This diploma thesis deals with the evaluation of the economic situation of ZLKL, s. r. o. using statistical methods. Primarily the thesis proceeds from financial records of the company which are put through the financial analysis. On the basis of its results the statistical analysis of chosen indicators is then accomplished. Using statistical methods it tries to analyse the development of each indicator, its trend, and to predict its future development. In the last part of the thesis, there is the evaluation of each indicator, and the formulation of suggestions and recommendations by whose implementation the company should achieve the bigger financial stability and the long-term stable management.

This thesis describes a new approach to the detection of protein stability change upon amino acid mutations. The main goal is to create a new meta-tool, which combines the outputs of eight well-established prediction tools and due to suitable method of consensus making, it is able to improve the overall prediction accuracy. The optimal strategy of combination of outputs of these tools is found by using a various number of machine learning methods. From all tested machine learning methods, KStar showed the highest prediction accuracy on the training dataset compiled from experimentally validated mutations originating from ProTherm database. Due to this reason, it is chosen as an optimal prediction technique. The general prediction abilities is validated on the testing dataset composed of multi-point amino acid mutations extracted also from ProTherm database. Since the multi-point mutations were not used for training any of integrated tools, we suppose that such comparison is objective. As a result, the developed meta-tool based on KStar technique improves the correlation coefficient about 0.130 on the training dataset and 0.239 on the testing dataset, respectively (the comparison is being made against the most succesful integrated tool). Based on the obtained results, it is possible to claim that machine learning methods are suitable technique for the problems from area of protein predictions.

This work describes a method of the secondary structure prediction of proteins based on cellular automaton (CA) model - CASSP. Optimal model and CA transition rule parameters are acquired by evolutionary algorithm. Prediction model uses only statistical characteristics of amino acids, so its prediction is fast. Achieved results was compared with results of other tools for this purpose. Prediction cooperation with a existing tool PSIPRED was also tested. It didn't succeed to beat this existing tool, but partial improvement was achieved in prediction of only alpha-helix secondary structure motif, what can be helful if we need the best prediction of alpha-helices. It was developed also a web interface of designed system.

E-Sports, also known as progaming (professional gaming) has grown a lot in the last few years. Professional gamers are regularly attending tournaments watched by hundreds of thousands of fans and with prize pools of millions of dollars. There are many video broadcasts of those events and recently betting on e-Sports has also become available. The main goal of this thesis was to create a mobile app for OS Android, which aims to utilize this growth and create a service of providing predictions of results of the e-Sports matches, similar to that existing in regular sports. The application in its current form receives the predictions via Google Cloud Messaging service and shows an Android notification on their arrival. The predictions are then stored on the device into SQLite database so they are available for further view and filtering. After the matches are finished, their results are shown in comparison to the predictions and balance of the predictions is calculated. Users can display information about their subscriptions and predictions under that subscriptions. The app was created in Android Studio IDE with appearance based on the material design guidelines. The app was tested on several devices of different brand and Android version, then it was placed on Google Play for open beta testing. In the future the app will be offered to the users of the service of providing predictions of results of the e-Sports matches.

This master’s thesis will look into the use of data mining in the area of segmentation and the prediction of onboarding candidates of a recruitment agency. The obtained results should serve to make company processes more effective concerning the processing of orders, and should also facilitate a more personal approach to candidates. The first chapter includes imperetive theoretical bases from the studies of Business Intelligence, data warehouses, data mining and marketing. Thereafter an analysis of the current state is presented with a focus on the capture of the key processes in processing and order. The last chapter looks at the proposed solution and implementation on the platform Microsoft SQL Server 2014. To conclude there are proposals of utilizing data mining in direct marketing.

For a long time, there has been little research on dynamic social networks. However, in recent years, there has been much more focus on this field and many techniques for analyzing temporal aspects of social networks were proposed. In this work, we studied a dynamic social network based on data retrieved from the Commercial Register. This registry contains information about all economic entities that operate in the Czech Republic, including people who hold functions in entities and their addresses of living. We applied several data analysis techniques including community tracing, clustering, and methods for identifying key actors to find important entities and individuals in the social network and inspect their changes over time. 1

This thesis describes and compares methods for statistical modeling of spatio- temporal data. Methods are extended by examples and numerical studies on real world data. Basic point of interest is statistical analysis of spatial data with unknown correlation structure and known position in space. Further analysis is focused on spatial data with temporal component - spatio-temporal data. Fi- nally, extremal values and their occurrences are discussed. The main aspiration of my thesis is to provide statistical tools for spatio-temporal data and analysis of extremal values of prediction. 1

The main goal of the thesis is to build upon the existing research literature and to explore the relationship of sport confidence and sports performance, among which a positive correlation is often found. The theoretical part summarizes the present findings regarding this connection, as well as how sport confidence generally works, what constructs it relates to, or how it is measured. In the empirical part, quantitative research on a sample of Sri Lankan swimmers between 17 and 19, executed using the Sport-Confidence Inventory (SCI; Vealey, Knight, 2002), is then presented. Its results showed that participants scoring high at least in one of the three SCI subscales were performing better, regardless of how well the other components were developed, as opposed to participants whose scores were moderate in all the three subscales. The results also pointed to significant differences between the genders, where it showed that men generally scored higher on the level of sport confidence. Series of recommendations for trainers and psychologists working with athletes, but also for potential follow-up studies, can be drawn from the outcomes. Keywords: sport-confidence, multidimensionality of confidence, performance prediction, competitive swimming, SCI

This diploma thesis deals with the discovery of factors that influence the household saving rate in the Czech Republic and Slovakia and the detection of any differences between these countries. In addition to the identification of significant determinants of saving, it investigates the development of Czech and Slovak household saving rate between 2000 and 2017. For this purpose a model is designed that describes the behaviour of the household saving rate over time, while allowing the prediction of values for the next period. Last but not least, recommendations on economic policy measures are set out on the basis of the findings.

Social networks can be helpful for the analysis of behaviour of people. An existing social network is rarely available, and its nodes and edges have to be inferred from not necessarily graph data. Link prediction can be used to either correct inaccuracies or to forecast links about to appear in the future. In this work, we study the prediction of miss- ing links in a social network inferred from real-world bank data. We review and compare both verified and modern approaches to link prediction. Following the advancements of deep learning in recent years, we primarily focus on graph neural networks, and their ability to scale to large networks. We propose an adjustment to an existing graph neural network method and show that its performance is either comparable with or outperform- ing the original method. The comparison is performed on two social networks inferred from the same data. We show that it is relatively hard to outperform the verified link prediction methods with graph neural networks. 1