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DNS Data Analysis for Mobile Device Identification Purposes
Sporni, Alex ; Bartík, Vladimír (referee) ; Burgetová, Ivana (advisor)
This bachelor's thesis deals with the problem of identification of mobile devices based on DNS data analysis. The thesis provides a theoretical introduction to the computer communication model. This thesis explains the importance of DNS in the terms of network communication between devices, It also presents the provided data sets, which contain real communication of mobile devices. These data sets must be with a suitable technique parsed and stored in a database to provide better data manipulation techniques in the later stages of implementation. This work further describes individual techniques of data processing. It also depicts in detail the methodologies for evaluating the relevance of TF-IDF and the application of cosine similarity to identify the mobile devices. The main output of this work is the evaluation of the achieved results.
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Robust Screen and Slide Detection in Video
Hanzel, Svätopluk ; Beran, Vítězslav (referee) ; Szőke, Igor (advisor)
The main goal of this bachelor thesis is implementation of a robust screen detector with slide synchronization using various techniques including neural networks, keypoints extraction and matching, text extraction using OCR and text matching. These methods are also analysed and compared to their possible alternatives.
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Prediction of Shopper Behaviour
Kačo, Adam ; Španěl, Michal (referee) ; Beran, Vítězslav (advisor)
The aim of this work is to create a model for predicting the behavior of those leaving. Such a model has many applications, both for brick-and-mortar stores and online stores. Benefits include, for example, customer satisfaction and comfort. In more detail, I am describing the basic issue of predicting the next products. I am describing recommender systems as a whole, as well as their basic categorization and the basics of the individual models of the recommender systems. I am describing model, that I have created, for the dataset, that I chose to use and a procedure for working with the given model to predict the next purchase. I am also presentimg the procedure of our implementation in detail, as well as the results of testing.
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Data Mining Methods for Text Analysis
Kozák, Ondřej ; Marcoň, Petr (referee) ; Dohnal, Přemysl (advisor)
This bachelor thesis explores the current methodology and possibilities of text mining and the subsequent application of some methods. The thesis described methods for preprocessing, methods for converting text to vector space and methods for text analysis and discusses their possible applications. The different preprocessing methods were applied to the text and then the conversion to vector space was demonstrated using simple methods such as BOW, Bag of n-grams, TF-IDF or with machine learning methods which are FastText and GloVe. LSA, LDA, TextRank and cosine similarity methods were applied to the extracted vectors to extract information from the text.
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Automatic Testing of JavaScript Restrictor Project
Bednář, Martin ; Pluskal, Jan (referee) ; Polčák, Libor (advisor)
The aim of the thesis was to design, implement and evaluate the results of automatic tests for the JavaScript Restrictor project, which is being developed as a web browser extension. The tests are divided into three levels - unit, integration, and system. The Unit Tests verify the behavior of individual features, the Integration Tests verify the correct wrapping of browser API endpoints, and the System Tests check that the extension does not suppress the desired functionality of web pages. The System Tests are implemented for parallel execution in a distributed environment which has succeeded in achieving an almost directly proportional reduction in time with respect to the number of the tested nodes. The benefit of this work is detection of previously unknown errors in the JavaScript Restrictor extension and provision of the necessary information that allowed to fix some of the detected bugs.
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DNS Data Analysis for Mobile Device Identification Purposes
Sporni, Alex ; Bartík, Vladimír (referee) ; Burgetová, Ivana (advisor)
This bachelor's thesis deals with the problem of identification of mobile devices based on DNS data analysis. The thesis provides a theoretical introduction to the computer communication model. This thesis explains the importance of DNS in the terms of network communication between devices, It also presents the provided data sets, which contain real communication of mobile devices. These data sets must be with a suitable technique parsed and stored in a database to provide better data manipulation techniques in the later stages of implementation. This work further describes individual techniques of data processing. It also depicts in detail the methodologies for evaluating the relevance of TF-IDF and the application of cosine similarity to identify the mobile devices. The main output of this work is the evaluation of the achieved results.
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Quality Analysis of Electronic Dictionaries Transformation
Stehlíková, Petra ; Škoda, Petr (referee) ; Kouřil, Jan (advisor)
The bachelor's thesis deals with electronic dictionaries, their formats and quality analysis of their conversions. The thesis describes Lexical Markup Framework format in detail. It also discusses the capabilities of advanced algorithms such as LSA for conversion quality analysis and the tools that can be used for the analysis. Based on this theoretical knowledge the scripts in Python language were created to analyze dictionaries in Lexical Markup Framework format.
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Robust Screen and Slide Detection in Video
Hanzel, Svätopluk ; Beran, Vítězslav (referee) ; Szőke, Igor (advisor)
The main goal of this bachelor thesis is implementation of a robust screen detector with slide synchronization using various techniques including neural networks, keypoints extraction and matching, text extraction using OCR and text matching. These methods are also analysed and compared to their possible alternatives.
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Analysis and Data Extraction from a Set of Documents Merged Together
Jarolím, Jordán ; Bartík, Vladimír (referee) ; Kreslíková, Jitka (advisor)
This thesis deals with mining of relevant information from documents and automatic splitting of multiple documents merged together. Moreover, it describes the design and implementation of software for data mining from documents and for automatic splitting of multiple documents. Methods for acquiring textual data from scanned documents, named entity recognition, document clustering, their supportive algorithms and metrics for automatic splitting of documents are described in this thesis. Furthermore, an algorithm of implemented software is explained and tools and techniques used by this software are described. Lastly, the success rate of the implemented software is evaluated. In conclusion, possible extensions and further development of this thesis are discussed at the end.
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Algorithms for anomaly detection in data from clinical trials and health registries
Bondarenko, Maxim ; Blaha, Milan (referee) ; Schwarz, Daniel (advisor)
This master's thesis deals with the problems of anomalies detection in data from clinical trials and medical registries. The purpose of this work is to perform literary research about quality of data in clinical trials and to design a personal algorithm for detection of anomalous records based on machine learning methods in real clinical data from current or completed clinical trials or medical registries. In the practical part is described the implemented algorithm of detection, consists of several parts: import of data from information system, preprocessing and transformation of imported data records with variables of different data types into numerical vectors, using well known statistical methods for detection outliers and evaluation of the quality and accuracy of the algorithm. The result of creating the algorithm is vector of parameters containing anomalies, which has to make the work of data manager easier. This algorithm is designed for extension the palette of information system functions (CLADE-IS) on automatic monitoring the quality of data by detecting anomalous records.
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