This master thesis deals with bankruptcy prediction models for construction companies doing business in Czech Republic. Terms important for understanding the issue are defined in the theoretical part. In analytical part, there are five current bankruptcy prediction models tested on the analysed sample and resulted accuracy compared with original ones. On the basis of knowledges acquired, there is developed a brand-new bankruptcy prediction model.

Master's thesis deals with the assessment of selected financial indicators of the company through a financial analysis and statistical methods, on the basis of which then evaluates the current situation of the company. The thesis is divided into three parts. The theoretical part contains the issues necessary for the analytical part. The analytical part is focused on the analysis of selected indicators and the subsequent application of statistical methods to predict their future development and to detect dependencies between the indexes. The last part formulates possible solutions to problems caused by financial indicators that do not reach the required values.

The diploma thesis evaluates the economic situation of BPS Bicycle Industrial s. r. o. time series analysis. The theoretical part describes financial indicators, time series analyzes and regression and correlation analysis. Based on the analyzes, suggestions were made to improve the current situation of the company. BPS has proven to be financially sound. Shortcomings to improvement were identified from the analyzes. For example, share of equity and debt, use of surplus funds and turnover of receivables and payables. This ratio needs to be addressed through greater use of debt. The system of sanctions is solution for problem the turnover of receivables and the use of surplus funds by investing in shares.

Kartézské genetické programován (CGP) je evoluc inspirovaná metoda strojového učen, která je primárně určená pro automatizovaný návrh programů a čslicových obvodů. CGP je úspěšné v řešen mnoha úloh z reálného světa. Avšak k nalezen inovativnch řešen obvykle potřebuje značný výpočetn výkon. Každý kandidátn program navržený pomoc CGP mus být spuštěn, aby se zjistilo, do jaké mry tento program řeš zadaný problém, a mohla mu být přiřazena fitness hodnota. Právě vyhodnocen fitness bývá výpočetně nejnáročnějš část návrhu pomoc CGP. Tato práce se zabývá využitm koevoluce prediktorů fitness v CGP za účelem zrychlen procesu evolučnho návrhu prováděného pomoc CGP. Prediktor fitness je malá podmnožina trénovacch dat použvaná pro rychlý odhad fitness hodnoty namsto náročného vyhodnocen objektivn fitness hodnoty. Koevoluce prediktorů fitness je optimalizačn metoda modelován fitness, která snižuje náročnost a frekvenci výpočtu fitness. V této práci je koevolučn algoritmus přizpůsoben pro CGP a jsou představeny a zkoumány tři přstupy k zakódován prediktorů fitness. Představená metoda je experimentálně vyhodnocena v pěti úlohách symbolické regrese a v úloze návrhu obrazových filtrů. Výsledky experimentů ukazuj, že pomoc této metody lze významně snžit výpočetn čas, který CGP potřebuje pro řešen zkoumané třdy úloh.

Master´s thesis deals with the assessment of selected financial indicators of the company using a statistical methods. The first part is focused on the theoretical description of financial indicators, time series analysis as well as regression and correlation analysis. The practical part contains a statistical analysis of selected indicators with subsequent prediction of indicators for the next two years. The practical part also contains a comparison of selected indicators with the industry average and a correlation analysis to determine the dependence of selected indicators. The last part contains suggestions to improve the situation of the company.

The thesis deals with the assessment of the economic situation of a selected company using statistical methods. The theoretical part describes the financial indicators, time series and regression analysis. The analytical part contains the calculation of selected indicators, which are subjected to statistical analysis. Based on the statistical analysis, the future development of indicators in the next two years is predicted. The last part contains suggestions that can lead to the improvement of the current situation of the company.

The diploma thesis deals with the evaluation of the economic situation of the company ABC, in the years 2012 to 2019. The first, theoretical part of the thesis describes financial indicators, regression and correlation analysis and time series. The practical part of the diploma thesis contains calculations of financial indicators, some of them are selected for statistical analysis, which further serves as a prediction of development in the next two years. The practical part also compares the results with the industry average. The last part of the work contains proposals that lead to the improvement of the financial situation of the selected company.

Antibody-mediated rejection (AMR) is the main cause of the kidney graft dysfunction and its failure after transplantation. Antibodies lead to vascular damage that is either acute or chronic and manifests as sudden or progressive graft dysfunction. Risk factors for development of AMR are time spent on haemodialysis, retransplantation, previous sensitisation against HLA antigens, and persistence of panel-reactive antibodies. Diagnosis is based on detection of deposits of C4d component of complement in peritubular capilaries and presence of donor-specific antibodies (DSA). We can also observe injury caused by antibodies against non-HLA antigens without detection of anti-HLA DSA. Use of "molecular microscope" can be beneficial in diagnosis and stratification of the risk of graft failure. High expression of ENDAT (endothelial activation and injury transcript) improves prediction of kidney graft failure more than C4d staining. Based on gene expression, the AMR scoring system correlates with the diagnosis of AMR and predicts graft loss in the future. The main goal of our work was to recognize patients at risk of AMR. In our study, we confirmed the efficacy and safety of acute AMR therapy with plasmaphereses and administration of intravenous immunoglobulins for improving outcomes of kidney transplantation....

My master's thesis on the topic of "Design of exercises for data mining - Classification and prediction" deals with the most frequently used methods classification and prediction. There are association rules, Bayesian classification, genetic algorithms, the nearest method neighbor, neural network and decision trees on the classification. There are linear and non-linear prediction on the prediction. This work also contains a summary of detail the issue of decision trees and a detailed algorithm for creating the decision tree, including development of individual diagrams. The proposed algorithm for creating the decision tree is tested through two tests of data dowloaded from Internet. The results are mutually compared and described differences between the two implementations. The work is written in a way that would provide the reader with a notion of the individual methods and techniques for data mining, their advantages, disadvantages and some of the issues that directly relate to this topic.

The aim of the thesis was to study and describe data mining methodology CRISP-DM. From the collected database of calls to the call center a prediction was performed, based on CRISP-DM methodology. In phase of test situation modeling four different testing methods were used: the k-NN, neural network, linear regression and super vector machine. The input attributes importance for further prediction was evaluated based on different selections. The results and findings may provide data for further more accurate forecasts in the future; not only in number of calls but also other indicators relevant to the call center.