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Protein Classification Techniques
Dekrét, Lukáš ; Zendulka, Jaroslav (referee) ; Burgetová, Ivana (advisor)
Main goal of classifying proteins into families is to understand structural, functional and evolutionary relationships between individual proteins, which are not easily deducible from available data. Since the structure and function of proteins are closely related, determination of function is mainly based on structural properties, that can be obtained relatively easily with current resources. Protein classification is also used in development of special medicines, in the diagnosis of clinical diseases or in personalized healthcare, which means a lot of investment in it. I created a new hierarchical tool for protein classification that achieves better results than some existing solutions. The implementation of the tool was preceded by acquaintance with the properties of proteins, examination of existing classification approaches, creation of an extensive data set, realizing experiments and selection of the final classifiers of the hierarchical tool.
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Genetic Programming in Prediction Tasks
Machač, Michal ; Mrázek, Vojtěch (referee) ; Sekanina, Lukáš (advisor)
This thesis introduces various machine learning algorithms which can be used in prediction tasks based on regression. Tree genetic programming and linear genetic programming are explained more thoroughly. Selected machine learning algorithms (linear regression, random forest, multilayer perceptron and tree genetic programming) are compared on publicly available datasets with the use of scikit-learn and gplearn libraries. A core part of this project is a new implementation of linear genetic programming which was developed in C++, tested on common symbolic regression problems and then evaluated on real datasets. Results obtained with the proposed system are compared with the results obtained with gplearn.
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Location Aware Analytics In The Context Of Mobile Network Performance Optimization
Urbanová, Lucie
The goal of this paper is to develop an estimation tool capable of predicting location aware network parameters, based on their previous field measurements and to perform a set of additional measurements to verify the accuracy of the tool. Additionally, the paper evaluates a number of regression methods in terms of their prediction accuracy, complexity and the amount of input data needed to create a prediction map of valid results.
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Novel methods for sleep analysis and classification
Navrátilová, Markéta ; Ronzhina, Marina (referee) ; Kolářová, Jana (advisor)
Tato diplomová práce se zabývá metodami pro analýzu a klasifikaci spánku. Popisuje jakjednotlivé spánkové fáze a vzorce biosignálů v průběhu spánku, tak metody pro klasifi-kaci. Příznaky jsou extrahovány na dodaných biosignálech ECG, EDA a RIP. Na základětěchto příznaků jsou klasifikovány jednotlivé spánkové fáze s využitím klasifikátoru ná-hodný les. Parametry klasifikátoru jsou optimalizovány a následně jsou vyhodnocenydosažené výsledky. Pomocí metod pro redukci dimenzionality je soubor příznaků analy-zován a výsledky jsou porovnány s výsledky ze standardní klasifikace. Řešení pro vizuali-zaci jak samotných nezpracovaných signálů, tak extrahovaných příznaků je navrhnuto aimplementováno. Dosažené výsledky jsou porovnány s publikovanými metodami.
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Classification of glioma grading in brain MRI
Olešová, Kristína ; Mézl, Martin (referee) ; Chmelík, Jiří (advisor)
This thesis deals with a classification of glioma grade in high and low aggressive tumours and overall survival prediction based on magnetic resonance imaging. Data used in this work is from BRATS challenge 2019 and each set contains information from 4 weighting sequences of MRI. Thesis is implemented in PYTHON programming language and Jupyter Notebooks environment. Software PyRadiomics is used for calculation of image features. Goal of this work is to determine best tumour region and weighting sequence for calculation of image features and consequently select set of features that are the best ones for classification of tumour grade and survival prediction. Part of thesis is dedicated to survival prediction using set of statistical tests, specifically Cox regression
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Segmentation of amyloid plaques in brains of trangenic rats based on microCT image data
Kačníková, Diana ; Kolář, Radim (referee) ; Chmelík, Jiří (advisor)
The presence of amyloid plaques in the hippocampus highlights the incidence of Alzheimer’s disease. Manual segmentation of amyloid plaques is very time consuming and increases the time that can be used to monitor the distribution of amyloid plaques. Distribution carries significant information about disease progression and the impact of potential therapy. The automatic or semi-automatic segmentation method can lead to significant savings in the time which are required when the disease has rapid progression. The description of amyloid plaques and the computed tomography are included in this work. In this diploma thesis are three implemented algorithms, two of them are based on published articles and one’s own methodological solution. The conclusion of the thesis is a quantitative evaluation of the accuracy of implemented segmentation procedures.
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Evaluation of the land cover in the military training area Libavá using Random Forest classifier
Žďánský, Vít ; Štych, Přemysl (advisor) ; Laštovička, Josef (referee)
Data land cover help us understand nature, how it develops, its uses and the influence that human actions have on it. Thanks to new methods in the remote sensing area, we can record these processes faster and at a larger scale than before. This thesis evaluates accuracy of the Random Forest (RF) and Maximum Likelihood (ML) classifiers using satellite data Sentinel-2 from the military training area Libavá. The military area went through a very specific development and the information regarding natural coverage in the region is missing. The classifier documentation contains 8 classes. The classification results from both algorithms are higher than 80 %. As expected, more accurate results were achieved using the Random Forest classifier. The most accurate classifications were of water surfaces and forests. The least accurate classifications were of agricultural land and sparse vegetation. Other classes varied in accuracy levels. This thesis' results are evaluated using error matrices, overall accuracy and the kappa coefficient. Keywords: classification, Random Forest, Maximum Likelihood, military training area, remote sensing, Sentinel 2, land cover, Libavá
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Comparison of Heuristic and Conventional Statistical Methods in Data Mining
Bitara, Matúš ; Žák, Libor (referee) ; Bednář, Josef (advisor)
The thesis deals with the comparison of conventional and heuristic methods in data mining used for binary classification. In the theoretical part, four different models are described. Model classification is demonstrated on simple examples. In the practical part, models are compared on real data. This part also consists of data cleaning, outliers removal, two different transformations and dimension reduction. In the last part methods used to quality testing of models are described.
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Tissue characterisation in spectral CT data
Poláková, Veronika ; Jan, Jiří (referee) ; Jakubíček, Roman (advisor)
This bachelor thesis deals with tissue characterisation in virtual monoenergetic images (VMI). Firstly, literature survey presents spectral CT which allows reconstructing VMI. Secondly, statistical evaluation of tissue CT numbers was made for all energies of VMI which were reconstructed. It was found that with growing energy of VMI CT number increases or decreases with different steepness depending on a type of tissue. As a consequence, the suitable VMI offer better contrast resolution between selected pairs of tissues, which enables better tissue segmentation and classification in these images.
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Tool for Classification of Lifestyle Traits Based on Metagenomic Data from the Large Intestine
Kubica, Jan ; Hon, Jiří (referee) ; Smatana, Stanislav (advisor)
This thesis deals with analysis of human microbiome using metagenomic data from large intestine. The main focus is placed on bacteria composition in a sample on different taxonomic levels regarding the lifestyle traits of an individual. For this purpose, a tool for classification of several attributes was created. It considers attributes like diet type and eating habits (vegetarian, vegan, omnivore), gluten and lactose intolerance, body mass index, age or sex. From range of machine learning perspectives considering K Nearest Neighbours (kNN), Random Forest (RF) and Support Vector Machines (SVM) were used. Datasets for training and final evaluation of the classifier were taken from American Gut project. The thesis also focuses on particular problems with metagenomic datasets like its multidimensionality, sparsity, compositional character and class imbalance.
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