Name
Lutsenko Yevgeniy Veniaminovich
Scholastic degree
•
Academic rank
professor
Honorary rank
—
Organization, job position
Kuban State Agrarian University
Web site url
Articles count: 272
The article presents results of the study to assess the effectiveness of credit funds in interacting agricultural (AES) and processing (PP) agricultural enterprises. The conducted studies are a continuation of the scientific work on the development of mathematical models of interaction of the enterprises of the AES and PP, are shown in the articles [1, 2, 3]. This article presents the authors’ developed set of models of management of credit funds of interacting enterprises of an agroindustrial complex. It includes mathematical models of economic efficiency of agricultural enterprises considering the use of loan funds, as well as the assessment of the maximum amount of interest rate of the loan and the minimum selling prices of units of finished agricultural products; a mathematical model of the economic efficiency of the processing plant taking into account credit obligations of the agricultural enterprise and a model for the calculation of the minimum selling prices of its finished products; a mathematical model of the economic efficiency of the combined entity with all its loans. We have proposed a model to calculate the minimum selling prices of its finished products
The quality of life of the population of the region is an
important integral criterion of estimation of efficiency
of activity of regional administration. The most
important strategic sector of the economy of the
Krasnodar region is the agro-industrial complex (AIC).
This poses the problem of management of the quality
of life of the region through the use of as the control
factor of the volume and direction of investment in
agriculture
The article is devoted to the solution of the problem which is the fact that on the one hand, the rating of Russian universities is in demand and on the other hand it hasn’t been created yet. The proposed idea of solving the problem consists in the application of domestic licensing of innovative intelligent technologies for these purposes: we have suggested using an automated system-cognitive analysis (ASC-analysis) and its software tools – the intelligent system called "Eidos". These methods are described in detail in this context. It is proposed to consider the possibility of applying these tools on the example of the Guardian University ranking. The article discusses its private criteria (indicators of universities). We specify the sources of data and the methods of their preparation for processing in "Eidos" system. In accordance with ASC-analysis methodology the article describes the installation of "Eidos", the data input into it, and the formalization of the subject area, synthesis and verification of models, their display and use to solve problems of assessment of the Guardian rating for Russian universities and research object modeling. It also discusses the prospects and ways of development of the integrated rating of Russian universities and operation of rating in adaptive mode. We have also specified the limitations of the proposed approach and the prospects of its development
Insects are a major component of natural biocenoses
and agrocenoses. One of the largest and most numerous
families are ground beetles (Carabidae); their
number, according to various estimates, is more than
30,000 species. For Carabidae beetles it is common to
have different ways of eating, a place of habitation,
occupied layers, seasonal and daily activity. They live
both on the surface and in the soil, more rarely on
bushes and trees. The types of the family of ground
beetles – active beetles with long, thin antennae of
uniform thickness, long elytra and long legs, adapted
to running. Their sizes vary from a few millimeters to
10 cm. As active predators, ground beetles play a huge
practical importance, destroying pests before reaching
the last threshold, thereby providing a natural regulation.
Based on the fact, that the number of beetles is
large, and their sizes are sometimes only a few millimeters,
there is a problem of determining the species
of these insects (or their identification), therefore it
took a special tool, which, on the one hand, facilitate
obtaining data about these insects, and on the other
hand, would increase their accuracy. This article proposes
a new (to this subject area) approach to identify
different species of ground beetles along their outer
contour with the use of software tools for automated
system-cognitive analysis (ASC-analysis) – the universal
cognitive analytical system called "Eidos,"
which is well-proven in the study of other objects. The
reason why it was decided to use this system is that
normal (standard) identification of ground beetles,
have certain disadvantages: the human factor (manifest
error in the determination); quite time consuming; the
inability to increase the number of criteria to improve
the reliability of the model comparison. This article
aims to overcome these drawbacks, by the use of universal
cognitive analytical system "Eidos", the automated
system-cognitive analysis (ASC-analysis). A numerical example is given
Adequate and effective assessment of the efficiency, effectiveness and quality of scientific activities of specific scientists and research teams is crucial for the information society and society based on knowledge. The solution to this problem is the subject of scientometrics and its purpose. The current stage of development scientometrics differs greatly from its previous appearance in the open as well as paid on-line access to huge amount of detailed data on a large number of indicators on individual authors and on scientific organizations and universities. In the world, there are well-known bibliographic databases: Web of Science, Scopus, Astrophysics Data System, PubMed, MathSciNet, zbMATH, Chemical Abstracts, Springer, Agris, or GeoRef. In Russia, it is primarily the Russian scientific citing index (RSCI). RSCI is a national information-analytical system, accumulating more than 9 million publications of Russian scientists, as well as information about citation of these publications from more than 6,000 Russian journals. There is a lot of data, so-called "Big data". The main primary scientometric indicator (based on which we build all the rest, such as the h-index) is the number of citations of the author's works, placed in the bibliographic database. This number of citations is determined by the software of RSCI using so-called "binding" which is a grammatical analysis