=Paper=
{{Paper
|id=Vol-2392/paper12
|storemode=property
|title=Technology for the Psychological Portraits Formation of Social Networks Users for the IT Specialists Recruitment Based on Big Five, NLP and Big Data Analysis
|pdfUrl=https://ceur-ws.org/Vol-2392/paper12.pdf
|volume=Vol-2392
|authors=Vasyl Lytvyn,Victoria Vysotska,Antonii Rzheuskyi
|dblpUrl=https://dblp.org/rec/conf/coapsn/LytvynVR19
}}
==Technology for the Psychological Portraits Formation of Social Networks Users for the IT Specialists Recruitment Based on Big Five, NLP and Big Data Analysis==
https://ceur-ws.org/Vol-2392/paper12.pdf
Technology for the Psychological Portraits Formation of
Social Networks Users for the IT Specialists Recruitment
Based on Big Five, NLP and Big Data Analysis
Vasyl Lytvyn[0000-0002-9676-0180], Victoria Vysotska[0000-0001-6417-3689],
Antonii Rzheuskyi[0000-0001-8711-4163]
Lviv Polytechnic National University, Lviv, Ukraine
Vasyl.V.Lytvyn@lpnu.ua1, Victoria.A.Vysotska@lpnu.ua2,
antonii.v.rzheuskyi@lpnu.ua3
Abstract. The specific features of the tasks of managing skilled human re-
sources during recruiting, which allow to identify them as a task of multicriteria
analysis and decision making in a fuzzy environment are highlighted in the arti-
cle. A generalized conceptual model of decision-making in the recruitment
tasks of IT specialists based on collected and analyzed data from social net-
works is proposed. It is substantiated that in order to increase the efficiency and
transparency of solutions during recruiting, it is expedient to use multi-criteria
optimization based on the TOPSIS method and show its advantages. The modi-
fication of the TOPSIS algorithm for recommending recruitment by qualified
human resources on the basis of the analysis of user profiles in social networks
for the Big Five model was proposed. The modification consists in integrating
additional constituent management components into the decision-making algo-
rithm, which provides calculation based on the analytical hierarchy process
(AHP) criteria of the psychological state of IT specialists. Using the methods
TOPSIS, AHP and scales for assessing the psychological state of skilled human
resources, an example of recruitment has been used to make experimental cal-
culations on the ranking of applicants, which demonstrated the efficacy of the
proposed approach.
Keywords: Big Five, NLP, Big Data Analysis, Social Network, Quantitative
Analysis, Recruitment, Content Analysis, TOPSIS, analytic hierarchy process,
AHP.
1 Introduction
Today, professional recruitment is a necessary and necessary process in every organi-
zation in any sphere of activity: its financial performance depends on the quality and
efficiency of the given process [1-13]. This is especially true for IT companies, in
which the fast-growing increase in the number of different projects in the precise
discussion of time spans requires continuous updating of the skilled workers of differ-
ent directions. Most of the professions and specialties in this area are many - more
than 30. Besides, in addition to the basic requirements for professionalism and the
education of IT specialists, the latest trend in recruiting is to take into account the
psychological portrait of a future employee as a level of communicative, openness of
experience, good faith, goodwill, leadership qualities, etc. Finding staff or recruiting
is done using different approaches and sources of information, the main sources are:
The internal database of the company or agency;
Sites for job search and cooperation with recruiting agencies;
Social capital (or search for candidates for acquaintance) and Mass-media;
Social networks, forums, blogs, etc. [1-13];
Employees of competitor companies, that is, the remuneration of specialists;
Universities, that is, the involvement of young specialists from higher educational
institutions.
This allows us to find professionals of a certain level for the corresponding position.
But it does not guarantee solving problems, compliance with the human factor for the
fulfillment of the respective official duties and the effectiveness of the work of the
respective claimant. As well as the acquired professional knowledge and the experi-
enced experience on the quality of work affects the psychological portrait of the very
IT specialist. One of the modern tasks of analyzing the psychological state of person-
ality when recruiting IT specialists is to analyze his profile in social networks (posts
on forums, comments on events, activity in social networks, etc.). The most popular
social networks for IT professionals are LinkedIn, Facebook, Twitter, Instagram,
Google+, Youtube. Analysis of the level of activity in relevant social networks of
relevant IT specialists and NLP analysis of the author's and content content of these
individuals will enable to automatically form the profile of the psychological state of
the applicant for the elected post. Ability to choose from a plurality of applicants, in
addition to the level of competencies and even the psychological factors without sub-
jectivity from the recruiter, will greatly improve the efficiency of recruitment for
companies. Qualitative recruitment will not only save them time and resources, but
will allow them to increase their own profits. The purpose of this study is to develop a
technology for making managerial decisions in recruiting tasks in accordance with the
above conceptual model and in verifying its effectiveness.
2 Influence of social networks for formation of psychological
portrait of the person
Social networking service is a web site or other Internet service that allows users to
create a public or semi-public questionnaire, make a list of users with whom they
have a connection and view their own links list and lists of other users. The nature and
nomenclature of connections may vary depending on the system. Unlike social net-
working services, the user is not in the center of the Internet community; the user's
relation to other members of the community is in the foreground. The focus of the
Internet community is on the user's contribution to achieving common goals, values
and communication. In social networks, the user is in the center of the system and can
belong to more than one group at a time [14-19].
Secondary connections in social networks are relatively weaker than secondary con-
nections in Internet communities. For example, if you have several social links in the
social network User2 , connections with unknown to him User2 will be weaker than
similar connections in the Internet community. In addition to providing useful ser-
vices, social networking Internet services are also a number of dangers [20-22].
Coordination of criminal gang activities.
Distribution of propaganda and information operations.
Infiltration of the circle of friends.
Analysis of social networks has a large potential for use in information operations,
since it allows us to investigate the attitude, outlook and communication of a wide
range of individuals, in particular [14-19]:
Analysis of social networks can help to find important information about the user
for their psychological analysis of recruiting. It also gives you the ability to direct
information to your chosen audience and influence perceptions of reality, decision-
making, or behavior of a particular group.
Social networking portals allow you to quickly exchange texts, scenes, videos of
various events in the process of their development. Watching this increases aware-
ness of current events or track the distribution of quality information, their fre-
quency to calculate the criterion of the model Big Five as a seam of ex-treversy,
neuroticism, openness, annoyance and benevolence.
The careful use of social networking portals can lead to secrecy disclosure. Instead,
the analysis can help determine the level of trust in the applicant for a position
where the notion of confidentiality is important.
There have been cases where crowdsourcing and monitoring of social networks in
Internet portals played an important role in the provision of humanitarian assis-
tance, since they quickly provided important information. The regular participation
of an individual in such events forms an appropriate opinion of him as a benevolent
person.
Accordingly, content analysis can be used to find individuals in the process of recruit-
ing (Fig. 1). Geocoded posts can complement the analysis, and help to assess the level
of activity of such personality civility. Due to the analysis, it is possible to expand the
analysis of the applicant for a post. The simpler classification of IT groups is suffi-
ciently well describes the main lines of work at the lower (production) level of the IT
company, that is, they are practically in all companies (test manager, project manager,
business analyst, sysadmin, designer, team lead, architect, programmer, IT sales).
