This thesis deals with the optimization of the supply process of a small business, especially with determining the optimal inventory level and demand forecasting, using tools of time series analysis. The final part gives a description of the creation of the program that is used to calculate individual indicators and forecasts. The aim is to increase the efficiency of the supply process, thereby reducing operating costs of the company.

The thesis deals with the trading on capital markets, the use of artificial intelligence, artificial neural networks, for modeling the behavior of stocks. The work contains a description of the capital markets, stock trading, methods of artificial intelligence. The main part of the thesis is the model for predicting the course and trend of shares, working in MATLAB, which serves as a support for trading decisions.

Tato diplomová práce se zabývá problematikou obchodování na komoditních trzích. Řešení problematiky spočívá ve využití umělé inteligence, konkrétně neuronových sítí, k technické analýze vývoje ceny vybrané komodity a snaze o co nejpřesnější predikci budoucího vývoje ceny pro podporu investičního rozhodování. Model neuronové sítě je vytvořen a použit pro predikci v programu MATLAB.

This thesis elaborates financial analysis of company J. P. PLAST, s.r.o. for period 2010 to 2013. Elementary methods of technical analysis, concretely horizontal and vertical analysis of financial statements, ratio indicators analysis and prediction of financial distress of company. Financial analysis is introduces from basic terms and explanation of individual indicators up to practical application of them and their interpretation. It should help to improve financial management, prepare supporting materials for impro-ving to economic situation of company and optimize managerial decision. In the conc-lusion section, there are proposed measures that may improve financial situation of the company. Application of the thesis will certainly be contribution for analysed company J. P. PLAST, s.r.o.

The web and mobile web traffic is growing rapidly due to service accessibility in densely populated areas and massive usage of smart hand held devices (smartphones, pads and Internet dongles). This is a challenge for mobile operators and service providers, because it forces systematically increasing network throughput capacity and in parallel to keep this investment on profitable basis. On the other side, the user satisfaction with mobile broadband service should be considered as well, in order to balance the user expectations and mobile operator investments. Our work provides extensive QoE study for different conditions in sense of mobile web service, web content, network conditions and end user device. Finally, the obtained results create basis for more dimensional overview of mobile web QoE and allow to recognize quality and saturation thresholds based on network parameters. Further, obtained data provide base for design three prediction models for prediction of MOS (one for notebook, one for mobile device) and acceptability.

This thesis is focused on predicting the impact of amino acid substitution on protein stability. The main goal is to create a consensual predictor that uses the outputs of chosen existing tools in order to improve accuracy of prediction. The optimal consensus of theese tools was designed using evolution strategies in three variants: 1/5 success rule, self-adaptation variant and the CMA-ES method. Then, the quality of calculated weight vectors was tested on the independent dataset. Although the highest prediction performance was attained by self-adaptation method, the differences between all three variants were not significant. Compared to the individual tools, the predictions provided by consensual methods were generally more accurate - the self-adaptation variant imporved the Pearson's corelation coeficient of the predictions by 0,057 on the training dataset. On the testing dataset, the improvement of designed method was smaller (0,040). Relatively low improvement of prediction performance (both on the training and the testing dataset) were caused by the fact, that for some records of testing dataset, some individual tools vere not able to provide their results. When omitting these records, consensual method improved the Pearson's corelations coeficient by 0,118.

This thesis deals with employing coevolutionary principles to the image filter design. Evolutionary algorithms are very advisable method for image filter design. Using coevolution, we can add the processes, which can accelerate the convergence by interactions of candidate filters population with population of fitness predictors. Fitness predictor is a small subset of the training set and it is used to approximate the fitness of the candidate solutions. In this thesis, indirect encoding is used for predictors evolution. This encoding represents a mathematical expression, which selects training vectors for candidate filters fitness prediction. This approach was experimentally evaluated in the task of image filters for various intensity of random impulse and salt and pepper noise design and the design of the edge detectors. It was shown, that this approach leads to adapting the number of target objective vectors for a particular task, which leads to computational complexity reduction.

Hlavním cílem této práce je obohatit hudební signály charakteristikami lidské řeči. Práce zahrnuje tvorbu audioefektu inspirovaného efektem talk-box: analýzu hlasového ústrojí vhodným algoritmem jako je lineární predikce, a aplikaci odhadnutého filtru na hudební audio-signál. Důraz je kladen na dokonalou kvalitu výstupu, malou latenci a nízkou výpočetní náročnost pro použití v reálném čase. Výstupem práce je softwarový plugin využitelný v profesionálních aplikacích pro úpravu audia a při využití vhodné hardwarové platformy také pro živé hraní. Plugin emuluje reálné zařízení typu talk-box a poskytuje podobnou kvalitu výstupu s unikátním zvukem.

This thesis discusses the issue of predicting of the effect of amino acid substitutions on protein funkcion, based on phylogenetic analysis method, inspired by tool MAPP. Significant number of genetic diseases is caused by nonsynonymous SNPs manifested as single point mutations on the protein level. The ability to identify deleterious substitutions could be useful for protein engineering to test whether the proposed mutations do not damage protein function same as for targeting disease causing harmful mutations. However the experimental validation is costly and the need of predictive computation methods has risen. This thesis describes desing and implementation of a new in silico predictor based on the principles of evolutionary analysis and dissimilarity between original and substituting amino acid physico-chemical properties. Developed algorithm was tested on four datasets with 74,192 mutations from 16,256 sequences in total. The predictor yields up to 72 % accuracy and in the comparison with the most existing tools, it is substantially less time consuming. In order to achieve the highest possible efficiency, the optimization process was focused on selection of the most suitable (a) third-party software for calculation of a multiple sequence alignment, (b) overall decision threshold and (c) a set of physico-chemical properties.

Cílem práce je seznámení se s problematikou uchovávání informace v DNA, provést rešerši na téma transpozony, bioinformatické nástroje a algoritmy, které jsou používány k jejich detekci v nasekvenovaných genomech a vytvořit tak stručný úvod do obsáhle problematiky, včetně jejího zasazení do kontextu současně probíhajícího výzkumu v dané oblasti. Na základě přehledu stávajících algoritmů a nástrojů pro detekci transpozonů je navržen a implementován nástroj pro hledání tzv. LTR transpozonů.