Paper Title

146 Pages·2017·6.75 MB·English

MINISTRY OF EDUCATION AND SCIENCE OF UKRAINE
SUMY STATE UNIVERSITY
UKRAINIAN FEDERATION OF INFORMATICS

PROCEEDINGS

OF THE VI INTERNATIONAL SCIENTIFIC

CONFERENCE

ADVANCED INFORMATION
SYSTEMS AND TECHNOLOGIES

AIST-2018
(Sumy, May 16–18, 2018)

SUMY
SUMY STATE UNIVERSITY
2018
The VIth International Conference «Advanced Information Systems and Technologies, AIST 2018»
16-18 May 2018, Sumy, Ukraine
UDC 004(063)
A

A Advanced Information Systems and Technologies: proceedings of the VI
international scientific conference, Sumy, May 16–18 2018 / Edited by
S. І. Protsenko, V. V. Shendryk – Sumy: Sumy State University, 2018 – 145 p.

This book comprises the proceedings of the VI International Scientific
Conference “Advanced Information Systems and Technologies, AIST-2018”.
The proceeding papers cover issues related to system analysis and modeling,
project management, information system engineering, intelligent data
processing, computer networking and telecomunications, modern methods and
information technologies of sustainable development. They will be useful for
students, graduate students, researchers who interested in computer science.

UDC 004(063)

ISSN 2311-8504 © Sumy State University, 2018

2
The VIth International Conference «Advanced Information Systems and Technologies, AIST 2018»
16-18 May 2018, Sumy, Ukraine

International Scientific Committee:

A.N. Chornous, Sc.D (Ukraine) V.I. Lytvynenko, Sc.D (Ukraine)
A.S. Dovbysh, Sc.D (Ukraine) N. B. Shakhovska, Sc.D (Ukraine)
O.A. Borisenko, Sc.D (Ukraine) E.A. Druzynin, Sc.D (Ukraine)
E.A. Lavrov, Sc.D (Ukraine) S.I. Dotsenko, Sc.D (Ukrain)
V.O. Lyubchak, PhD (Ukraine) T.V. Kovalyuk, PhD (Ukraine)
S.I. Protsenko, Sc.D (Ukraine) A. Pakštas, PhD (United Kingdom)
A.M. Кulish, Sc.D (Ukraine) O.Romanko, PhD (Canada)
M.M. Glybovets, Sc.D (Ukraine) І. Polik, PhD (USA)
Yu. I. Grytsyuk , Sc.D (Ukraine) V.Kalashnikov, Sc.D (Mexico)
I.V. Grebennik, Sc.D (Ukraine) S. Berezyuk, PhD (Canada)
O.O. Yemets, Sc.D (Ukraine) P. Davidsson, PhD (Sweden)
D.D. Peleshko, Sc.D (Ukraine M. Biagi, PhD (Italy)

Organizing Committee:

Protsenko S. I., Sc.D, chairman (Ukraine);
Shendryk V. V., PhD, co-chairman(Ukraine);
Vaschenko S. M., PhD, co-chairman (Ukraine);
Parfenenko Y. V., PhD (Ukraine), Nahornyi V. V. , PhD (Ukraine),
Zakharchenko V. P. (Ukraine), Shendryk S.O. (Ukraine),

Boiko O. V., PhD, executive secretary (Ukraine).

Contacts:

Address:AIST conference, Sumy State University

2 Rimsky-Korsakov Str., Sumy, 40000, Ukraine
website: www.aist.sumdu.edu.ua
е-mail: [email protected].
3
The VIth International Conference «Advanced Information Systems and Technologies, AIST 2018»
16-18 May 2018, Sumy, Ukraine
CONTENTS

