Scientific Journal of KubSAU

The article describes the synthesis and verification of statistical and system-cognitive models of the influence of environmental factors on the quality of life of the population of the region. This stage of the ASC-analysis is performed in the system called "Eidos". As a result, we have created and validated (verification stage) all the specified systemic cognitive models. It is expected that reliability for the models of knowledge is sufficiently high for a given subject area, that is why we can state the discovery of a dependence of life expectancy and causes of death from environmental conditions. Typically, knowledge models are approximately 20% higher in accuracy than statistical models, which operate on the principle of positive pseudo-prediction. Making decisions based on the model of Abs (matrix of absolute frequencies) is not appropriate because of the different number of instances of classes (generalized categories) and dependence of the solutions of this amount. In the model called Prc2 (conditional and unconditional percentage distribution) the dependence of the model values of the number of examples in classes has been removed, but the accuracy of it is usually same low as in the Abs. In addition, for decision-making based on this model, one has to compare the values of conditional and unconditional probabilities manually, which is laborious and hardly possible for large dimensional models. The knowledge model called Inf3, based on a measure similar to the Chi-square, is the result of the automated comparison of values of conditional and unconditional probabilities presented in the model of Prc1, which is similar to Prc2, and usually has a fairly high accuracy, especially considering the high complexity of the subject area, which we simulated. Therefore, in accordance with the technology of the ASC-analysis data conversion into information, and afterwards - into knowledge, it is the model of Inf3 which is planned to be used for the solution of problems of identification, forecasting, decision-making and exploring the modeled subject area, through the study of its models

Processes and machines of Agro-engineering systems with good reason can be considered as complex multiparameter natural and technical systems. In these systems there are numerous and diverse physical, chemical and biological processes. On the one hand, these processes have a significant impact on the performance of these systems. On the other hand, they are extremely difficult to describe in the form of meaningful analytical models based on equations. As a result, the development of meaningful analytical models is associated with a large number of simplifying assumptions that reduce the validity of these models. However, mathematical modeling of processes and machines of Agro-engineering systems is necessary for the development of both their designs and application technologies. Thus, there is a problem that is proposed to be solved with the use of phenomenological information and cognitive models. These models are based on the theory of information and describe the simulated system purely externally as a "black box", but it is meaningful. System-cognitive models can be built directly on the basis of empirical data using the intellectual system called "Eidos". This is done by model technology and methodology and is much less time-consuming and much faster than the development of meaningful analytical models. On the other hand, phenomenological system-cognitive models can be sufficient to determine rational design features and parameters of processes and machines of Agro-engineering systems. In addition, such phenomenological models can be considered as a first step in the development of meaningful analytical models. A numerical example is given

Agronomy systems with good reason can be considered as complex multiparameter natural and technical systems. In these systems, there are numerous and diverse physical, chemical and biological processes. On the one hand, these processes have a significant impact on the performance of these systems. On the other hand, they are extremely difficult to be described in the form of meaningful analytical models based on equations. As a result, the development of meaningful analytical models is associated with a large number of simplifying assumptions that reduce the validity of these models. Usually we consider linear univariate models for agronomic systems, whereas practices are necessary for nonlinear multiparameter models. Thus, we face the problem proposed to be solved by the application of a phenomenological meaningful systemic cognitive models. These models are created using automated system-cognitive analysis (ASC-analysis) using the intellectual system called "Eidos" directly based on empirical data and used for the decision of tasks of forecasting, decision support and research of the modeled subject area. In this case, empirical data can be large, incomplete (fragmented), noisy, presented in different types of measuring scales (nominal, ordinal and numerical) and in different units of measurement. The comparability of the processing of heterogeneous data is ensured by the fact that they are all converted into units of measurement of the amount of information. A numerical example has been given

The article considers the application of Eidos intellectual technologies for implementation of developed veterinary and medical diagnostics statistical tests without programming in the convenient form for the individual and mass testing, the analysis of the results and development of the individual and group recommendations. It is possible to merge several tests in one supertest