Each group must possess not only relevant professional qualifications and experience,
but also psychological features. According to the European e-competence Framework,
the list of IT specialties forms 23 specialties, in particular [23]:
1. Account Manager, 13. ICT Trainer,
2. Business Analyst, 14. Network Specialist,
3. Business Information Manager, 15. Project Manager,
4. Chief Information Officer (CIO), 16. Quality Assurance Manager,
5. Database Administrator, 17. Service Desk Agent,
6. Developer, 18. Service Manager,
7. Digital Media Specialist, 19. Systems Administrator,
8. Enterprise Architect, 20. Systems Analyst,
9. ICT Consultant, 21. Systems Architect,
10. ICT Operations Manager, 22. Technical Specialist,
11. ICT Security Manager, 23. Test Specialist.
12. ICT Security Specialist,
Actions
Candidates
Request
Social
Content Networks
Recruiting Report
system
Request
Rules Request
Support
service
The company
Fig. 1. The process of finding information about the applicant for a job at recruiting.
The list of professions somehow not got the following, which are even in Ukraine
[23]:
1. Technical Lead, 5. Recruiter,
2. Sales Manager, 6. Human Resource Manager,
3. Information Developer (Technical 7. Data scientist,
writer), 8. Top Manager.
4. Designer,
Although the hierarchy recommended in the main IT professions is the most com-
monly used in Ukraine [23]:
1. Postgraduate studies, 9. Software Engineer,
2. Systems Architector, 10. Programmer,
3. Senior Executive, 11. Administrator,
4. Senior Manager, 12. Associate Software Engineer,
5. Manager, 13. Recruiter,
6. Associate Manager, 14. Testers,
7. Team Leader, 15. Assistant, assistant professor.
8. Senior Software Engineer,
Even for the last list, any recruiter can be confused when performing his work - which
of the candidate’s best suits the psychological criteria for the recommended position
is best. In addition, as a rule, recruits of the era do not have a degree in psychology.
Therefore, additional automatic analysis of profiles of applicants in social networks in
absentia will reduce him time when the list of candidates for the rating is formed. This
will work under the condition that, in cooperation with the IT cluster, a list of criteria
for recruited posts will be formed and agreed upon. Next, it's a matter of working out
large amounts of social networking data using Big Five, NLP and Big Data Analysis
(Fig. 2) [24-37]. Analysis of posts in social networks, along with related metadata
[38-43], can reveal future Timelifts. Image classification algorithms help you find out
what kinds of images are popular on social networks, and, along with the link to the
terrain, track changes in preferences and personal attitudes to different things [44-57].
However, recruiters are limited by national legislation and can not fully disclose the
potential of analyzing social networks in relevant internet portals without the consent
of the applicant [58-73].
Recommended
Action
Social
Networks
Not
Recommended
Rules
Action
Candidates
Fig. 2. The process of forming a recommendation for a post based on the analysis of candidate
profiles in social networks.
3 Model Big Five for determination of the psychological state of
personality
Regardless of its professionalism and level of experience, the Big Five model is used
to determine the main features of a psychological portrait of a person. But it is usually
used with the testing of these personalities. And on the sow of their answers build a
corresponding psychological portrait. The modified version of the definition based on
the Big Five model is the proposed method by the Chinese Bai Shuotian [6-7] - analy-
sis of the profiles of Internet users of social networks, the frequency of clicks, posts,
alarms, emoticons, etc. (Fig. 3). For more advanced analysis, methods of content
analysis and NLP methods for collecting statistics based on the context-the emotional
coloring of messages from a particular user, that is, the frequency of words with emo-
tional coloration (negative, positive and neutral in relation to the total), are used.
Recruitment algorithm based on the analysis of profiles in social networks.
Moderators Rules
Recommended
List
Analitics
Rules Report
List
Results
Results
Not
Recommended
Candidates Report
Social Parser
Actions Networks Requests
Recruiters
Company
Fig. 3. Recruiting process based on the analysis of profiles in social networks
1. Periodic generalization of requirements for a position to formulate recommenda-
tions [41] of candidates for the model Big Five on the basis of a structured tech-
nique of the analytical hierarchy process (AHP, decision-making in a multicenter
setting).
2. Identification of IT specialists as a user of social networks.
3. Automatic extraction of user profile data into social networks and its activity for a
certain period of time (lyrics, posts, author's content, etc.) using modern methods
of Big Data Analysis.
4. Analysis of the collected content by modern methods of NLP [23-37].
5. Formation of the profile of the psychological state of the IT specialist in the Big
Five Medal based on the results obtained in the previous stage (submission of as-
sessments according to the criteria on a certain scale).
6. Comparison of the received profile of the applicant with the requirements for the
position and the provision of an in-depth report. In the case of several applicants
for one position, the use of the TOPSIS method to form rating recommendations
for the position of applicants in accordance with the requirements of deployed re-
porting [75].
7. The use of Machine Learning methods to study the system for recommending posi-
tions according to Big Five - analysis of user profiles in social networks.
Model Big Five is a hierarchical model of personality, which describes the five
main features that make up the personality of a person. For the best memorization in
the English literature, five rice are in the acronym OCEAN [1-13]:
Openness to experience. The false perception of innovation, art, emotions, adven-
tures, unusual ideas, curiosity and diversity of experience. Designed for gamers,
web sites, animations, and more
Conscientiousness. The tendency to be organized and reliable, demonstrate self-
discipline, act obediently, strive to achieve and give preference to the planned, ra-
ther than spontaneous, behavior. High conscientiousness is often perceived as ob-
stinacy and obsession. Inherent to programmers. Low conscientiousness is associ-
ated with flexibility and spontaneity (inherent in business analysts), but it can also
be manifested as failure and lack of reliability. This is a negative signal for many
specialties, not just for IT business.
Extraversion. Energy, positive emotions, sociability, and the tendency to seek
stimulation in the company of others and talkativeness. Rice is inherent not only
for Tollmasters, but also for managers, business analysts, coaches, tormenjedzhe-
ram and designers. High extroversion is often perceived as seeking attention and
domination. Low extroversion causes a protected, reflective personality that can be
perceived as remote or self-absorbing (developers, datasansists, programmers, sys-
tem architects, etc.). Extrovert IT specialists tend to be more dominant in a social
setting, unlike introverted people who can act more shy and cautious in this envi-
ronment.
Agreeableness. The tendency to be sympathetic and co-operative, not suspicious
and antagonistic to others. It is also an indicator of a trusting and altruistic charac-
ter, as well as whether a person is generally well-nurtured or not. High coherence is
often seen as naivete or obedience - good performers. A low figure is often an indi-
cator of the competitiveness or complexity of a person (algorithmizers), which can
be considered as amateurs arguing or unreliable.
Neuroticism. The tendency to be prone to psychological stress. The tendency is
easy to experience unpleasant emotions such as anger, anxiety, depression and vul-
nerability. Neuroticism also refers to the degree of emotional stability and impulse
control, and it is sometimes called emotional resistance. High stability manifests it-
self as a stable and calm person (Timeliders), but it can be regarded as immovable
and indifferent. Low stability turns out to be reactive and exciting personality, of-
ten found in dynamic individuals (business analysts), but can be perceived as un-
stable or dangerous. In addition, people with a higher level of neuroticism, as a
rule, have worse psychological well-being.
