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CLUSTERING ALGORITHM FOR LARGE GROUPS OF EXPERTS BASED ON THE INTERPRETIVE STRUCTURAL MODELING METHOD
Е.М. Gerasimenko , P.S. Gerasimenko6-212025-12-30Abstract ▼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 -
SOLUTION OF THE MAXIMUM EVACUATION FLOW PROBLEM DASED ON HESITANT FUZZY AGGREGATIPN OPERATORS
Е. М. Gerasimenko, E.V. Nyzhov2021-11-14Abstract ▼Evacuation modeling is an urgent problem that has attracted more and more interest in recent
years. Today, flow theory in macroscopic evacuation allows researchers to find solutions to
optimization problems by treating aggrieved as a homogeneous mass. The main difficulty in constructing
evacuation scenarios is the necessity to take into account the internal uncertainty of the
network. In addition to the inherent ambiguity, the nodes of the network have limited capacities
and can store the flow as well as direct an additional flow to a sink in a given order. Thus, an
expert is a key figure in fuzzy modeling who must evaluate the order of intermediate nodes in order
to obtain the flow. If the decision-maker doubts during the choice of the membership function of an
alternative in relation to an attribute due to possible sub-attributes, he / she can set out all possible
evaluations of the alternative. Therefore, this article discusses the problem of maximum evacuation
with intermediate storage in nodes and compiling a priority of shelters. The hesitant fuzzy
hybrid averaging aggregation operator is used to determine the priority of intermediate nodes.
This evacuation scenario is the safest, since the maximum number of victims can be sent to the
safest shelters, using the capacities of intermediate nodes, so that the amount of incoming flow at
the intermediate node can exceed the outgoing flow. After finding the priority list of vertices, atransport network that is correspond to the residual network is constructed, and the flow is searched for, taking into account the storage of the flow in the shelters. A numerical example is
given to illustrate the proposed algorithm -
AN INTELLIGENT SENTIMENT ANALYSIS SYSTEM FOR MEASURING CUSTOMER LOYALTY AND MAKING DECISIONS BASED ON FUZZY LOGIC
E.M. Gerasimenko, V.V. Stetsenko2021-11-14Abstract ▼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. -
APPLICATION OF FUZZY LOGIC FOR MAKING DECISIONS ABOUT EVACUATION IN CASE OF FLOODING
Е.М. Gerasimenko, V.V. Kureichik, S.I. Rodzin, A.P. Kukharenko2022-11-01Abstract ▼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. -
BASIC APPROACHES TO EXTRACTING TEXTUAL INFORMATION (OVERVIEW)
V.V. Kureichik, P.S. Gerasimenko2024-10-08Abstract ▼This article is devoted to the review of known and modern approaches, methods and algorithms of
full-text search. A brief history of the solution of the problem of search in unstructured text data, its development
and relevance are described. The main task of search in text data is formulated. The definition of
the database index is given. The target function of the search information system is defined in general
terms and possible compromise variations of its parameters when solving various applied problems are
described. A generalized architecture of a modern search information system is given with the division of
the search problem into two phases: the primary extraction of relevant records and their subsequent ranking
to form the final search results. The article provides basic descriptions of the main algorithms and
methods of full-text search, such as: search by terms (logical search), search using trees and their varieties
(B-trees, UB-trees, tries), search based on n-grams (including search based on frequency representation),
use of the vector space model (VSM), search based on an inverted (reverse) index, search using the apparatus of fuzzy logic and bioinspired methods. The main advantages and disadvantages of these methods
are given, their applicability in various conditions is described, and possible methods for optimizing
the search for text data to improve the accuracy, speed of search and efficiency of resource use are considered.
Possible promising directions in the field of solving the problem of primary information extraction
are presented. Some methods for determining the similarity of text records for solving the ranking
problem based on the apparatus of fuzzy logic are given. The article touches upon the issues of increasing
the relevance of primary extraction using artificial intelligence methods, neural networks, fuzzy logic and
bioinspired methods, in particular methods for expanding the search query and/or expanding the processed
text records. The influence of the boundary conditions of the search system construction on increasing
its efficiency is described. In conclusion, the article summarizes the review and discusses the prospects
for further development of various full-text search methods. -
ALGORITHM FOR SEARCHING AND ACQUISITION OF KNOWLEDGE BASED ON TECHNOLOGIES FOR PROCESSING AND ANALYZING TEXTS IN NATURAL LANGUAGE
Е.М. Gerasimenko, Y.А. Kravchenko, D.А. Shanenko2024-11-10Abstract ▼The article is devoted the topical scientific problem of increasing the efficiency of processing and
analyzing text information when solving problems of searching and acquiring knowledge. The relevance of
this task is related to the need to create effective means of processing the accumulated huge amount of
poorly structured data containing important, sometimes hidden knowledge that is necessary for building
effective control systems for complex objects of different nature. The algorithm of search and knowledge
acquisition in processing and analyzing textual information proposed by the author is characterized by the
use of low-level deterministic rules that allow for qualitative text simplification based on the exclusion of
words invariant to meaning from textual information. The algorithm relies on domain elaboration that
allows to create lists of domain-specific words, which allows for high quality text simplification. In this
task, the input data are streams of textual information (profile descriptions) extracted from online recruiting
platforms; the output information is represented by sentences formed in the form of a triple "subjectverb-
object", reflecting the granules of knowledge obtained during text processing. The use of this order of
units constituting a sentence is due to the fact that this order is the most widespread in the Russian language,
although other variations of the order are possible in the texts themselves without losing the general
meaning. The main idea of the algorithm is to split a large corpus of text into sentences, then filter the
resulting sentences based on the keywords entered by the user. Subsequently, the sentences are further
split into components and simplified depending on the type of received component (verbal, nominal).
