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Neural Networks in R
Arzumanov, Eduard ; Bašta, Milan (advisor) ; Žižka, David (referee)
The aim of this work was to present the issue of neural network, which is still, despite the fact it exist and has been applied for several years, remains quite unknown for a considerably big part of public and academical environment. The aim of the practical part was to verify via practical application if neural network are truly a better instrument of statistical analysis, than the commonly used ones, especially when the goal is to analyze and describe complex processes and relationships between them. Further aim of the work was to investigate and describe the relationships between the development of trading volumes of Apple shares and the shares of competitive companies regarding the market of smart phones such as Google, HTC, Nokia, Samsung using neural network models. The attainment of these goals was realized through a rather extensive description of neural networks theory as well as the presentation of valuable theoretical tools for avoiding the frequent barriers occurring during the practical implementation. This practical application was realized via software called R, which has widely spread lately due to its availability and a vast range of flexibility, which is provided to users. The value of this work is familiarization and the creation of an integrated knowledge within readers about the issue of neural networks and the deliverance of a proof, that neural networks are indeed a better tool compared to the commonly used ones (ARMA models, linear regression). The author of the work gained a lot of useful knowledge about neural networks, learned how to use them in practice especially in the environment of R software, by which he shifted his proficiency with the current software to a whole new level.
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Application of the Artificial Intelligence in the Real Estate Valuation
Štechová, Edita ; Witzany, Jiří (advisor) ; Fičura, Milan (referee)
The main purpose of this study is to develop a predictive model capable to forecast residential real estate prices in the city of Prague using Artificial Intelligence methods. The first part of this study discusses fundamentals of Artificial Neural Networks and Fuzzy Inference Systems in the context of real estate valuation. The second part demonstrates a development and testing of such models using a dataset of real estate market transactions. In the third part, results are compared to Multiple Regression and an explanatory power of each model is evaluated. Conclusions of this research are: (1) Artificial Neural Networks and Fuzzy Inference Systems give more accurate estimates of market values of residential real estates than Multiple Regression; (2) Artificial Neural Networks and Fuzzy Inference Systems represent an efficient way of modeling and analyzing residential real estate prices in Prague.
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Možnosti předpovědi finanční krize
Salvetová, Veronika
This bachelor thesis examines methods for prediction of financial crisis. Using the statistical program Statistica 12, selected statistical predictive instruments are evaluated in their ability to predict along one dimensional time series the structural break related to the emergence of the financial crisis that developed in the years 2007-2008. The quality of the predictive models is evaluated using selected statistical criteria. The results show that contemporary predictive mathematical methods are not very good tools for prediction of crises. This is the basis for further discussion. On the basis of this information, this text offers suggestion for improving predictive instruments and better prevention of crises.
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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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