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Klasifikace odrůd hrachu setého pomocí neuronových sítí
Dobešová, Anna
This thesis is focused on classification of pea varieties by means neural networks procedure. Variety testing is an applied branch, which is strictly legally regulated. New methods and techniques are only slowly implemented. One option for an improving of the process of granting plant variety rights is to perform an image analysis of tested plants morphometric features. In the thesis, own solution for the classification of shape characteristics of pea by neural networks is designed and implemented. The analysis is focused on standards of pea. Achieved results are globally unique, because there is no other known application for classification of shape characteristics of plants by image analysis during variety registration. An advantage of the solution is fully automated process of analysis, which is realized in MATLAB computing environment. The neural network was trained by data from fields trials provided by National Plant Variety Office of Central Institute for Supervising and Testing in Agriculture. The solution will be adopted within practical testing procedures.
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Nástroj pro vzdálené použití NNSU algoritmu pro separaci dat (uživatelský manuál)
Hakl, František
Tento manuál popisuje základní použití serveru NNSU (paralelní implementace neuronové sítě s přepínacími jednotkami), který umožnuje vzdálený přístup k implementaci algoritmu NNSU a jeho pilotní použití na separování dat zaslaných na server. Účelem této volně přístupné aplikace je otestování vhodnosti separátoru na separaci uživatelských dat. Obsahem tohoto uživatelského návodu jsou informace postačující k využívání NNSU serveru, které popisují zpusob práce s daty určenými k separaci, způsob definování použité neuronové sítě, zadání výpočtu a metody hodnocení výsledné kvality separace.
Fulltext: content.csg -  PDF
Plný tet: v1200-13 - PDF
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Using data mining to manage an enterprise.
Prášil, Zdeněk ; Pour, Jan (advisor) ; Novotný, Ota (referee)
The thesis is focused on data mining and its use in management of an enterprise. The thesis is structured into theoretical and practical part. Aim of the theoretical part was to find out: 1/ the most used methods of the data mining, 2/ typical application areas, 3/ typical problems solved in the application areas. Aim of the practical part was: 1/ to demonstrate use of the data mining in small Czech e-shop for understanding of the structure of the sale data, 2/ to demonstrate, how the data mining analysis can help to increase marketing results. In my analyses of the literature data I found decision trees, linear and logistic regression, neural network, segmentation methods and association rules are the most used methods of the data mining analysis. CRM and marketing, financial institutions, insurance and telecommunication companies, retail trade and production are the application areas using the data mining the most. The specific tasks of the data mining focus on relationships between marketing sales and customers to make better business. In the analysis of the e-shop data I revealed the types of goods which are buying together. Based on this fact I proposed that the strategy supporting this type of shopping is crucial for the business success. As a conclusion I proved the data mining is methods appropriate also for the small e-shop and have capacity to improve its marketing strategy.
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Modern trends in the area of computer physics
SURYNEK, Radek
The theme of the thesis is to make a list few fundamental modern methods which can be used in computerized physics. The thesis describes parallel computing, neural networks,genetic algorithms, fuzzy logic. Every chapter include theoretical description, simplified mathematical expression, proposals of technical solution. Applications are briefly mentioned here too. The printed matter is completed with a few simple examples. The closing part of the thesis acquired information about these methods and outlines their future development.
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Aplikace neuronových sítí a metody ROC v klasifikačních úlohách
Pokorný, Martin
The disseratation theses deals with the problem of cost-sensitive binary classification by means of neural networks applied in economical prediction tasks, especially in the field of financial distress prediction. The first part contains the review of existing research in this area and the challenging key points related to cost-sensitive classification are set there. After that, the application of existing Receiver Operating Characteristics (ROC) method, which is able to solve mentioned problems, is discussed and the possibility of its wider use in economical prediction is proposed. The methodology of ROC analysis application is shown in medical and economical experiment of classification with neural networks.
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Artificial Intelligence Methods in geoinformatics
Voženílek, V. ; Dvorský, J. ; Húsek, Dušan
The book "Artificial Intelligence Methods in geoinformatics", (editors: Voženílek, V. - Dvorsky, J. - Husek, D) issued by the University of Palacky in Olomouc (in 2011. 184 pp. ISBN 978-80-244-2945-8) is the final outcome of the project GACR 205/09/1079 - "Artificial intelligence methods in GIS." The aim of the book was to summarize the results of research on the application of artificial intelligence methods for processing, visualization and interpretation of spatial information in the area of geographic information systems. The project was a joint research activity of experts from various research fields of Palacky University in Olomouc, VSB-Technical University of Ostrava, and Institute of Computer Science, Academy of Sciences Czech Republic targeted to the integration of methods used in solving problems of artificial intelligence and methods of processing spatial data, whose integration is essential for the development of new modern research methods and technologies.
