Scientific Journal of KubSAU

Automated system-cognitive analysis (ASC-analysis) for images provides automatic identification of specific characteristics of the given images from the color of the pixels and image edges, the synthesis of generalized images of pictures (classes), identifying the most and the least specific image features for the class, determining values of features of images for their differentiation, deletion low-value characteristics (abstraction) from the model, problem solving for quantitative comparison of specific images with generalized images of classes and generalized images of the classes with each other, and objectives of the study of the simulated subject area by studying its model. The work discusses the new features of the ASC-analysis and its implementing intellectual system called "Eidos" for identifying features of images using their spectral analysis, formation of the generalized spectra of classes, the task of comparison of images of specific objects to classes and classes with each other in their spectra. For the first time, it became possible to form the generalized spectra of classes with weights of the colors according to their degree of specificity and unspecific features for classes, and it is not the intensity of the color in the spectrum, but the amount of information in the color on the linking the object with that color to the class. In fact, there is a question of generalization of spectral analysis by using intelligent cognitive technologies and information theory in the spectral analysis. First, everyone is talking about the fact that spectral lines contain information about which element or substance is included in the object, but no one bothered to count what exactly the amount of information is and then use it to determine the composition of the object pattern recognition methods based on the use of this information. Second, spectral analysis is traditionally used to determine the elemental and molecular composition of the object; we propose to use it not only for that, but also to identify any images. A numerical example has been given

The work discusses various examples of physical systems which state is determined by the logarithmic law - quantum and classical statistical systems and relativistic motion in multidimensional spaces. It was established that the Fermi-Dirac statistics and BoseEinstein-Maxwell-Boltzmann distribution could be described by a single equation, which follows from Einstein's equations for systems with central symmetry. We have built the rate of emergence of classical and quantum systems. The interrelation between statistical and dynamic parameters in supergravity theory in spaces of arbitrary dimension was established. It is shown that the description of the motion of a large number of particles can be reduced to the problem of motion on a hypersphere. Radial motion in this model is reduced to the known distributions of quantum and classical statistics. The model of angular movement is reduced to a system of nonlinear equations describing the interaction of a test particle with sources logarithmic type. The HamiltonJacobi equation was integrated under the most general assumptions in the case of centrally-symmetric metric. The dependence of actions on the system parameters and metrics was found out. It is shown that in the case of fermions the action reaches extremum in fourdimensional space. In the case of bosons there is a local extremum of action in spaces of any dimension

Traditionally, control decisions are made by solving repeatedly the forecasting problem for different values of control factors and choosing a combination of them that ensures the transfer of the control object to the target state. However, real control objects are affected by hundreds or thousands of control factors, each of which can have dozens of values. A complete search of all possible combinations of values of control factors leads to the need to solve the problem of forecasting tens or hundreds of thousands or even millions of times to make a single decision, and this is completely unacceptable in practice. Therefore, we need a decision-making method that does not require significant computing resources. Thus, there is a contradiction between the actual and the desired, a contradiction between them, which is the problem to be solved in the work. In this work, we propose a developed algorithm for decision-making by solving the inverse forecasting problem once (automated SWOT analysis), using the results of cluster-constructive analysis of the target states of the control object and the values of factors and a single solution of the forecasting problem. This determines the relevance of the topic. The purpose of the work is to solve the problem. By decomposing the goal, we have formulated the following tasks, which are the stages of achieving the goal: cognitive-target structuring of the subject area; formalization of the subject area (development of classification and descriptive scales and gradations and formation of a training sample); synthesis, verification and increasing the reliability of the model of the control object; forecasting, decision-making and research of the control object by studying its model. The study uses the automated system-cognitive analysis and its software tools (the intelligent system called "Eidos") as a method for solving the set tasks. As a result of the work, we propose a developed decision-making algorithm, which is applicable in intelligent control systems. The main conclusion of the work is that the proposed approach has successfully solved the problem

Antibacterial chemotherapeutic drugs, which include antibiotics and synthetic antimicrobial agents, are widely used in veterinary medicine for the prevention and treatment of diseases caused by microorganisms. Antibacterial agents can be classified by type of action and chemical structure. It is also known that when several drugs are used in combination with each other, they interact within the body with each other, which can lead to strengthening or weakening of their action. For these reasons, it is of scientific and practical interest to develop a classification of antibiotics by their characteristics and principle of action (task 1), as well as by mutual compatibility (task 2). The article solves these problems using a new method of agglomerative cognitive clustering, implemented in automated system-cognitive analysis (ASK-analysis). This method of clustering has a number of advantages over the known traditional methods of clustering. These advantages allow us to obtain clustering results that are understandable to specialists and amenable to meaningful interpretation, which are well consistent with the experts ' assessments, their experience and intuitive expectations, which is often a problem for classical clustering methods. The article provides detailed numerical examples of solving two problems. The universal automated system called "Eidos", which is a tool of ASK-analysis, is in full open access on the author's website: http://lc.kubagro.ru/aidos/_Aidos-X.htm. Numerical examples of solving veterinary problems with the use of artificial intelligence technologies are placed as cloud Eidos-applications and are available to everyone

