Scientific Journal of KubSAU

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

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 creation of artificial intelligence systems is one of important and perspective directions of development of modern information technology. As there are many alternatives to artificial intelligence systems, there is a need to evaluate mathematical models of these systems. In this article, we consider a solution of the problem of identifying classes of levels of pay to employees on their characteristics. To achieve this goal it requires free access to test the source data and methodology, which will help to convert the data into the form needed for work in artificial intelligence systems. A good choice is a database of test problems for systems of UCI artificial intelligence repository. In this work we have used data base on teaching effectiveness for three regular semesters and two summer semesters of 151 teaching assistant (TA) assignments at the statistics Department of the University of Wisconsin-Madison. The most reliable in this application was the model of the INF4. The accuracy of the model in accordance with Lmeasure made up 0,809, which is much higher than the reliability of expert evaluations, which is equal to about 70%. To assess the reliability of the models in the ASC-analysis and in the system of "Eidos" we use F-criterion of van Ritbergen and its fuzzy multiclass generalization proposed by Professor E. V. Lutsenko

In forensics there is an urgent need to determine the type of rifle (automatic, rifle, large caliber pistol) depending on its used ammunition found at the scene of the use of weapons. We offer a solution to this problem with the use of new innovative method of artificial intelligence: automated system-cognitive analysis (ASC-analysis) and its program toolkitwhich is a universal cognitive analytical system called "Eidos". In the "Eidos" system we have implemented the software interface that allows posting of images and identifying their outer contours. By multivariable typing, the system creates a systemic-cognitive model, the use of which, if the model is sufficiently accurate, may be helpful in solving problems of system identification, prediction, classification, decision support and research of the modeled object by studying its model. For this task the following stages: 1) input images of ammunitions into the "Eidos" system and creation of their mathematical models; 2) the synthesis and verification of the models of generalized images of ammunition for types of weapons based on the contour images of specific munitions (multiparameter typing); 3) improving the quality of the model by separating classes for typical and atypical parts; 4) quantification of the similarities-the differences between specific types of munitions with generic images of different types of ammunition of the weapon (system identification); 5) quantification of the similarity-differences between types of ammunition, i.e. cluster-constructive analysis of generalized images of ammunition. A numerical example is given. We also possess a successful experience of solving similar problems in other subject areas

From a huge number of the organisms inhabiting our planet, insects make 70%, being the most numerous of the invertebrate animal classes numbering more than 2 million types. It is difficult to find such place where it would be impossible to meet representatives of this huge class. They completely took over the entire environment - water, the land, air. For them, it is the common characteristic: complex instincts, omnivorous, high fecundity, and for some of them – a public way of life. Insects can be found at tremendous heights, reaching the level of 5000 meters, and they inhabit the desert where it practically never rains, not to mention the absence of any vegetation. Deep caves where no sunlight, nor the conditions for food and existence of living organisms — it is also the habitat of insects, they can be found far beyond the Arctic circle, and even on many Islands of Antarctica, where in addition to lifeless rock, it would seem that there is nothing else. Among insects, one of the largest and most numerous families are the ground beetles (Carabidae). They subtly respond to changes in soil and vegetation, hydrothermal and micro-climatic conditions of the environment, which makes them a convenient model subject to various environmental and Zoological researches. Ground beetles belong to a large number of genera and species, often difficult to see, in this regard, we use many different signs to diagnose. We have taken into consideration the coloration, body shape, external structure, surface structure, size, and arrangement of the genitals and chaetotaxy. Due to the fact, that the number of ground beetles is enormous, and, using their appearance, it is very difficult to determine their generic identity, there is a need of automation of the identification process, due to which we require a special mechanism that would increase the accuracy of these insects. In the previous work of the authors (http://ej.kubagro.ru/2016/05/pdf/01.pdf) we considered the further possibility of using the method of ASC- analysis to classify insects, not only in species but also in genera, orders, thereby increasing the reliability of determination of ground beetles, which will be done in this article. A numerical example is given. We also have gained a successful experience of solving such problems in other subject areas. This article can be considered as a continuation of the series of works dedicated to governmental use of the automated system-cognitive analysis (ASC-analysis) and its software tools – the system of "Eidos"

The creation of artificial intelligence systems is one of important and perspective directions of development of modern information technology. As there are many alternatives to artificial intelligence systems, there is a need to evaluate mathematical models of these systems. In this work, we consider a solution of the problem of identifying classes of levels of pay of employees on their characteristics. To achieve this goal, it requires free access to test the source data and methodology, which will help to convert the data into the form needed for work in artificial intelligence systems. A good choice is the databases from the site: http://allexcel.ru/gotovyetablitsy-excel-besplatno. In this work, we have used the database called "The database table of employees, payments calculation". The most reliable in this application was the model of the INF4 based on semantic appropriate measure of information of A. Kharkevich with integral criteria of "Amount of knowledge". The accuracy of the model is 0.960, which is much higher than the reliability of expert evaluations, which is equal to about 70%. To assess the reliability of the models in the ACS-analysis and the system called "Eidos" we have used F-criterion of van Ritbergen and fuzzy multiclass generalization proposed by Professor E. V. Lutsenko

