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Automatic Cryptocurrencies Trading
Vorobiev, Nikolaj ; Hrubý, Martin (referee) ; Rozman, Jaroslav (advisor)
This thesis focuses on the trading in the cryptocurrency market. The theoretical part of the thesis describes the principles of trading, technical analysis, trading systems and recurrent neural networks. After conducting a search of brokers, Binance is chosen as a trading broker and real-time data provider; CryptoDataDownload is chosen as a historical data provider. After getting acquainted with the technologies used, elements of information trading systems are designed, enabling communication with remote servers and with each other, for the purpose of trading, obtaining and concurrent processing of user's, historical or real-time data. The resulting systems should provide to the user manual, semi-automatic (according to the plan) or automatic (according to the decisions of recurrent neural network, learned on historical data) trading and ability to respond to a change in the market. Furthermore, the thesis moves to the practical level, including implementation and experiments on created systems. In the final part of the thesis, the results are evaluated and the possibilities for improvement and expansion are described.
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Advanced scoring of sleep data
Jagošová, Petra ; Novotná, Petra (referee) ; Ronzhina, Marina (advisor)
The master´s thesis is focused on advanced scoring of sleep data, which was performed using deep neural network. Heart rate data and the movement information were used for scoring measured using an Apple Watch smartwatch. After appropriate pre-processing, this data serves as input parameters to the designed networks. The goal of the LSTM network was to classify data into either two groups for sleep and wake or into three groups for wake, Non-REM and REM. The best results were achieved by network doing classification of sleep vs. wake using the accelerometer. The statistical evaluation of this best-designed network reached the values of sensitivity 71,06 %, specificity 57,05 %, accuracy 70,01 % and F1 score 81,42 %.
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Using artificial intelligence to monitor the state of the machine
Popara, Nikola ; Bražina, Jakub (referee) ; Kovář, Jiří (advisor)
This thesis is focus on monitoring state of machine parts that are under the most stress. Type of artificial intelligence used in this work is recurrent neural network and its modifications. Chosen type of neural network was used because of the sequential character of used data. This thesis is solving three problems. In first problem algorithm is trying to determine state of mill tool wear using recurrent neural network. Used method for monitoring state is indirect. Second Problem was focused on detecting fault of a bearing and classifying it to specific category. In third problem RNN is used to predict RUL of monitored bearing.
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Chatbot Based on Artificial Neural Networks
Čechák, Jiří ; Beneš, Karel (referee) ; Szőke, Igor (advisor)
The thesis describes an implementation and the way generative chatbot operates. The chatbot was implemented in Python using artificial neural networks and is based on a sequence-to-sequence principle. The final chatbot contains three models, which can be trained and used for conversations in a created GUI. After training of all three models, the chatbot was then tested by using BLEU metric. It was also tested by some users who compared the quality of its generated answers with the quality of answers created by already an existing chatbot Cleverbot. For a better understanding of the given problematics, there is a simple description of the basic terms, such as artificial intelligence, artificial neural networks, the difference between closed and open domain, word embedding and a basic description of the chatbots and their types, including their advantages, disadvantages and usage.
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Analysis of GPON frames using machine learning
Tomašov, Adrián ; Horváth, Tomáš (referee) ; Holík, Martin (advisor)
Táto práca sa zameriava na analýzu vybraných častí GPON rámca pomocou algoritmov strojového učenia implementovaných pomocou knižnice TensorFlow. Vzhľadom na to, že GPON protokol je definovaný ako sada odporúčaní, implementácia naprieč spoločnosťami sa môže líšiť od navrhnutého protokolu. Preto analýza pomocou zásobníkového automatu nie je dostatočná. Hlavnou myšlienkou je vytvoriť systém modelov za použitia knižnice TensorFlow v Python3, ktoré sú schopné detekovať abnormality v komunikácií. Tieto modely používajú viaceré architektúry neuronových sietí (napr. LSTM, autoencoder) a zameriavajú sa na rôzne typy analýzy. Tento systém sa naučí na vzorovej vzorke dát a upozorní na nájdené odlišnosti v novozachytenej komunikácií. Výstupom systému odhad podobnosti aktuálnej komunikácie v porovnaní so vzorovou komunikáciou.
