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Data analysis from the manufacturing process
Krčmář, Martin ; Honzík, Petr (referee) ; Zezulka, František (advisor)
This thesis deals with the classification of production data using algorithms: neural networks, decision trees and naive bayesian classifier. The neural network is dedicated forward multilayer networks with a learning algorithm of backpropagation. In thesis, these algorithms are described and evaluated their pros and cons. Another part deals with the development of the program in C# for creating these algorithms. The last part is devoted to the evaluation of the results. Bachelor thesis contains a sample of generated clasification models decision tree and bayesian classifier.
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Diagnosing Parkinson's disease from analysis of speech recording
Vymlátil, Petr ; Trzos, Michal (referee) ; Lněnička, Jakub (advisor)
This thesis is focused on diagnosing Parkinson’s disease from analysis of speech recording. Introduction of this work deals with description of voice production mechanism, it’s basic qualities and influence of hypokinetic dysarthria on speech. In next chapter, there is described voice signal and some methods of it’s preprocessing. Next part continues dealing with description of chosen individual symptoms, which are needed for PD diagnosing, followed by definition of chosen reduction methods and classifiers. There is a comparison of classify succes of naive bayes classifier, depending on chosen reduction method in last chapter of this work.
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Analysis of experimental ECG
Mackových, Marek ; Kolářová, Jana (referee) ; Ronzhina, Marina (advisor)
This thesis is focused on the analysis of experimental ECG records drawn up in isolated rabbit hearts and aims to describe changes in EKG caused by ischemia and left ventricular hypertrophy. It consists of a theoretical analysis of the problems in the evaluation of ECG during ischemia and hypertrophy, and describes an experimental ECG recording. Theoretical part is followed by a practical section which describes the method for calculating morphological parameters, followed by ROC analysis to evaluate their suitability for the classification of hypertrophy and at the end is focused on classification.
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Machine-Learning in Natural Language Processing
Otrusina, Lubomír ; Šilhavá, Jana (referee) ; Smrž, Pavel (advisor)
This beachelor's thesis deals with word sense disambiguation problem using the machine learning techniques. There are shortly presented problems of word sense disambiguation and its timeline. There are described methods and approaches, especially the naive Bayes classifier that is implemented in the system. There's illustrated a simple example of using this classifier. In a practical section is described project of system based on naive Bayes classifier including description of various algorithms used in the system. Finally there are described evaluation and analysis of the system. This created system took part in an international competition on semantic evaluation workshop SemEval-2007.
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Machine-Learning Methods in Natural Language Processing
Vodička, Jan ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
Bachelor's thesis deals with sentiment analysis using machine learning methods, mainly naive bayes classifier. Input text can be classified as positive or negative message. There are used several data sources for create of automatic annotated corpus - social network Twitter, price comparator Heureka, movie database ČSFD and restaurant portal Scuk. These sources are compared in terms of performance in assessing the sentiment. Consequently, the final training dataset is created and used at almost real-time Twitter sentiment analysis.
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Word Sense Disambiguation
Kraus, Michal ; Glembek, Ondřej (referee) ; Smrž, Pavel (advisor)
The master's thesis deals with sense disambiguation of Czech words. Reader is informed about task's history and used algorithms are introduced. There are naive Bayes classifier, AdaBoost classifier, maximum entrophy method and decision trees described in this thesis. Used methods are clearly demonstrated. In the next parts of this thesis are used data also described. Last part of the thesis describe reached results. There are some ideas to improve the system at the end of the thesis.
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Zlepšování učinnosti prevence v telemedicíně
Nálevka, Petr ; Svátek, Vojtěch (advisor) ; Berka, Petr (referee) ; Štěpánková, Olga (referee) ; Šárek, Milan (referee)
This thesis employs data-mining techniques and modern information and communication technology to develop methods which may improve efficiency of prevention oriented telemedical programs. In particular this thesis uses the ITAREPS program as a case study and demonstrates that an extension of the program based on the proposed methods may significantly improve the program's efficiency. ITAREPS itself is a state of the art telemedical program operating since 2006. It has been deployed in 8 different countries around the world, and solely in the Czech republic it helped prevent schizophrenic relapse in over 400 participating patients. Outcomes of this thesis are widely applicable not just to schizophrenic patients but also to other psychotic or non-psychotic diseases which follow a relapsing path and satisfy certain preconditions defined in this thesis. Two main areas of improvement are proposed. First, this thesis studies various temporal data-mining methods to improve relapse prediction efficiency based on diagnostic data history. Second, latest telecommunication technologies are used in order to improve quality of the gathered diagnostic data directly at the source.
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