Software & Systems

Articles of journal № 2 at 2019 year.

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21. The quantum genetic algorithm in the problems of intelligent control modeling and supercomputing [№2 за 2019 год]
Authors: Ulyanov, S.V., N.V. Ryabov
Visitors: 6267
This paper considers the use of the quantum genetic algorithm for automatic selection of the optimum type and kind of correlation in the quantum structure of fuzzy inference. When solving intelligent and cognitive control tasks based on quantum soft computing and the principles of quantum deep machine learning, it is important to choose the type and kind of quantum correlation. It is an additional physical and informational computing resource in the formation of the laws of the time variation of the gains of traditional regulators located at the lower (performing) level of the intelligent control system structure. This approach is essential for the realization of adaptive and self-organizing processes of knowledge bases and guaranteed to achieve the control objectives under contingency control situations. Successful solution of the problem of choosing the type and kind of quantum correlations allows strengthening the successful search for solutions of algorithmically un-solvable problems at the classical control level. A genetic algorithm is a powerful computational intelligence toolkit for random searching of effec-tive solutions for poorly formalized tasks. However, it has a big disadvantage when used on a classic computer: low speed and dependence on the expert’s choice of a decision-making space. The paper describes the types of quantum genetic algorithms based on a combination of quantum and classical calculations, and an algorithm consisting only of quantum calculations. In such algorithm, a population can be composed of only one chromosome in a state of superposition. Immersion in the quantum structure of the fuzzy inference quantum genetic algorithm provides a synergistic effect and allows realizing quantum fuzzy inference on a classical processor. The new effect is based on the quantum genetic algorithm extracting information hidden in the clas-sical state laws change over time the gains of traditional regulators on a new unexpected situation con-trol. Such synergistic effect is possible only with end-to-end intellectual information technology of quantum computing and is absent at the classical level of application of the classical computing tech-nology.

22. Item-based recommender system with statistical learning for unauthorized customers [№2 за 2019 год]
Author: A.V. Filipyev
Visitors: 6380
The paper aims to reveal that using statistical learning approaches for recommender systems makes personal communication with customers better than the expert opinion regarding this question does. The author uses a cosine similarity distance as a basis for developing a machine learning recommenda-tion model. However, this distance has high calculation costs, therefore the paper considers the ways of solving this problem. The probability matrix of purchasing one item with another was calculated in or-der to weight cosine similarity and to avoid the situation when unpopular products are put at the top of a recommendation list. A weighted sum model joins cosine similarity and probability matrices and buildes recommendation sequences. User-based collaborative filtering is the most popular algorithm to build personal recom-mendation. However, it is useless when it is impossible to identify a user in the system. The developed algorithm based on cosine similarity distances, probability matrix and weighted sums allows building an item-to-item recommendation model. The main idea of this approach is to offer additional products to clients when only products in a cart are known. The item-to-item recommendation algorithm has shown advantages of using statistical machine learning approaches in order to improve communication with clients through a mobile application and a website. An integrated recommendation module has re-vealed that developing a data-driven culture is a right way of many modern companies.

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