Since the interpretation of results depends on the natural language that a person
thinks, many countries of the world have their own versions of testing. The model is
based on variables that are most widely spoken in the natural language through the
use of a certain emotional color in the lexicon of the corresponding adjectives, nouns
and rotations, as well as the frequency of their use. At the same time, the assumption
is assumed that the natural linguistic person is formed:
M BF C Ext , C Agr , C Cns , C Nrt , C Ops , Ext, Agr, Cns, Nrt , Ops , (1)
where the components of the tuple:
extraversion (engagement): sociability, assertiveness or rest, passivity, that is
С Exp Exp(C,U Exp ) through parameters U Exp ( u1Exp is sociability / sociability
through the frequency and content of emoticons and liches at posts and friends'
messages, u2Exp is perseverance / rest due to the frequency of reactions to the
actions of friends, u3Exp is activity / passivity due to the frequency of activity in the
profile);
benevolence (pleasure): kindness, trust, warmth or hostility, selfishness, distrust,
that is С Agr Agr(C,U Agr ) through parameters U Agr ( u1Agr is kindness /
hostility through reaction to new events, establishing new contacts and answering
new queries. u2Agr is trust / distrust due to the response to the invitation, u3Agr is
warmth / egoism through frequent posts about oneself in relation to posts about
social events);
conscientiousness (reliability): organization, thoroughness, reliability or careless-
ness, negligence, unreliability, that is С Cns Cns(C,U Cns ) through parameters
U Cns ( u1Cns is organization / negligence due to the quality of author's posts, the
degree of their chaos, u2Cns is solidity / serenity through the richness author posts,
u3Cns is reliability / unreliability due to the reaction of friends to the author's
events, the frequency of friends' greetings with the events, the frequency of
answers to requests and the posts of friends);
emotional stability: relaxation, balance, resistance or neuroticism is nervousness,
depression, irritability, neuroticism, that is С Nrt Nrt (C,U Nrt ) through
parameters U Nrt ( u1Nrt is relaxation / nervousness through the frequency and
content of emoticons, nicknames and posts to friends' posts, u2Nrt is Balance /
depression due to content content, emoticons, and likes, u3Nrt is stability /
irritability due to the frequency of responses);
culture, openness to experience: spontaneity, creativity or limited, intermediate,
narrow interests, that is С Ops Ops(C,U Ops ) , where C is content from user
social networking profiles, U Ops is rules for calculating the criterion, that is.
U Ops {u1Ops , u2Ops , u3Ops , u4Ops } ( u1Ops is the frequency of occurrence of words
related to kindness/anger; u2Ops is frequency of occurrence of words related to trust
/ distrust, u3Ops is the frequency of occurrence of words related to heat / hostility,
u4Ops is the frequency of occurrence of words related to sincerity / egoism).
The following parameters are the main indicators for forming the criteria for social
user behavior according to the Big Five model:
frequency and content of emotional coloration of likes for a certain period of time;
frequency and content of emotional coloration of emoticons for a certain period of
time;
frequency and content of emotional coloring of posts;
the frequency and frequency of activities in the social network;
average period of stay in social networks, taking into account the period (working
time, non-working hours, season, etc.);
participation and activity in groups;
number of friends;
frequency of greetings of friends;
the presence / absence of pages, their number and frequency of updates;
presence / absence of author's content (photos, videos, author's posts, etc.), its vol-
umes and frequency / time of appearance;
the presence / absence of information about yourself, the level of its completeness;
the frequency and content of the vocabulary and the broadcasts of the applicant /
author's / general content;
the frequency and content of the comments of friends on the broad content of the
applicant;
proportionality of accessible public content to the closed view for all visitors.
This list can be continued, depending on the possibility of the social network itself. So
in LinkedIn it is possible to further analyze the frequency of recommendations on the
skills from friends (the level of trust of the surrounding to the applicant). Factors of
the behavior model Big Five in research denote differently, but the overall content of
the model is fairly stable and based on the following postulates []:
all adult persons can be characterized by a specific combination of personality
traits, influencing thoughts, feelings and behavior (on individuality);
characteristics of the person being studied are endogenous basic tendencies (of
origin);
the features develop in the childhood, finally formed in adulthood and retain their
immutability in the adapted subjects (on development);
features are organized hierarchically, from narrow and specific to wide, general-
ized dispositions (about structure).
The described models are close to the theory of individuality. Theories of personality
traits are intermediate between typological and idiographic (clinical) approaches to
the study of individuality. However, for its use it is difficult to determine the correla-
tion between different characteristics without the introduction of vertical and horizon-
tal measurements, which are the basis of the hierarchy within the system of personali-
ty. Interpretation of rice as a situational sustained appearance also raises doubts.
However, this does not forbid the use of the possibility of isolating and predicting
personality traits. Today, the well-known corporation has developed an algorithm that
defines features of the personality in English (Spanish) authoring text of 100 words or
more. IBM Watson Personality Insights attempts to apply a linguistic analysis to de-
termine the psychological portrait of its author along a piece of text [74]. The service
itself is paid, it is offered for analysis of clients by blogs, tweets, and entries in the
forum. But he has a demo where you can download a text of just one hundred words
(it can be more, but one is the minimum). And the car, like a real oracle, will give a
deployed personality trait. True, the text should be in English or Spanish [74].
To get a psychological portrait, simply insert a piece of any text written by the
person in question in the appropriate field, and then click on the "Analyze" button.
The program with a small delay gives a brief description, and when scrolling down
the page you will find more detailed data. The text is analyzed for the presence of
certain marked words, and the results are ordered by recognized psychological
metrics, such as the Big Five - the assessment of personality on five characteristics,
such as consciousness, friendliness, extraversion, emotionality and openness of ex-
perience.
The service has APIs, open access documentation, and also a section on GitHub.
According to [74] the results of the analysis of the text of Richard Branson under
the heading "My idea of paradise" showed that the entrepreneur prefers imagina-
tions rather than facts, believes in the best in people, trusts them, and in many of
his actions is very independent. These data, if considered relevant, can be useful
not only for analyzing the client base, but also for HR-specialists in evaluating ap-
plicants.
However, the service users evaluate ambiguously. Quora has a related topic, where
many participants believe that the service of accuracy of characteristics can be
compared to horoscopes, they say, in any collection of very general phrases, eve-
ryone can see themselves. But subjective thought. Without a large number of statis-
tical data, with close cooperation with psychologists, one can not fully confirm the
effectiveness, efficiency and quality of the results. In parallel, with modern recruit-
ing, the recruits themselves do not have a psychologist's education, and their sub-
jectivity significantly influences the outcome of the formation of proposals for po-
sitions among applicants. It also does not solve the problem of qualitative selection
of personnel. Therefore, companies often suffer losses and lose time searching for
relevant professionals in the current fast-paced time and growing rates of IT busi-
ness development. But a couple of drawbacks to using the method.
The main thing in our opinion is the disadvantage of such a system - it is possible
to pick up in advance or write a text that will be interpreted as beneficial to the ap-
plicant. Nowadays there is only one source of an independent, unique collection of
content that has been collected over a long period of time by a specific personality,
the user of social networks. As a person has not tried, she can not play a role for a
long time (years), not showing himself in relationships with people around him in
the same social network. The history of the user profile of a social network user is
an objective information about his psychological state based on the Big Five mod-
el. Only appropriate methods of data extraction and NLP should be used to form
the set of criteria for its psychological portrait for recruiting.
Another disadvantage is that when creating requirements for the positions of IT
professionals, the company usually uses other criteria such as communicative, re-
sponsible, leadership qualities, teamwork and creativity. This list may be expand-
ed, but it is usually a fundamental indicator. By collaborating with representatives
of an IT cluster with a specialist in psychology, this problem can be easily solved
using, for example, a structured technique of the analytical hierarchy process
(AHP, decision making in a multi-arterial setting) as shown in Fig. 4.
We will use the web resource http://victana.lviv.ua/matrytsia (Fig. 5) for a conditional
example in the absence of statistical research in this area and active close cooperation
on these issues with the IT cluster and psychologists using approximate calculations
(Fig. 6). In fig. 7. and Table 1 shows the results of calculations of the criteria for our
conventional case.
