The thesis deals with problems of automatic detection of premature ventricular contractions in ECG records. One detection method which uses a convolutional neural network and LSTM units is implemented in the Python language. Cardiac cycles extracted from one-lead ECG were used for detection. F1 score for binary classification (PVC and normal beat) on the test dataset reached 96,41 % and 81,76 % for three-class classification (PVC, normal beat and other arrhythmias). Lastly, the accuracy of the classification is evaluated and discussed, the achieved results for binary classification are comparable to the results of methods described in different papers.

The diploma thesis deals with the analysis and evaluation of calculations in the Vedanta canteen of the company Jitřenka-Zlatá studna s.r.o. The aim of the work is to propose changes that will improve the current situation of the company. The proposals will be based on an analysis of the current state of cost classification, the method of calculation and the calculation system.

My diploma thesis deals about face detection in picture. I try to outline problems of computer vision, artificial intelligence and machine learning. I described in details the proposed detection by Viola and Jones, which uses AdaBoost learning algorithm. This method was deliberately chosen for speed and detection accuracy. This detector was made in programming language C / C + + using the OpenCV library. To a final learning was used database of faces images „MIT CVCL Face Database“. The main goal was to propose the face detector utilizable also in video-sequences.

My master's thesis on the topic of "Design of exercises for data mining - Classification and prediction" deals with the most frequently used methods classification and prediction. There are association rules, Bayesian classification, genetic algorithms, the nearest method neighbor, neural network and decision trees on the classification. There are linear and non-linear prediction on the prediction. This work also contains a summary of detail the issue of decision trees and a detailed algorithm for creating the decision tree, including development of individual diagrams. The proposed algorithm for creating the decision tree is tested through two tests of data dowloaded from Internet. The results are mutually compared and described differences between the two implementations. The work is written in a way that would provide the reader with a notion of the individual methods and techniques for data mining, their advantages, disadvantages and some of the issues that directly relate to this topic.

The thesis is focused on methods of machine learning used for recognising the first stage of schizophrenia in images from nuclear-magnetic resonance. The introduction of this paper is focused primarily on physical principles. Further in this work, the attention is given to registration methods, reduction of data set and machine learning. In the classification part, simmilarity rates, support vectors´ method, K-nearest neighbour classification and K-means are described. The last stage of theoretical part is focused on evaluation of the clasification. In practical part the results of reduction data set by methods PCA, CRLS-PCA and subjects PCA are described. Furthermore, the practical part is focused on pattern recognition by methods K-NN, K-means and test K-NN method on real data. Abnormalities which are recognised by some classification methods can distinguish patients with schizophrenia from healthy controls.

Automatical electrochemical measurements are sources of large data sets intended for further analysis. This work deals with classification, evaluation and processing of electrochemical signals using artificial neural networks. Due to high dimensionality of input data, an autoassociative neural network (AANN) is used in this work. This type of network performs dimensionality reduction via filtering the input data into relatively small number of principal parameters at the bottleneck output. These extracted parameters can be used for classification, evaluation and additional modelling of analyzed data trough the reconstructive part of this network. Furthermore, this work deals with implementation of a feedforward neural network in OpenCL language.

The subject of this thesis is a method proposed for automated evaluation of the parameters of X-ray of cystic disorders in human jawbones. The main problem in medical diagnostic is the low repeatability due to the subjective evaluation of images without using a tool for image processing. In this thesis are described the basic steps of image processing, various methods of image segmentation and chosen segmentation method live-wire. Selected segments were processed in the ImageJ Java environment. In the cystic regions their basic statistical and shape properties were evaluated. The obtained values were used for learning the classification model (decision tree) in the environment RapidMiner. This model was used to create a plug-in for automatic classification of the type of cysts in the program ImageJ.

The aim of the diploma project is to find a method through which it will be possibleto classify the selected emotion from speech. At the beginning of the work deals with the description of the human body and their voice-generating operation. Furthermore, the text deals with the problem of the human voice into digital form.Great attention is paid to the parameters of the speech signal with an emphasis on describing the symptoms to help the selected emotion. The work deals with therecognition of emotions and a description of some of them. The main part is finding the best methods to reduce symptoms of segmental and suprasegmental speech utterances. The results of success was achieved by comparing the classification of selected emotions when using multiple methods and compare their results. The most important criterion in assessing the results ofthe reduction parameters of the speech signal, based on previous research in this area.

This master‘s thesis deals with the use of higher order cumulants for classification of cardiac cycles. Second-, third-, and fourth-order cumulants were calculated from ECG recorded in isolated rabbit hearts during experiments with repeated ischemia. Cumulants properties useful for the subsequent classification were verified on ECG segments from control and ischemic group. The results were statistically analyzed. Cumulants are then used as feature vectors for classification of ECG segments by means of artificial neural network.

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.