SESSION 1 SYSTEM ANALYSIS AND MODELING

BRANCH AND BOUND METHOD FOR SOLVING OF LINEAR STOCHASTIC
COMBINATORIAL OPTIMIZATION PROBLEM ON ARRANGEMENTS
Oleg Iemets, Tetiana Barbolina ....................................................................................................... 8
CLIENT SOLVENCY ESTIMATION USING INTELLECTUAL DATA ANALYSIS
APPROACH
Petro Bidyuk, Vira Huskova, Oleksandr Terentiev ..................................................................... 12
FUNCTIONS OF DISPROPORTIONALITY AND THEIR APPLICATION
Viktor V.Avramenko ....................................................................................................................... 16
COGNITIVE MODELING OF POTENTIAL PHARMACY DEMAND MANAGEMENT
Hanna Ropalo ................................................................................................................................... 21
INFORMATION TECHNOLOGY FOR QUALITY MANAGEMENT OF ENGINEERING
PRODUCTS
Evgeniy Lavrov, Mykola Bahmach, Galyna Mihalevska ............................................................. 25
INTRODUCTION OF AN IRREGULAR GRID WITH RESPECT TO THE SPATIAL
COORDINATE FOR THE METHOD OF LINES
Olga Dmytriyeva, Nadiia Huskova................................................................................................. 30
THE APPROXIMATION SURFACE REVIEW OF THE MULTIDIMENSIONAL TARGET
FUNCTION FOR SURROGATE OPTIMIZATION PROBLEMS
Ruslana Trembovetska, Volodymyr Halchenko, Volodymyr Tychkov ...................................... 34
THE PROBLEMS OF AUTOMATION OF FINANCIAL TRANSACTIONS SUBJECT TO THE
SIGNS OF INTERNAL FINANCIAL MONITORING
Serhii Mynenko ................................................................................................................................ 39
WEB-BASED APPLICATION FOR PLANNING THE STRUCTURE OF HYBRID
RENEWABLE ENERGY SYSTEM
Anastasia Verbytska, Yulia Parfenenko, Olha Boiko, Ozhikenov Kasymbek Adilbekovich,
Assem Kabdoldina ........................................................................................................................... 43

SESSION 2 PROJECT MANAGEMENT

CONVERGENSE OF AGILE AND TRADITIONAL METHODOLOGIES IN IT PROJECTS
Juliya Kasianenko ............................................................................................................................ 48
SESSION 3 E-LEARNING TECHNOLOGIES

GAME MODEL OF BLENDED LEARNING IN A UNIFIED LEARNING ENVIRONMENT OF
THE SUMY STATE UNIVERSITY
Inga Vozna, Serhey Shapovalov ..................................................................................................... 53
DEVELOPMENT OF A TECHNIQUE FOR THE CREATION OF VIRTUAL SIMULATORS OF
ARTILLERY WEAPONS
Natalia Fedotova, Tetiana Yasinska............................................................................................... 57
ELECTRONIC MILITARY TRAINING DEVICE
Kateryna Savytska, Viktoria Kudrytska, Natalia Fedotova ........................................................ 61
INFORMATION SYSTEM FOR ACCOUNT OF STUDENTS WORK
Mariia Mova, Volodymyr Nahornyi, Olga Zagovora, Maksym Portianoi ................................. 65
INFORMATION TECHNOLOGY FOR MODELING OF HUMAN-MACHINE INTERACTIONS
Evgeniy Lavrov, Nadiia Pasko, Yan Voitsekhovskyi, Ruslan Plaks, Galyna Mihalevska ........ 69
THE SIMULATOR ON THE TOPIC “LAND AND DOIG METHOD” OF THE DISTANCE
LEARNING COURSE “OPTIMIZATION METHODS AND OPERATIONS RESEARCH”:
DEVELOPMENT AND SOFTWARE REALIZATION
Oleksandr Syvokin, Oleg Iemets .................................................................................................... 72
PECULIARITIES OF DISTANCE LEARNING TECHNOLOGIES APPLICATION IN
TRAINING FUTURE OFFICERS FOR THE ARMED FORCES OF UKRAINE
Olena Pavlichenko, Yurii Kalynovs’kyi, Halyna Serhieieva ....................................................... 76