Position
Communicativeness Responsibility Leadership Team work Creativity
Openness Conscientiousness Extraversion Agreeableness Neuroticism
Fig. 4. Structured technique of AHP for calculation of job criteria by Big Five model
Fig. 5. Web Resource Interface for AHP
Fig. 6. An example of constructing a matrix of criteria for AHP
Fig. 7. Results of calculation of criteria for our conditional example
Table 1 shows that criterion A (openness) for a candidate is more important than oth-
ers, while criteria D (benevolence) and E (neuroticism) can even be neglected.
Table 1. Generalized Global Priorities
1 2 3 4 5
Criteria Global priorities
0.417 0.263 0.160 0.097 0.062
A 0.417 0.417 0.417 0.417 0.417 0.417
B 0.263 0.263 0.263 0.263 0.263 0.263
C 0.160 0.160 0.160 0.160 0.160 0.160
D 0.097 0.097 0.097 0.097 0.097 0.097
E 0.062 0.062 0.062 0.062 0.062 0.062
4 The process of determining the suitability of a person's
psychological state for an IT specialty
On the basis of a comprehensive approach to the accounting of the specifics of the
processes of human resources management, a generalized conceptual model of deci-
sion-making in recruiting tasks will be provided by the following set of information:
A set of feasible alternatives between applicants for a post
Cnd {cnd 1 , cnd 2 , , cnd n } {cnd i , i 1, n} ;
A set of criteria for choosing Big Five that characterize the psychological state of
applicants / candidates (alternatives) – U {U Ext ,U Agr ,U Cns ,U Nrt ,U Ops } or
U {U1 , U 2 , , U m } {U j , j 1, m} , where m=5, since there are only 5 model
criteria Big Five;
A set of subcriteria characterizing each of the criteria –
U j {u j1 , u j 2 , ..., u jT } {u jt , t 1, T } ;
The area of determining the values of each individual criterion – Vcr;
A set of social networks where information about the applicant for the decision-
making procedure is collected and analyzed – Ssn;
A set of relationships between profiles in various Social Networks – Rps;
The relationship between sets Cnd, U та Ssn – Rss;
Linguistic expressions reflecting the degree of satisfaction of applicants to private
criteria (degree of affiliation) – Lrd.
Relations between criteria and private criteria – Rcp.
In order to achieve the goal as structured technique AHP, TOPSIS (The Technique
for Order Preference by the Similarity to the Ideal Solution) has been selected in re-
cruiting tasks, which allows to eliminate a number of disadvantages of existing in-
strumental approaches. The method is modified to the conditions of the accepted con-
ceptual model of decision-making in recruiting tasks [75-80]. The main idea of the
TOPSIS method is that the best bidder should have not only the greatest proximity to
the ideal solution, but also beyond all other contenders for an unacceptable solution.
Here, the best (optimal) solution is a vector that contains the maximum values for
each criterion for all applicants, and the unacceptable (worst) solution is the vector
containing the minimum values for each criterion. As follows from the essence of the
TOPSIS method, using the latter can quite effectively solve the problem of fuzzy
multicriteria optimization, which make up the mathematical basis of decision support
in human resource management tasks. Under the multicriteria optimization in the
decision-making theory, we mean the choice of the best solution among potential
applicants. The TOPSIS method is one of the effective tools for promoting recruiting
and experts in formulating their goals and subjective advantages, structuring the set of
criteria, evaluating applicants in the decision making process in fuzzy mathematics,
linguistic variables, fuzzy sets and fuzzy numbers. The solution of the optimization
problem using TOPSIS involves the need to translate the values of qualitative linguis-
tic variables, expressing the degree of satisfaction of one or another applicant to the
criteria, in fuzzy numbers [75]. The fuzzy number is a fuzzy subset of a universal set
of real numbers having a normal and convex membership function for which there is
a carrier value, where the membership function is equal to one, while the function of
membership decreases when the maximum or left moves from its maximum. Accord-
ing to [75] the fuzzy opinions of experts, for example, psychologists, formulated in
terms of natural language, or independent indicators, calculated on the basis of analy-
sis of social networks. These indicators can be described by fuzzy triangular and
fuzzy trapezoidal numbers. In this paper, given the need to ensure the stability of the
criteria to the boundaries of the interval of validity, an obscure trapezoidal number is
used. Formation of judgments of the expert as a result of the analysis of the
applicant's activity in social networks in the form of a fuzzy trapezoidal number in
practice is realized in this way. The investigated object according to the selected
criterion is evaluated by the expert by a four-digit number h (h1 , h2 , h3 , h4 ) , where
hi are real numbers. The essence of this procedure is that the value of the criterion is
in the range from h1 to h4 , but most likely it is within the range from h2 to h3 . If in
the four the average numbers will be equal, that is h2 h3 , then a fuzzy trapezoidal
number turns into an unclear triangular number. Using operations on membership
functions based on the segment principle, operations on fuzzy numbers [75] are
introduced. When using TOPSIS, some operations on fuzzy numbers should be taken
into account. Let two fuzzy trapezoidal numbers be set as h (h1 , h2 , h3 , h4 ) and
g ( g1 , g 2 , g 3 , g 4 ) . Below are the operations of summation, the difference and the
product of these numbers:
h g h1 g1 , h2 g 2 , h3 g 3 , h4 g 4 ,
h g h1 g 4 , h2 g 3 , h3 g 2 , h4 g1
(2)
h g h1 g1 , h2 g 2 , h3 g 3 , h4 g 4
h f h1 f , h2 f , h3 f , h4 f
The distance between two fuzzy trapezoidal numbers is determined from the expres-
sion [75]:
1
wo (h, g ) ((h1 g1 ) 2 (h2 g 2 ) 2 (h3 g3 ) 2 (h4 g 4 ) 2 ) . (3)
4
If h = g , that is h and g equivalent, then wo (h, g ) 0 . To implement the
method, it is necessary to operate with linguistic variables and their values, expressing
verbal scales for measuring signs. At the same time, the levels are arranged in order of
increasing intensity of the manifestation of these features. In this case, the number of
values (gradations) of the linguistic variables is seven. Table 2 shows the 7-level
values of the linguistic variable and their corresponding fuzzy trapezoidal numbers.
Table 2. Linguistic values and their corresponding fuzzy trapezoidal numbers
N Scale Linguistic values Rating False trapezoidal numbers
1 F poorly 0-25 (0,0,1,2)
2 FX unsatisfactorily 26-49 (1,2,2,3)
3 E fairly 50-60 (2,3,4,5)
4 D satisfactorily 61-70 (4,5,5,6)
5 C добре 71-79 (5,6,7,8)
6 B very good 81-87 (7,8,8,9)
7 A perfectly 88-100 (8,9,10,10)
According to Table 2, numeric matching can be found for each value of the linguistic
variable. For example, the numerical correspondence of the linguistic value "partially
good", which is one of the gradation of the measurement of properties, is determined
by the 100-point rating system as (5, 6, 7, 8). We present an algorithm for multi-
criteria optimization of recruiting tasks based on the TOPSIS method and the results
of the analysis of the applicants' activity. Cnd {cnd i , i 1, n} in social networks
Ssn {sl , l 1, v} . Let the following components of recruiting tasks be known:
Cnd {cnd i , i 1, n} is a set of candidate;
U {U j , j 1, m} is a set of criteria by model Big Five;
U j {u jt , t 1, T j } is a set of private criteria as sub-criteria of the Big Five model
Ssn {sl , l 1, v} is a set of social networks;
rcp j , j 1, m are the coefficients of the relative importance of the criteria (
U {u j , j 1, m} );
rcp jt , t 1, T , j 1, m are the coefficients of the relative importance of private
criteria ( u j {u jt , t 1, T j } );
rpsl , l 1, v is coefficients of reliability of social networks.
