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• CLUSTERING ALGORITHM FOR LARGE GROUPS OF EXPERTS BASED ON THE INTERPRETIVE STRUCTURAL MODELING METHOD

  Е.М. Gerasimenko , P.S. Gerasimenko

  6-21

  2025-12-30

  Abstract ▼

  This article presents an algorithm for achieving consensus in social networks during large‑scale group decision‑making with incomplete probabilistic fuzzy information containing elements of uncertainty, which takes into account the trust relationships among experts. A method for clustering experts based on interpretive structural modelling is proposed. It serves both to classify experts and to enhance the efficiency of consensus achievement.The study examines trust propagation and aggregation operators for probabilistic fuzzy information with elements of uncertainty. These operators enable indirect trust assessment and determination of experts’ weight coefficients. As a result, it becomes possible to form several subsets of experts and to determine weight coefficients for a large number of experts based on their mutual trust relationships. Based on the clustering of experts and the calculated indirect trust relationship between experts, decision‑making in emergency situations is carried out by achieving consensus, taking into account fluctuating probabilistic fuzzy information, and the best evacuation alternative is identified.
  The assessments provided by experts in the form of probabilistic fluctuating fuzzy values allow for effective modelling of doubts, uncertainty, and inconsistencies in expert evaluations when a group of experts or various expert organisations are involved. At the same time, it becomes possible to take into account different expert assessment values in multi‑criteria decision‑making tasks when experts cannot agree on common membership degrees. The algorithm allows classifying a large group of experts into several subsets based on their social trust relationships. This method prevents the formation of overlapping subsets and does not require pre‑setting clustering parameters. It relies exclusively on social trust relationships between experts, thereby avoiding the issue of subjective intervention in the clustering process. Compared to traditional clustering methods, the interpretive structural modelling‑based clustering approach effectively reveals the hierarchical structure of relationships among experts. It also minimizes the number of participants in large‑scale group decision‑making within a social network by reducing the dimensionality of the expert set. Clustering experts based on the interpretive structural modelling method significantly enhances the efficiency and feasibility of large‑scale group decision‑making

• APPLICATION OF FUZZY LOGIC FOR MAKING DECISIONS ABOUT EVACUATION IN CASE OF FLOODING

  Е.М. Gerasimenko, V.V. Kureichik, S.I. Rodzin, A.P. Kukharenko

  2022-11-01

  Abstract ▼

  We are talking about natural disasters, such as flooding, which can be predicted a few
  hours before they occur so that evacuation of the population can be organized. Evacuation
  means that people in disaster areas must leave these areas and reach shelters. The article pr esents
  an analysis of the decision-making process on evacuation, the main criteria determining
  the decision and the main stages of using fuzzy logic to make a decision on evacuation based on
  qualitative and quantitative values of the decision-making criteria. These stages include selection
  of criteria, determination of qualitative input and output variables, fuzzification of variables,
  definition of the base of fuzzy rules, construction of fuzzy inference, visualization of results
  and sensitivity analysis. When modeling, the following criteria were taken into account: the
  predicted flood level, the level of danger, the vulnerability of the area of the expected flood and
  the possibility of safe evacuation. The predicted flood level was based on the parameters of the
  maximum level and the rate of water rise. The hazard level reflected the physical characteristics
  of the flood and its potential impact on the safety of people in the flood area. The vulnerability
  of the area of the expected flood was defined as the inability at the local level to prevent people
  from direct contact with flood waters during the event. The possibility of safe evacuation was
  defined as a set of limitations and potential negative aspects that could delay or hinder the successful
  evacuation. The description of qualitative variable criteria for making a decision on the
  need for evacuation, examples of determining the base of fuzzy rules are presented. The fuzzy
  model is implemented using Matlab Fuzzy Logic Toolbox. The procedure of fuzzy inference and
  interpretation of the solution and a model of several scenarios and flood situations are described.
  The method by which a fuzzy model of decision-making on evacuation can be applied in
  combination with a geoinformation system is considered. The actions related to the need for
  evacuation for various scenarios and circumstances are presented.

