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Chatbot Based on Artificial Neural Networks
Červíček, Petr ; Novotný, Ondřej (referee) ; Szőke, Igor (advisor)
The thesis pursues the implementation of the chatbot based on neural networks. It uses Long short term memory networks, which remember long-term dependencies. Chatbot was implemented in Python with superstructure Keras and is based on sequence-to-sequence. Chatbot was also tested by BLEU and given to users, who chatted with the chatbot. For a better understanding of the given problematics, there is simple description of the chatbot history and used technologies.
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Algorithmic Trading Using Twitter Data
Kříž, Jakub ; Plchot, Oldřich (referee) ; Szőke, Igor (advisor)
This master's thesis describes creation of prediction system. This system predicts future market development based on stock exchange data and twitter messages analysis. Tweets from two different sources are analysed by mood dictionaries or via recurrent neural networks. This analysis results and technical analysis of stock exchange data results are used in multilayer neural network for prediction. A business strategy is created and tested based on results of this prediction. Design and implementation of prediction system is described in this thesis. This system achieved revenue increase more than 25 % of some business strategies by tweets analysis. However this improvement applies for certain data and timeframe.
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Data analysis from the manufacturing process
Krčmář, Martin ; Honzík, Petr (referee) ; Zezulka, František (advisor)
This thesis deals with the classification of production data using algorithms: neural networks, decision trees and naive bayesian classifier. The neural network is dedicated forward multilayer networks with a learning algorithm of backpropagation. In thesis, these algorithms are described and evaluated their pros and cons. Another part deals with the development of the program in C# for creating these algorithms. The last part is devoted to the evaluation of the results. Bachelor thesis contains a sample of generated clasification models decision tree and bayesian classifier.
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Electromagnetic analysis
Kolofík, Josef ; Reichert, Pavel (referee) ; Martinásek, Zdeněk (advisor)
This thesis deals with electromagnetic analysis and applications of electromagnetic side channel. The first and second part describes the basics of cryptography, function of cryptographic module and side-channel attacks. The third part discusses the electromagnetic analysis, construction of probe, a description of the laboratory workplace, the electromagnetic emission of PIC16F84A, AES and preparation for laboratory measurements. The fourth part describes specific laboratory measurements and extracting the useful signal. In the fifth part of the thesis presents the results of processing the measured values, the outputs generated by scripts and found the link between measured curves and AES encryption key. In the sixth part of the thesis are analyzed the basics of defense against side channel attack.
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The use of artificial intelligence in the capital markets to reduce the risks of trading
Orság, Štěpán ; Budík, Jan (referee) ; Dostál, Petr (advisor)
This thesis deals with the prediction of trading at financial markets and by using the prediction is trying to reduce the risks of entering at the market. The prediction has been work out by using of artificial intelligence. The artificial intelligence is in this thesis represented by neural networks witch model and predict market behavior. The thesis contains a description of the financial markets, exchange trading and its analysis, and artificial intelligence methods. The main part of this thesis is a model for prediction of prices of a particular instrument. This model was developed in MATLAB and should serve as a support for making business decisions. Its aim is to predict the direction and magnitude of movement the price level for the next trading day. The output of this model is processed using the platform MetaTrader 4. At the end are evaluated possible gains from this solution.
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The use of artificial intelligence in cryptography
Lavický, Vojtěch ; Rosenberg, Martin (referee) ; Babnič, Patrik (advisor)
Goal of this thesis is to get familiar with problematics of neural networks and commonly used security protocols in cryptography. Theoretical part of the thesis is about neural networks theory and chooses best suitable type of neural network to use in cryptographic model. In practical part, a new type of security protocol is created, using chosen neural network.
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Artificial Intelligence
Ragas, Luděk ; Žouželková Bartošová, Marie (referee) ; Sedláček, Pavel (advisor)
Cílem této bakalářské práce je poskytnout náhled do rozsáhlého oboru umělé inteligence. Téze nejprve poskytuje definici umělé inteligence a krátký přehled její historie. Poté práce stručně popisuje technologie umělé inteligence, jako jsou neuronové sítě, expertní systémy a genetické algoritmy. Na závěr téze popisuje vliv umělé inteligence na společnost a její pozici v ní.
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Parameter Identification for Elastic-plastic Material Models from Experimental Data
Jeník, Ivan ; Šebek, František (referee) ; Kubík, Petr (advisor)
This master's thesis deals with the identification of the material flow curve from record of tensile test of smooth cylindrical specimen. First, necessary theory background is presented. Basic terms of incremental theory of plasticity, tensile test procedure and processing its outputs are described. Furthermore, possibilities of mathematical expression of the elastic-plastic material constitutive law, thus mathematical expression of the material flow curve itself. Mechanism of ductile damage of material is explained briefly as well. Overview of recent methods of the flow curve identification is given, focused on cases, when the stress distribution in a specimen is not uniaxial. That is either kind of analytic correction of basic formulas derived for uniaxial stress state, or application of mathematical optimization techniques combined with numerical simulation of the tensile test. Also unusual method of neural network is mentioned. For 8 given materials, the flow curve identification was performed using different methods. Namely by analytic correction, optimization, sequential identification and neural network. Algorithms of the last two methods were modified. Based on assessment of obtained results, application field and adjusting the parameters of single algorithms was recommended. It showed up, that an effective way to the accurate and credible results is the combination of different methods during flow curve identification procedure.
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