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Utilization of artificial intelligence in vibrodiagnostics
Dočekalová, Petra ; Huzlík, Rostislav (referee) ; Zuth, Daniel (advisor)
The diploma thesis deals with machine learning, expert systems, fuzzy logic, genetic algorithms, neural networks and chaos theory, which fall into the category of artificial intelligence. The aim of this work is to describe and implement three different classification methods, according to which the data set will be processed. The GNU Octave software environment was chosen for the data application for licensing reasons. Further evaluate the success of data classification, including visualization. Three different classification methods are used for comparison, so that we can compare the processed data with each other.
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Decision trees
Patera, Jan ; Sáblík, Václav (referee) ; Honzík, Petr (advisor)
This diploma thesis presents description on several algorithms for decision trees induction and software RapidMiner. The first part of the thesis deals with partition and terminology of decision trees. There’re described all algorithms for decision tree construction in RapidMiner. The second part deals with implementation and comparison of chosen algorithms. The application was developed in C++. Based on the real datesets the comparisson of different algorithms was realized using Rapid Miner 4.0.
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Detection of modern Slow DoS attacks
Jurek, Michael ; Jonák, Martin (referee) ; Sikora, Marek (advisor)
S rozvojem propojených zařízení v síti internet se počet útoků zvětšuje. Útočníci můžou zneužít takového zranitelného zařízení a vytvořit (D)DoS útok proti své oběti. Tyto útoky se stávají čím dál tím víc sofistikovanější. Proto byla vytvořena nová kategorie DoS útoků s názvem Pomalé DoS útoky, u kterých se útočník snaží napodobit chování standardního uživatele. Útočník se snaží využít všech možností, které mu transportní či aplikační protokol umožňují jako např. náhodné zahazování paketů, neodesílání nebo pozdržování zpráv. Na druhou stranu tvorba vlastních aplikačních výplní těchto protokolů může způsobit stav odepření služby na cíleném aplikačním serveru. Tato práce navrhuje klasifikaci síťových toků a volbu parametrů, které můžou pomoci s detekcí pomalých DoS útoků. Mezi vybranými pomalými DoS útoky jsou Slow Read, Slow Drop a Slow Next. Pro každý útok je popsán proces komunikace z pohledu transportní a aplikační vrstvy. Dále jsou vybrány důležité parametry popisující tyto útoky a v neposlední řadě jsou diskutovány metody a nástroje umožňující tvorbu takových útoků. Tato práce se zabývá možnostmi a nástroji tvorby spojení pro útok a diskutuje základní komunikační koncepty tvorby paralelních spojení. Dále je navržen vlastní generátor pomalých DoS útoků s velkým množstvím parametrů, pomocí nichž může útočník definovat vlastní pomalé DoS útoky. Následující část popisuje testovací prostředí pro testování generovaných útoků, scénáře a nástroje zachycování síťového provozu pro tvorbu vlastního datového souboru, jež je dále použit pro detekci pomalých DoS útoků pomocí metod strojového účení s učitelem. Konrétně jsou použity rozhodovací stromy a náhodné lesy k výběrů důležitých paramterů či sloupců použitelných pro detekci pomalých DoS útoků.
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Recognition of Handwritten Digits
Dobrovolný, Martin ; Mlích, Jozef (referee) ; Herout, Adam (advisor)
Recognition of handwritten digits is one of computer vision problematics that can not be solved with 100 % success these days. This document describes a method for handwritten digits recognizing based on shape features and randomized tree classifiers. These methods are known for their long time machine learning and quick characters recognizing. This method is due to use of relative angles among key locations and is nearly invariant to substantial affine and nonlinear deformations.
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Word Sense Disambiguation
Kraus, Michal ; Glembek, Ondřej (referee) ; Smrž, Pavel (advisor)
The master's thesis deals with sense disambiguation of Czech words. Reader is informed about task's history and used algorithms are introduced. There are naive Bayes classifier, AdaBoost classifier, maximum entrophy method and decision trees described in this thesis. Used methods are clearly demonstrated. In the next parts of this thesis are used data also described. Last part of the thesis describe reached results. There are some ideas to improve the system at the end of the thesis.
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Implementation of Algorithms Based on Decision Trees in C#
Grolig, Lukáš ; Pešek, Martin (referee) ; Stríž, Rostislav (advisor)
This bachelor thesis is focused on selection of data mining algorithms based on decision trees for an analytical system developed under the project System for the Internet security increase based on malware spreading analysis. Selected algorithms are described in greater detais, as well as their implementation in the C# language. These algorithms are then tested with regards to their training speed and classification accuracy. Finally, this thesis presents further conclusions and recommendations based on performed experiments.
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Utilization of artificial intelligence in technical diagnostics
Konečný, Antonín ; Huzlík, Rostislav (referee) ; Zuth, Daniel (advisor)
The diploma thesis is focused on the use of artificial intelligence methods for evaluating the fault condition of machinery. The evaluated data are from a vibrodiagnostic model for simulation of static and dynamic unbalances. The machine learning methods are applied, specifically supervised learning. The thesis describes the Spyder software environment, its alternatives, and the Python programming language, in which the scripts are written. It contains an overview with a description of the libraries (Scikit-learn, SciPy, Pandas ...) and methods — K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Trees (DT) and Random Forests Classifiers (RF). The results of the classification are visualized in the confusion matrix for each method. The appendix includes written scripts for feature engineering, hyperparameter tuning, evaluation of learning success and classification with visualization of the result.
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Data Mining
Stehno, David ; Hynčica, Tomáš (referee) ; Honzík, Petr (advisor)
The aim of the thesis was to study and describe data mining methodology CRISP-DM. From the collected database of calls to the call center a prediction was performed, based on CRISP-DM methodology. In phase of test situation modeling four different testing methods were used: the k-NN, neural network, linear regression and super vector machine. The input attributes importance for further prediction was evaluated based on different selections. The results and findings may provide data for further more accurate forecasts in the future; not only in number of calls but also other indicators relevant to the call center.
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Advanced Data Mining in Cardiology
Mézl, Martin ; Provazník, Ivo (referee) ; Sekora, Jiří (advisor)
The aim of this master´s thesis is to analyse and search unusual dependencies in database of patients from Internal Cardiology Clinic Faculty Hospital Brno. The part of the work is theoretical overview of common data mining methods used in medicine, especially decision trees, naive Bayesian classifier, artificial neural networks and association rules. Looking for unusual dependencies between atributes is realized by association rules and naive Bayesian classifier. The output of this work is a complex system for Knowledge discovery in databases process for any data set. This work was realized with collaboration of Internal Cardiology Clinic Faculty Hospital Brno. All programs were made in Matlab 7.0.1.
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Analysis of Mobile Devices Network Communication Data
Abraham, Lukáš ; Bartík, Vladimír (referee) ; Burgetová, Ivana (advisor)
At the beginning, the work describes DNS and SSL/TLS protocols, it mainly deals with communication between devices using these protocols. Then we'll talk about data preprocessing and data cleaning. Furthermore, the thesis deals with basic data mining techniques such as data classification, association rules, information retrieval, regression analysis and cluster analysis. The next chapter we can read something about how to identify mobile devices on the network. We will evaluate data sets that contain collected data from communication between the above mentioned protocols, which will be used in the practical part. After that, we finally get to the design of a system for analyzing network communication data. We will describe the libraries, which we used and the entire system implementation. We will perform a large number of experiments, which we will finally evaluate.
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