|
|
Information Extraction from Loosely Structured Text
Minárik, Matej ; Bartík, Vladimír (referee) ; Burget, Radek (advisor)
Nowadays we are speaking about Web 2.0, which means the web of documents rather than the web of data. Documents are mostly unstructured, or just partially structured, but search engines need data in structured form in order to provide better search results. The process of extracting structured data from partially structured documents is the main goal of this work. In this work we are analyzing information extraction methods, namely classification methods, which need annotated training data, in order to create their inner model. We also analyze methods, which do not need training. These methods are initialized with a few data examples we are interested in extracting. We propose an extraction method in order to extract therapeutic indications and active substances from medical information sheets.
|
|
|
Bioinformatics Tool for Prediction of Protein Solubility
Hronský, Patrik ; Burgetová, Ivana (referee) ; Martínek, Tomáš (advisor)
This master's thesis addresses the solubility of recombinant proteins and its prediction. It describes the subject of protein synthesis, as well as the process of recombinant protein creation. Recombinant protein synthesis is of great importance for example to pharmacologic industry. This synthesis is not a simple task and it does not always produce viable proteins. Protein solubility is an important factor, determining the viability of the resulting proteins. It is of course favourable for companies, that take part in recombinant protein synthesis, to focus their effort and their resources on proteins, that will be viable in the end. In this regard, bioinformatics is of great help, as it is capable, with the help of machine learning, of predicting the solubility of proteins, for example based on their sequences. This thesis introduces the reader to the basic principles of machine learning and presents several machine learning methods, used in the field of protein solubility prediction. It deals with the definition of a dataset, which is later used to test selected predictors, as well as to train the ensemble predictor, which is the main focus of this thesis. It also focuses on several specific protein solubility predictors and explains the basic principles upon which they are built, as well as the results of their testing. In the end, it presents the ensemble predictor of protein solubility.
|
|
|
Prediction of Values on a Time Line
Maršová, Eliška ; Bařina, David (referee) ; Zemčík, Pavel (advisor)
This work deals with the prediction of numerical series whose application is suitable for prediction of stock prices. They explain the procedures for analysis and works with price charts. Also explains the methods of machine learning. Knowledge is used to build a program that finds patterns in numerical series for estimation.
|
|
|
Fundamental Analysis of Numerical Data for Automatic Trading
Huf, Petr ; Szőke, Igor (referee) ; Černocký, Jan (advisor)
This thesis is aimed to exploitation of fundamental analysis in automatic trading. Technical analysis uses historical prices and indicators derived from price for price prediction. On the opposite, fundamental analysis uses various information resources for price prediction. In this thesis, only quantitative data are used. These data sources are namely weather, Forex, Google Trends, WikiTrends, historical prices of futures and some fundamental data (birth rate, migration, \dots). These data are processed with LSTM neural network, which predicts stocks prices of selected companies. This prediction is basis for created trading system. Experiments show major improvement in results of the trading system; 8\% increase in success prediction accuracy thanks to involvement of fundamental analysis.
|
|
|
Optical Character Recognition Using Convolutional Networks
Csóka, Pavel ; Behúň, Kamil (referee) ; Hradiš, Michal (advisor)
This thesis aims at creation of new datasets for text recognition machine learning tasks and experiments with convolutional neural networks on these datasets. It describes architecture of convolutional nets, difficulties of recognizing text from photographs and contemporary works using these networks. Next, creation of annotation, using Tesseract OCR, for dataset comprised from photos of document pages, taken by mobile phones, named Mobile Page Photos. From this dataset two additional are created by cropping characters out of its photos formatted as Street View House Numbers dataset. Dataset Mobile Nice Page Photos Characters contains readable characters and Mobile Page Photos Characters adds hardly readable and unreadable ones. Three models of convolutional nets are created and used for text recognition experiments on these datasets, which are also used for estimation of annotation error.
|
|
|
Musical genre classification
Káčerová, Erika ; Říha, Kamil (referee) ; Uher, Václav (advisor)
The aim of this bachelor thesis is creating a system for automatic music genre recognition. The thesis deals with two main issues, which are feature extraction of a genre and machine learning process. For the purpose of feature extraction a source code is written in JAVA programming language based on jAudio library. Six machine learning models are created in RapidMiner Studio software. The most appropriate one of them, Neural Networks method is then improved and tested on different parts of songs from database.These database contains 250 training songs and 25 test songs from five music genres: classical music, disco, drum and bass, hip hop and rock.
|
|
|
Automatic recognition of meaning in texts
Jeleček, Jiří ; Dvořák, Pavel (referee) ; Povoda, Lukáš (advisor)
As part of this work it was designed and implemented a system using data mining techniques from the text in order to detect emotions in Czech, English and German language texts. Because the system is built mostly on machine learning techniques, was designed and created training set, which was later used to build the model classifier using the selected algorithms.
|
|
| |
|
|
Segmentation of MR images using machine learning algorithms
Dorazil, Jan ; Mikulka, Jan (referee) ; Dvořák, Pavel (advisor)
This thesis concerns with magnetic resonance image segmentation using Random Forests algorithm. Employed technologies accomplishing the specified task include C++ progra- mming language with libraries ITK and OpenCV. This work descibes the technique of processing images from loading through preprocessing to the actual segmentation. The outcome from this work is a programme that automatically segmentates MR images of mouse’s head to the brain and the surroundings.
|
|
|
Feature Selection Based on Combination of Uncorrelated Evaluation Functions
Vaculík, Karel ; Klusáček, Jan (referee) ; Honzík, Petr (advisor)
In order to process large amount of data, it is necessary to use computers. It is possible to use statistical methods or machine learning in some cases. In either case, data can be represented with large number of features. Selection of suitable subset of features can be crucial for efficient processing. This thesis explores a subgroup of feature selection methods which are called filter methods. Comparison of such methods is carried out and the results are used in the design of a new method. This new method uses a combination of existing methods.
|
guest ::
