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License plate recognition
Trkal, Ondřej ; Hynčica, Tomáš (referee) ; Jirsík, Václav (advisor)
This thesis deals with the recognition of license plates using neural networks with backpropagation learning. The theoretical section is a brief summary of the principle of creating a new license plate, computer vision and neural networks with backpropagation learning. The practical part describes the design of methods used to detect single-line license plates of cars in the Czech Republic. In this work has been tested several ways to describe the signs and examined the effect of these descriptions and topology of neural networks for quality license plate recognition.
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Weather Forecast Based on Different Sources
Hořák, Martin ; Kořenek, Jan (referee) ; Novotný, Tomáš (advisor)
Bachelor thesis deals with the weather forecast through the artificial neural network with backpropagation method. The forecast is based on data obtained from freely accessible services offering weather information. The created application downloads and saves the forecasts from servers and creates a new forecast. The results of the prediction are being compared with the reality and the original services.
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Neural network for style transfer
Kadlec, Filip ; Matoušek, Radomil (referee) ; Hůlka, Tomáš (advisor)
In this bachelor’s thesis we describe machine learning, types of artificial neural networks and internal processes of neural networks, such as feedforward data processing and training neural networks. We are also pursuing comparison and description of libraries (such as TensorFlow and Keras), which are suitable for neural networks implementation. In the practical part of thesis, we are dealing with problem called artistic style transfer with convolutional neural network.
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Optical character recognition from image data
Marinič, Michal ; Uher, Václav (referee) ; Burget, Radim (advisor)
The thesis is concerned with optical character recognition from image data with different methods used for character classification. In the first theoretical part it focuses on explanation of all important parts of system for optical character recognition. The latter practical part of the thesis describes an example of image segmentation, the implementation of artificial neural networks for image recognition and create simple training set of data for the evaluation of the network. It also describes the process of training Tesseract tool and its implementation in a simple application EasyTessOCR for character recognition.
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The Use of Artificial Intelligence on Stock Market
Skočík, Michal ; Pekárek, Jan (referee) ; Budík, Jan (advisor)
Diploma thesis is focused on problematics of artificial neural networks and their usage on capital markets. There is a software created as a part of this diploma thesis which can load input data and create neural network that serves for share price forecast. This program is created in numerical computing environment MATLAB. Created neural network is tested under simulation of business model. Results are discussed upon examination of results of simulation.
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Algorithmic Trading Using Artificial Neural Networks
Šeda, Jan ; Pešán, Jan (referee) ; Szőke, Igor (advisor)
The capability to be able to determine the future progression on the worlds stock exchange is an important issue, which has become discernible in the last decades. An important role of this progression lies within the fast advancements in computerized technology.Aforementioned document describes a mechanism used for prediction of the future price of a certain stock. The strategy of trading is build upon this mechanism, and the core of this prediction system is an artificial neural network. Inputs used in this network are indicators derived from technical analysis. This trading system was implemented into historical trades and successfully tested.
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Prediction of Time Series Using Statistical Methods
Beluský, Ondrej ; Bidlo, Michal (referee) ; Schwarz, Josef (advisor)
Many companies consider essential to obtain forecast of time series of uncertain variables that influence their decisions and actions. Marketing includes a number of decisions that depend on a reliable forecast. Forecasts are based directly or indirectly on the information derived from historical data. This data may include different patterns - such as trend, horizontal pattern, and cyclical or seasonal pattern. Most methods are based on the recognition of these patterns, their projection into the future and thus create a forecast. Other approaches such as neural networks are black boxes, which uses learning.
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Mathematical Modeling of Company Efficiency Using Neural Networks in Maple
Bartulec, Tomasz ; Vašátko, Jiří (referee) ; Chvátalová, Zuzana (advisor)
The goal of this thesis is to study the possibilities of Artificial neural network as an innovative mathematical methods for financial analysis of company performance, to find out what are today´s requests for performance evaluation of companies are and to identify possible ways how to use this relatively new concept in this area. When processing the possibilities of the computer program Maple for mathematical calculations will be applied. Intermediate objectives are then acquainted with the basic principle on which the artificial neural networks works, to analyze the financial performance of specific company and evaluate potential predictive abilities of the proposed network. The result of the work should be evaluating the success of this approach to financial analysis and evaluation of its use in practice.
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Melody Harmonization
Trnkóci, Andrej ; Jaroš, Jiří (referee) ; Fapšo, Michal (advisor)
Computer scientists have long been considering music as a particularly interesting art Indeed, the history of computer music is almost as long as the history of computer science. Programs to compose music, or to make music" at various levels of the composition process have been designed since the 50s. This bachelor's thesis surveys the main approaches in the field of automatic harmonization, i.e. the problem of producing musical arrangements (scores) from given melodies, and focuses on the most widely used techniques to do so. The main goal of this paper is the issue of design and implementation of a software system for an automatic music harmonization which should learn the rules of harmony from the database of midi file. In the paper. In this thesis I describe existing systems for harmonization and furthermore I focus mainly on principles of machine learning - theory and application of Artificial Neural Networks and their use for harmonization.
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Artificial Intelligence and Industry 4.0
Hirsch, Radim ; Kroupa, Jiří (referee) ; Kovář, Jiří (advisor)
The aim of this work is to provide an overview of the application of artificial intelligence methods in the context of Industry 4.0. The first chapter defines the concept of industry 4.0, previous development of the industry and inclusion of the scientific field of artificial intelligence in this concept. The second chapter is focused on the applications of artificial intelligence methods in the field of machining, manufacturing industry, automation and energetics. The work concludes with evaluation of methods, their advantages and disadvantages from the point of view of individual practical applications and mentions possible directions of future development.
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