|
|
COVID-19 disease classification based on analysis of chest X-rays
Šteflík, Dominik ; Kiac, Martin (referee) ; Myška, Vojtěch (advisor)
This diploma thesis addresses the development and evaluation of artificial intelligence algorithms for classifying COVID-19 disease from chest X-ray images. Given the severity and impact of the COVID-19 pandemic on the global population, the ability to rapidly and accurately diagnose diseases from radiographic images has become critical. This study synthesizes current advancements in image processing and deep learning to evaluate the application of several novel classification methods in practice. Using a dataset obtained from a Czech medical environment, these methods are analyzed and validated in order to examine their effectiveness and accuracy in real life scenarios. The methods chosen for this study, COVID-Net, DarkCovidNet, and CoroNet, were selected due to their availability, widespread use and proven effectiveness in the field. The core of the thesis is the design of a convolutional neural network tailored to extract and learn from the subtle features present in X-ray images indicative of COVID-19. This initiative confronted significant challenges posed by variable acquisition parameters of X-ray images, which can substantially affect diagnostic accuracy. The uniformity of these parameters is crucial for reliable analysis, underscoring the importance of rigorous preprocessing techniques. In response, advanced normalization, contrast adjustment, and augmentation procedures were implemented to standardize the input data. The convolutional network itself employs a series of convolutional, pooling, and fully connected layers, optimized to handle the nuanced variations present in medical imaging data. Notably, the network architecture incorporates an attention mechanism, implemented through a Squeeze-and-Excitation block, to dynamically adjust the importance of different channels in the input image. By integrating these elements, the network model is trained to focus on significant features within the X-ray images, allowing it to distinguish subtle indicators of COVID-19 effectively. Furthermore, this work discusses the potential of integrating these AI-driven diagnostic tools into existing healthcare infrastructures to enhance early detection and treatment of COVID-19. The findings indicate that leveraging artificial intelligence in medical imaging can substantially aid in managing and controlling disease outbreaks, ultimately contributing to better health outcomes.
|
|
|
Evolutionary Analogue Amplifier Optimisation
Bielik, Marek ; Zachariášová, Marcela (referee) ; Bidlo, Michal (advisor)
Táto práca demonštruje možnosti využitia evolučných algoritmov, konkrétne evolučných stratégií, v doméne dizajnu analógových zosilňovačov. Do implementácie je zahrnutý ngSPICE simulátor, ktorý je použitý na vyhodnotenie optimalizovaných riešení a v práci je navrhnutých niekoľko vyhodnocovacích metód. Práca tiež zahŕňa experimenty a ich výsledky, ktoré boli použité na určenie najvodnejších parametrov evolučných stratégií. Cieľom bolo optimalizovať hodnoty súčiastok jedno a dvoj stupňových zosilňovačov s bipolárnymi tranzistormi v zapojení so spoločným emitorom. Výsledkom je nástroj umožňujúci návrh zosilňovačov s ľubovoľným zosilnením v rámci možností daného obvodu bez použitia akéhokoľvek matematického aparátu.
|
|
|
UNIVERSAL BASIC OPRESSION
Růžičková, Martina ; Jánoščík,, Václav (referee) ; Sterec, Pavel (advisor)
Master's thesis Polyamory Design Unit (PDU) explores the possibilities of collaboration between experts being active in fine arts, product design, graphic design, architecture and philosophy in order to create a speculative future scenario. Together with Jana Trundova, Simon Barak, Ondrej Mohyla and Lukas Likavcan, I create the concept and the presentation structure for a housing complex, which is designed for polyamoric coexistence of human and non-human entities. Such a coexistence is made possible by full automation of work and global implementation of universal basic income. These initial parameters constitute a big emancipatory potential, that could change present meaning of the concept of polyamory and thus redefine networks of relations in bigger scales too.
|
|
|
Simulation Based Matchmaking Optimisation
Eštvan, Ivan ; Chlubna, Tomáš (referee) ; Milet, Tomáš (advisor)
This bachelor's thesis focuses on designing a working matchmaking system and simulation environment for a First Person Shooter like game and their implementation within Unreal Engine 4. It introduces various types of matchmaking systems used in today's games and explains some basic concepts used in Unreal Engine 4 to implement such environments. Implemented system then takes the input data, with information about players, creates matches by using our own matchmaking and performs a simulation of them, providing the simulation results of created matches for further analysis.
|
|
|
The Use of Artificial Intelligence for Decision Making
Nezbedová, Katarína ; Pekárek, Jan (referee) ; Dostál, Petr (advisor)
This bachelor thesis deals with the Tamari attractor problem and its application for forming a prediction model. The core of the work is to create a simulation program in the MATLAB development environment and to use it to create and compare several case studies of a predictive model based on different parameters. This model is graphically illustrated and supplemented by economic interpretation.
|
|
|
Running Motion Analysis
Eliáš, Radoslav ; Kolářová, Jana (referee) ; Goldmann, Tomáš (advisor)
Cieľom tejto práce je analyzovať pohyb a držanie tela pri behu. Systém pracuje so záznamom z dvoch kamier, zboku a zozadu. Využíva nástroj na detekciu postoja ľudského tela založenú na konvolučnej metóde. Práca porovnáva niekoľko detektorov. Výsledný systém používa detektor OpenPose a implementuje knižnicu s výpočtami pre rôzne metriky používane na ohodnotenie formy behu. Výsledky sú zobrazené v multiplatformnej aplikácii. Ohodnotená bola niekoľkými experimentmi na osobnej dátovej sade videí behu.
|
|
|
RPG Game in Unity with Procedural Elements
Líška, Samuel ; Vlnas, Michal (referee) ; Milet, Tomáš (advisor)
The main objective of this thesis is to create 2D top-down RPG game with a focus on procedural generation in Unity. This thesis contains a summary of information about videogames, procedural content generation, game engines, and Unity itself. This thesis also contains solution design and implementation of the game. Perlin noise and its processing into the biome with the usage of Whittaker diagram has been used. Multiple systems to enhance gameplay are described as well. Lastly, this thesis contains testing and evaluation with a small survey.
|
|
|
Deep Neural Network Optimization
Bažík, Martin ; Wiglasz, Michal (referee) ; Sekanina, Lukáš (advisor)
The goal of this thesis was to design, implement and analyze various optimizations of deep neural networks, in order to improve the observed parameters. The optimizations are based on modification of the data representation used by neural network operations and searching for the best combination of its hyper-parameters. The convolutional neural networks used for these optimizations were built on LeNet-5 architecture and trained on MNIST, CIFAR-10, and SVHN datasets. The neural networks and their optimizations were implemented within Tiny-dnn library using C++ programming language.
|
|
|
Algorithmic Trading Using Artificial Neural Networks
Poláček, Samuel ; Beneš, Karel (referee) ; Szőke, Igor (advisor)
Algorithmic trading of many kinds of assets is not a new field at all. Domain of neural networks provides many tools, which are usefull in this field. This bachelor thesis discusses cryptocurrency trading algorithms using artificial neural network. In theoretical section of this thesis the basic theory and terms the stock market trading is based on is discussed. After the basic idea of cryptocurrencies is defined and used technical tools are introduced, the practical section starts. Sufficient configuration of neural network topology and hyperparameters values are obtained by many experiments. Subsequently after many experiments with indicators of technical analysis, acceptable neural network input configuration is obtained. Created neural network model combined with defined trading strategy generates profit.
|
|
| |
guest ::
