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Emotion Recognition from Acted and Spontaneous Speech
Atassi, Hicham ; Přibil, Jiří (referee) ; Zahradník, Pavel (referee) ; Smékal, Zdeněk (advisor)
Dizertační práce se zabývá rozpoznáním emočního stavu mluvčích z řečového signálu. Práce je rozdělena do dvou hlavních častí, první část popisuju navržené metody pro rozpoznání emočního stavu z hraných databází. V rámci této části jsou představeny výsledky rozpoznání použitím dvou různých databází s různými jazyky. Hlavními přínosy této části je detailní analýza rozsáhlé škály různých příznaků získaných z řečového signálu, návrh nových klasifikačních architektur jako je například „emoční párování“ a návrh nové metody pro mapování diskrétních emočních stavů do dvou dimenzionálního prostoru. Druhá část se zabývá rozpoznáním emočních stavů z databáze spontánní řeči, která byla získána ze záznamů hovorů z reálných call center. Poznatky z analýzy a návrhu metod rozpoznání z hrané řeči byly využity pro návrh nového systému pro rozpoznání sedmi spontánních emočních stavů. Jádrem navrženého přístupu je komplexní klasifikační architektura založena na fúzi různých systémů. Práce se dále zabývá vlivem emočního stavu mluvčího na úspěšnosti rozpoznání pohlaví a návrhem systému pro automatickou detekci úspěšných hovorů v call centrech na základě analýzy parametrů dialogu mezi účastníky telefonních hovorů.
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Automatic detection of ischemia in ECG
Noremberczyk, Adam ; Potočňák, Tomáš (referee) ; Ronzhina, Marina (advisor)
This thesis discusses the utilization of the artificial neural networks (ANN) for detection of coronary artery disease (CAD) in frequency area. The first part of this thesis is orientated towards the theoretical knowledge. Describes the issue of ECG pathological changes. ECQ are converted to frequency area. Described statistical methods and methods for automatic detection of CAD and MI. Explained the issue of the perceptron and ANN. The second deals with use of Neural Network Toolbox MATLAB®. This part focuses on counting and finding suitable parameters and making connection of band. At the end of the thesis UNS is used to detect ischemic parameters and the results are discussed. Average values for the best settings are 100% accuracy.
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Overview of Actual Approaches to Classifications
Brezánský, Tomáš ; Rozman, Jaroslav (referee) ; Zbořil, František (advisor)
This bachelor thesis deals with an overview of current approaches to classifications. It describes various approaches to classifications and their algorithms, focuses on neural networks, Bayesian classifiers and decision trees. The main task of this work is to perform experiments with three classification algorithms, namely, the ID3 algorithm, the RCE neural network and the naive Bayesian classifier. The work contains experiments with given algorithms and evaluates the obtained results.
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A convolutional neural network for image segmentation
Mitrenga, Michal ; Petyovský, Petr (referee) ; Jirsík, Václav (advisor)
The aim of the bachelor thesis is to learn more about the problem of convolutional neural networks and to realize image segmentation. This theme includes the field of computer vision, which is used in systems of artificial intelligence. Special Attention is paid to the image segmentation process. Furthermore, the thesis deals with the basic principles of artificial neural networks, the structure of convolutional neural networks and especially with the description of individual semantic segmentation architectures. The chosen SegNet architecture is used in a practical application along with a pre-learned network. Part of the work is a database of CamVid images, which is used for training. For testing, a custom image database is created. Practical part is focused on CNN training and searching for unsuitable parameters for network learning using SW Matlab.
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Classification of the vascular tree in fundus images
Tebenkova, Iuliia ; Kolář, Radim (referee) ; Odstrčilík, Jan (advisor)
Retinal image analysis plays a very important role, as human gets around 90% of environment information with the help of eyes. Automation of process of retinal image analysis promotes to improve the efficiency of retinal medical examinations. The following thesis is dedicated to automatic classification methods of retinal vascular system images obtained from a digital fundus camera. Vessel classification method using classifier on the base of neural networks, which is trained and then tested on the retinal vessel segments, is investigated and implemented. In this thesis anatomical retinal survey, properties of image data from digital fundus camera and retinal image classification methods are briefly described. The last chapter is devoted to the evaluation of efficiency of retinal vessel classification with automatic methods.
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Classification of sleep phases using polysomnographic data
Králík, Martin ; Kozumplík, Jiří (referee) ; Ronzhina, Marina (advisor)
Aim of this thesis is the classification of polysomnographic data. The first part of the thesis is a review of mentioned topic and also the statistical analysis of classification features calculated from real EEG, EOG and EMG for evaluating of the features suitability for sleep stages scoring. The second part is focused on the automatic classification of the data using artificial neural networks. All the results are presented and discussed.
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Implementation of Mining Modules of Data Mining System on NetBeans Platform
Stríž, Rostislav ; Bartík, Vladimír (referee) ; Šebek, Michal (advisor)
Data collecting plays an important role in many aspects of today's businesses and quality information is the key to success. Process called Knowledge Discovery in Databases makes possible to extract hidden information that can be used further in our efforts. Main goal of this thesis is to describe an addition to such Data Mining System. Main objective is to create data mining module for NetBeans application, developed for demonstrational purposes by Faculty of Information Technology. New module is going to be able to mine information from Oracle database server via unusual use of Genetic Algorithm. This thesis describes the whole process of module implementation, begining with theoretical basics through coding details to final testing and summary.
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Multi-Label Classification of Text Documents
Průša, Petr ; Očenášek, Pavel (referee) ; Bartík, Vladimír (advisor)
The master's thesis deals with automatic classifi cation of text document. It explains basic terms and problems of text mining. The thesis explains term clustering and shows some basic clustering algoritms. The thesis also shows some methods of classi fication and deals with matrix regression closely. Application using matrix regression for classifi cation was designed and developed. Experiments were focused on normalization and thresholding.
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The Use of SVM in Environment of Financial Markets
Štechr, Vladislav ; Prochocká, Kristína (referee) ; Budík, Jan (advisor)
This thesis deals with use of regression or classification based on support vector machines from machine learning field. SVMs predict values that are used for decisions of automatic trading system. Regression and classification are evaluated for their usability for decision making. Strategy is being then optimized, tested and evaluated on foreign exchange market Forex historic data set. Results are promising. Strategy could be used in combination with other strategy that would confirm decisions for entering and exiting trades.
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The decision boundary
Gróf, Zoltán ; Hynčica, Tomáš (referee) ; Jirsík, Václav (advisor)
The main aim of this master's thesis is to describe the subject of the implementation of decision boundaries with the help of artificial neural networks. The objective is to present theoretical knowledge concerning this field and on practical examples prove these statements. The work contains basic theoretical description of the field of pattern recognition and the field of feature based representation of objects. A classificator working on the basis of Bayes decision is presented in this part, and other types of classificators are named as well. The work then deals with artificial neural networks in more detail; it contains a theoretical description of their function and their abilities in the creation of decision boundaries in the feature plane. Examples are shown from literature for the use of neural networks in corresponding problems. As part of this work, the program ANN-DeBC was created using Matlab, for the generation of practical results about the usage of feed-forward neural networks for the implementation of decision boundaries. The work contains a detailed description of this program, and the achieved results are presented and analyzed. It is shown as well, how artificial neural networks are creating decision boundaries in the form of geometrical shapes. The effects of the chosen topology of the neural network and the number of training samples on the success of the classification are observed, and the minimal values of these parameters are determined for the successful creation of decision boundaries at the individual examples. Furthermore, it's presented how the neural networks behave at the classification of realistically distributed training samples, and what methods can affect the shape of the created decision boundaries.
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