The purpose of the task is to rank the applicants on the basis of activity ratings in
social networks, taking into account the reliability of the latter. The solution to the
problem involves the following sequence of actions:
Step 1. To conduct multi-criteria optimization of recruiting tasks based on the
TOPSIS method, it is necessary first of all to get rid of the hierarchical structuring of
the criteria. To this end, based on the AHP Saati method, we use the coefficients of
the relative importance of the criteria and the private criteria to determine the weights
[75], which will be taken into account by the integral criterion U {U j , j 1, m} . In
m Tj
a formalized form, the product rcp j , where j 1
rcp j 1 and rcp
t 1
jt 1 , where
U
is determined rcp jt is the weight of the private criterion u jt in the calculation of the
integral criterion U {u j , j 1, m} , that is rcp jt rcp jt rcp j . As a result, the two-
U
level hierarchical structure of the selection criteria U {U j , j 1, m} , characterizing
the applicant for a position, is reduced to a one-step hierarchy. In the next steps for
simplification of the indexes, all private criteria are combined in a single set S.
S {u jt , j 1, m, t 1, T j } {u z , z 1, Z } , z T j 1 t , j 1, m, t 1, T j , T0 0 . (4)
Here Z is the total number of private criteria that characterize the applicant for a
m
position, that is Z T . In this case, rcp rcp .
j 1
j z
U
jt
Step 2. Degrees of membership (matching) of applicants to private criteria are
estimated linguistic values and are expressed by fuzzy trapezoidal numbers
Trn l ( wizl ) (eizl , f izl , g izl , hizl ) . So, for example, if the degree of satisfaction
(affiliation) of the applicant cnd i private criterion u z on the basis of analysis of the
applicant's activity in the social network l estimated value "good", then it is
expressed as wizl (7, 8, 8, 9) , and if this assessment is "very good", then
wizl (8, 9, 10, 10) etc. As a result of the Big Five assessing the degree of
membership of applicants for a job by private criteria, we obtain the following matrix:
Trn l [wizl ], l 1, q {eizl , f izl , g izl , hizl }, l 1, q . (5)
Step 3. This step involves preliminary calculation of the reliability ratios of social
networks rpsl , l 1, q . To this end, a modification of the method was introduced,
which involves integrating into an algorithm an additional step, which involves
calculating and introducing the reliability coefficients of social networks involved in
the bidding process. From given the reliability coefficients of social networks
rpsl , l 1, q matrix is formed
Trn rpsl [ wizrpsl ], l 1, q {eizrpsl , f izrpsl , g izrpsl , hizrpsl }, l 1, q . (6)
Elements of this matrix are trapezoidal numbers that express the degree of satisfaction
of the applicant cnd i private criteria u z taking into account the reliability of social
networks and calculated as follows:
eizrpsl eizl rps l ; f izrpst f izl rps l ; g izrpsl g izl rps l ; hizrpsl hizl rps l . (7)
Step 4. The single matrix is determined:
Trn rpsl [ wizrpsl ], l 1, q {eizrpsl , f izrpsl , g izrpsl , hizrpsl }, l 1, q
. (8)
Trn iz [ wiz ] {eiz , f iz, g iz , hiz }
The elements of this matrix are defined as follows:
q
1
eiz {min eizrpsl , l 1, q}; f iz f izrpsl ;
q l 1
(9)
q
1
g iz g izrpsl ; hiz {max hizrpsl , l 1, q}.
q l 1
Step 5. Elements of the matrix Trniz [ wiz ] {eiz , f iz, g iz , hiz } multiply by the
weight of the private criteria. As a result of this operation a weighted fuzzy matrix is
constructed Trnizrcp [wizrcp ] {eizrcp , f izrcp , g izrcp , hizrcp } here:
eizrcp eiz rcp z ; f izrcp fiz rcp z ; gizrcp giz rcp z ; hizrcp hiz rcp z . (10)
Step 6. The resulting matrix is normalized. This method Hsu and Cehn is used [],on
the basis of which are determined hz max hiz , i 1, n . Next on the basis of the
rcp
expression:
f rcp g rcp h rcp
rcp
e
TrnizN wizN eizN , f izN , g izN , hizN iz , iz , iz , iz . (11)
z
h h z h z h z
The elements of a normalized decision matrix are determined.
Step 7. Based on the weighted meanings, the ideal positive (best) solution is
determined (IPS) Cnd * . To this end, for everyone u z , z 1, Z are chosing
hz* {max hizN , i 1, n} (12)
matrix is formed
Cnd * [hz* ] [(h1* , h1* , h1* , h1* ), ... , (hZ* , hZ* , d Z* , hZ* )] (13)
According to the words (11) hz* 1 for z , that is, all elements of the matrix Cnd *
are equal to units.
Step 8. The ideal negative (worst) solution is calculated (INS) Cnd . To this aim, for
everyone u z , z 1, Z are chosing
ez {min eizN , i 1, n} (14)
and the following matrix is formed:
Cnd [ez ] [(e1 , e1 , e1 , e1 ), ... , (eZ , eZ , eZ , eZ )] (15)
Step 9. Using the formula (3), according to the individual values of each individual
criterion, the distance of the applicants to IPS:
1 N
H z* (cns i , Cnd * ) ((eiz h z* ) 2 ( f izN h z* ) 2 ( g izN h z* ) 2 (hizN h z* ) 2 ) (16)
4
On the basis of the obtained results a vector is formed [ H * ] [ H1* , ..., H Z* ] .
Step 10. By the individual values of each individual criterion, the distance of the
applicants to INS:
1 N
H z (cnd i , Cnd ) ((eiz e z ) 2 ( f izN e z ) 2 ( g izN e z ) 2 (hizN e z ) 2 ) (17)
4
On the basis of the obtained results a vector is formed [ H ] [ H1 , ..., H Z ] .
Step 11. Determine the distance of each of the applicants to IPS and INS:
Z Z
H * (cnd i ) ( H (cnd , Cnd ) ; D (cnd ) ( H (cnd , Cnd ) .
z 1
*
z i
* 2
i
z 1
z i
* 2
(18)
Step 12. Calculates the integral index (coefficient of proximity) for each comparable
bidder as the ratio of the calculated distance for him from the ideal worst decision to
the sum of distances to the best and worst decisions:
H (cnd i )
H (cnd i ) H * (cnd i ) H (cnd i ); (cnd i ) . (19)
H (cnd i )
In accordance with the value of the coefficient of proximity (cnd i ) it is possible to
rank applicants. So, the closer to the unit value of the coefficient of proximity
(cnd i ) , the better the candidate is compared. The largest value of the integral
indicator (cvdi ) determines the best bidder, that is, the optimal solution. The least
value (cnd i ) matches the worst bidder.
5 Conclusions
In the conditions of the active development of innovative information technologies,
human resources, including IT specialists, have become the main strategic resource of
companies, which ensures their long-term competitiveness and achieve their goals.
