Given the volatility of the stock market and the multitude of financial variables at play, forecasting the value of stocks can be a challenging task. Nonetheless, such prediction task presents a fascinating problem to solve using machine learning. The stock market can be affected by news events, social media posts, political changes, investor emotions, and the general economy among other factors. Predicting the stock value of a company by simply using financial stock data of its price may be insufficient to give an accurate prediction. Investors often openly express their attitudes towards various stocks on social medial platforms. Hence, combining sentiment analysis from social media and the financial stock value of a company may yield more accurate predictions. This thesis proposes a method to predict the stock market using sentiment analysis and financial stock data. To estimate the sentiment in social media posts, we use an ensemble-based model that leverages Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) models. We use an LSTM model for the financial stock prediction. The models are trained on the AAPL, CSCO, IBM, and MSFT stocks, utilizing a combination of the financial stock data and sentiment extracted from social media posts on Twitter between the years 2015-2019. Our experimental results show that the combination of the financial and sentiment information can improve the stock market prediction performance. The proposed solution has achieved a prediction performance of 74.3%.

This work examines the use of convolutional neural networks with a focus on semantic and instance segmentation of cells from microscopic images. The theoretical part contains a description of deep neural networks and a summary of widely used convolutional architectures for image segmentation. The practical part of the work is devoted to the creation of a convolutional neural network model based on the U-Net architecture. It also contains cell segmentation of predicted images using three methods, namely thresholding, the watershed and the random walker.

<div>With the constant advancement of modern radio technology, the safety of radio communication has become a growing concern for us. Communication has become an essential component, particularly in the application of modern technology such as unmanned aerial vehicle (UAV). As a result, it is critical to ensure that a drone can fly safely and reliably while completing duties. Simultaneously, machine learning (ML) is rapidly developing in the twenty-first century. For example, ML is currently being used in social media and digital marking for predicting and addressing users' varies interests. This also serves as the impetus for this thesis. The goal of this thesis is to combine ML and radio communication to identify and classify UAV interference with high accuracy.</div><div>In this work, a ML approach is explored for detecting and classifying jamming attacks against orthogonal frequency division multiplexing (OFDM) receivers, with applicability to UAVs. Four types of jamming attacks, including barrage, protocol-aware, single-tone, and successive-pulse jamming, are launched and analyzed using software-defined radio (SDR). The jamming range, launch complexity, and attack severity are all considered qualitatively when evaluating each type. Then, a systematic testing procedure is established, where a SDR is placed in the vicinity of a drone to extract radiometric features before and after a jamming attack is launched. Traditional ML methods are used to create classification models with numerical features such as signal-to-noise ratio (SNR), energy threshold, and important OFDM parameters. Furthermore, deep learning method (i.e., convolutional neural networks) are used to develop classification models trained with spectrogram images filling in it. Quantitative indicators such as detection and false alarm rates are used to evaluate the performance of both methods. The spectrogram-based model correctly classifies jamming with a precision of 99.79% and a false-alarm rate of 0.03%, compared to 92.20% and 1.35% for the feature-based counterpart.</div>

In this thesis is handled the problem of mushroom detection and recognition in natural environment. Convolutional neural networks are used. The beginning of this thesis is dedicated to the theory of neural networks. Further is solved the problem of object detection and classification. Using neural network trained for classification is solved also the task of localization. Results of trained CNNs are analised.

This master’s thesis deals with optical character recognition. The first part describes the basic types of optical character recognition tasks and divides algorithm into individual phases. For each phase the most commonly used methods are described in the next part. Within the character recognition phase the problematics of artificial neural networks and their usage in given phase is explained, specifically multilayer perceptron and convolutional neural networks. The second part deals with requirements definition for specific application to be used as feedback for robotic system. Convolution neural networks and CNTK library for deep learning using algorithm implementation in .NET is introduced. Finally, the test results of the individual phases of the proposed solution and the comparison with the open source Tesseract engine are discussed.

This Master's Thesis is focused on the principles of neural networks, primarily convolutional neural networks (CNN). It introduces the evolutionary optimization in the context of neural networks. One of existing libraries devoted to the CNN design was chosen (Keras), analysed and used in image classification tasks. An optimization technique based on cartesian genetic programming that should reduce the complexity of CNN's computation was proposed and implemented. The impact of the proposed technique on CNN behaviour was evaluated in a case study.

The goal of this thesis is to firstly design and implement an application for embeddedsystems which will classify MNIST numbers and secondly optimize energy and memoryrequirements of this network. The basics of neural networks, Cortex-M processor cores andembedded devices are described in the theoretical part. Followed by implementation details.Networks learning is done with Python and Theano library on a PC. The network is thenconverted to C for a board STM32F429 Discovery. Last part consist of network optimization,which focuses on convolution, dot product and number representation of weights and biasesof the network.

This thesis deals with real-time human activity recognition (eg, running, walking, driving, etc.) using sensors which are available on current mobile devices. The final product of this thesis consists of multiple parts. First, an application for collecting sensor data from mobile devices. Followed by a tool for preprocessing of collected data and creation of a data set. The main part of the thesis is the design of convolutional neural network for activity classification and subsequent use of this network in an Android mobile application. The combination of previous parts creates a comprehensive framework for detection of user activities. Finally, some interesting experiments were made and evaluated (eg, the influence of specific sensors on detection precision).

Ne vždy je jednoduché získání dostatečně velké a kvalitní datové sady s obrázky zřetelných artefaktů, ať už kvůli nedostatku ze strany zdroje dat nebo složitosti tvorby anotací. To platí například pro radiologii, nebo také strojírenství. Abychom mohli využít moderní uznávané metody strojového učení které se využívají pro klasifikaci, segmentaci a detekci defektů, je potřeba aby byla datová sada dostatečně velká a vyvážená. Pro malé datové sady čelíme problémům jako je přeučení a slabost dat, které způsobují nesprávnou klasifikaci na úkor málo reprezentovaných tříd. Tato práce se zabývá prozkoumáváním využití generativních sítí pro rozšíření a vyvážení datové sady o nové vygenerované obrázky. Za použití sítí typu Conditional Generative Adversarial Networks (CGAN) a heuristického generátoru anotací jsme schopni generovat velké množství nových snímků součástek s defekty. Pro experimenty s generováním byla použita datová sada závitů. Dále byly použity dvě další datové sady keramiky a snímků z MRI (BraTS). Nad těmito dvěma datovými sadami je provedeno zhodnocení vlivu generovaných dat na učení a zhodnocení přínosu pro zlepšení klasifikace a segmentace.

The aim of the following thesis was to study the issue of optical disc and retinal vessels segmentation in ophthalmologic sequences. The theoretical part of the thesis summarizes the principles of different approaches in the field of deep learning, which are used in connection with the given issue. Based on the theoretical part, methods for optical disk segmentation and retinal vessel segmentation based on the convolutional neural networks Linknet, PSPNet, Unet and MaskRCNN are proposed. The practical part of the thesis deals with the description of their implementation and subsequent evaluation.