In the author's interpretation we consider concepts and methods of science, such as science, knowledge, model, gnosticism and agnosticism, the principle of Ashby, facts, empirical regularity, empirical law, scientific law, and others. We have formulated the main problem of the science, concluding that cognitive abilities of a human are limited and do not provide effective knowledge in a very large volume of data. The solution to this problem is to look at ways of automation of scientific research. Traditionally, we use information-measuring systems and automated systems research (ASNI) for this. However, the mathematical methods used in these systems, impose strict impracticable requirements to the source data, which dramatically reduces the effectiveness and applicability of these systems in practice. Instead of having to submit to the source data impracticable requirements (like the normality of the distribution, absolute accuracy and complete replications of all combinations of values of factors and their full independence and additivity) automated system-cognitive analysis (ASC-analysis) offers (without any pre-processing) to understand the data and thereby convert them into information and then convert this information to knowledge by its application to achieve targets (i.e. for controlling) and for solution for problems of classification, decision support and meaningful empirical research of the modeled subject area. ASC-analysis is a systematic analysis, considered as a method of scientific cognition. This is a highly automated method of scientific knowledge that has its own developed and constantly improving software tool – an intellectual system called "Eidos". The system of "Eidos" has been developed in a generic setting, independent of any domain and can be applied in all subject areas, in which people apply their natural intelligence. The "Eidos" system is a tool of cognition, which greatly increases the possibility of natural intelligence, just like microscopes and telescopes multiply the possibilities of vision (but in this case only if you have this possibility). The study proposes a new view of the models: phenomenological meaningful model, which is currently represented only by systemic cognitive models, and which is currently in the middle between empirical and theoretical knowledge. The system called "Eidos" is considered as a tool of automation of the learning process, providing meaningful synthesis of phenomenological models directly on the basis of empirical data

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

In this article we consider application of the automated systemic and cognitive analysis (ASK-analysis), its mathematical model – a systemic information theory and the program tools realizing them – the intellectual Eidos system, for input (digitization) of images from graphic files, synthesis of the generalized images of classes, their abstraction, classification of the generalized images of classes (clusters and constructs), comparison of concrete images with the generalized images (identification) of classes, comparisons of classes with each other and creations of the generalized images of genus of ground beetles on the basis of images of the types. The new approach to digitization of images of ground beetles based on use of a polar frame, the center of weight of the image and its external contour is offered. Before digitization of images, their transformations standardizing the provision of images, their sizes and an angle of rotation can be applied. Therefore, the results of digitization and the ASK-analysis of images can be invariant (are independent) concerning their situation, the sizes and turn. There is a successful experience of the solution of similar tasks in other subject domains. This article can be considered as a continuation of series of the works devoted to application of the automated systemic and cognitive analysis (ASK-analysis) and its program tools – the Eidos system

One of the "points of growth" of applied statistics is methods of reducing the dimension of statistical data. They are increasingly used in the analysis of data in specific applied research, such as sociology. We investigate the most promising methods to reduce the dimensionality. The principal components are one of the most commonly used methods to reduce the dimensionality. For visual analysis of data are often used the projections of original vectors on the plane of the first two principal components. Usually the data structure is clearly visible, highlighted compact clusters of objects and separately allocated vectors. The principal components are one method of factor analysis. The new idea of factor analysis in comparison with the method of principal components is that, based on loads, the factors breaks up into groups. In one group of factors, new factor is combined with a similar impact on the elements of the new basis. Then each group is recommended to leave one representative. Sometimes, instead of the choice of representative by calculation, a new factor that is central to the group in question. Reduced dimension occurs during the transition to the system factors, which are representatives of groups. Other factors are discarded. On the use of distance (proximity measures, indicators of differences) between features and extensive class are based methods of multidimensional scaling. The basic idea of this class of methods is to present each object as point of the geometric space (usually of dimension 1, 2, or 3) whose coordinates are the values of the hidden (latent) factors which combine to adequately describe the object. As an example of the application of probabilistic and statistical modeling and the results of statistics of non-numeric data, we justify the consistency of estimators of the dimension of the data in multidimensional scaling, which are proposed previously by Kruskal from heuristic considerations. We have considered a number of consistent estimations of dimension of models (in regression analysis and in theory of classification). We also give some information about the algorithms for reduce the dimensionality in the automated system-cognitive analysis