The emphasis in this matter is on recruiting IT specialists. In addition to the basic
requirements for professionalism and IT education, the latest trend in recruiting is to
take into account the psychological portrait of a future employee as a level of com-
municativeness, openness of experience, good faith, goodwill, leadership qualities,
etc. One of the modern tasks of analyzing the psychological state of personality when
recruiting IT specialists is to analyze his profile in social networks (posts on forums,
comments on events, activity in social networks, etc.). Therefore, the development of
new conceptual approaches and promising IT management of human resources be-
comes of special relevance and practical significance. In recruiting, the challenge of
recruitment is of great importance, since, with the presence of qualified personnel, the
company can function successfully. Therefore, the issues of the adoption of personnel
decisions, free of subjectivity, are very relevant. The complexities faced by compa-
nies in the process of identifying the applicant, the most acceptable requirements for a
particular post in terms of professional suitability, and from the point of view of com-
pliance with corporate style and psychological compliance, necessitate the develop-
ment and improvement of automated approaches for recruiting. The paper proposes a
methodological approach to solving recruiting tasks based on the analysis of data on
the applicant from social networks using multi-criteria optimization based on the
TOPSIS method. The use of the TOPSIS method in human resources management
tasks allows to increase the adequacy of the decisions made by ranking according to
the degree of proximity to the ideal solution ensures the objectivity and transparency
of the adopted managerial decisions and provides opportunities for expanding the
scope of multicriteria optimization methods. The authors apply the TOPSIS method
modification, which consists in integrating into the algorithm an additional step,
which involves calculating and introducing the reliability coefficients of social net-
works involved in the procedure for evaluating the applicants' psychological state
based on their activity with emotional coloration (neutral, positive and negative).
The advantages of the proposed approach to multicriteria optimization based on the
modified TOPSIS method to support decision-making in recruitment are as follows:
Absence of the need to compile a fuzzy rule base;
Mathematical reasonableness and relative simplicity of calculations of integral
indicators, allowing to carry out ranking of alternative solutions, to carry out fur-
ther analysis and choice of the final solution;
Lack of constraints on the number of applicants and the parameters of the for-
mation of criteria for evaluation according to the activity in social networks, which
characterize the object of research;
Accounting in the algorithm of making decisions on the competence of the years of
training involved in the decision-making process;
Accounting for the hierarchical structure of the criteria describing the applicants;
Ability to rank applicants according to their degree of proximity to the ideal solu-
tion.
The article presents a step-by-step demonstration of the possibilities of the proposed
method in the process of multi-criteria analysis and decision-making on the example
of the selection and recruitment task. Conducting alternative calculations for decision-
making based on a ball assessment system and a comparative analysis of the results of
the two methods shows the effectiveness of the proposed method. Using the described
methodological approach as the mathematical basis of a computer system for decision
support in recruiting tasks can become an effective tool for preparing and taking ef-
fective decisions in human resource management in IT business.
References
1. Best, H.: Does personality matter in politics? Personality factors as determinants of par-
liamentary recruitment and policy preferences. In: Comparative Sociology, vol. 10(6),
928-948. (2011)
2. Rife, S. C., Cate, K. L., Kosinski, M., Stillwell, D.: Participant recruitment and data collec-
tion through Facebook: The role of personality factors. In: International Journal of Social
Research Methodology, vol. 19(1), 69-83. (2016)
3. Ryan, T., Xenos, S.: Who uses Facebook? An investigation into the relationship between
the Big Five, shyness, narcissism, loneliness, and Facebook usage. In: Computers in hu-
man behavior, vol. 27(5), 1658-1664. (2011)
4. Buettner, R.: A framework for recommender systems in online social network recruiting:
An interdisciplinary call to arms. In: 47th Hawaii International Conference on System Sci-
ences, 1415-1424. (2014)
5. Stoughton, J. W., Thompson, L. F., Meade, A. W.: Big five personality traits reflected in
job applicants' social media postings. In: Cyberpsychology, Behavior, and Social Network-
ing, vol. 16(11), 800-805. (2013)
6. Shuotian Bai, Tingshao Zhu, Li Cheng: Big-Five Personality Prediction Based on User
Behaviors at Social Network Sites. In: http://arxiv.org/pdf/1204.4809v1.pdf. (2012).
7. Shuotian Bai: List of computer science publications by Shuotian Bai. Access mode:
http://dblp.uni–trier.de/pers/hd/b/Bai:Shuotian. (2016).
8. Nedunchezhian, P., Jacob, S. G.: Social influence algorithms and emotion classification for
Prediction of Human Behavior: A Survey. In: International Conference on Recent Trends
and Challenges in Computational Models (ICRTCCM), 55-60. (2017)
9. Chirayil Subhash, S.: Personality Analysing on Watson Cloud by tracking the digital foot-
prints of the user. In: Doctoral dissertation, Dublin, National College of Ireland. (2015)
10. Mostafa, M., Crick, T., Calderon, A. C., Oatley, G.: Incorporating emotion and personali-
ty-based analysis in user-centered modelling. In: International Conference on Innovative
Techniques and Applications of Artificial Intelligence, 383-389. (2016).
11. Lytvyn, V., Pukach, P., Bobyk, І., Vysotska, V.: The method of formation of the status of
personality understanding based on the content analysis. In: Eastern-European Journal of
Enterprise Technologies, 5/2(83), 4-12 (2016)
12. Chyrun, L., Kis, I., Vysotska, V., Chyrun, L.: Content monitoring method for cut for-
mation of person psychological state in social scoring, 2018 IEEE 13th International Sci-
entific and Technical Conference on Computer Sciences and Information Technologies,
CSIT 2018, 106-112 (2018)
13. Chyrun, L., Vysotska, V., Kis, I., Chyrun, L.: Content Analysis Method for Cut Formation
of Human Psychological State, Proceedings of the 2018 IEEE 2nd International
Conference on Data Stream Mining and Processing, DSMP 2018, 139-144 (2018)
14. Fedushko, S.: Development of a software for computer-linguistic verification of socio-
demographic profile of web-community member. In: Webology, vol. 11(2), 126. (2014)
15. Korzh, R., Peleschyshyn, A., Syerov, Yu., Fedushko, S.: The cataloging of virtual
communities of educational thematic. In: Webology, vol. 11(1), article 117. (2014)
16. Korzh, R., Peleshchyshyn, A., Fedushko, S., Syerov, Y.: Protection of University
Information Image from Focused Aggressive Actions. In: Advances in Intelligent Systems
and Computing, Springer, vol. 543, 104-110. (2017)
17. Korzh, R., Peleshchyshyn, A., Syerov, Yu., Fedushko, S.: University’s Information Image
as a Result of University Web Communities’ Activities. In: Advances in Intelligent
Systems and Computing, Springer, Vol. 512, 115-127. (2017)
18. Korobiichuk, I., Fedushko, S., Juś, A., Syerov, Y.: Methods of Determining Information
Support of Web Community User Personal Data Verification System. In: Advances in
Intelligent Systems and Computing, Springer, vol. 550, 144-150. (2017)
19. Korzh, R., Fedushko, S., Peleschyshyn, A.: Methods for forming an informational image
of a higher education institution. In: Webology, vol. 12(2), article 140. (2015)
20. Basyuk, T.: Popularization of website and without anchor promotion. In: Int. Scientific and
Technical Conference on Computer science and information technologies, 193-195. (2016)
21. Basyuk, T.: Innerlinking website pages and weight of links. In: International Scientific and
Technical Conference on Computer science and information technologies, 12-15. (2017)
22. Basyuk T.: The Popularization Problem of Websites and Analysis of Competitors. In:
Advances in Intelligent Systems and Computing II, vol. 689, Springer, 54-65. (2018)
23. Lytvyn, V., Vysotska, V., Pukach, P., Bobyk, І., Pakholok, B.: A method for constructing
recruitment rules based on the analysis of a specialist's competences. In: Eastern-European
Journal of Enterprise Technologies, 6/2(84), 1-14 (2016)
24. Lytvyn, V., Vysotska, V., Dosyn, D., Burov, Y.: Method for ontology content and
structure optimization, provided by a weighted conceptual graph, Webology, 15(2), pp. 66-
85 (2018)
25. Vasyl, Lytvyn, Victoria, Vysotska, Dmytro, Dosyn, Roman, Holoschuk, Zoriana,
Rybchak: Application of Sentence Parsing for Determining Keywords in Ukrainian Texts.