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Personal Voice Activity Detection
Sedláček, Šimon ; Landini, Federico Nicolás (referee) ; Švec, Ján (advisor)
Cílem této práce je implementovat, otestovat a vyhodnotit řečníkem podmíněnou metodu pro detekci hlasu ( Voice Activity Detection , VAD) nazvanou Personal VAD. Pro detekci využívá tato metoda LSTM neuronových sítí a jejím účelem je vytvoření systému schopného spolehlivě detekovat řečové signály cílového řečníka při zachování vlastností typického VAD systému co se velikosti modelu, odezvy a nízkých nároků na zdroje týče. Systém je trénován pro klasifikaci řečových rámců do tří tříd: neřeč, řeč necílového a řeč cílového řečníka. Za tímto účelem využívá metoda speaker embedding vektory pro reprezentaci cílového řečníka jako součást vstupních příznaků. Některé z náročnějších variant systému využívají skórování rámců systémem pro verifikaci řečníka, což vede ke zvýšení spolehlivosti klasifikace. Vedle základní metody skórování představené v originálním článku byly navrženy dvě modifikace, jež základní metodu překonaly a zlepšily spolehlivost výsledného systému i v akusticky náročných prostředích.
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Holistic License Plate Recognition Based on Convolution Neural Networks
Le, Hoang Anh ; Hradiš, Michal (referee) ; Špaňhel, Jakub (advisor)
Main goal of this work was to create a holistic license plate reader, with an emphasis on achieving the highest possible accuracy on low quality images. Combination of convolutional and recurrent neural networks was designed and implemented, with usage of LSTM and CTC, where the inputs are cut-outs from the entire license plate. Competitive networks were also implemented to compare results. Networks were compared on a total of 4 datasets and the results were, that my design has achieved the best results with a recognition accuracy of 97.6%.
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Deep learning based sound records analysis
Kramář, Denis ; Říha, Kamil (referee) ; Přinosil, Jiří (advisor)
This master thesis deals with the problem of audio-classification of the chainsaw logging sound in natural environment using mainly convolutional neural networks. First, a theory of grafical representation of audio signal is discussed. Following part is devoted to the machine learning area. In third chapter, some of present works dealing with this problematics are given. Within the practical part, used dataset and tested neural networks are presented. Final resultes are compared by achieved accuracy and by ROC curves. The robustness of the presented solutions was tested by proposed detection program and evaluated using objective criteria.
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Financial market analysis using deep learning algorithm
Nimrichter, Adam ; Burget, Radim (referee) ; Mašek, Jan (advisor)
The thesis deals with methods for analysis of financial markets, focused on cryptocurrencies. The theoretical part, in a context of virtual currencies, describes block-chain technology, financial indicators and neural networks with recurrent architectures. Main goal is to create a system for giving a recommendation either for buy, or sell the currency. The system consists of designed financial strategy and predicted value of the currency, for which is used financial indicators and LSTM neural network. Tests were performed on Bitcoin, Litecoin and Ethereum historical data from year 2017.
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New Techniques in Neural Networks Training - Connectionist Temporal Classification
Gajdár, Matúš ; Švec, Ján (referee) ; Karafiát, Martin (advisor)
This bachelor’s thesis deals with neural network and their use in speech recognition. Firstly,there is some theory about speech recognition, afterwards we show theory around neural networks in connection with connectionist temporal classification method. In next chapter we introduce toolkits, which were used for training of neural networks and also experiments done by them to find out impact of connectionist temporal classification method on precisionin phoneme decoding. The last chapter include summarization of work and overall evaluation of experiments.
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