The rapidly developing processes in global information development of our society have had a significant impact on education. Recently, in agricultural and other universities the amount of generated and processed pedagogical information has dramatically increased. Spontaneously and purposefully, electronic databases of educational information and educational portals have been created. All these works require a significant investment of time and effort of the teaching staff (PPP) of higher education institutions and a large number of technical experts in the field of information technology; they also require appropriate computer and communications equipment. All this is already an accomplished fact. On the other hand, the question arises about the degree of meaningfulness and expediency of certain aspects of this process in the form in which it is actually carried out, and evaluation of its impact on the mission of the University in General: "Training high-quality professionals", in particular for the regional agro-industrial complex (AIC). Apparently, now this process develops spontaneously, and no one has planned it, considering on the one hand, the costs of various kinds on its implementation and on the other hand - ensuring its effectiveness in achieving the objectives and getting the intended desired results both in physical and valuable forms. The meaning and justification of this process can give only a substantial positive impact on improving the quality of education, and only when it has adequate and reasonable cost. For a reasoned response to these questions, the authors propose to apply the theory of reflexive management active objects, automated system-cognitive analysis (ASC-analysis), functional cost analysis (FCA) and the method of "Direct costing". A foundation for solving the problem: this is a great experience in teaching and research activities, a successful experience of the application of ASC-analysis and the FSA for personnel management; a software tool of ASC-analysis which is an intellectual system called "Eidos" (full open free access)

The aim of this work is to study the strength and the direction of the influence of morphological and biochemical properties of tomatoes on the quantitative, qualitative, financial and economic results of their cultivation and the degree of determinism of these results. Achieving this goal is of great scientific and practical interest for scientists, breeders and vegetable growers-practitioners. This allows breeders to obtain new high-performance varieties of tomato hybrids, and farms to choose hybrids, the cultivation of which is most effective from a financial and economic point of view. To achieve this goal, we use automated system-cognitive analysis (ASC-analysis) and its software tool which is the intelligent system called "Eidos". A numerical example based on real data on tomato hybrids has been considered in detail

The article is devoted to the use of automated system-cognitive analysis for the study of probiotics for the growth of bulls. Moreover, as growth indicators we have not used live weight, but indices characterizing the shape and proportions of the body of animals. A detailed numerical example of solving the problem using real data is given

The quality of life of the population of the region is an important integral criterion of estimation of efficiency of activity of regional administration. Quality of life is mostly influenced by environmental factors. This article proposes to solve the problem of research of the influence of environmental factors on various aspects of quality of life by using ASC-analysis

14 January 2019 at the website of the higher attestation Commission of the Russian Federation http://vak.ed.gov.ru/87 the information appeared: "About refining of scientific specialties and their respective fields of science where publications are included in the List of peer-reviewed scientific publications, where basic scientific results of dissertations on competition of a scientific degree of candidate of Sciences, on competition of a scientific degree of the doctor of Sciences must be published ". It is reported that according to the recommendation of the HAC for other publications included in the List of groups of scientific specialties, the work on refining scientific specialties and branches of science will be continued in 2019. This work is a continuation of the author's series of works on cognitive linguistics. It offers innovative intelligent technology to automate the solution of the problem formulated by the higher attestation Commission of the Russian Federation above. With the use of the automated system-cognitive analysis (ASC-analysis) and its programmatic toolkit which is intellectual system called "Eidos" directly on the basis of official texts of passports of scientific specialties of the higher attestation Commission of the Russian Federation, there were established their semantic kernels, and then, implemented the automatic classification of scientific texts (articles, monographs, textbooks, etc.) on the specialties and groups of specialties of the higher attestation Commission of the Russian Federation. Traditionally, this task is solved by dissertation councils, as well as editorial boards of scientific publications, i.e. by experts, on the basis of expert assessments, in an informal way, on the basis of experience, intuition and professional competence. However, the traditional approach has a number of serious drawbacks that impose significant limitations on the quality and volume of analysis. Therefore, the efforts of researchers and developers to overcome these limitations are relevant. Currently, there are all grounds to consider these restrictions as unacceptable, because they are not only necessary, but also quite possible to overcome. Thus, there is a problem, the solution of which is the subject of consideration in this article. A detailed numerical example of solving the problem on real data is given as well

In peach orchards of the Russian humid subtropics, leaf curl is the most dangerous and harmful disease. Due to the high degree of danger from the peach leaf curl, for the first time in this region the main task was to analyze peach leaf curl development on weather conditions. In order to solve the problem, it is proposed to apply a new innovative intellectual technology: automated system-cognitive analysis (ASK-analysis) and its software tools - the “Eidos” system. In order to build the model, based on our own observations and the experience of Russian and foreign colleagues, it was decided to use the following factors: the sum of temperatures above +4 ° C of the current year (for the period from January to April), the sum of temperatures above +4 ° C of the previous year (for the whole year), the sum of precipitation of the current year (for the period from January to April), the sum of precipitation of the previous year (for the whole year), the number of hours of infection (in the current year). It was established that such factors as the number of hours of infection, the sum of temperatures above +4 ° C in April and during the period from January to April, as well as the sum of precipitation in March and April, are the most important in the dynamics of peach leaf curl development and spread. High rates of leaf curl spread and development are caused by the number of hours of infection in the range of 1440 ... 2064 hours, as well as by low air temperatures in March and April (the sum of temperatures above +4 °C – 89,4-240,4° and 283,7-316,7°, respectively) and high air temperatures - in January and February (the sum of temperatures above +4 ° C – 155,3-259,6° and 243,5-280,1°, respectively)