The model is developed for the intellectualized decision-making support system on financing of cyber security means of transport cloud-based computing infrastructures, given the limited financial resources. The model is based on the use of the theory of multistep games tools. The decision, which gives specialists a chance to effectively assess risks in the financing processes of cyber security means, is found. The model differs from the existing approaches in the decision of bilinear multistep quality games with several terminal surfaces. The decision of bilinear multistep quality games with dependent movements is found. On the basis of the decision for a one-step game, founded by application of the domination method and developed for infinite antagonistic games, the conclusion about risks for players is drawn. The results of a simulation experiment within program implementation of the intellectualized decision-making support system in the field of financing of cyber security means of cloudbased computing infrastructures on transport are described. Confirmed during the simulation experiment, the decision assumes accounting a financial component of cyber defense strategy at any ratios of the parameters, describing financing process.
The article is devoted to the problem of voice signals recognition means introduction in the system of distance learning. The results of the conducted research determine the prospects of neural network means of phoneme recognition. It is also shown that the main difficulties of creation of the neural network model, intended for recognition of phonemes in the system of distance learning, are connected with the uncertain duration of a phoneme-like element. Due to this reason for recognition of phonemes, it is impossible to use the most effective type of neural network model on the basis of a multilayered perceptron, at which the number of input parameters is a fixed value. To mitigate this shortcoming, the procedure, allowing to transform the non-stationary digitized voice signal to the fixed quantity of mel-cepstral coefficients, which are the basis for calculation of input parameters of the neural network model, is developed. In contrast to the known ones, the possibility of linear scaling of phoneme-like elements is available in the procedure. The number of computer experiments confirmed expediency of the fact that the use of the offered coding procedure of input parameters provides the acceptable accuracy of neural network recognition of phonemes under near-natural conditions of the distance learning system. Moreover, the prospects of further research in the field of development of neural network means of phoneme recognition of a voice signal in the system of distance learning is connected with an increase in admissible noise level. Besides, the adaptation of the offered procedure to various natural languages, as well as to other applied tasks, for instance, a problem of biometric authentication in the banking sector, is also of great interest.