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The Use of Artificial Intelligence on Stock Market
Kuna, Martin ; Veselý, Karel (referee) ; Dostál, Petr (advisor)
Diplomová práce se zabývá aplikací vybraných metod umělé inteligence v prostředí kapitálových, potažmo akciových, trhů. Konkrétně se zaměřuje na využití umělých neuronových sítí pro predikci trendu a na možnost aplikace genetických algoritmů k řešení problému optimalizace investičního portfolia. Obsahuje návrh řešení uvedených problémů v praxi. Návrhy jsou koncipovány ve formě modelů zpracovaných ve vývojovém prostředí Matlab.
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The analysis of biochemical and hematological data in athletes
Molnárová, Monika ; Korvas, Pavel (referee) ; Chlíbková, Daniela (advisor)
This thesis is focused on patophysiology changes in organism of ultramaraton runners. It analyze the blood parameters idnicating patological changes. It describes the principles of analysis and laboratory testing not only in laboratories, but also in terrain conditions. The thesis includes statistical testing and evaluation of hematological and biochemical parameters indicating severe patologies. With the use of Spearman correlation coefficient are determined the relations between the parameters. The relation between the found parameters are then further analyzed by regression. The goal of the thesis is finding a way to predict biological parameters by using a group of other parameters. In the practical part of the work is described the algorithm for prediction, the network testing results and other ways to improve the prediction algorithm.
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Mixing of Predictors in Parameter Estimation
Podlesna, Yana ; Kárný, Miroslav
This bachelor thesis deals with the design of the method for solving the curse of dimensionality arising in the quantitative modeling of complex interconnected systems. The employed predictive models are based on a discrete Markov process. Prediction is based on estimating model parameters using Bayesian statistics. This work contains method for reducing the amount of data needed for prediction in systems with a large number of occurring states and actions. Instead of estimating a predictor dependent on all parameters, the method assumes the use of several predictors, which arise from estimating parametric models based on dependences on different regressors. The behavioral properties of the proposed method are illustrated by simulation experiments.
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Driver Steering Model for Simulation Algorithms
Tmejová, Tereza ; Zháňal, Lubor (referee) ; Hejtmánek, Petr (advisor)
This diploma thesis deals with the creation of a computation driver model. In the first part, there is an overview on driver models for longitudinal and lateral control. Next, driving maneuvres that could be selected for testing of driver model are described. In the practical part, there is created a computational driver model, whose task is to follow required path. The resulting model is tested on three driving maneuvers - steady turning, moose test and slalom. Finally, this model is tested on the passage of a real track. For all these tracks, a comparison is made and the success of the model is evaluated.
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Prediction of the Effect of Mutation on Protein Solubility
Velecký, Jan ; Martínek, Tomáš (referee) ; Hon, Jiří (advisor)
The goal of the thesis is to create a predictor of the effect of a mutation on protein solubility given its initial 3D structure. Protein solubility prediction is a bioinformatics problem which is still considered unsolved. Especially a prediction using a 3D structure has not gained much attention yet. A relevant knowledge about proteins, protein solubility and existing predictors is included in the text. The principle of the designed predictor is inspired by the Surface Patches article and therefore it also aims to validate the results achieved by its authors. The designed tool uses changes of positive regions of the electric potential above the protein's surface to make a prediction. The tool has been successfully implemented and series of computationally expensive experiments have been performed. It was shown that the electric potential, hence the predictor itself too, can be successfully used just for a limited set of proteins. On top of that, the method used in the article correlates with a much simpler variable - the protein's net charge.
