Title: Methods of artificial intelligence and their use in prediction Author: Lubomír Šerý Department: Department of Probability and Mathematical Statistics Supervisor: Ing. Marek Omelka, Ph.D., Department of Probability and Mathe- matical Statistics Abstract: In the presented thesis we study field of artificial intelligence, in par- ticular we study part dedicated to artificial neural networks. At the beginning, concept of artificial neural networks is introduced and compared to it's biological base. Afterwards, we also compare neural networks to some generalized linear models. One of the main problems of neural networks is their learning. Therefore biggest part of this work is dedicated to learning algorithms, especially to pa- rameter estimation and specific computational aspects. In this part we attempt to bring in an overview of internal structure of neural network and to propose enhancement of learning algorithm. There are lots of techniques for enhancing and enriching basic model of neural networks. Some of these improvements are, together with genetic algorithms, introduced at the end of this work. At the very end of this work simulations are presented, where we attempt to verify some of the introduced theoretical assumptions and conclusions. Main simulation is an application of concept of neural...

of dissertation thesis Prognostic and Predictive Factors in Breast Cancer The mRNA Expression of Selected Genes in Normal and Tumor Breast Tissue Samples and Theirs Clinical Value in Breast Cancer L.Šefrhansová Background: The aim of this work was to describe and to evaluate possibilities of prognosis and prediction in breast cancer. Within the framework of this study-work we carry out a prospective clinical study. The aim of this prospective study was to detect mRNA MMP-7, p53 and TIMP-1 expression in normal and tumor breast tissue samples and to determine the clinical and prognostic significance of our results. Prognosis and prediction: The tumor size, lymph node status, presence of distant metastasis, differentiation of the tumor, perivascular invasion, mitotic activity, expression of ER, PR and HER2 receptors are the basic prognostic factors in breast cancer. Age under/above 35 years was included among independent prognostic breast cancer factors in 2005. It is approved to use uPA/PAI to assess prognosis in node negative breast cancer patients. The hormone receptor status and HER-2 receptor status are the only two predictive markers associated with the target therapy. OncotypeDX analysis could be use to predict the disease recurrence interval of patients with estrogen positive and node negative...

The diploma thesis deals with the impact of the economic crisis in the retail sector in the Czech Republic during the years from 2000 to 2013. The aim of this work is to verify and followers finds an eventual impact of the economic crisis on selected indicators in retailing. The analysis is focus on total sales, employment and average monthly wage in the retail sector. The predictions of future development variables for the next two years are made for adequate models. The conclusion summarizes the main results of the work and for making appropriate recommendations for the development of this sector.

This dissertation thesis deals with the prediction and optimization of real data using artificial neural networks methods in decision-making. Usage of artificial neural networks includes finding of various suitable types, topologies and learning algorithms of artificial neural networks to solve prediction of real-world data sets. Optimization solutions in the context of work focused on neural networks used in topology optimization learning algorithms and neural networks own calculation. The first part is a summary of current trends in applications development and the utilization of artificial neural networks. The review of the literature is the selection of the most common models of artificial neural networks for prediction of real data needs. Subsequently, the implementation of the defined application of artificial neural networks in support of decision-making process is made. On the basis of determining the value of the decision making process are defined methods for prediction of real data using classical methods. As classical methods are considered methods of statistical prediction models for time series. After that, the methods of artificial neural networks and their application on prediction task are described. In the next part, experiments involving the choice of type, topology, learning algorithms and optimization possibilities of artificial neural networks is tested on real-world data sets. The implementation presents the prediction of selected real-world economic values; namely a set of household expenditures and goods transport is used. Results obtained with artificial neural networks are compared with real value of the index and also with selected models of statistical time series prediction results.

The aim of this diploma thesis is to create an analysis of the number, structure and demographic dynamics of the population of micro-region Mikulovsko in the years 2003--2013. In that period were analyzed following indicators: birthrate, abortion, mortality, marriage, divorce, migration and overall population growth in the micro-region. Through the use of an appropriate trend function was predicted a development of selected indicators by the year 2018. The reason for the elaboration of this work is the absence of such a detailed analysis of the micro-region development strategy. The work can be used as a basis for its processing.

This thesis evaluates the demographic and socio-economic situation of the city of Prague through several indicators analyzed over a period of at least twelve years. The indicators relate to the age structure of Prague population and demographic aging, economic activity and structure and immigration of foreigners into the city of Prague. Their development is compared with the Czech Republic as a whole and with selected Prague districts. Their further development in future years is also predicted, based on the time series method and equalization through adequate trend function. Finally the thesis provides a summary of the results and proposes possible measures to improve the situation in Prague in those areas that have proved problematic.

Thesis on "Expert systems analysis of insurance fraud in vehicle area" deals with insurance fraud in the Czech Republic, including the introduction of the legal process and the general division of insurance fraud and the realities of dealing with insurance losses. The practical part of the thesis deals with expert analysis of solution of claims, including detection and prediction of insurance fraud. The aim of this thesis is to provide statistical analysis of insurance fraud, ie. Evaluation of variations and correlations and statistical dependencies of various markers of insurance fraud in the Czech Republic.

The diploma thesis "South Moravian Region's Economic Development Analysis" is focused on structure and economic development analysis of South Moravian Region. It compares available data with other regions of the Czech Republic and other selected European regions. Following part is focused on a statistical prediction of future development of the South Moravian Region's GDP and complex evaluation and future recommendations.

Submitted diploma theses engaged the topic solvency and bancruptcy models and its objektive is to analyze financial situation of four agricultural companies and for companies of the energy suppliers. Theoretical part brings an outline of commonly used solvency and bancruptcy models and newly published models. Theoretical part stems from original authors. Practical part brings an application of chosen models on companies and examines their ability of rating the companies. Based on results is evaluated the financial situation of companies in time. The end of thesis brings the evaluation of the model´s appropriateness for application on given sectors and submits recommendtions for using these models.

The theme of the air transport is becoming increasingly popular among people nowadays. Therefore, this Master thesis deals with a statistical analysis of the Czech airline Czech Airlines j.s.c. The system of timelines from 2003 to 2014 was used to analyse the number of transported passengers regarding passenger traffic and the number of transported goods and mail within the air freight transport. The theoretical part of the paper is based on a literary review where basic terms of the air transport and timelines are described. The aim of the analysis is to apply particular trendy features on indicators of the research. According to the selected trend, predictions for the following the three seasons are made. Subsequently, the statistical analysis is evaluated and recommendations aimed at improving the future situation of the company are made.