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Methodology of development and deployment of Business Intelligence solutions in Small and Medium Sized Enterprises
Rydzi, Daniel ; Jandoš, Jaroslav (advisor) ; Vlček, Radim (referee) ; Slánský, David (referee)
Dissertation thesis deals with development and implementation of Business Intelligence (BI) solutions for Small and Medium Sized Enterprises (SME) in the Czech Republic. This thesis represents climax of author's up to now effort that has been put into completing a methodological model for development of this kind of applications for SMEs using self-owned skills and minimum of external resources and costs. This thesis can be divided into five major parts. First part that describes used technologies is divided into two chapters. First chapter describes contemporary state of Business Intelligence concept and it also contains original taxonomy of Business Intelligence solutions. Second chapter describes two Knowledge Discovery in Databases (KDD) techniques that were used for building those BI solutions that are introduced in case studies. Second part describes the area of Czech SMEs, which is an environment where the thesis was written and which it is meant to contribute to. This environment is represented by one chapter that defines the differences of SMEs against large corporations. Furthermore, there are author's reasons why he is personally focusing on this area explained. Third major part introduces the results of survey that was conducted among Czech SMEs with support of Department of Information Technologies of Faculty of Informatics and Statistics of University of Economics in Prague. This survey had three objectives. First one was to map the readiness of Czech SMEs for BI solutions development and deployment. Second was to determine major problems and consequent decisions of Czech SMEs that could be supported by BI solutions and the third objective was to determine top factors preventing SMEs from developing and deploying BI solutions. Fourth part of the thesis is also the core one. In two chapters there is the original Methodology for development and deployment of BI solutions by SMEs described as well as other methodologies that were studied. Original methodology is partly based on famous CRISP-DM methodology. Finally, last part describes particular company that has become a testing ground for author's theories and that supports his research. In further chapters it introduces case-studies of development and deployment of those BI solutions in this company, that were build using contemporary BI and KDD techniques with respect to original methodology. In that sense, these case-studies verified theoretical methodology in real use.
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Business Intelligence principles and their use in questionnaire investigation
Hanuš, Václav ; Maryška, Miloš (advisor) ; Novotný, Ota (referee)
This thesis is oriented on practical usage of tools for data mining and business intelligence. Main goals are processing of source data to suitable form and test use of chosen tool on the test case. As input data I used database which was created as result of processing forms from research to verify the level of IT and economics knowledge among Czech universities. These data was modified into the form, which allows processing them via data mining tools included in Microsoft SQL Server 2008. I choose two cases for verification the potentials of these tools. First case was focused on clustering using Microsoft Clustering algorithm. Main task was to sort the universities into the clusters by comparing their attributes which was amounts of credits of each knowledge group. I had to deal with two problems. It was necessary to reduce the number of groups of subjects, otherwise there was a danger of creation too many clusters which I couldn't put the name on. Another problem was unequal value of credits in each group and this problem caused another problem with weights of these groups. Solution was at the end quite simple. I put together similar groups to bigger formation with more general category. For unequal value, I used parameter for each of new group and transform it to scale 0-5. Second case was focused on prediction task using Microsoft Logistic Regresion algorithm and Microsoft Neural Network algorithm. In this case was the goal to predict the number of presently studying students. I had a historical data from years 2001-2009. A predictive model was processed based on them and I could compare the prediction with real data. In this case, it was also necessary to transform the source data, otherwise it couldn't be processed by tested tool. Original data was placed into the view instead of table and contained not only wished objects but more types of these. For example divided by a sex. Solution was in creation of new table in database where only relevant objects for test case were placed. Last problem come up when I tried to use prediction model to predict data for year 2010 for which there wasn't real data in the table. Software reported an error and couldn't make prediction. During my research on the Microsoft technical support I find some threads which refer to similar problem, so it's possible that this is a system error whit will be fix in forthcoming actualization. Fulfillment of these cases provided me enough clues to determine abilities of these tools from Microsoft. After my former school experience with data mining tools from IBM (former SSPS) and SAS, I can recognize, if tested tools can match these software from major data mining supplier on the market and if it can be use for serious deployment.
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