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Intelligent Manager of Fantasy Premier League Game
Vasilišin, Maroš ; Burgetová, Ivana (referee) ; Hynek, Jiří (advisor)
Hra Fantasy Premier League poskytuje miliónom hráčov po celom svete možnosť stať sa na chvíľu manažérom svojho vlastného klubu. Výsledky a bodové ohodnotenie v hre závisia na správnom predvídaní, ako sa budú hráči chovať v skutočných futbalových zápasoch. Ak by pri tomto rozhodovaní pomáhal software na predikciu a analýzu budúcich výkonov hráčov, výsledky v hre sa môžu rapídne zlepšiť. Táto diplomová práca sa zaoberá návrhom a implementáciou predikčného modelu, ktorý využíva neurónové siete na predikcie časových radov počas celej sezóny v hre. Boli použité metódy na spracovanie dát o hráčoch a kluboch za posledné 4 sezóny. Výkonnosť a presnosť predikčných metód boli testované na dátach z poslednej sezóny Premier League a predikcie algoritmu sa vo väčšine prípadov blížili realite. Ak by sa užívateľ držal predikčného modelu v hre stopercentne, získal by väčší počet bodov ako bežný hráč, ktorý žiadny predikčný model nepoužíva.
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Prediction of the Effect of Mutation on Protein Solubility
Marko, Július ; Smatana, Stanislav (referee) ; Hon, Jiří (advisor)
Protein solubility is a key problem in production of functional proteins. Prediction of the effect of mutation on protein solubility could save a lot of time and money, as it would provide in silico prediction of solubility enhancing mutations before performing deep mutational scanning in laboratory. In this work, new predictor of the effect of mutation on protein solubility SoluProtMut is introduced that is based on machine learning methods. Most of the existing predictors predict the effect from the amino acid sequence. In addition to the sequence, the tool presented in this work also uses the spatial structure of the protein, which can significantly increase it's accuracy.
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Optimization of Run Configurations of k-Wave Jobs
Sasák, Tomáš ; Jaroš, Marta (referee) ; Jaroš, Jiří (advisor)
This thesis focuses on scheduling, i.e. correct approximation of configurations used to run k-Wave simulations on supercomputers from the IT4Innovations infrastructure. Especially, for clusters Salomon and Anselm. A single work is composed of a set which contains many simulations. Every simulation is executed by some code from the k-Wave toolbox. To calculate the simulation, it is necesarry to select a suitable configuration, which means the amount of supercomputer resources (number of nodes, i.e. cores), and the duration of the rental. Creation of an ideal configuration is complicated and is even harder for an inexperienced user. The approximation is made based on the empiric data, obtained from multiple executions of different sets of simulations on given clusters. This data is stored and used by a set of approximators, which performs the actual approximation by methods of interpolation and regression. The text describes the implementation of the final scheduler. By experimenting, the most efficient methods for this problem has found out to be Akima spline, PCHIP interpolation and cubic spline. The main contribution of this work is creation of a tool which can find suitable configuration for k-Wave simulation without knowing the code or having lots of experience with its usage.
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Prediction of spontaneous preterm labor - role of midwife
Plojharová, Tereza ; Pařízek, Antonín (advisor) ; Přáda, Jan (referee)
This bachelor thesis focuses on the issue of premature birth prediction. Premature delivery is one of the most serious problems in obstetrics and the cause of many perinatal complications. The incidence in the Czech Republic today is around 7,2 %. The problem of prediction of this serious pathology is false negativity or converselyfalse positivity. Regarding the latter, women are unnecessarily hospitalized and treated. It is very important to find a method that could detect as many impending premature births as possible and at the same time show the lowest possible false positivity. However, this is very difficult. Premature birth is caused multifactorial effects. The aim of the study was to determine the premature birth prediction using vaginal ultrasound cervicometry in combination with the examination of fetal fibronectin levels in the transvaginal fluid and the effectiveness of the QUIPP algorithm in predicting the premature birth. These goals were accompanied by 6 hypotheses that helped to fulfill the goals. From January 2019 to March 2020, asymptomatic women with a high risk of premature birth in the week of pregnancy 22+0 to 25+6 were examined. Also, women who then gave birth at the Department of Gynecology and Obstetrics of the First Faculty of Medicine and General Teaching Hospital in...
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Travel Time Prediction
Mudroch, Andrej
This paper discusses the methods of travel time prediction based on the usage of machine learning and historical data. The developed prediction models are described as well as the data sources which were used as the input of the prediction models. Finally, the comparison of the models‘ performance is shown, providing proof the developed models have ability to outperform the widely used model based on instantaneous travel time that is not using statistical learning.
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