The field of marketing was used as an example in this work, and the keywords were "social media".
The author has developed an algorithm for for knowledge search and acquisition based on natural language
text processing and analysis technologies, and a software implementation of the proposed algorithm
has been performed. A number of metrics were used as efficiency evaluation methods: the Flash-
Kincaid index; the Coleman-Liau index; and the automatic readability index. The conducted computational
experiments have confirmed the effectiveness of the proposed algorithm in comparison with analogues
that use neural networks to solve similar problems -
TEXT SENTIMENT ANALYSIS BASED ON FUZZY RULES AND INTENSITY MODIFIERS
Е.М. Gerasimenko, V.V. Stetsenko2024-08-12Abstract ▼Expressing feelings is a hidden part of hard life and communication. To create computers that can
better serve humanity, computer science continues to research into developing machine learning algorithms
that can process text data and perform sentiment analysis tasks on natural language texts. Additionally,
the availability of online reviews and increased end-user expectations are driving the development
of system intelligence that can automatically categorize and share user reviews. Every year, research
in this area has discovered more and more emotions in text, but only a small part of it has been devoted to
the use of fuzzy logic. This mainly happens because the researchers often use binary classification – «positive
» and «negative», less often adding a third class – «neutral». The use of fuzzy logic helps to determine
emotions, and not just «good» and «bad», but the degree of these emotions. The number of classes is defined
by determines of the level of detail. Previously, we proposed a fuzzy dictionary-based sentiment
model, in this paper we propose an improved text sentiment determination model based on a sentiment
dictionary (SentiWordNet) and fuzzy rules. To determine the accuracy and precision of sentiment analysis,
coefficients were applied to observe the emotional load of words of different parts of speech and action
modifiers that contribute to the strengthening or weakening of emotional tones. The quantitative value of
the sentiment of the text is obtained by aggregating normalized data by emotional classes using fuzzy result
methods. As a result of the study, it was found that taking into account all modifiers can significantly
increase the accuracy of the method previously proposed by the authors, and also ensures the determination
of boundaries when determining a detailed assessment of relationships in 7 classes (“very positive”,
“positive”, “somewhat positive”, “neutral” , “somewhat negative”, “negative”, “very negative”). -
DECISION SUPPORT FOR PREVENTION AND ELIMINATION OF THE EMERGENCIES’ CONSEQUENCES BASED ON THE INFORMATION STRUCTURING FUZZY METHOD
Е.М. Gerasimenko, D.Y. Kravchenko, Y.А. Kravchenko, E.V. Kuliev2023-06-07Abstract ▼The article is devoted to solving the scientific problem of decision support for the prevention
and elimination of emergencies’ consequences based on solving the problem of structuring information.
The relevance of this task is due to the need to develop theoretical foundations for optimizing
the risk of adverse effects on human health and the environment in connection with emergencies.
The authors give definitions to the main terms of the studied subject area. A formalized
statement of the problem to be solved is presented. A detailed emergencies’ classification with a
description of the presented classes’ features is given. The system of rules for decision support in
emergencies should have a multi-level hierarchy, which allows for the construction of variousdecision-making trajectories on a top-down basis. The most suitable model for building such an information
space is an ontological structure that provides the creation of the necessary multi-level
hierarchy, taking into account all the parameters and criteria that affect the development of the situation.