In: Computer Science and Information Technologies, Proc. of the Int. Conf. CSIT, 326-
331 (2017)
26. Lytvyn, V., Vysotska, V., Uhryn, D., Hrendus, M., Naum, O.: Analysis of statistical
methods for stable combinations determination of keywords identification. In: Eastern-
European Journal of Enterprise Technologies, 2/2(92), 23-37 (2018)
27. Vysotska, V., Hasko, R., Kuchkovskiy, V.: Process analysis in electronic content
commerce system. In: Proceedings of the International Conference on Computer Sciences
and Information Technologies, CSIT 2015, 120-123 (2015)
28. Lytvyn, V., Vysotska, V., Pukach, P., Nytrebych, Z., Demkiv, I., Senyk, A., Malanchuk,
O., Sachenko, S., Kovalchuk, R., Huzyk, N.: Analysis of the developed quantitative
method for automatic attribution of scientific and technical text content written in
Ukrainian, Eastern-European Journal of Enterprise Technologies, 6(2-96), 19-31 (2018)
29. Vysotska, V., Chyrun, L.: Analysis features of information resources processing. In:
Computer Science and Information Technologies, Proc. of the Int. Conf. CSIT, 124-128
(2015)
30. Lytvyn, V., Sharonova, N., Hamon, T., Vysotska, V., Grabar, N., Kowalska-Styczen, A.:
Computational linguistics and intelligent systems. In: CEUR Workshop Proceedings, Vol-
2136 (2018)
31. Lytvyn V., Vysotska V., Pukach P., Nytrebych Z., Demkiv І., Kovalchuk R., Huzyk N.:
Development of the linguometric method for automatic identification of the author of text
content based on statistical analysis of language diversity coefficients, Eastern-European
Journal of Enterprise Technologies, 5(2), 16-28 (2018)
32. Vysotska, V., Kanishcheva, O., Hlavcheva, Y.: Authorship Identification of the Scientific
Text in Ukrainian with Using the Lingvometry Methods, 2018 IEEE 13th International
Scientific and Technical Conference on Computer Sciences and Information Technologies,
CSIT 2018 – Proceedings 2, 34-38 (2018)
33. Vysotska, V., Fernandes, V.B., Lytvyn, V., Emmerich, M., Hrendus, M.: Method for
Determining Linguometric Coefficient Dynamics of Ukrainian Text Content Authorship,
Advances in Intelligent Systems and Computing, 871, 132-151 (2019)
34. Lytvyn, V., Vysotska, V., Burov, Y., Bobyk, I., Ohirko, O.: The linguometric approach for
co-authoring author's style definition, Proceedings of the 2018 IEEE 4th International
Symposium on Wireless Systems within the International Conferences on Intelligent Data
Acquisition and Advanced Computing Systems, IDAACS-SWS 2018, 29-34 (2018)
35. Lytvyn, V., Vysotska, V., Dosyn, D., Lozynska, O., Oborska, O.: Methods of Building
Intelligent Decision Support Systems Based on Adaptive Ontology, Proceedings of the
2018 IEEE 2nd International Conference on Data Stream Mining and Processing, DSMP
2018, 145-150 (2018)
36. Vysotska, V., Burov, Y., Lytvyn, V., Demchuk, A.: Defining Author's Style for Plagiarism
Detection in Academic Environment, Proceedings of the 2018 IEEE 2nd International
Conference on Data Stream Mining and Processing, DSMP 2018, 128-133 (2018)
37. Vysotska, V., Lytvyn, V., Hrendus, M., Kubinska, S., Brodyak, O.: Method of textual
information authorship analysis based on stylometry, 2018 IEEE 13th International
Scientific and Technical Conference on Computer Sciences and Information Technologies,
CSIT 2018, 9-16 (2018)
38. Rzheuskyi, A., Matsuik, H., Veretennikova, N., Vaskiv, R.: Selective Dissemination of
Information – Technology of Information Support of Scientific Research. In: Advances in
Intelligent Systems and Computing III, vol. 871, 235-245. (2019)
39. Kazarian, A., Holoshchuk, R., Kunanets, N., Shestakevysh, T., Rzheuskyi, A.: Information
support of scientific researches of virtual communities on the Platform of Cloud Services.
In: Advances in Intelligent Systems and Computing III, vol. 871, 301-311. (2019)
40. Lypak, H., Rzheuskyi, A., Kunanets, N., Pasichnyk, V.: Formation of a consolidated
information resource by means of cloud technologies. In: International Scientific-Practical
Conference on Problems of Infocommunications Science and Technology, 157-160 (2018)
41. Rzheuskyi, A., Kunanets, N., Stakhiv, M.: Recommendation System "Virtual Reference".
In: Computer Sciences and Information Technologies (CSIT), vol. 1, 203-206.(2018)
42. Babichev, S., Gozhyj, A.P., Kornelyuk, A.I., Litvinenko, V.I.: Objective clustering
inductive technology of gene expression profiles based on SOTA clustering algorithm. In:
Biopolymers and Cell. vol. 33(5), 379–392. (2017)
43. Gozhyj, A., Kalinina, I., Gozhyj, V.: Fuzzy cognitive analysis and modeling of water
quality. In: 9th IEEE International Conference on Intelligent Data Acquisition and
Advanced Computing Systems: Technology and Applications (IDAACS), 289-294. (2017)
44. Bidyuk, P., Gozhyj, A., Kalinina, I., Gozhyj, V.: Methods for processing uncertainties in
solving dynamic planning problems. In: 12th International Scientific and Technical
Conference on Computer Sciences and Information Technologies, 151-156. (2017)
45. Bidyuk, P., Gozhyj, A., Kalinina, I., Gozhyj, V.: Analysis of uncertainty types for model
building and forecasting dynamic processes. In: Advances in Intelligent Systems and
Computing II, vol. 689, Springer-Verlag, 66-78. (2018)
46. Bidyuk, P. at al.: The Methods Bayesian Analysis of the Threshold Stochastic Volatility
Model. In: International Conference on Data Stream Mining and Processing, 70-75. (2018)
47. Babichev, S. at al.: A fuzzy model for gene expression profiles reducing based on the
complex use of statistical criteria and Shannon entropy. In: Advances in Computer Science
for Enginering and Education, vol. 754, Springer, 545-554. (2018)
48. Sachenko, S., Beley, O.: The information System of control risks. In: International
Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology
and Aplications IDAACS, 270-274. (2001)
49. Sachenko, S., Pushkar, M., Rippa, S.: Intellectualization of Accounting System. In:
Intelligent Data Acquisition and Advanced Computing Systems: Technology and
Applications, 536-538. (2007)
50. Sachenko, S., Rippa, S., Krupka Y.: Pre-Conditions of Ontological Approaches
Application for Knowledge Management in Accounting. In: Intelligent Data Acquisition
and Advanced Computing Systems: Technology and Applications, 605-608. (2009)
51. Sachenko, S., Rippa, S., Golyash, I.: Improving the Information Security Audit of
Enterprise Using XML Technologies. In: Intelligent Data Acquisition and Advanced
Computing Systems: Technology and Applications, 933-937. (2011)
52. Sachenko, S., Lendyuk, T., Rippa, S.: Simulation of Computer Adaptive Learning and
Improved Algorithm of Pyramidal Testing. In: Intelligent Data Acquisition and Advanced
Computing Systems, vol. 2, 764-770. (2013)
53. Furgala, Yu.M., Rusyn, B.P.: Peculiarities of melin transform application to symbol
recognition. In: International Conference on Advanced Trends in Radioelectronics,
Telecommunications and Computer Engineering (TCSET), 251-254. (2018)
54. Kapustiy, B.E., Rusyn, B.P., Tayanov, V.A.: Peculiarities of application of statistical
detection criteria for problems of pattern recognition. In: Journal of Automatioin and
Inrormation Science, vol. 37(2), 30-36. (2005)
55. Rusyn, B., Kosarevych, R., Lutsyk, O., Korniy, V.: Segmentation of atmospheric clouds
images obtained by remote sensing. In: International Conference on Advanced Trends in
Radioelectronics, Telecommunications and Computer Engineering, 213-216. (2018)
56. Kapustiy, B.O., Rusyn, B.P., Tayanov, V.A.: A new approach to determination of correct
recognition probability of set objects. In: Upravlyaushchie Sistemy i Mashiny, vol. 2, 8-12.