• INTELLIGENT RECOMMENDER SYSTEM FOR SPATIAL ANALYSIS

  S.L. Belyakov, А. V. Bozhenyuk, N. А. Golova, К.S. Yavorchuk, I.N. Rosenberg

  14-26

  2025-07-31

  Abstract ▼

  The work is devoted to the analysis of mechanisms of formation of recommendations and
  evaluation of the user's reaction to them in the interactive mode of work with the geoinformation
  system. One of the important areas of application of recommender systems is the search and decision-
  making in spatial situations. A peculiarity of this class of problems is the uncertainty of task definition and ambiguity of decision evaluation. Users are often faced with problems that do not
  have a clear formulation. To try to solve them, it is necessary not only to designate the direction of
  solution search, but also to find an adequate sequence of tasks with clearly formulated input and
  output data. Recommendations in such cases are designed in a dialogue with the user-analyst to
  develop a strategy for finding solutions. In this paper we study a smart recommendation system
  using the experience of dialog interaction. We propose a model of adaptation to the mental image
  of the problem, which builds the user, taking into account the levels of situational awareness and
  cognitive load. The peculiarity of the model is the use of visual cartographic objects, which are
  indicators of the state of the mental image. A recommendation is represented by a set of objects
  that are introduced into the field of cartographic analysis. This implicitly induces a certain semantic
  direction of increasing situational awareness. A criterion of satisfaction with the recommendation
  is suggested. A diagram of recommender system states, which describes the selection of context,
  adequate to the problem being solved, is given. The context is understood as an information
  object, capable of providing program functions and data for solving problems of a limited class.
  A sequence of contexts in an analysis session is considered as a precedent of experience. Indicators
  of trend, tendency and rhythm are proposed for possible chains of contexts. The degree of
  semantic proximity of precedents to the current course of search for a solution is estimated by
  these indicators. Their use will increase the speed of adaptation.

• INTELLIGENT RECOMMENDER SYSTEM FOR SPATIAL ANALYSIS

  S.L. Belyakov, А.V. Bozhenyuk, N.А. Golova, К.S. Yavorchuk, I.N. Rosenberg

  14-26

  2025-07-30

  Abstract ▼

  The work is devoted to the analysis of mechanisms of formation of recommendations and
  evaluation of the user's reaction to them in the interactive mode of work with the geoinformation
  system. One of the important areas of application of recommender systems is the search and decision-
  making in spatial situations. A peculiarity of this class of problems is the uncertainty of task definition and ambiguity of decision evaluation. Users are often faced with problems that do not
  have a clear formulation. To try to solve them, it is necessary not only to designate the direction of
  solution search, but also to find an adequate sequence of tasks with clearly formulated input and
  output data. Recommendations in such cases are designed in a dialogue with the user-analyst to
  develop a strategy for finding solutions. In this paper we study a smart recommendation system
  using the experience of dialog interaction. We propose a model of adaptation to the mental image
  of the problem, which builds the user, taking into account the levels of situational awareness and
  cognitive load. The peculiarity of the model is the use of visual cartographic objects, which are
  indicators of the state of the mental image. A recommendation is represented by a set of objects
  that are introduced into the field of cartographic analysis. This implicitly induces a certain semantic
  direction of increasing situational awareness. A criterion of satisfaction with the recommendation
  is suggested. A diagram of recommender system states, which describes the selection of context,
  adequate to the problem being solved, is given. The context is understood as an information
  object, capable of providing program functions and data for solving problems of a limited class.
  A sequence of contexts in an analysis session is considered as a precedent of experience. Indicators
  of trend, tendency and rhythm are proposed for possible chains of contexts. The degree of
  semantic proximity of precedents to the current course of search for a solution is estimated by
  these indicators. Their use will increase the speed of adaptation

