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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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Deep learning based QRS delineator
Malina, Ondřej ; Hejč, Jakub (referee) ; Smíšek, Radovan (advisor)
This thesis deals with the issue of automatic measurement of the duration of QRS complexes in ECG signals. Special emphasis is then placed on the possibility of automatic detection of QRS complexes while exciting cardiac tissue with a pacemaker. The content of this work is divided into four logical units, where the first part deals with the heart as an organ. It describes the origin and spread of excitement in the heart, its possible pathologies and their manifestations in ECG recording, it also deals with pacing and measuring ECG recording during simultaneous pacing. The second part of the thesis contains a brief introduction to the topic of machine and deep learning. The third part of the thesis contains a search of current approaches using methods based on deep learning to solve the detection of QRSd. The fourth part deals with the design and implementation of its own model of deep learning, able to detect the beginnings and ends of QRS complexes from ECG recordings. It describes the data preprocessing implemented in the MATLAB programming environment. The actual implementation of the model was performed in the Python using the PyTorch and NumPy moduls.
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The Use of Artificial Intelligence for Decision Making in the Firm
Volný, Miloš ; Budík, Jan (referee) ; Dostál, Petr (advisor)
This thesis is concerned with future trend prediction on capital markets on the basis of neural networks. Usage of convolutional and recurrent neural networks, Elliott wave theory and scalograms for capital market's future trend prediction is discussed. The aim of this thesis is to propose a novel approach to future trend prediction based on Elliott's wave theory. The proposed approach will be based on the principle of classification of chosen patterns from Elliott's theory by the way of convolutional neural network. To this end scalograms of the chosen Elliott patterns will be created through application of continuous wavelet transform on parts of historical time series of price for chosen stocks.
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Recurrent Neural Networks for Speech Recognition
Nováčik, Tomáš ; Karafiát, Martin (referee) ; Veselý, Karel (advisor)
This master thesis deals with the implementation of various types of recurrent neural networks via programming language lua using torch library. It focuses on finding optimal strategy for training recurrent neural networks and also tries to minimize the duration of the training. Furthermore various types of regularization techniques are investigated and implemented into the recurrent neural network architecture. Implemented recurrent neural networks are compared on the speech recognition task using AMI dataset, where they model the acustic information. Their performance is also compared to standard feedforward neural network. Best results are achieved using BLSTM architecture. The recurrent neural network are also trained via CTC objective function on the TIMIT dataset. Best result is again achieved using BLSTM architecture.
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Text Recognition Enhanced by Writer Identity
Trněný, Matěj ; Kišš, Martin (referee) ; Kohút, Jan (advisor)
The objective of this theses was to implement a neural network for text recognition enhanced by writers identity. Adversarial learning method was selected for this purpose. Usefulness of this method was verified by experiments. This net should yield better results on data which are not similar to data contained in training data set. Accuracy of the resulting net was compared to method single-task learning and method multi-task learning. Net implementing single-task learning method has reached average character recognition error of 7, 995%, net implementing multi-task learning method has reached average error of 7, 565% and net implementing adversarial learning method has reached average error of 7, 573%. In comparison to the net implementing single-task learning multi-task learning has improvement of 5, 38% and adversarial learning has reached improvement of 5, 28%.
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Generating Animations with Neural Networks
Dráber, Filip ; Kohút, Jan (referee) ; Hradiš, Michal (advisor)
Ačkoli je snímání pohybu už tak nástrojem, který má animátorům pomoci zjednodušit ty nejsložitější aspekty tvorby realistických animací, spousta námahy je stále ukrytá v anotování a strukturalizaci těchto dat. Tento problém řeším návrhem neuronové sítě, která může být natrénována na datovém souboru nasnímaného pohybu tak, aby reprodukovala lidský pohyb, který je vizualizován v aplikaci, které umožňuje uživateli tento pohyb ovládat. Také experimentuji s různými metodami trénování autoregresivního modelu, a na základě toho určuji, která metoda nejlépe vyvažuje dobu trénování a výkon. Dalším postřehem je, jak přidání ovládacích hodnot do vlastností generovaných snímků ovlivňuje použití rekurentních neuronových sítí pro tento úkol.
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Web application for Cybersecurity Job Ads Analysis
Turek, Adam ; Sikora, Marek (referee) ; Ricci, Sara (advisor)
Cílem bakalářské práce je vytvoření interaktivní celosvětové mapy zobrazující databázi pracovních inzerátů ve webové aplikaci a provedení filtrování podle různých parametrů, kde je následně provedena analýza strojového učení. Také mapa zobrazuje počet inzerátů na pracovní pozice podle příslušných států. Webová aplikace je vytvořena pomoci JavaScriptové knihovny ReactJS spojené s LeafletJS, které zajišťují hlavní funkcionalitu. Část se strojovým učením a změna skriptů je realizována pomocí programovacího jazyku Python. Práce popisuje teoretickou část a implementaci jednotlivých funkcí mapy a dále se zabývá popisem a úspěsnou úpravou skriptů pro účely provedení strojového učení.
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