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Language Identification of Text Document
Cakl, Jan ; Pešán, Jan (referee) ; Szőke, Igor (advisor)
The thesis deals with a language identification of a text document. The final program includes three different implementation methods of language identification. The first method is based on a frequency statistics of N-gram. The second one represents Markov chains and the last one uses the simulated neural net for the identification purposes. The result is implemented in the Python language.
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Music Improvisation
Angelov, Michael ; Hradiš, Michal (referee) ; Fapšo, Michal (advisor)
The thesis deals with problems concerning algorithmic music compositon, especially the domain of musical improvisation. There is an opening presentation of some of existing tools and approaches that are commonly used in domain of computer music. Consenquently there is a proposal of a new system, using main principles of markov chains and prediction suffix trees (PST) with description of its implementation. The main task of developped application is to analyze an external MIDI recording that is proposed to the system by user and create a new and inovative musical material in MIDI format that would sound close to the original recording giving an impression of a computer improvised music to the listener.
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Fuzzy Markov chains and their use in risk management
Šindelková, Petra ; Vymazal, Tomáš (referee) ; Misák, Petr (advisor)
This thesis deals with the application of Markov chains for the production of concrete products. The theoretical part is focused on clarifying the concepts of risk management and describes the procedures for dealing with classical Markov chains. There are presented basics of fuzzy logic and finally there is explained the procedure using fuzzy logic in calculating of classical Markov chains in the subsection entitled Fuzzy Markov chains. The practical part describes production process, namely concrete pavements. On this production process is applied knowledge from the theoretical part and there is a comparison and evaluation of two methods of Marcov chains calculation (classic and fuzzy approach).
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Cross-channel attribution modelling
Žárský, Jiří ; Šperková, Lucie (advisor) ; Vraná, Lenka (referee)
This bachelor thesis focuses on attribution in the context of online marketing and studies available evaluation models for the performance of advertising campaigns. This performance is measured on the basis of the campaigns' effectiveness in catching the attention of customers and generating revenue. Data containing information about users' interactions with real advertising campaigns were used for the analysis. Prior to solving the attribution problem, data from the AdForm platform were cleansed and transformed into the required structures. This process is automated by the ETL tool called Keboola. Afterwards, data are analyzed using various attribution modelling techniques such as simple heuristics, the Shapley Value or Markov chains. The thesis discusses the theoretical side, as well as the actual application of these models. In the last section, the results of individual models are interpreted, taking into account the campaign costs. The interpretation is performed in the Tableau visualization software, using metrics such as the return on advertisement spending. This thesis presents a critical assessment of attribution models based on predetermined criteria. A scheme of data transformations, which can be used for future analyses of advertisement campaigns, was also created as part of this work. The thesis further includes a chapter discussing issues potentially leading to inaccuracies in the models' results.
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Optimization of Algae Population Growth Using Markov Chains and
Zouharová, Martina ; Kalčevová, Jana (advisor) ; Černý, Michal (referee)
The thesis deals with the task of refining the constructional and operational parameters of a tubular photobioreactor in order to maximise the growth rate of algae contained in the cultivation suspension. It builds on a basic growth model of the Porhydrium sp. alga, and focuses on the optimization of external irradiance, which is one of the key determinants of algae growth. Two distinct methodological approaches are applied: analytic approach, which employs Markov Chains, and simulation approach, which relies on agent-based simulations. In the analytic part, we introduce the construction of state transition matrix for a Markov Chain that accounts for varying irradiance inside the photobioreactor (in contrast to constant-irradiance methods that have been published so far). In the simulation part, we devised an agent-based model of algae population that enables us to analyze the system behaviour while interactively changing the model parameters. In the context of the results from both the analytic and simulation part, we conclude by suggesting the optimal level of external irradiance.
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