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Data Mining
Slezák, Milan ; Hynčica, Ondřej (referee) ; Honzík, Petr (advisor)
The thesis is focused on an introduction of data mining. Data mining is focused on finding of a hidden data correlation. Interest in this area is dated back to the 60th the 20th century. Data analysis was first used in marketing. However, later it expanded to more areas, and some of its options are still unused. One of methodologies is useful used for creating of this process. Methodology offers a concise guide on how you can create a data mining procedure. The data mining analysis contains a wide range of algorithms for data modification. The interest in data mining causes that number of data mining software is increasing. This thesis contains overviews some of this programs, some examples and assessment.
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Neural networks and evolutionary algorithms
Vágnerová, Jitka ; Rychtárik, Milan (referee) ; Hrubeš, Jan (advisor)
Objective of this master's thesis is optimizing of neral network topology using some of evolutionary algorithms. The backpropagation neural network was optimized using genetic algorithms, evolutionary programming and evolutionary strategies. The text contains an application in the Matlab environment which applies these methods to simple tasks as pattern recognition and function prediction. Created graphs of fitness and error functions are included as a result of this thesis.
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Computer analysis of sport matches
Židlík, Pavel ; Balík, Miroslav (referee) ; Atassi, Hicham (advisor)
This work deals with the possibility of a fast football match analysis from audio part of record with the possibility of implementation of some methods for other than football matches as well. The first intention was concentrated on detection of whiz of the soccer whistle that has specific frequency in its specter, which is out of common speech frequency. After detection harmonic frequency , the attention was focused on the definition of whiz meaning. Referee was helpful with the issue as he informed me about the number of whiz styles and provided me with referential samples for whiz classification. Neural network with back propagation was used for definition of whiz meaning. Another subject for detection of important moments of the match was concentration on the commentator’s basic tone. In case the commentator is really excited with the match, his basic speech tone automatically intensifies with every important action of the game. Analysis of commentator’s intensified basic speech tone was realized in this work too. Also the national hymns of teams playing against each other are a significant moment of the match. That is why detection of a hymn became another subject of analysis. Advantages of MFCC were used to obtain audio signal feature, from which 20 coefficients were gained. These were used as an entrance for classifier based on neural network with back propagation. For easy usage of these methods a graphic user interface with possibility of well-arranged look on gained results and also with possibility of replaying chosen section was created.
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Detection of cardiac cells in microscopic image
Musikhina, Ksenia ; Hrubeš, Jan (referee) ; Rychtárik, Milan (advisor)
This work is devoted to problem of detection of cardiac cells in microscopic picture. All possible means of preprocessing and segmentation were considered with the aim to choose the most suitable method for further classification. Different methods of classification were be testing: method of objects attributes and classifier based on neural network. As a result was obtained the number of living and dead cardiac cells and percentage of them. The electivity of classification methods was calculated by sensitivity and specificity. The user’s interface was created for improvement of clearness classification in MATLAB environment.
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Traffic Signs Detection and Recognition
Číp, Pavel ; Honec, Peter (referee) ; Horák, Karel (advisor)
The thesis deals with traffic sign detection and recongnition in the urban environment and outside the town. A precondition for implementation of the system is built-in camera, usually in a car rear-view mirror. The camera scans the scene before the vehicle. The image data are transfered to the connected PC, where the data are transformation to information and evalutations. If the sign was detected the system is visually warned the driver. For a successful goal is divided into four separate blocks. The first part is the preparing of the image data. There are color segmentation with knowledge of color combination traffic signs in Czech Republic. Second part is deals with shape detection in segmentation image. Part number three is deals with recognition of inner pictogram and its finding in the image database. The final part is the visual output of displaying founded traffic signs. The thesis has been prepader so as to ensure detection of all relevant traffic signs in three basic color combinations according to existing by Decree of Ministry of Transport of Czech Republic. The result is the source code for the program MATLAB. .
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ECG signal classification
Smělý, Tomáš ; Harabiš, Vratislav (referee) ; Hrubeš, Jan (advisor)
This thesis deals with classification of different types of time courses of ECG signals. Main objective was to recognize the normal cycles and several forms of arrhythmia and to classify the exact types of them. Classification has been done with usage of algorithms of Neural Networks in Matlab program, with its add-on (Neural Network Toolbox). The result of this thesis is application, which makes possible to load an ECG signal, pre-process it and classify its each cycle into five classes. Percentage results of this classification are in the conclusion of this thesis.
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Analysis of AVG signals
Musil, Václav ; Sekora, Jiří (referee) ; Rozman, Jiří (advisor)
The presented thesis discusses the basic analysis methods of arteriovelocitograms. The core of this work rests in classification of signals and contribution to possibilities of noninvasive diagnostic methods for evaluation patients with peripheral ischemic occlusive arterial disease. The classification employs multivariate statistical methods and principles of neural networks. The data processing works with an angiographic verified set of arteriovelocitogram dates. The digital subtraction angiography classified them into 3 separable classes in dependence on degree of vascular stenosis. Classification AVG signals are represented in the program by the 6 parameters that are measured on 3 different places on each patient’s leg. Evaluation of disease appeared to be a comprehensive approach at signals acquired from whole patient’s leg. The sensitivity of clustering method compared with angiography is between 82.75 % and 90.90 %, specificity between 80.66 % and 88.88 %. Using neural networks sensitivity is in range of 79.06 % and 96.87 %, specificity is in range of 73.07 % and 91.30 %.
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Application of neural networks for classification of T-wave alternations
Procházka, Tomáš ; Harabiš, Vratislav (referee) ; Hrubeš, Jan (advisor)
This thesis deals with analysis of T-wave Alternans (TWA), periodical changes of T wave in ECG signal. Presence of these alternans may predict higher risk of sudden cardiac death. From the several possible ways of TWA classification, the training algorithms of self organizing maps are used in this thesis. Result of the thesis is a program, which in the first step detects QRS complexes in the signal. Then, in the next step, gained reference points are used for T-waves detection. Detected waves are represented by a vector of significant points, which is used as an input for artificial neural network. Final output of the program is a decision about presence of TWA in the signal and its rate.
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Use of neural networks in classification of heart diseases
Skřížala, Martin ; Tannenberg, Milan (referee) ; Hrubeš, Jan (advisor)
This thesis discusses the design and the utilization of the artificial neural networks as ECG classifiers and the detectors of heart diseases in ECG signal especially myocardial ischaemia. The changes of ST-T complexes are the important indicator of ischaemia in ECG signal. Different types of ischaemia are expressed particularly by depression or elevation of ST segments and changes of T wave. The first part of this thesis is orientated towards the theoretical knowledges and describes changes in the ECG signal rising close to different types of ischaemia. The second part deals with to the ECG signal pre-processing for the classification by neural network, filtration, QRS detection, ST-T detection, principal component analysis. In the last part there is described design of detector of myocardial ischaemia based on artificial neural networks with utilisation of two types of neural networks back – propagation and self-organizing map and the results of used algorithms. The appendix contains detailed description of each neural networks, description of the programme for classification of ECG signals by ANN and description of functions of programme. The programme was developed in Matlab R2007b.
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Speech Recognition (digit)
Kantar, Martin ; Minář, Petr (referee) ; Matoušek, Radomil (advisor)
The aim of this diploma thesis is to explain what speech is and what are its constituents. I mention commonly used methods which are used for preparation of signals which we use for recognition. Schematic examples show principles of current recognizers of speech, their advantages and disadvantages. I made speech recognition program for 0-9 numerals in Matlab for neural nets learning.
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