• KNOWLEDGE FOR ARGUMENTATION IN COMPARISON OF SPATIAL SITUATIONS

  S.L. Belyakov, N.А. Golova, К.S. Yavorchuk, I. N. Rosenberg

  2022-05-26

  Abstract ▼

  The traditionally used way to assess the quality of the solution proposed by an intelligent
  system is to explain the course of logical inference. Knowledge about reasoning is used to argue
  the choice of a solution option. The sequence of applied rules, the facts used and the confirmed
  hypotheses are considered arguments that should convince the user of the validity of the formed
  conclusion. The disadvantage of this method of explanation is that it reflects a formally correct,
  but devoid of semantic content, course of reasoning. The argumentation of the solution obtained is
  based on the tracing protocol, which is essentially no different from debugging information when
  tracing programs. The argumentation in this case is far from the meaning of the situation. The
  meaning is understood as a given set of transformations of the situation that preserve the immutability
  of its perception by a human analyst. Knowledge about the semantic content of situations
  should be presented in a special fashion. In this paper, we consider a representation containing a
  precent and its permissible transformations. In this form, spatial situations in geoinformation systems
  are described. For argumentation, it is proposed to use special relations between images of
  situations. The concept of the area of applicability of the image is introduced. The mutual arrangement of the spatial-temporal and semantic shell of images and the areas of their applicability
  is considered as a carrier of the relationship. Information about relationships is extracted from the
  structure of the cartographic database. The relations of inheritance, aggregation, composition,
  generalization and association of classes of objects are considered. Knowledge for argumentation
  is provided by the rules for determining the reliability index of expert conclusion for individual
  relationships and their combinations. A method of automatic rule generation is proposed.
  The relations for comparison of levels of reliability of rules are given.

• HYBRID METHODIC FOR PRACTICAL IMPLEMENTATION OF THE SYSTEM OF DECISION-MAKING ON PRIORITY REGULATION

  S.A. Tkalich

  2022-03-02

  Abstract ▼

  The task of building a decision-making system within the framework of automated systems of
  accident-free control of technological processes based on forecasting models is considered.
  The analysis of models and methods of emergency forecasting is presented. The task of developing
  a methodology for practical implementation of the system based on the integral criterion of accident-
  free control, taking into account the time reserves to bring the process to a normal state
  (emergency forecasting system) and the resource component (preventive maintenance system) is
  formulated. The conclusion is made about the expediency of building decision-making systems and
  automated control systems based on forecasting models, as the most promising approach to solving
  the problem of accident-free control of technological processes. The principle of building a
  decision-making system is based on the use of the integral criterion of accident-free management.
  The block diagram of the algorithm for calculating the integral criterion of accident-free control is
  presented. Hybrid methodology for practical realization of such systems on the basis of priority
  regulation, which includes a standard regulator, is offered. The procedure of formation of priority
  regulators according to the forecast data is described. A block diagram of the algorithm of the
  priority regulator, which determines the critical parameter on the basis of sensitivity theory, is
  presented. In case of a positive forecast on an accident, the critical parameter is selected by the
  maximum of the sensitivity coefficient, and the minimum or maximum value of the parameter depending
  on the sign of its rate of change is fed to the standard regulator from the matrix of critical
  values as a set point. The structure of the decision-making system based on the concept of accident-
  free control of technological processes is given. The station of accident-free control forms the
  data for the decision-making module on the basis of the compositional model of emergency forecasting
  and the integral criterion of accident-free control. Algorithm block diagram of the decision
  making module for priority regulation is given.

