The goal of cryptography is to make it impossible to take a cipher and reproduce the original plain text without the corresponding key. With good cryptography, your messages are encrypted in such a way that brute force attacks against the algorithm or the key are all but impossible. Good cryptography gets its security by using incredibly long keys and using encryption algorithms that are resistant to other form attack. The neural net application represents a way of the next development in good cryptography. This paper deals with using neural network in cryptography, e.g. designing such neural network that would be practically used in the area of cryptography. This paper also includes an experimental demonstration.

The article is focused on an analysis and pattern recognition in time series, which are fractal in nature. The proposal methodology is based on an interdisciplinary approach that combines artificial neural networks, analytic programming, Elliott wave theory and knowledge modelling. The heart of the methodology are a methods, which is able to recognize Elliott waves structures including their deformation in the charts and helps to more efficient prediction of its trend. The functionality of the proposed methodology was validated in experimental simulations, for whose implementation was designed and created an application environment. Experimental simulations have shown that the method is usable to a wider class of problems than the theory itself allows only Elliott waves.This paper introduces a methodology that allows analysis of Elliot wave's patterns in time series for the purpose of a trend prediction.

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