Scientific Journal of KubSAU

This work is a continuation of the author's series of works on cognitive veterinary medicine. The present period is characterized by the appearance of huge volumes of texts in different languages in the open access, generated by people. Currently, these texts are accumulated in various electronic libraries and bibliographic databases (WoS, Scopus, RSCI, etc.), as well as on the Internet on various sites. All these texts have specific authors, dates and can belong simultaneously to many non-alternative categories and genres, in particular: educational; scientific; artistic; political; news; chats; forums and many others. The solution of the generalized problem of attribution of texts is of great scientific and practical interest, i.e. studying these texts, which would reveal their probable authors, date of creation, the ownership of these texts to the above generalized categories or genres, and might evaluate the similarities - differences of authors and texts according to their content, highlight key words etc. To solve all these problems it seems necessary to form the generalized linguistic images of texts into groups (classes), i.e. to form semantic kernels of classes. A special case of this problem is the creation of the semantic kernel in various scientific specialties of the HAC of the Russian Federation and the automatic classification of scientific texts in the areas of science. Traditionally, this task is solved by dissertation councils, i.e. experts, on the basis of expert assessments, i.e. 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. Currently, there are all grounds to consider these restrictions as unacceptable, because they can be overcome. Thus, there is a problem, the solutions of which are the subject of consideration in this article. Therefore, the efforts of researchers and developers to overcome them are relevant. Therefore, the aim of the work is to develop an automated technology (method and tools), as well as methods of their application for the formation of the semantic core of veterinary medicine by automated system-cognitive analysis of passports of scientific specialties of the HAC of the Russian Federation and automatic classification of texts in the areas of science. A detailed numerical example of solving the problem on real data has been given as well

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

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 purpose of the article is to use automated system-cognitive analysis (ASC-analysis) to study the impact of agrotechnological factors on the yield and quality of wheat and the use of the created models to solve the problems of forecasting, decision support and research of the simulated domain through the study of its model. To achieve this goal, the following tasks are set and solved, obtained by decomposition of the goal and are the stages of its achievement: Task 1: to formulate the idea and concept of solving the problem; Task 2: to justify the choice of method and tool for solving the problem; Task 3: to apply the selected method and tool to achieve this goal: cognitive structuring of the subject area; formalization of the subject area; synthesis and verification of the model; improving the quality of the model and the choice of the most reliable model; solution in the most reliable model of diagnostic problems (classification, recognition, identification), decision support and research of the simulated subject area by studying its model. Task 4: describe the effectiveness of the proposed solution. Task 5: to consider the limitations and shortcomings of the proposed solution to the problem and the prospects for its development by overcoming these limitations and shortcomings. A detailed numerical example of solving the problems based on 217 real examples of wheat cultivation in the fields of the Krasnodar region is given. For readers, it is possible to download this numerical example and install it on your computer to study

At present, databases of 27-year observations of various adverse weather conditions and dangerous hydrometeorological phenomena leading to social and economic losses on the territory of Russia are in full open free access. Some of these natural hazards also cause significant damage to agriculture, especially crop production, horticulture and viticulture. Therefore, a great scientific and practical interest is the intellectual analysis of these data, which will create more favorable conditions for the prediction of such adverse events and decision-making, taking into account their possible negative impact on human activity. To achieve this goal, it is necessary to solve the following tasks, which are obtained by decomposition of the goal and are the stages of its achievement: Task 1: cognitive structuring of the subject area. Task 2: preparation of initial data and formalization of the subject area. Task 3: synthesis and verification of statistical and system-cognitive models and selection of the most reliable model. Task 4: solving problems in the most reliable model: - subtask 4.1. Forecasting (diagnostics, classification, recognition, identification); - subtask 4.2. Support decision-making; - sub-task 4.3. Study of the simulated subject area by studying its model (cognitive diagrams of classes and values of factors, agglomerative cognitive clustering of classes and values of factors, nonlocal neurons and neural networks, 3d-integral cognitive maps, cognitive functions). It is proposed to use automated system-cognitive analysis (ASC-analysis) to solve the tasks. The article provides a detailed numerical example illustrating the solution of all these problems