• AN INTELLIGENT SENTIMENT ANALYSIS SYSTEM FOR MEASURING CUSTOMER LOYALTY AND MAKING DECISIONS BASED ON FUZZY LOGIC

  E.M. Gerasimenko, V.V. Stetsenko

  2021-11-14

  Abstract ▼

  This paper presents an intelligent approach of measuring customer loyalty to a specific
  product based on the analysis of comments. General sentiment analysis in tweets and messages is
  quite common, but task-oriented analysis of user opinions and measuring their level of loyalty is a
  new idea. The tricky part of doing task-oriented sentiment analysis lies in measuring customer
  loyalty to a particular product based on how customers feel about that product itself. The resulting
  data on the level of customer loyalty to the product can help a new customer to make a decision on
  a specific product, taking into account its various characteristics and feedback from previous customers. The dataset was a large dataset of online customer testimonials from Amazon.com. The set of
  initial data is a set of reviews, from which the proposed approach forms an aggregated assessment of
  opinions, then a fuzzy logic model is used to measure customer loyalty to the product. In the proposed
  approach, the input text is first processed using such methods as tokenization, removal of stop words,
  lemmatization, then the parts of speech are marked and the polarity of the reviews is analyzed, then
  fuzzy logic methods are applied to the obtained aggregated estimates to determine the degree of customer
  loyalty to the product. This work used various open API libraries such as SentiWordNet, Stanford
  CoreNLP, etc. The approach used focuses on identifying the sentiment of reviews, which can be
  positive, negative and neutral. In our study, we used a triangular membership function, also known
  as trimf, because it supports three variables and creates a relationship between them. The implementation
  of the approach ensures high accuracy in determining loyalty to e-commerce products, which
  is superior to previous approaches, and the use of fuzzy logic has significantly increased the values of
  such indicators as precision, recall, and F-measure.

• ALGORITHM OF EFFECTIVE CONTROLS FOR NONSTOCHASTIC CAUSAL MODELS IN THE ABSENCE OF OBSERVABLE VARIABLES FOR SYSTEMS OF DECISION MAKING CONTROL

  A.N. Tselykh, V.S. Vasilev , L.A. Tselykh

  2021-11-14

  Abstract ▼

  The paper deals with the problem of reproducing the decision-making process by a person under
  conditions of uncertainty and incompleteness of the initial data. The decision-maker relies on his
  belief system, which includes a shared vision of the system in relation to which the decision is being
  made. The system is presented in the form of a causal model created on the basis of human mental
  representations. These models are directed graphs, on the arcs of which the causal relationship is
  expressed in the form of labels with a sign that determines the direction of change in the state of the
  system. The vertices of this directed graph are high-level abstraction concepts. This graph simulates
  the functioning of a real system. Thus, we investigate the problem of predicting and controlling human
  actions based on non-stochastic causal models in the absence of observable variables for use in
  decision support systems and expert systems. Decision-making is considered from the point of view of
  the choice of objects of application of managerial influences - the factors of the model. In this study,
  we show that the application of the proposed algorithm can facilitate decision-making regarding the
  choice of control actions that support the achievement of the tactical and strategic goals of the decision
  maker. It should be noted that the algorithm implements an automatic selection of the regularization
  parameter, which makes the development and application of the proposed algorithm available
  to users who do not have sufficient mathematical training. The convergence of the sequence of Lagrange
  multipliers of an effective control algorithm is proved. The theorem on resonance in a nonstochastic
  causal mod-el, represented by a directed graph, which is determined by the range of admissible
  values of the damping coefficient in the control model, is proved. It is expected that the introduction
  of this tool into decision support systems will in-crease the reliability of decisions regarding
  the operation of the system as a whole. The choice of control actions using the proposed algorithm
  has high efficiency and productivity. Thus, the results presented in the study can be useful for
  developing applications in intelligent systems.