and search in databases for works of the author, for relevant links from references in the works of various authors. However, the problem is, as experience shows, that authors make a very large number of simply incorrect and incomplete references in the reference lists, very far from standard. Currently, the software that RSCI uses does not automatically bind these invalid references, and this requires human intervention. But, centrally, to do this is not possible by experts of RSCI because of the huge amount of work, and distributed work for a large number of specialists in the field still requires a centralized moderation. As a result, the work for binding references to the literary sources is very slow and a huge amount of links is unbound. This leads to an underestimation of nanomatrices indicators of both individual authors and research teams that cannot be considered acceptable. The solution to this problem is offered by applying the automated system-cognitive analysis (ASC-analysis) and its programmatic Toolkit – intellectual system called "Eidos". This work provides a numerical example of the intellectual anchor of the real incorrect references to the works of the author on the basis of a small amount of real scientific data that are publicly available free on-line access to the RSCI
A diversified corporation is a highly complex multivariable dynamic system. The application of classical forecasting methods applied to such objects has encountered a number of difficulties, due to its economic nature. In the article, we substantiate the requirements to the forecasting method; on the basis of these requirements we can select the method and its software tool
The performance indicators of a trading company in
physical and monetary terms is significantly affected
by the types and volumes of purchased and sold
products, and which she purchased suppliers and the
consumers sold. However, the solution to the problem
of choosing the rational range of products faces
considerable cost of computational and human
resources, and lack of baseline data, and in real
dimensions this problem has no solution. The paper
proposes such a solution is very economical in costs of
different types of resources based on the application of
information theory, cognitive and control theory
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 present a solution of
the problem of identifying classes of salary levels of
employees depending 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 artificial
intelligence of UCI repository. In this work we used
the database called "Wine Data Set" from the Bank's
original task of artificial intelligence from repository
UCI. The most reliable in this application was the
model of the INF4 based on semantic, according to
A. Kharkevich, integral criteria of "Amount of
knowledge". The accuracy of the model is 0,916,
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
the system of "Eidos" we used the F-criterion of van
Ritbergen and fuzzy multiCLASS generalization
proposed by Professor E. V. Lutsenko (L-measure)
The creation of artificial intelligence systems is one
of important and perspective directions of
development of modern information technology.
Since there are many alternatives of mathematical
models of systems of artificial intelligence, there is a
need to assess the quality of these models, which
requires their comparison. To achieve this goal we
require free access to the source data and
methodology, which allows to convert these data
into a form needed for processing in artificial
intelligence. A good choice for these purposes is a
database of test problems for systems of artificial
intelligence of repository of UCI. In this work we
used the database "Iris Data Set" from the bank's
original task of artificial intelligence – UCI
repository, which solved the problem of
formalization of the subject area (development of
classification and descriptive dials and graduations
and the encoding of the source data, resulting
training sample, essentially representing a
normalized source data), synthesis and verification
statistical and system-cognitive models of the
subject area, identify colors with classes, which
serve varieties of Iris, as well as studies of the
subject area by studying its model. To solve these
problems we used the automated system-cognitive
analysis (ASC-analysis) and its programmatic
Toolkit – intellectual system called "Eidos"
The authors have developed and manufactured a large
number of different designs of relative helical drums
for mixing animal feed. We have conducted 749 field
experiments with the drums of the 10 different designs
with different parameters modes of operation. In all
experiments, we measured the quality of the feed mixture.
However, directly based on empirical data, rational
choice of design features and parameters of the
operation modes of the reels is not possible. For this,
you must first develop a model reflecting these empirical
data. The construction of meaningful analytical
models of different types of drums is a difficult and
demanding scientific task, the complexity of which is
due to the large variety and complexity of forms of
drums and their mode of usage, a large number of diverse
physical factors affecting the processes in the
drum. As a consequence, the development of analytical
models associated with a large number of simplifying
assumptions that reduce their versatility and reliability.
Therefore, it is important to search of a mathematical
method and software tools provide a quick and simple
for the user to identify and influence the design of the
drum and the parameters of the operating modes on the
quality of the feed mixture directly on the basis of empirical
(experimental) data. The work proposes a solution
to this problem with the use of a new universal
innovative method of artificial intelligence: automated
system-cognitive analysis (ASC-analysis) and its programmatic
Toolkit – universal cognitive analytical
system called "Eidos". In the system of "Eidos" we have implemented a software interface that provides
direct input into the system large amounts of empirical
data from Excel file. Created on their basis in the system
of "Eidos" system-cognitive model allows the visual
form to reflect the effect of the structure of the
drum and the parameters of the operating modes on the
quality of the resulting feed mixture and to develop on
this basis the science-based and appropriate recommendations
for the rational choice of design features
and parameters of the modes relative to the screw
drums. We have also given a numerical example