There is growing interest to analyze electroencephalogram (EEG) signals with the objective of classifying schizophrenic patients from the control subjects. In this study, EEG signals of 15 schizophrenic patients and 19 age-matched control subjects are recorded using twenty surface electrodes. After the preprocessing phase, several features including autoregressive (AR) model coefficients, band power and fractal dimension were extracted from their recorded signals. Three classifiers including Linear Discriminant Analysis (LDA), Multi-LDA (MLDA) and Adaptive Boosting (Adaboost) were implemented to classify the EEG features of schizophrenic and normal subjects. Leave-one (participant)-out cross validation is performed in the training phase and finally in the test phase; the results of applying the LDA, MLDA and Adaboost respectively provided 78%, 81% and 82% classification accuracies between the two groups. For further improvement, Genetic Programming (GP) is employed to select more informative features and remove the redundant ones. After applying GP on the feature vectors, the results are remarkably improved so that the classification rate of the two groups with LDA, MLDA and Adaboost classifiers yielded 82%, 84% and 93% accuracies, respectively.
Several algorithms have been developed for time series forecasting. In this paper, we develop a type of algorithm that makes use of the numerical methods for optimizing on objective function that is the Kullbak-Leibler divergence between the joint probability density function of a time series xi, X2, Xn and the product of their marginal distributions. The Grani-charlier expansion is ušed for estimating these distributions.
Using the weights that have been obtained by the neural network, and adding to them the Kullback-Leibler divergence of these weights, we obtain new weights that are ušed for forecasting the new value of Xn+k.
The paper deals with application of MF-ARTMAP neural network on
financial fraud data. The focus was on classification of data into 5 types of fraud based on expert knowledge with the aim to achieve the tool with highest classification accuracy. The fraud was characterized by 22 features and the verbal features were encoded into numerical values to be able to use them in the classification proceduře. The results show that in the čase of sufficient data (fraud) representation neural networks could be used with success; in case there are rather small examples, expert generated rules are preferred.
Hospitals must index each case of inpatient medical care with codes from the International Classification of Diseases, 9th Revision (ICD-9), under regulations from the Bureau of National Health Insurance. This paper aims to investigate the analysis of free-textual clinical medical diagnosis documents with ICD-9 codes using state-of-the-art techniques from text and visual mining fields. In this paper, ViSOM and SOM approaches inspire several analyses of clinical diagnosis records with ICD-9 codes. ViSOM and SOM are also used to obtain interesting patterns that have not been discovered with traditional, nonvisual approaches. Furthermore, we addressed three principles that can be used to help clinical doctors analyze diagnosis records effectively using the ViSOM and SOM approaches. The experiments were conducted using real diagnosis records and show that ViSOM and SOM are helpful for organizational decision-making activities.
Credit risk assessment, credit scoring and loan applications approval are one of the typical tasks that can be performed using machine learning or data mining techniques. From this viewpoint, loan applications evaluation is a classification task, in which the final decision can be either a crisp yes/no decision about the loan or a numeric score expressing the financial standing of the applicant. The knowledge to be used is inferred from data about past decisions. These data usually consist off both socio-demographic and economic characteristics of the applicant (e.g., age, income, and deposit), the characteristics of the loan, and the loan approval decision. A number of machine learning algorithms can be used for this purpose. In this paper we show how this task can be performed using the LISp- Miner system, a tool that is under development at the University of Economics, Prague. LISp-Miner is primarily focused on mining for various types of association rules, but unlike "classical" association rules proposed by Agrawal, LISp-Miner in- troduces a greater variety of different types of relations between the left-hand and right-hand sides of a rule. Two other procedures that can be used for classification task are implemented in LISp-Miner as well. We describe the 4ft-Miner and KEX procedures and show how they can be used to analyze data related to loan applications. We also compare the results obtained using the presented algorithms with results from standard rule-learning methods.
The energy utilization of the alternative fuels is one of the main topics for future developments of recoverable sources in the European Union and in the Czech Republic. The aim of research is combustion tests in the fluidized-bed boiler Foster Wheeler located at Štětí. The experiments are carried out for Czech brown coal, wood, sewage sludge and wastes including analyses and recommendations for optimal thermal utilization and minimizing harmful emissions. The second step is thermal analyses of coal, alternative fuel- wood pellets and sewage sludge from treatment plant. From the results of experiments and thermal modeling it is clear that 15 % of alternative fuels can be used in the large fluidized-bed boilers located in the Czech Republic., Pavel Kolat, Bohumir Čech, Dagmar Juchelková, Helena Raclavská and Juraj Leško., and Obsahuje bibliografii
Geophysical data are used not only, in geological mapping, exploration of mineral resources, hydrogeology, but are also important for other branches such as environmental protection, civil engineering and archeology. That is why, within the project CzechGeo/EPOS (www.czechgeo.cz), geophysical data access is solved as a separate topic under the guidance of the Czech Geological Survey (hereinafter CGS). In accordance with the current needs of national and international activities (INSPIRE, EPOS, IAGA), an inventory of available data, its consolidation and harmonization according to national and international standards is conducted. The aim is to store securely and permanently valuable data, which in many cases cannot be reinstated. On the example from the Nízký Jeseník Mts. possible advantages and utilization of Archive data for mapping and verification of the movement tendencies gained from GNSS networks – EPN, EAST SUDETEN and MORAVA are demonstrated. Very valuable information for the interpretation of structural and tectonic conditions is provided by geophysical data (seismic reflection profiles, gravity and magnetic data, etc.) in the area of interest, especially in terms of monitoring the main fault systems and the character of the basement structures.
Review article presents utilisation of coal in industry, coal in power generation, gas production, Czech energy policy and outlook and application of clean coal technology in the Czech power stations. The further exploitation and utilisation of Czech coal resources requires the implementation of development programme aimed at the application of the new, economically effective technologies, of coal mining, utilisation, gasification. Emissions of GHG in CR., Václav Roubíček, Pavel Kolat and Jaroslav Buchtele., and Obsahuje bibliografii
The purpose of this study was to determine the production of metalloproteinases (MMP) 2 and 9 following UV-B irradiation in human corneal epithelial cells and fibroblasts. Epithelial cells and fibroblasts were separated from human donor corneas and exposed to UV-B lamp irradiation for 20, 40, 80 and 120 s. Media samples were collected at 8, 24, 48 and 72 h and gelatinase A and B production was assayed by the ELISA test. Statistical significance of production was assessed by the paired t-test. Increased production of MMP-2 was found in human corneal fibroblasts in response to UV-B irradiation. A statistically significant production of MMP-2 was not observed in human corneal epithelial cells following UV-B exposure. We did not detect any increase in MMP-9 after irradiation in either epithelial cells or fibroblasts. MMP-2 is produced by the corneal fibroblasts in the acute phase after UV-B irradiation. MMP-9 is not released in vitro following UV-B irradiation damage and therefore does not directly participate in the pathophysiology of acute photokeratitis., I. Kozák, D. Klisenbauer, T. Juhás., and Obsahuje bibliografii