Sommelier evaluates the quality of wine on the basis of their subjective feelings. At the same time, what the sommelier says when evaluating wine, it is difficult or impossible to rationally understand for the uninitiated to this art. The process of assessing the quality of wine by sommelier can not be formalized and is carried out entirely at the sensual level. Sometimes, different sommeliers differently evaluate the same wine poured from the same barrel into bottles of different prestige, with stickers differing in the number of stars. This raises at least two legitimate and natural questions. The first question is whether any subjective sommelier evaluations of the quality of wine are connected with its objective physical and chemical properties? The second question arises in the case of a positive answer to the first one: is it possible to analyze the objective methods of physical and chemical properties of wine to predict its subjective assessment by various sommeliers or some "generalized sommelier", generalizing many such subjective assessments? This article is devoted to obtaining reasoned answers to these questions. The purpose of this work, which is of great scientific and practical interest, is to create a model that provides an automated assessment of the quality of wine based on the analysis of its objective physical and chemical properties, coinciding with its sommelier-evaluation. To achieve this goal, we use Automated system-cognitive analysis (ASC-analysis) and its software tools – the intelligent system called "Eidos". A detailed numerical example based on 1599 real-world examples of sommelier evaluation of wine quality with known physical and chemical properties is considered. In addition to the answer to the two questions in the article, there is a study of the created system-cognitive model

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

A number of authors (Milaeva I. V., Zaitsev S. Yu., Dovzhenko N. A., Tsarkov D. V., Tsarkova M. S., 2015) have proposed a regression model and a method for indirect measurement of fat and protein content in cow's milk by its dynamic surface tension, which has a number of advantages over the traditional approach. The model reflects the objectively existing relations between the content of fat and protein in cow's milk and parameters of centigrams of dynamic surface tension at the interface of the milk/air. These relationships are revealed by the authors of the method wit mathematical processing of 112 empirical samples. For this purpose, they have applied regression and correlation analysis (in MS Excel). Since the content of fat and proteins in milk largely determines its quality, this problem refers to the problems of qualimetry. However, this problem of qualimetry also refers to typical problems of pattern recognition (multi-parameter typing and system identification), and therefore it is possible that the quality of its solution can be improved by the use of artificial intelligence methods, in particular cognitive and information technologies. Moreover, these relationships are quite complex. The use of intelligent technologies also creates additional opportunities for the study of the simulated subject area by studying its model. All this is of great scientific and practical interest for both researchers and practitioners. To solve all these problems in the work we have applied Automated System-Cognitive analysis (ASC-analysis) and its software tool which is an intelligent system called "Eidos". A numerical example based on real data has been considered in detail as well

There are many opinions on the question of what kind of society we live in at the beginning of the XXI century. Previously, it was believed that this is a post-industrial society. There was even an opinion that it would be a society of developed socialism or even a communist society. After that, the opinion on this has changed. First, modern society was called the information society, and then the society based on knowledge. The latest news in this area is that it seems that modern society is a digital society, that is, a society, based on digital technologies, digital communications, digital information processing and transmission technologies, as well as digital artificial intelligence technologies. In a digital society and science people must move to digital intelligent research technologies. In particular, the question arises as to whether veterinary medicine in a digital society should not also become cognitive veterinary medicine. This work is devoted to a detailed and reasoned (according to the authors) answer to this question. The methodology and terminology in this new field is not yet established and is not generally accepted. Therefore, in this work a lot of attention is paid to the logic and methodology of scientific knowledge, terminological issues and definitions of concepts

In previous works, the authors solved the problem of cognitive structuring and formalization of the subject area, synthesis and verification of system-cognitive models, predicting the impact of nomenclature and sales volumes on the profit and profitability of the trading company, decision support for the selection of such nomenclature and sales volumes, which cause a given target profit and profitability of the company. This work is devoted to the study of the simulated subject area by studying its SC-model