(2005)
57. Rusyn, B., Prudyus, I., Ostap, V.: Fingerprint image enhancement algorithm. In: The
Experience of Designing and Application of CAD Systems in Microelectronics-
Proceedings of the 6th International Conference (CADSM), 193-194. (2001)
58. Rusyn, B., Torska, R., Kobasyar, M.: Application of the cellular automata for obtaining
pitting images during simulation process of the growth. In: Advances in Intelligent
Systems and Computing, vol. 242, 299-306. (2014)
59. Varetskyy, Y., Rusyn, B., Molga, A., Ignatovych, A.: A new method of fingerprint key
protection of grid credential. In: Advances in Intelligent and Soft Computing, vol. 84, 99-
103. (2010)
60. Oksanych, I., Shevchenko, I., Shcherbak, I., Shcherbak, S.: Development of specialized
services for predicting the business activity indicators based on micro-service architecture.
In: Eastern-European Journal of Enterprise Technologies, vol. 2(2-86), 50-55. (2017)
61. Galushka, I., Shcherbak, S.: Devising a mathematical model for pattern-based enterprise
data integration. In: Eastern-European Journal of Enterprise Technologies, vol. 2(9), pp.
59-64. (2015)
62. Shcherbak, S.S.: Interoperability web application models based on microformats. In: Int.
Crimean Conf.: Microwave and Telecommunication Technology (CriMiCo), 57-58. (2011)
63. Nazarkevych, M., Klyujnyk, I., Nazarkevych, H.: Investigation the Ateb-Gabor Filter in
Biometric Security Systems. In: Data Stream Mining & Processing, 580-583. (2018)
64. Nazarkevych, M., Klyujnyk, I., Maslanych, I., Havrysh, B., Nazarkevych, H.: Image
filtration using the Ateb-Gabor filter in the biometric security systems. In: Perspective
Technologies and Methods in MEMS Design (MEMSTECH), 276-279. (2018)
65. Nazarkevych, M., Buriachok, V., Lotoshynska, N., Dmytryk, S.: Research of Ateb-Gabor
Filter in Biometric Protection Systems. In: International Scientific and Technical
Conference on Computer Sciences and Information Technologies (CSIT), 310-313. (2018)
66. Nazarkevych, M., Oliarnyk, R., Dmytruk, S.: An images filtration using the Ateb-Gabor
method. In: Computer Sciences and Information Technologies (CSIT), 208-211. (2017)
67. Nazarkevych, M., Kynash, Y., Oliarnyk, R., Klyujnyk, I., Nazarkevych, H.: Application
perfected wave tracing algorithm. In: Ukraine Conference on Electrical and Computer
Engineering (UKRCON), 1011-1014. (2017)
68. Nazarkevych, M., Oliarnyk, R., Troyan, O., Nazarkevych, H.: Data protection based on
encryption using Ateb-functions. In: International Scientific and Technical Conference
Computer Sciences and Information Technologies (CSIT), 30-32. (2016)
69. Vitynskyi, P., Tkachenko, R., Izonin, I., Kutucu, H.: Hybridization of the SGTM Neural-
like Structure through Inputs Polynomial Extension. In: International Conference on Data
Stream Mining & Processing (DSMP), 386-391. (2018)
70. Tkachenko, R., Izonin, I., Kryvinska, N., Chopyak, V., Lotoshynska, N., Danylyuk, D.:
Piecewise-linear Approach for Medical Insurance Costs Prediction using SGTM Neural-
Like Structure. In: Informatics & Data-Driven Medicine (IDDM), 170-179. (2018)
71. Tepla, T.L., Izonin, I.V., Duriagina, Z.А., Tkachenko, R.О., Trostianchyn, А.М.,
Lemishka, І.А., Kulyk, V.V., Kovbasyuk, Т.М.: Alloys selection based on the supervised
learning technique for design of biocompatible medical materials. In: Archives of
Materials Science and Engineering, vol. 93/1, 32-40. (2018)
72. Izonin, I., Trostianchyn, A., Duriagina, Z., Tkachenko, R., Tepla, T., Lotoshynska, N.: The
Combined Use of the Wiener Polynomial and SVM for Material Classification Task in
Medical Implants Production. In: International Journal of Intelligent Systems and
Applications (IJISA), vol. 10(9), 40-47. (2018)
73. Tkachenko, R., Duriagina, Z., Lemishka, I., Izonin, I., Trostianchyn, A.: Development of
machine learning method of titanium alloys properties identification in additive
technologies. In: EasternEuropean Journal of Enterprise Technologies, vol. 3, 12(93), 23-
31. (2018)
74. Mahmud, J.: IBM Watson Personality Insights: The science behind the service. In: IBM
Corporation, https://developer. ibm. com/watson/blog/2015/03/23/ibm-watson-personality-
insights-science-behind-service. (2015)
75. Mammadova, M. H., Jabrayilova Z. H.: Multi criteria optimization of human resource
management problems based on the modified topsis method. In: Eastern-European Journal
of Enterprise Technologies, vol. 2(4 (74)), 48-62. (2015)
76. Mammadova, M. H., Djabrailova, Z. G.: Decision-Making Support in Human Resource
Management Based on Multi-Objective Optimization. In: TWMS Journal of Pure and
Applied Mathematics, vol. 9(1), 52-72. (2018)
77. Calvo-Armengol, A., Jackson, M. O.: The effects of social networks on employment and
inequality. In: American economic review, vol. 94(3), 426-454. (2004)
78. Chen, C. T., Hung, W. Z.: Choosing project leader based on interval linguistic TOPSIS
and social network technology. In: 2012 International conference on Fuzzy Theory and Its
Applications (iFUZZY2012), 310-315. (2012)
79. Clais, P., Leseur, G., Lasfar, O.: U.S. Patent Application No. 14/527,548. (2015)
80. Kotsilieris, T., Pavlaki, A., Christopoulou, S., Anagnostopoulos, I.: The impact of social
networks on health care. In: Social Network Analysis and Mining, vol. 7(1), 18. (2017).