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This thesis describes the design to increase efficiency of furniture production in a small company. The aim is to summarize the current situation at the new production program and recommend purchase of new machines and their selection. This new machines are deployed in the foor plan in several variants and is choosen the best version. Another aim is to prepare a budget expenses and revenues from the existing machine park and new machine park to determine the effectiveness of investments.

No description available.

No description available.

As the number of computers and other smart devices grows in every aspect of human life, the amount of malicious software (malware) also grows. Such software tries to disrupt computer usage. Therefore one of the challenges for computer science is to divide the Malware into classes according to its behaviour. The thesis summarizes known ways to look at the problem at hand, some of them are extensions of known approaches, while others are completely new. They are all implemented, tested and compared. We also propose few ideas for future research. Powered by TCPDF (www.tcpdf.org)

The objective of this work is to explore the intrusion detection prob- lem and create simple rules for detecting specific intrusions. The intrusions are explored in the realistic CSE-CIC-IDS2018 dataset. First, the dataset is analyzed by computing appropriate statistics and visualizing the data. In the data visu- alization various dimensionality reduction methods are tested. After analyzing the dataset the data are normalized and prepared for the training. The training process focuses on feature selection and finding the best model for the intrusion detection problem. The feature selection is also used for creating rules. The rules are extracted from an ensemble of Decision Trees. At the end of this work, the rules are compared to the best model. The experiments demonstrate that the simple rules are able to achieve similar results as the best model and can be used in a rule-based intrusion detection system or be deployed as a simple model. 1

Goal of this thesis is to create an online service, suitable for approximation of user's position inside a building via his mobile device. For this goal a known network of Wi-Fi access-points was used in combination with a machine learning algorithms. Algorithms were trained and tested on the ground floor in building Q of Mendel University in Brno. The Algorithms and the online service were tested with automated tests as well as in real environment with a mobile application, which was also created as a part of this thesis. Results have shown that reliability and accuracy of service is highly dependent on localized mobile device. Success rate floated between 43~\% and 66~\%, depending on a mobile device.

Thesis describe theory behind neuroevolution. Then it describes both design and creation of simulated environment for autonomous agent and its training with library Neataptic in environments with various difficulty. Thesis also describes pro- cess of designing frontend for visualization of results and backend for faster training of agents. At the end it describes resulting agents and proposes enhancements to existing solution.

The notion of salience in the discourse analysis models how the activation of referred objects evolves in the flow of text. The salience algorithm was defined and tested briefly in an earlier research, we present a reproduction of its results in a larger scale using data from the Prague Discourse Treebank 1.0. The results are then collected into an accessible shape and analyzed both in their visual and quantitative form in the context of the two main resources of the salience - coreference relations and topic-focus articulation. Finally, attempts are made with using the salience information in the machine learning NLP tasks of document clustering and topic modeling. Powered by TCPDF (www.tcpdf.org)

The notion of salience in the discourse analysis models how the activation of referred objects evolves in the flow of text. The salience algorithm was already defined and tested briefly in an earlier research, we present a reproduction of its results in a larger scale using data from the Prague Discourse Treebank 1.0. The results are then collected into an accessible shape and analyzed both in their visual and quantitative form in the context of the two main resources of the salience - coreference relations and topic-focus articulation. Finally, attempts are made with using the salience information in the machine learning NLP tasks of document clustering and topic modeling. Powered by TCPDF (www.tcpdf.org)