This master’s thesis is oriented on human resource management. This thesis analyzes the efficiency of the company with a focus on personal character weaknesses, ie. the organizational structure, competence of manager, combination compensation, system of empleyee appraisal and the stress load at work. It contains proposals for solutions on improving work efficiency through personnel changes.

The diploma thesis deals with artificial intelligence utilization for predictions on stock markets.The prediction is unconventionally based on Bayes' probabilistic model theorem and on its based Naive Bayes classifier. I the practical part algorithm is designed. The algorithm uses recognized relations between identifiers of technical analyze. Concretely exponential running averages at 20 and 50 days had been used. The program output is a graphic forecast of future stock development which is designed on ground of relations classification between the identifiers

The master’s thesis deals with the use of artificial intelligence as support for managerial decision making in the company. This thesis contains the application which utilize genetic and graph algorithms to optimize the location of production facilities and logistic warehouses according to transport cost aspects.

This master's thesis deals with artificial neural networks and possibilities of their use on stock market. In next chapters of this thesis there are provided design and implementation of stock prices prediction tool. The implementation is done with use of the MATLAB software. The created prediction tool is then tested in a simple trading simulation and achieved results are discussed in the end

This diploma thesis deals with the prediction of financial time series on capital markets using artificial intelligence methods. There are created several dynamic architectures of artificial neural networks, which are learned and subsequently used for prediction of future movements of shares. Based on the results an assessment and recommendations for working with artificial neural networks are provided.

This thesis deals with a complex analysis and prediction of foreign exchange markets. It uses advanced artificial intelligence methods, namely neural networks and chaos theory. It introduces unconventional approaches and methods of each of these areas, compares them and uses on a real problem. The core of this thesis is a comparison of several prediction models based on completely different principles and underlying theories. The outcome is then a selection of the most appropriate prediction model called NAR + H. The model is evaluated according to several criteria, the pros and cons are discussed and approximate expected profitability and risk are calculated. All analytical, prediction and partial algorithms are implemented in Matlab development environment and form a unified library of all used functions and scripts. It also may be considered as a secondary main outcome of the thesis.

This diploma thesis deals with the path planning by swarm intelligence. In the theoretical part it describes the best known methods of swarm intelligence (Ant Colony Optimization, Bee Swarm Optimization, Firefly Swarm Optimization and Particle Swarm Optimization) and their application for path planning. In the practical part particle swarm optimization is selected for the design and implementation of path planning in the C#.

The goal of this master's thesis is to create an artificial intelligence for the Bang! game. There is a full description of the Bang! game, it's entire rules, player's using strategy principles and game analysis from UI point of view included. The thesis also resumes methods of the artificial intelligence and summarizes basic information about the domain of game theory. Next part describes way of the implementation in C++ language and it's proceeding with use of Bayes classification and decision trees based on expert systems. Last part represent analysis of altogether positive results and the conclusion with possible further extensions.

The thesis concentrates on using recurrent neural networks in computer vision. The theoretical part describes the basic knowledge about artificial neural networks with focus on a recurrent architecture. There are presented some of possible applications of the recurrent neural networks which could be used for a solution of real problems. The practical part concentrates on face recognition from an image sequence using the Elman simple recurrent network. For training there are used the backpropagation and backpropagation through time algorithms.

This thesis deals with the prediction of trading at financial markets and by using the prediction is trying to reduce the risks of entering at the market. The prediction has been work out by using of artificial intelligence. The artificial intelligence is in this thesis represented by neural networks witch model and predict market behavior. The thesis contains a description of the financial markets, exchange trading and its analysis, and artificial intelligence methods. The main part of this thesis is a model for prediction of prices of a particular instrument. This model was developed in MATLAB and should serve as a support for making business decisions. Its aim is to predict the direction and magnitude of movement the price level for the next trading day. The output of this model is processed using the platform MetaTrader 4. At the end are evaluated possible gains from this solution.