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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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Machine Learning as a Tool for the Prediction of the Effect of Mutations on Protein Stability
Dúbrava, Juraj Ondrej ; Martínek, Tomáš (referee) ; Musil, Miloš (advisor)
The main focus of this thesis is the prediction of the effect of amino acid substitutions on protein stability. My goal was to develop a predictive tool for the classification of the effect of mutations by combining several machine learning techniques. The implemented predictor, which utilizes SVM and Random forest methods, has achieved higher accuracy than any of the integrated methods. The novel predictive tool was compared with the existing ones using independent testing dataset. The predictor has yield 67 % accuracy and MCC 0,3.
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Machine Learning Optimization of KPI Prediction
Haris, Daniel ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
This thesis aims to optimize the machine learning algorithms for predicting KPI metrics for an organization. The organization is predicting whether projects meet planned deadlines of the last phase of development process using machine learning. The work focuses on the analysis of prediction models and sets the goal of selecting new candidate models for the prediction system. We have implemented a system that automatically selects the best feature variables for learning. Trained models were evaluated by several performance metrics and the best candidates were chosen for the prediction. Candidate models achieved higher accuracy, which means, that the prediction system provides more reliable responses. We suggested other improvements that could increase the accuracy of the forecast.
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Recognition of vehicles using signals sensed by smartphone
Nevěčná, Leona ; Vítek, Martin (referee) ; Smíšek, Radovan (advisor)
Thanks to the development in recent years, the placement of miniaturized sensors such as accelerometers, gyroscopes, magnetometers, global positioning system receivers (GPS), microphones or others to commercially sold smartphones is increasing. Use of these sensors (which are to be found in the smartphone) for human activity recognition with health care improvement in mind is a discussed theme. Advantages of the use of smartphone for human movement monitoring lies in the fact that it is a device that the person measured carries with them and there are no additional costs. The disadvantages are a limited storage and battery. Therefore, only accelerometer, gyroscope, magnetometer, and microphone were chosen because their combination achieves best results. GPS sensor was excluded for its lack of reliability in sampling and for being energy demanding. Features were computed from the measured data and used for learning of the classification model. The highest accuracy was achieved with the use of a machine learning method called Random Forest. The main goal of this work was to create an algorithm for transportation mode recognition using signals sensed by a smartphone. The created algorithm succeeds in classification of walk, car, bus, tram, train, and bike in 97.4 % with 20 % holdout validation. When tested on a new set of data from the tenth volunteer, the resulting accuracy counted as average form classification recall for each transportation mode reached 90.49 %.
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Comparison of different models for forecasting of Czech electricity market
Kunc, Vladimír ; Krištoufek, Ladislav (advisor) ; Kopečná, Vědunka (referee)
There is a demand for decision support tools that can model the electricity markets and allows to forecast the hourly electricity price. Many different ap- proach such as artificial neural network or support vector regression are used in the literature. This thesis provides comparison of several different estima- tors under one settings using available data from Czech electricity market. The resulting comparison of over 5000 different estimators led to a selection of several best performing models. The role of historical weather data (temper- ature, dew point and humidity) is also assesed within the comparison and it was found that while the inclusion of weather data might lead to overfitting, it is beneficial under the right circumstances. The best performing approach was the Lasso regression estimated using modified Lars. 1
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Software using random forest for risk prediction of heart valve surgery patients
HERMANUTZ, Georg
CASPeR - Cardiac surgery prediction tool for risk stratification of heart valve surgeries is presented. The base builds a machine learning pipeline for training a random forest classifier which predicts the mortality after a certain amount of days after the surgery was performed. The classifier also offers a list of potential risk factors through its in build feature selection. With a survival analysis the groups "high-risk" and "low-risk" are compared with each other to check for statistical difference. The tool uses "Shiny" a R package which offers a web frame work to develop data analysis visualizations for the User Interface. CASpeR is delivered as a Microsoft Windows standalone desktop application, that comes with a .exe installer and a detailed manual.
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Machine Learning Methods for Credit Risk Modelling
Drábek, Matěj ; Witzany, Jiří (advisor) ; Málek, Jiří (referee)
This master's thesis is divided into three parts. In the first part I described P2P lending, its characteristics, basic concepts and practical implications. I also compared P2P market in the Czech Republic, UK and USA. The second part consists of theoretical basics for chosen methods of machine learning, which are naive bayes classifier, classification tree, random forest and logistic regression. I also described methods to evaluate the quality of classification models listed above. The third part is a practical one and shows the complete workflow of creating classification model, from data preparation to evaluation of model.
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Valuation of real estates using statistical methods
Funiok, Ondřej ; Pecáková, Iva (advisor) ; Řezanková, Hana (referee)
The thesis deals with the valuation of real estates in the Czech Republic using statistical methods. The work focuses on a complex task based on data from an advertising web portal. The aim of the thesis is to create a prototype of the statistical predication model of the residential properties valuation in Prague and to further evaluate the dissemination of its possibilities. The structure of the work is conceived according to the CRISP-DM methodology. On the pre-processed data are tested the methods regression trees and random forests, which are used to predict the price of real estate.
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Semantic Recognition of Comments on the Web
Stříteský, Radek ; Harár, Pavol (referee) ; Povoda, Lukáš (advisor)
The main goal of this paper is the identification of comments on internet websites. The theoretical part is focused on artificial intelligence, mainly classifiers are described there. The practical part deals with creation of training database, which is formed by using generators of features. A generated feature might be for example a title of the HTML element where the comment is. The training database is created by input of classifiers. The result of this paper is testing classifiers in the RapidMiner program.
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3D Slicer Extension for Tomographic Images Segmentation
Chalupa, Daniel ; Jakubíček, Roman (referee) ; Mikulka, Jan (advisor)
This work explores machine learning as a tool for medical images' classification. A literary research is contained concerning both classical and modern approaches to image segmentation. The main purpose of this work is to design and implement an extension for the 3D Slicer platform. The extension uses machine learning to classify images using set parameters. The extension is tested on tomographic images obtained by nuclear magnetic resonance and observes the accuracy of the classification and usability in practice.
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