• TRANSFORMING THE DECISION-MAKING EXPERIENCE

  S. L. Belyakov, M. L. Belyakova, S.A. Zubkov, N.A. Golova, K.S. Yavorchuk

  2021-01-19

  Abstract ▼

  The problem of transferring the experience of decision-making in situational analysis using
  geoinformation systems is considered. The need for intellectual support from the geoinformation
  system is due to the fact that spatial objects and connections of the real world are extremely dynamic.
  Under these conditions, it is not possible to apply analytical models of processes and phenomena
  due to the incompleteness and inconsistency of the information describing them. Statistical
  models depend on a large number of factors, which vary as the geographical location of the
  situation changes. An alternative is to use the experience of experts who are able to make effective
  decisions in local spatial situations. The lack of control over the reuse of experience is a problem.
  The knowledge gained in developing solutions in one locality can lead to inadequate solutions in
  another locality. The experience of solving a problem in the same area loses its significance over
  time. In this paper, we propose a representation of knowledge in the form of an image consisting
  of a center and acceptable transformations of the center. Image transformation functions that
  perform knowledge transfer are introduced. The properties of transformation functions that carry
  procedural knowledge about the images of situations are analyzed. The use of the identified properties
  for the formation of a test plan for software transformation procedures is considered. Thecriteria for successful transformation are studied. The optimization problem of finding the best
  transformation function in the GIS knowledge base is formulated. A generalized method of transforming
  experience is proposed. An example of the synthesis of transformation methods for selecting
  an operational call center is given. The image of the situation of making a decision about
  choosing a land plot for a service center is transformed into the specified area on the GIS map.

• STATEMENT THE PROBLEM OF SIMULATION OF DECISION-MAKING PROCESSES IN COMPLEX ORGANIZATIONAL-TECHNICAL SYSTEMS

  G.V. Gorelova

  2020-07-10

  Abstract ▼

  The article considers the features of organizational - technical systems (SOTS), belonging to the class of complex. SOTS may include robotic complexes, automated production, electronic sys-tems and devices used to transmit and convert information, etc. In the modern sense, SOTS are information and technical systems and are currently not only technical objects, they can also be classified as cyberphysical systems (CPS). The effectiveness of SOTS can be determined by many criteria, which should vary in content and time depending on the goals, stage of the existence of SOTS, the influence of internal and external environment. This determines the specificity of mana-gerial decision-making processes in them, requiring preliminary simulation modeling, especially at the design stages of these systems. The general formulation of the simulation problem is given, based on the combination of three approaches to the solution: cognitive, multi-criteria and multi-stage, probabilistic uncertainty. Which are combined into a single complex. Models of a multi-stage decision-making process, a probabilistic model problem of the nominal optimum and cogni-tive modeling of complex systems are proposed A demo example is presented, consisting of the development of a cognitive map of conditional SOTS, functioning in the presence of threats, modeling of functioning scenarios on a cognitive map with hypothetical changes in control and dis-turbing influences on the system. It is shown that at certain stages of decision-making with varia-tions of criteria-based assessments and control actions, it is possible to suppress threats to the system, as well as increase its effectiveness. Simulation was performed using the author's software system CMLS. The developed mathematical and software are designed for intelligent control sys-tems for the rational behavior of complex objects.

• A TIME SERIES FORECASTING METHOD BASED ON COGNITIVE FUZZY MODELING AND REGRESSION ANALYSIS

  А.I. Guseva , R.М. Romanov

  157-178

  2025-12-30

  Abstract ▼

  The relevance of the study stems from the low effectiveness of traditional time series forecasting methods under conditions of high uncertainty and limited data, which are typical of weakly formalized systems. The aim of the work is to develop and substantiate a time series forecasting method based on a hybrid approach that integrates cognitive fuzzy modelling, regression analysis, and the analytic network process. Within the study, a systematic review and comparative analysis of existing forecasting methods was carried out, including approaches based on fuzzy logic, neural network and cognitive modelling, as well as ensemble and hybrid methods, and their limitations were identified when dealing with small samples, nonlinear dependencies, and uncertainty. The proposed method includes: the construction of fuzzy cognitive maps, defuzzification of linguistic assessments, clustering of factors, application of the analytic network process to determine priorities, and the formation of a weighted regression model. The model undergoes statistical validation using the , , , and  metrics, as well as diagnostic checks of the assumptions underlying regression analysis, including tests for multicollinearity and autocorrelation. Application of the method reduced  from 0.38 to 0.22,  from 0.30 to 0.18, and  from 11.65 % to 7.12 %, thereby confirming an improvement in the accuracy and robustness of forecasts under limited data compared with classical multiple regression. The novelty of the proposed method lies in the integration of cognitive modelling, regression analysis, and the analytic network process, whereby the strengths of each component compensate for their individual limitations, providing more accurate and robust forecasting under the uncertainty inherent in the system under study. The practical significance of the work consists in the possibility of applying the proposed method to support decision-making and to enhance the validity of forecasts in various subject domains and situations characterized by a limited number of observations, a substantial role of expert judgments, and a complex structure of causal relationships between indicators over time

