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Ab initio study of phase stability of multicomponent alloys
Fikar, Ondřej ; Brož, Pavel (referee) ; Černý, Miroslav (referee) ; Zelený, Martin (advisor)
Ab initio methods are based on purely theoretical findings of quantum physics that can be used to predict among others physical, chemical and mechanical properties of materials. Due to rapid increase in accessibility of computational resources in the recent decades the theoretical prediction of material properties became an integral part of materials design. This work is focused on theoretical prediction of phase stability and solubility of solid solutions. Ab initio calculations based on Density Functional Theory were performed using Projector-Augmented Waves method and thermal dependencies of thermodynamic quantities were obtained using phonon calculations and Monte Carlo simulations. Attention is paid to alloys mainly based on aluminium, silver and magnesium, which were investigated in order to assess the reliability and precision of theoretical predictions of solubility in the solid state. Phase stability of solid solutions was evaluated multiple times including different energy contributions and using various methods in order to determine the influence of each contribution and method on the prediction accuracy. Calculated solubilities are compared with experimental data provided using the CALPHAD method.
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Detection of Harmfulness of Communication Partners and Their Networks
Kučera, Rostislav ; Homoliak, Ivan (referee) ; Očenášek, Pavel (advisor)
With the growing dependence of the population on electronic devices, the risk of data loss or misuse also increases. As the number of attacks in computer networks rises, systems for detecting malicious traffic become more important. The goal of this work is a theoretical analysis and implementation of modules for detecting malicious computer communication using machine learning methods, specifically a neural network model, and statistical analysis, which are deployed within the extended intrusion detection system Snort.
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Information Extraction from Wikipedia
Jurišica, Rudolf ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
The goal of this thesis is to reduce the number of unknown referenced entities in Czech Wikipedia articles. This has been achieved by using some existing solutions, created by the KNOT research group at FIT BUT, and then by creating a set of programs. These programs are automatically run every month, when a new version of Wikipedia is released. They will automatically add new names to the knowledge base, generate their derived forms, and edit the articles themselves directly on Wikipedia.
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System for Recognizing Disinformation in Web Environment
Večerka, Lukáš ; Žádník, Martin (referee) ; Strnadel, Josef (advisor)
This work deals with the design, implementation, and verification of a system for automatic recognition of disinformation on the web. It addresses the issue of disinformation spread in the online environment and its impact on society. It focuses on training several Czech transformer language models for disinformation recognition and further automatic extraction of content from Czech online newspapers and their analysis using text classification and natural language processing through deep learning methods. The results of these analyses are then presented in a web user interface with the aim of providing a platform for verifying articles, authors, and sources. The interface could be used for data annotation by experts for continuous improvement of language models.
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Detection of parking space availability based on video
Kužela, Miloslav ; Zelený, Ondřej (referee) ; Frýza, Tomáš (advisor)
Detekování obsazenosti parkovacích míst je často řešeno použitím senzorů umístěných v blízké lokaci parkovacího místa. Se vzrůstem strojového učení je možnost využití této technologie za použití kamer a detekčních algoritmů. Práce se zabývá právě vytvořením a použitím takového modelu k detekci obsazenosti parkoviště. Probírá existující modely a detektory, vytvoření vlastního datasetu s konkrétní strukturou, vytvoření a naučení různých typů modelů a probrání vysledků při testování daných modelů na vlastních záznamech z parkovací plochy. Poté následné vytvoření webové aplikace na které můžou návštěvnící parkoviště pozorovat obsazenost parkoviště. Vše za použití programovacího jazyka Python s knihovnami Torchvision.
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Monitoring fitness activities using a wearable device
Toman, Adam ; Trčka, Tomáš (referee) ; Tofel, Pavel (advisor)
Bachelor thesis deals with the issue of fitness tracking with wearable device. Theoretical part describes the historical development of wearable devices, the base theory behind fitness activities and resistence training and methods of using of wearable devices for classification and evaluation of these activities. Aim for the practical part of this thesis was to develop an algorithm able to classify and evaluate activities through chosen recorded metrics. Practical part is followed by overall result evaluation and discussion.
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Using of neural network for detection of heart rhythm disturbances from ECG data and accelerometer signal
Aleksandrenko, Borys ; Ředina, Richard (referee) ; Bulková, Veronika (advisor)
This bachelor's thesis addresses the issue of detecting heart rhythm disorders from EKG and accelerometer signals using machine learning. First, an analysis of the possibilities for detecting heart rhythm disorders from these signals was conducted through a theoretical review. In the next part, a methodology was proposed for detecting two rhythm disorders: inappropriate sinus tachycardia and chronotropic incompetence. The methodology was further supplemented with adaptive filtering of EKG signals using signals from the accelerometer. In the third part of the thesis, a database of samples was created for training machine learning models proposed in the methodology. The next section included the description and implementation of the models. In the fifth part of the thesis, an application for detecting heart rhythm disorders using the proposed methodology was developed in the Python programming language. Finally, a discussion and evaluation of the results were conducted.
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Evolutionary Design of Neural Networks
Kastner, Jan ; Hurta, Martin (referee) ; Sekanina, Lukáš (advisor)
The thesis deals with the implementation of a problem-solving method for the automated design of convolutional neural networks (CNN) architectures. The optimization of two fundamental and often conflicting characteristics, the number of parameters and the quality of CNN classification, is performed using a multi-criteria optimization genetic algorithm (NSGA-II). To encode this problem, the Cartesian genetic programming (CGP) technique is used, which enables the wide range of CNN architectures to be represented, and at the same time, the searched area can be appropriately limited by parameterization. Experiments were performed on the MNIST dataset to understand the effect of population size on the quality of the resulting solution. It is also evident from the results of the experiments that the quality of the architectures found can compete with already established models. This is therefore an alternative approach that does not require human intervention compared to manual design.
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Using artificial intelligence to automate trading
Čermák, František ; Hůlka, Tomáš (referee) ; Matoušek, Radomil (advisor)
This thesis deals with the use of artificial intelligence for automating stock trading. The main objective was to investigate current technologies applied in algorithmic trading and then to design and develop an automated trading system using artificial intelligence. The work focuses on various aspects of algorithmic trading, including high frequency trading, cloud solutions, machine learning, blockchain and smart contracts. It also explores the applications of AI in trading, such as predictive analytics and natural language processing, and discusses the ethical and regulatory challenges associated with this technology. The design and development of an automated trading system is described in detail, including system architecture, choice of programming languages and tools, and implementation of trading algorithms. The results show that the use of artificial intelligence can significantly increase the efficiency and accuracy of stock trading, but technological and ethical risks must be considered. This thesis makes a significant contribution to research in the field of algorithmic trading and provides a foundation for further research in optimizing trading algorithms and integrating new technologies.
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A modern approach to measuring antibiotic susceptibility of microbial cultures using machine learning
Lepík, Jakub ; Burget, Radim (referee) ; Čičatka, Michal (advisor)
The bachelor's thesis focuses on antibiotic susceptibility testing (AST), specifically enhancing and automating the assessment of the disk diffusion method using machine learning and object detection architectures. Thanks to the TensorFlow development platform and extensive dataset, on which custom detection models like EfficientDet were trained, processing a wide range of input data is enabled. This brings the possibility of using mobile devices alongside traditional laboratory equipment when evaluating this method. By employing additional image processing techniques and the OpenCV library, a custom algorithm for measuring the size of inhibitory zones was developed, which, along with the detection models, is integrated within the application module developed by Bruker Daltonics GmbH & Co. KG. This module, created using the ASP.NET platform, is a precise and valuable tool for assisting personnel in microbiological laboratories.
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