The main elements of this ontological model are entities and relationships between them, the
presence of which at the upper level of decomposition will indicate the risk of an emergency, and at
each lower level it will expand the taxonomy of a detailed description of emergencies’ possible situations
and the necessary actions to prevent or eliminate them consequences. The processing of this
ontological model of rules is implemented on the basis of the structuring information fuzzy method
proposed by the authors in emergencies, which differs from known analogs by the use of a new generalized
criterion for optimizing the choice of decision support alternatives. The originality of the
optimization formulation of the structuring problem lies in the assessment of the information elements
contextual binding to a certain class of emergency situations, interdisciplinary, taking into account
the presence of many links between subject areas, as well as taking into account the decrease in the
level of information efficiency about the course of emergencies over time. -
SENTIMENT ANALYSIS OF TEXT REVIEWS USING TONE DICTIONARIES AND FUZZY SET CARDINALITY
Е.М. Gerasimenko, V.V. Stetsenko2023-02-17Abstract ▼Sentiment or opinion analysis aims to determine the polarity of people's opinions in relation
to any product, service, event or any person. One of the most common methods used in sentiment
analysis of text content is natural language processing. Sentiment analysis of natural language
text can be assessed using numerous methodologies such as machine learning algorithms and
statistical tools, while the application of fuzzy logic is not common. The use of fuzzy logic was
chosen for the following reasons. First, fuzzy logic handles linguistic uncertainty well. This way of
defining the problem leads to a reduction in bias, both positively and negatively. Secondly, learn ing approaches based on fuzzy rules are fundamentally different from those learning approaches
that are widely used in sentiment classification, such as support vector machines, naive Bayes,
etc., as they relate to generative learning, i.e. i.e. the goal of learning is to assess the degree to
which an instance belongs to each individual class. The proposed model for sentiment analysis of
text reviews is based on the use of tone lexicons using fuzzy logic and consists of four main stages.
The steps include tokenization, word bag model formulation, sentiment fuzzy score formulation,
and polarity assignment. In the proposed model, the power of the fuzzy set is used as a measure of
the evaluation of the indicators of the polarity of words. Word polarity values are obtained by
applying two sentiment lexicons: SentiWordNet and AFINN. Two versions of the model were created
depending on the type of vocabulary used: based on SentiWordNet and AFINN. Comparison
of the presented approach based on fuzzy logic with other dictionary-based methods demonstrates
the superiority of the developed models based on the application of fuzzy logic. -
INTELLIGENT METHOD OF KNOWLEDGE EXTRACTION BASED ON SENTIMENT ANALYSIS
E.M. Gerasimenko, V.V. Stetsenko2020-11-22Abstract ▼The paper explores the impact of age and gender in sentiment analysis, as this data can help
e-commerce retailers increase sales by targeting specific demographic groups. The data set used
was created by collecting book reviews. A questionnaire was created containing questions about
preferences in books, as well as age groups and gender information. The article analyzes segmented
data on the subject of moods depending on each age group and gender. Sentiment analysis
was performed using various machine learning (ML) approaches, including maximum entropy,
support vector method, convolutional neural network, and long short-term memory. This paper
investigates the impact of age and gender in sentiment analysis, because this data can help
e-commerce retailers to increase sales by targeting specific demographic groups, as well as increase
the satisfaction of the needs of people of different age and gender groups. The dataset used
is generated by collecting book reviews. A questionnaire was created containing questions about
preferences in books (user opinions of e-books, paperbacks, hardbacks, images and audiobooks),
as well as data on age group and gender. In addition, the questionnaire also contains information
on a positive or negative opinion regarding preferences, which served as the basis for reliability
for the classifiers. As a result, 900 questionnaires were received, which were divided into groups
according to gender and age. Each specific group of data was divided into training and test one.
Segmented data were analyzed for sentiment analysis depending on age group and gender.
The age group “over 50 years old” showed the best results in comparison with all other age
groups in all classifiers; data in the female group performed higher accuracy compared to data from
the groups without gender information. The high scores shown by these groups indicate that sentiment
analysis approaches are able to predict moods in these groups better than in others. Sentiment
analysis was performed using a variety of machine learning (ML) approaches, including maximum
entropy, support vector machines, convolutional neural networks, and long short term memory. -
SOLUTION OF THE PARTIALLY REVERSAL MODELLING TASK OF THE MINIMUM COST FLOW FINDING IN FUZZY CONDITIONS
E. M. Gerasimenko2020-11-22Abstract ▼This article is devoted to the development of an algorithm for solving the problem of modeling
a partially reversal flow of minimum cost in a fuzzy transportation network. The minimum cost
flow problem is a central problem in transportation planning and evacuation modelling. The relevance
of these tasks is due to necessity to find optimal transportation routes in terms of cost andtransfer the maximum flow along them. This article is devoted to solving this problem in fuzzy
conditions, since the apparatus of the theory of fuzzy sets allows you to set network parameters,
such as the capacity of road sections, the cost of transportation in a fuzzy form. This method of
assignment is convenient in situations where there is a lack of data on the modeled object, linguistic
nature of data, measurement errors, etc. In the problems of evacuation modelling, which occur
spontaneously, there is also a lack of accurate information about the capacity and cost of transportation.
The contraflow concept, which was used in the paper, allows increasing the total flow
by reversing traffic. The lane reversal technique is a modern technique for increasing the transmitted
traffic by increasing the network output capacity. The use of traffic reversal allows releasing
congested sections of the road and redistributing traffic towards unloaded roads, eliminating congestion
and "traffic jams" on the roads. A method of operating with fuzzy numbers is proposed,
which does not lead to "blurring" of the boundaries of the resulting number and allows operating
with fuzzy boundaries at the last iterations, while at the rest of the previous iterations, calculations
are performed only with the centers of fuzzy numbers. A numerical example is considered that
illustrates the operation of the proposed algorithm.
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