-Condition monitoring is an important and challenging task actual for many areas of industry, medicine and economics. Nowadays it is necessary to provide on-line monitoring of the complex systems status, e.g. the steel production, in order to avoid faults, breakdowns or wrong diagnostics. In the present paper a novel machine learning method for the automated condition monitoring is presented. Neural Clouds (NC) is a novel data encapsulation method, which provides a confidence measure regarding classification of the complex system conditions. The presented adaptive algorithm requires only the data which corresponds to the normal system conditions, which is typically available. At the same time the fault related data acquisition is expensive and fault modeling is not always possible, especially in case one is dealing with steel production, power stations operation, human health condition or critical phenomena in financial markets. These real word applications are also presented in the paper.

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Abstract-Two artificial neural networks of different types were applied to profiles of gene expression in most aggressive human brain tumor -glioblastoma -and in normal brain tissue. The results of gene expression profiles classification are presented. First method -self organizing maps -gave good dis crimination of profiles on the trained map. Another ANN -perceptron -showed a good result of classification -more then 95% of the test data set were successfully classified. Due to high correlations between some gene expression values one can suppose, that number of genes necessary for successful classification may be reduced.

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