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Finding guanine quadruplexes in DNA using decision trees
Kotrys, Kryštof ; Šťastný, Jiří (referee) ; Kaura, Patrik (advisor)
This bachelor’s thesis is focused on creating a functional decision tree model for detection of guanine quadruplexes in the DNA. The first part of the thesis summarizes theoretical knowledge in the fields of local DNA structures, computational prediction methods of guanine quadruplexes and decision trees. The second part describes the creation of a decision tree model, followed by a statistical comparison of the results with the G4Hunter algorithm.
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Methods for Classification of WWW Pages
Svoboda, Pavel ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
The main goal of this master's thesis was to study the main principles of classification methods. Basic principles of knowledge discovery process, data mining and using an external class CSSBox are described. Special attantion was paid to implementation of a ,,k-nearest neighbors`` classification method. The first objective of this work was to create training and testing data described by 'n' attributes. The second objective was to perform experimental analysis to determine a good value for 'k', the number of neighbors.
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Detection of Malicious Domain Names
Setinský, Jiří ; Perešíni, Martin (referee) ; Tisovčík, Peter (advisor)
The bachelor thesis deals with the detection of artificially generated domain names (DGA). The generated addresses serve as a means of communication between the attacker and the infected computer. By detection, we can detect and track infected computers on the network. The detection itself is preceded by the study of machine learning techniques, which will then be applied in the creation of the detector. To create the final classifier in the form of a decision tree, it was necessary to analyze the principle of DGA addresses. Based on their characteristics, the attributes were extracted, according to which the final classifier will be decided. After learning the classification model on the training set, the classifier was implemented in the target platform NEMEA as a detection module. After final optimizations and testing, we achieved a accuracy of the classifier of 99%, which is a very positive result. The NEMEA module is ready for real-world deployment to detect security incidents. In addition to the NEMEA module, another model was created to predict the accuracy of datasets with domain names. The model is trained based on the characteristics of the dataset and the accuracy of the DGA detector, whose behavior we want to predict.
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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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Analysis of Classification Methods
Juríček, Jakub ; Zendulka, Jaroslav (referee) ; Burgetová, Ivana (advisor)
This work deals with the classification methods used in the knowledge discovery from data process and discusses the possibilities of their validation and comparison. Through experiments, the work focuses on the analysis of four selected methods: Naive Bayes classificator, decision tree, neural network and SVM. Factors influencing basic characteristics such as training speed, classification speed, accuracy are examined. A part of the thesis is a desktop application, which is a tool for training, testing and validation of individual methods. Eleven reference data sets are selected for experimental purposes. At the end of this work experimental results of comparison and observed characteristics of classification methods are summarized.
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The idnetification of the objects in the imege
Zavalina, Viktoriia ; Fliegel, Karel (referee) ; Boleček, Libor (advisor)
Master´s thesis deals with methods of objects detection in the image. It contains theoretical, practical and experimental parts. Theoretical part describes image representation, the preprocessing image methods, and methods of detection and identification of objects. The practical part contains a description of the created programs and algorithms which were used in the programs. Application was created in MATLAB. The application offers intuitive graphical user interface and three different methods for the detection and identification of objects in an image. The experimental part contains a test results for an implemented program.
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Design of exercises for data mining - Classification and prediction
Martiník, Jan ; Malý, Jan (referee) ; Burget, Radim (advisor)
My master's thesis on the topic of "Design of exercises for data mining - Classification and prediction" deals with the most frequently used methods classification and prediction. There are association rules, Bayesian classification, genetic algorithms, the nearest method neighbor, neural network and decision trees on the classification. There are linear and non-linear prediction on the prediction. This work also contains a summary of detail the issue of decision trees and a detailed algorithm for creating the decision tree, including development of individual diagrams. The proposed algorithm for creating the decision tree is tested through two tests of data dowloaded from Internet. The results are mutually compared and described differences between the two implementations. The work is written in a way that would provide the reader with a notion of the individual methods and techniques for data mining, their advantages, disadvantages and some of the issues that directly relate to this topic.
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ANALYSIS AND DEFINITION OF DECISION PROBLEMS OF EXPERT IN REAL ESTATE VALUATION
Krejza, Zdeněk ; Bradáč, Albert (referee) ; Abraham, Karel (referee) ; Tichá, Alena (advisor)
The thesis deals with the decision-making of the expert in real estate valuation. Due to the complexity of the process and the difficulties of valuation it can be assumed that the decision will be an arduous process. It is obvious that the choice of an expert is crucial to the result of the valuation process. This topic is currently relatively little explored, and therefore the work will deal with the analysis and formulation of decision problems expert in real estate valuation. The thesis analyses the current status of forensic engineering and decision-making regarding to real estate valuation. The general decision-making process, divided into seven steps, is adapted to the requirements of expert decision-making in real estate valuation. As in the managerial decision-making process, property valuation is also divided into three levels. These three levels considered the described fundamental decision problems that lead to the formulation of the expert decision-making principles in real estate valuation. For better understanding the extensiveness of the decision-making process in the valuation of real estate the author created a decision tree respectively schemes whose functionality has been verified at the end of the thesis, exemplified with the help of a specific case study of the determined price in real estate valuation.
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Mining Modules of Data Mining System on NetBeans Platform
Henkl, Tomáš ; Lukáš, Roman (referee) ; Zendulka, Jaroslav (advisor)
The master's thesis deals with the knowledge discover in databases and with the extending of the data mining systems in the Oracle environment developed at the VUT FIT. The system kernel conception incorporates an interface that enables the adding of data mining modules. The objective of the thesis is to learn this interface and implement and embed the data mining module for decision-tree classification into the application. In addition, the thesis compares the application with similar commercial product SAS Enterprise Miner
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