave software environment was chosen for the data application for licensing reasons. Further evaluate the success of data classification, including visualization. Three different classification methods are used for comparison, so that we can compare the processed data with each other.
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Utilization of artificial intelligence in technical diagnostics
Konečný, Antonín ; Huzlík, Rostislav (referee) ; Zuth, Daniel (advisor)
The diploma thesis is focused on the use of artificial intelligence methods for evaluating the fault condition of machinery. The evaluated data are from a vibrodiagnostic model for simulation of static and dynamic unbalances. The machine learning methods are applied, specifically supervised learning. The thesis describes the Spyder software environment, its alternatives, and the Python programming language, in which the scripts are written. It contains an overview with a description of the libraries (Scikit-learn, SciPy, Pandas ...) and methods — K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Trees (DT) and Random Forests Classifiers (RF). The results of the classification are visualized in the confusion matrix for each method. The appendix includes written scripts for feature engineering, hyperparameter tuning, evaluation of learning success and classification with visualization of the result.
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Classification of eMail Communication
Piják, Marek ; Herout, Adam (referee) ; Szőke, Igor (advisor)
This diploma's thesis is based around creating a classifier, which will be able to recognize an email communication received by Topefekt.s.r.o on daily basis and assigning it into classification class. This project will implement some of the most commonly used classification methods including machine learning. Thesis will also include evaluation comparing all used methods.
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Financial health models and bankruptcy prediction models
ONDOKOVÁ, Lucie
The main aim of the master thesis is to compare of different methodologies of financial health models and bankruptcy prediction models and their cause to company classification. The work deals with the applicability of models on the sample of 45 prosperous companies and 45 companies that were initiating in insolvency process. Sample contain about 33 % companies from building industry, 33 % retail, 16,7 % manufacturing industry and 16,7 % of the other industries mainly services. The special kind of contingency table - the confusion matrix - is used in the methodology to calculate sensitivity, specificity, negative predictive, false positive rate, accuracy, error and other classification statistics. Overall model accuracy is obtained as a difference between accuracy and error. Dependencies of models are acquired based on Pearson´s correlation coefficient. The changes (removing of grey zone and testing new cut-off points) in models are tested in the sensitivity analysis. In practise part there are about 12 financial models calculated (Altman Z´, Altman Z´´, Index IN99, IN01 and IN05, Kralicek Quicktest, Zmijewski model, Taffler model and its modification, Index Creditworthiness, Grunwald Site Index, Doucha´s Analysis). Only two financial indicators (ROA and Sales / Assets) in results were important as crucial part for more than one model. Then are classifications of companies in models determined. It shows that the best models according to overall accuracy are Zmijewski and Altman´s Z´´. On the other hand the worst models are index IN99 and both versions of Taffler´s model. The classification is not caused excessively by extreme values, year of the model creation or country of the origin (hypothesis 1). Based on results it is suggested that the bankruptcy prediction is an accurate forecaster of failure up to three years prior to bankruptcy in most examined models (hypothesis 2). It is observed that the type of model and industry influence the classification of models. In the end, the changes based on sensitivity analysis in the worst companies are made. All of three changes have increased overall classification accuracy of models.
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State of the art speech features used during the Parkinson disease diagnosis
Bílý, Ondřej ; Smékal, Zdeněk (referee) ; Mekyska, Jiří (advisor)
This work deals with the diagnosis of Parkinson's disease by analyzing the speech signal. At the beginning of this work there is described speech signal production. The following is a description of the speech signal analysis, its preparation and subsequent feature extraction. Next there is described Parkinson's disease and change of the speech signal by this disability. The following describes the symptoms, which are used for the diagnosis of Parkinson's disease (FCR, VSA, VOT, etc.). Another part of the work deals with the selection and reduction symptoms using the learning algorithms (SVM, ANN, k-NN) and their subsequent evaluation. In the last part of the thesis is described a program to count symptoms. Further is described selection and the end evaluated all the result.
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Comparison of selected classification methods for multivariate data
Stecenková, Marina ; Řezanková, Hana (advisor) ; Berka, Petr (referee)
The aim of this thesis is comparison of selected classification methods which are logistic regression (binary and multinominal), multilayer perceptron and classification trees, CHAID and CRT. The first part is reminiscent of the theoretical basis of these methods and explains the nature of parameters of the models. The next section applies the above classification methods to the six data sets and then compares the outputs of these methods. Particular emphasis is placed on the discriminatory power rating models, which a separate chapter is devoted to. Rating discriminatory power of the model is based on the overall accuracy, F-measure and size of the area under the ROC curve. The benefit of this work is not only a comparison of selected classification methods based on statistical models evaluating discriminatory power, but also an overview of the strengths and weaknesses of each method.
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Quality measures of classification models and their conversion
Hanusek, Lubomír ; Hebák, Petr (advisor) ; Řezanková, Hana (referee) ; Skalská, Hana (referee)
Predictive power of classification models can be evaluated by various measures. The most popular measures in data mining (DM) are Gini coefficient, Kolmogorov-Smirnov statistic and lift. These measures are each based on a completely different way of calculation. If an analyst is used to one of these measures it can be difficult for him to asses the predictive power of a model evaluated by another measure. The aim of this thesis is to develop a method how to convert one performance measure into another. Even though this thesis focuses mainly on the above-mentioned measures, it deals also with other measures like sensitivity, specificity, total accuracy and area under ROC curve. During development of DM models you may need to work with a sample that is stratified by values of the target variable Y instead of working with the whole population containing millions of observations. If you evaluate a model developed on a stratified data you may need to convert these measures to the whole population. This thesis describes a way, how to carry out this conversion. A software application (CPM) enabling all these conversions makes part of this thesis. With this application you can not only convert one performance measure to another, but you can also convert measures calculated on a stratified sample to the whole population. Besides the above mentioned performance measures (sensitivity, specificity, total accuracy, Gini coefficient, Kolmogorov-Smirnov statistic), CPM will also generate confusion matrix and performance charts (lift chart, gains chart, ROC chart and KS chart). This thesis comprises the user manual to this application as well as the web address where the application can be downloaded. The theory described in this thesis was verified on the real data.
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