• APPLICATION OF TOPSIS FUZZY METHOD FOR DECISION MAKING USING SYNTACTICALLY INDEPENDENT LINGUISTIC VARIABLES

  А.V. Bozhenyuk , I.А. Dubchak , О.V. Kosenko

  272-282

  2026-09-10

  Abstract ▼

  Uncertain and imprecise data are typical for multicriteria problems, making fuzzy set theory an adequate tool for their solution. The objective of this paper is to apply the TOPSIS method in a fuzzy environment using syntactically independent linguistic variables. Due to the frequent occurrence of fuzzy concepts in decision-making data, crisp values are insufficient for modelling real-world situations. In the proposed approach, the evaluation of each alternative and the weight of each criterion are expressed by syntactically independent linguistic variables, whose arbitrary values are determined through the semantics of the base terms. An approach to calculating arbitrary (non-base) values of linguistic variables used by an expert is considered. The base values of the syntactically independent linguistic variables used are specified by triangular fuzzy numbers. The proposed approach is well suited for solving group decision-making problems in a fuzzy environment, where an expert or group of experts is not constrained by a limited number of fuzzy assessments. Here, fuzzy variables representing the values of the syntactically independent linguistic variables "criterion importance" and "criterion score" are considered as weighting coefficients for criterion importance and evaluation of qualitative criteria. The paper proposes using the centre of gravity method to calculate the distance between two triangular fuzzy numbers. In accordance with the fuzzy TOPSIS concept, a proximity coefficient is defined to determine the ranking order of all alternatives by calculating the distances to the fuzzy positive ideal solution (PIS) and the fuzzy negative ideal solution (NIS). An example is provided to illustrate the proposed approach

• THE ADJACENCY MATRIX RECONSTRUCTION ALGORITHM FOR CAUSAL GRAPH MODELS IN THE ABSENCE OF OBSERVABLE VARIABLES

  A. N. Tselykh , V.S. Vasilev, L. A. Tselykh

  2021-11-14

  Abstract ▼

  The paper deals with the problem of modeling complex systems in the absence of observable
  variables. To solve this problem, it is proposed to use causal graph models. The class of causal
  models considered here is defined as non-stochastic causal models with unobservable variables.
  These models are presented in the form of a directed graph, created on the basis of human mental
  representations. In this case, on the arcs, causality is expressed in the form of some marks with a
  sign that determines the direction of change in the state of the system. The considered causal models
  include heterogeneous, complex and qualitative types of variables that illustrate the nonnumerical
  nature of nodes and links and, as a consequence, the absence and impossibility of obtaining
  time series data. In the absence of observable variables and the impossibility of conducting
  experiments, the problem of reconstructing the adjacency matrix of the causal graph model becomes
  much more complicated. It is required to obtain a model with a certain spectral decomposition
  that implements the main function of the modeled system. Based on this concept, a new method
  for reconstructing the adjacency matrix is proposed, implemented on the basis of the corresponding
  causal propagation matrix or transmission matrix. The idea is to use combinatorial optimization
  based on spectral graph theory to generate data from a qualitative non-stochastic causal
  model and reconstruct an adjacency matrix using that data. In this case, the eigenvectors are
  identified as key objectives of the matrix reconstruction process, which postulates a fundamental
  approach based on the spectral properties of the graph. The results of computational experiments
  on solving the problem of reconstructing the adjacency matrix for causal graph models in the absence
  of observable variables using the developed algorithm have shown that the algorithm effectively
  reconstructs matrices from the given parameters with admissible similarity indices. The
  convergence of the approximation to the solution of the matrix reconstruction algorithm is proved
  no slower than with the speed of a geometric progression. From a technical point of view, the
  advantage of the algorithm is the implementation of a tool for automatic adjustment of the regularization
  parameter, suitable for users without prior mathematical knowledge.