The article deals with the similarity and difference of nosological images in veterinary medicine using a new method of agglomerative clustering implemented in Automated system-cognitive analysis (ASC-analysis) on a small numerical example. This method is called Agglomerative cognitive clustering. This method differs from the known traditional facts: a) parameters of a generalized image of the cluster are computed not as averages from the original objects (classes) or their center of gravity, and are defined using the same underlying cognitive operations of ASC-analysis, which is used for the formation of generalized images of the classes on the basis of examples of objects and which is really correct and provides a synthesis; b) as a criterion of similarity we do not use Euclidean distance or its variants, and the integral criterion of non-metric nature: "the total amount of information", the use of which is theoretically correct and gives good results in non-orthonormal spaces, which are usually found in practice; c) cluster analysis is not based on the original variables, matrices of frequency or a matrix of similarities (differences) dependent on the measurement units of the axes, and in the cognitive space in which all the axes (descriptive scales) use the same unit of measurement: the quantity of information, and therefore, the clustering results do not depend on the original units of measurement features. All this makes it possible to obtain clustering results that are understandable to specialists and can be interpreted in a meaningful way that is in line with experts' assessments, their experience and intuitive expectations, which is often a problem for classical clustering methods

In the article, on a small numerical example, we consider the similarity and difference of symptoms and syndromes according to their diagnostic meaning, i.e. according to the information they contain about the belonging of conditionals of animals to different nosological images. This problem can be solved for veterinary with the use of a new method of agglomerative cognitive clustering, implemented in Automated System-Cognitive analysis (ASC-analysis). This method of clustering differs from the known traditional methods in: a) in this method, the parameters of the generalized image of the cluster are calculated not as averages from the original objects (symptoms) or their center of gravity, but are determined using the same basic cognitive operation of ASC-analysis, which is used to form generalized images of the classes based on examples of objects and which really correctly provides a generalization; b) the similarity criterion is not the Euclidean distance or its variants, but the integral criterion of non-metric nature: "the total amount of information", the application of which is theoretically correct and gives good results in unortonormated spaces, which are usually found in practice; c) cluster analysis is carried out not on the basis of initial variables, frequency matrices or matrix of similarity (differences), depending on the units of measurement on the axes (measurement scales), but in cognitive space, in which one unit of measurement is used for all axes: the amount of information, and therefore the results of clustering do not depend on the initial units of measurement of features of objects. All this allows us to get the results of clustering, understandable to specialists and amenable to meaningful interpretation, well-consistent with the experts ' assessments, their experience and intuitive expectations, which is often a problem for classical clustering methods

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

This article briefly discusses a new innovation (brought to a level that ensures its practical use) method of artificial intelligence: automated system-cognitive analysis (ASC-analysis) and its programmatic toolkit which is called intellectual system "Eidos". A detailed numerical example of the solution demonstrating the technology of creating a veterinary diagnostic test of gastrointestinal diseases of horses is given. As the source data, we use data from the UCI repository, kindly given by Mary McLeish and Matt Cecile (Department of computer science of University of Guelph, Ontario, Canada N1G 2W1, with the support of a sponsor: Will Taylor. The developed test is used to solve the problems of diagnosis, decision support and examining the simulated subject area by studying its model. The results of the study can be used by anyone, due to the fact that Eidos the universal automated system, which is a tool of ask-analysis, is in full open free access on the author's website at: http://lc.kubagro.ru/aidos/_Aidos-X.htm, and numerical examples of solving veterinary problems with the use of artificial intelligence technologies are placed as a cloud Eidos-application 129

Meat Academy website http://meatinfo.ru has a comparative table of breeds of cattle on 8 indicators, from which 2 are text and 6 are numerical http://meatinfo.ru/info/show?id=197. It is a natural question for business executives, which of these breeds are similar throughout the system of indicators characterizing them, and which ones differ and to what extent. There is also the question of which indicators are similar and different in meaning and by how much. This article is devoted to the solution of these problems. The results of the study can be used by anyone, due to the fact that Eidos the universal automated system, which is a tool of ask-analysis, is in full open free access on the author's website at: http://lc.kubagro.ru/aidos/_Aidos-X.htm, and numerical examples of solving the mentioned problems with the use of artificial intelligence technologies are placed as a cloud Eidos-application #131

The researchers obtained data on the morphological, biochemical and trace element composition of the blood of bull-calves of Hereford breed of different sizes. In this regard, scientists and business executives have three natural questions: 1) whether it is possible to predict the size and thus the meat productivity of bulls using these blood indicators; what are the strength and direction of the influence of certain values of blood indicators on the size and weight of bulls; what blood indicators are similar in meaning, and what are different and how much (to what extent). The article is devoted to the reasoned answers to these questions by applying modern methods of mathematical and numerical modeling to solve the corresponding problems. The results of the study can be used by anyone, due to the fact that Eidos the universal automated system, which is a tool of ask-analysis, is in full open free access on the author's website at: http://lc.kubagro.ru/aidos/_Aidos-X.htm, and numerical examples of solving the mentioned problems with the use of artificial intelligence technologies are placed as a cloud Eidos-application #133