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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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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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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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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 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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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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Automatic diagnosis of the 12-lead ECG using deep learning
Blaude, Ondřej ; Chmelík, Jiří (referee) ; Provazník, Valentine (advisor)
The aim of this diploma thesis is to investigate the problematics of automatic ECG diagnostics, namely on twelve-lead recordings. This problem is solved by standard methods such as random forest, artificial neural networks or K-nearest neighbors. However, thanks to its ability to independently extract symptoms, deep learning methods are also popular. All these methods are described in the theoretical part. In the practical part, deep learning models were designed, functionality support was verified using data from the PhysioNet database. Two pilot models were created and subsequently optimized. From the entire parameter optimization procedure, three models are available, of which the best accuracy achieves an F1 score of 87.35% and 83.7%, and the second best achieves an F1 score of 77.74% and an accuracy of 84.53%. The results achieved are discussed and compared with those of similar publications.
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Detection of Fake News Using Machine Learning
Koreň, Matej ; Zbořil, František (referee) ; Hříbek, David (advisor)
This thesis focuses on the use of machine learning in fake news detection. For this purpose, four models have been selected – Bayesian, Decision Tree, Support Vector Machine and a Neural Network. In five experiments on various datasets, these models were trained, tested, evaluated and compared with state-of-the-art methods. Final implementation is in the form of a Python package, which allows it’s users to replicate this procedure with their own data. Beyond the assignment, Slovak dataset Dezinfo SK was created.
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Diagnostics of the state of the machine/machine components with the help of fuzzy sets
Horák, Roman ; Zuth, Daniel (referee) ; Marada, Tomáš (advisor)
Diploma thesis deals with the use of fuzzy logic in the field of technical diagnostics. The thesis is divided into theoretical and practical parts. The theoretical part describes technical diagnostics, fuzzy logic and genetic algorithm. The theoretical part is followed by a practical part in which fuzzy logic is tested on the Iris dataset and then the acquired knowledge is applied to a technical dataset evaluating machine fault conditions. At the end of the chapters of both datasets, the results are summarized and evaluated. The last chapter of the practical part is devoted to the description of the developed scripts in software Matlab 2022b. Part of the work are attachments in which the created FIS models and written scripts are stored.
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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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