|
|
Business Process Mining
Skácel, Jan ; Kreslíková, Jitka (referee) ; Bartík, Vladimír (advisor)
This thesis explains business process mining and it's principles. A substantial part is devoted to the problems of process discovery. Further, based on the analysis of specific manufacturing process are proposed three methods that are trying to identify shortcomings in the process. First discovers the manufacturing process and renders it into a graph. The second method uses simulator of production history to obtain products that may caused delays in the process. Acquired data are used to mine frequent itemsets. The third method tries to predict processing time on the selected workplace using asociation rules. Last two mentioned methods employ an algorithm Frequent Pattern Growth. The knowledge obtained from this thesis improve efficiency of the manufacturing process and enables better production planning.
|
|
| |
|
|
Analysis of Product Reviews
Klocok, Andrej ; Doležal, Jan (referee) ; Smrž, Pavel (advisor)
Online store customers generate vast amounts of product and service information through reviews, which are an important source of feedback. This thesis deals with the creation of a system for the analysis of product and shop reviews in the czech language. It describes the current methods of sentiment analysis and builds on current solutions. The resulting system implements automatic data download and their indexing, subsequently sentiment analysis together with text summary in the form of clustering of similar sentences based on vector representation of the text. A graphical user interface in the form of a web page is also included. A review data set with a total of more than six million reviews was created during the semester along with an interface for easy data export.
|
|
| |
|
| |
|
|
Emotional State Recognition and Classification Based on Speech Signal Analysis
Černý, Lukáš ; Atassi, Hicham (referee) ; Smékal, Zdeněk (advisor)
The diploma thesis focuses on classification of emotions. Thesis deals about parameterization of sounds files by suprasegment and segment methods with regard for next used of these methods. Berlin database is used. This database includes many of sounds records with emotions. Parameterization creates files, which are divided to two parts. First part is used for training and second part is used for testing. Point of interest is self-organization network. Thesis includes Matlab´s program which can be used for parameterization of any database. Data are classified by self-organization network after parameterization. Results of hits rates are presented at the end of this diploma thesis.
|
|
|
Machine learning for analysis of MR images of brain
Král, Jakub ; Říha, Ivo (referee) ; Provazník, Ivo (advisor)
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.
|
|
|
Biologically Inspired Methods of Object Recognition
Truhlář, Martin ; Hradiš, Michal (referee) ; Juránek, Roman (advisor)
This document describes a method for biologically inspired pattern recognition. Furthermore, it explains the process of image processing and various stages of information extraction for classification. Support Vector Machine method is used for classification, but there are other classification methods explained. It explains how to test and work with the method itself. Results for each model set of classifiers and their advantages and disadvantages are summarized in the conclusion.
|
|
| |
|
|
Automated recognition of selected terrain features from their cartographic representation
Sykora, Matúš ; Bayer, Tomáš (advisor) ; Brodský, Lukáš (referee)
Automated recognition of selected terrain features from their cartographic representation. This diploma thesis is dedicated to automatic classification of selected terrain shapes and their cartographic representation. The main aim of this thesis is to design methodological approach for automatic recognition of terrain shapes (hills and valleys) with the use of Machine Learning (Deep Learning). The first part of suggested method divides rough terrain segmentation into two categories, which will be then classified with convolutional neural network. The second part of the thesis is dedicated to the very classification of pre-segmented terrain shapes using Machine Learning. Both parts of the processing are using photos SRTM30 as an input data. The whole proposed method was developed in Python programming language with the usage of Arcpy, TensorFlow and Keras libraries. Keywords: Digital cartography, GIS, terrain shapes, Machine Learning, Deep Learning, recognition, classification, segmentation
|
guest ::