SESSION 4 INFORMATION SYSTEMS ENGINEERING

AUTOMATED RECORDING AND PROCESSING OF LECTURE ATTENDANCE DATA
USING RFID STUDENT CARDS
Alexander Kalashnikov, Hongwei Zhang, Misko Abramriuk ..................................................... 81
HIERARCHICAL ALGORITHM OF THE MACHINE LEARNING FOR THE SYSTEM OF
FUNCTIONAL DIAGNOSTICS OF THE ELECTRIC DRIVE
Anatoly Dovbysh, Victoria Zimovets ............................................................................................. 85
PHYSICAL AND MATHEMATICAL MODELLING OF PITTING CORROSION
Nutthawut Suchato, Roger Light, Richard Smith, Alexander Kalashnikov .............................. 89
SECURE MOBILE APPLICATION DEVELOPMENT
Roman Yatsenko, Viktor Obodiak, Valerii Yatsenko .................................................................. 93
WEB SERVICE FOR MONITORING THE PRICES OF ONLINE STORES
Borys Kuzikov, Maksym Vynohradov ........................................................................................... 97

5
SESSION 5 INTELLIGENT DATA PROCESSIN

FEATURES OF CALCULATING RATINGS IN INFORMATION SYSTEMS
Olga Pronina, Elena Piatykop ...................................................................................................... 102
MATHEMATICAL MODEL OF THE MANAGING STRATEGY SELECTION IN PUBLIC
CATERING ESTABLISHMENT (BASED ON MARKOV DECISION-MAKING PROBLEM) ......
Liliana Danilova ............................................................................................................................. 106
OBJECT DETECTION BASED ON GROWING CONVOLUTIONAL NEURAL NETWORK
FOR AUTONOMOUS SYSTEMS
Viacheslav Moskalenko, Alona Moskalenko, Artem Korobov, Borys Lypivets ...................... 110
ON-BOARD GEOGRAPHIC INFORMATION SYSTEM OF IMAGES’ IDENTIFICATION
Juliy Simonovskiy, Vladislav Piatachenko, Nikita Mironenko ................................................. 115

SESSION 6 COMPUTER NETWORKING AND TELECOMMUNICATIONS

DETECTION AND PREVENTING LEAKS OF SENSITIVE DATA ON COMPUTER SYSTEMS
Mihail Babiy ................................................................................................................................... 120
CROWDSOURCED MEASUREMENT: REALISATION, PRIVACY AND SECURITY IN THE
FRAME OF SUMY STATE UNIVERSITY WIRELESS NETWORK QUALITY ANALYSE
Borys Kuzikov, Sergey Panchenko............................................................................................... 123

SESSION 7 MODERN METHODS AND INFORMATION TECHNOLOGIES OF
SUSTAINABLE DEVELOPMENT

INVESTIGATION OF THE INFLUENCE OF INFORMATION MANAGEMENT ON THE
DEVELOPMENT OF THE COUNTRY
Vitaliya Koibichuk ......................................................................................................................... 129
MODELING THE PROBABLE LOSSES OF BANKS FROM THEIR INVOLVEMENT IN THE
PROCESS OF LEGALIZATION (LAUNDERING) OF INFLAMMABLE FUNDS
Anton Boiko, Tetiana Dotsenko .................................................................................................... 133
REGIONAL SUSTAINABILITY ASSESSMENT THROUGH MULTIVARIATE STATISTICAL
ANALYSIS
Tetiana Marynych, Stanislav Smolenko ...................................................................................... 137
SIMULATION OF SCORING OF THE BANK’S BORROWERS CREDITWORTHINESS
Konstantin Gritsenko .................................................................................................................... 141

6
SESSION 1
SYSTEM ANALYSIS AND MODELING

7
The VIth International Conference «Advanced Information Systems and Technologies, AIST 2018»
16-18 May 2018, Sumy, Ukraine
Branch and Bound Method for Solving of Linear
Stochastic Combinatorial Optimization Problem
on Arrangements
Oleg Iemets1, Tetiana Barbolina2
1Poltava University of Economics and Trade, Ukraine, 2V.G. Korolenko National Pedagogical University, Ukraine,
[email protected]

Abstract –The article deals with the solving of HAh A,h A,...,h A
linear combinatorial optimization problems on a set of 1 2 s
arrangements under probabilistic uncertainty. (where h A , iJ is numeric characteristic of
i s
We propose to branch the common set of
random variable A) is an eigenvector. Suppose that
arrangements assigning certain possible values for
eigenvector satisfies
some of the variables. The way of bound computing is
proposed and substantiate. The algorithm of branch h ABih Aih B
i i i
and bound method is formulated.
Keywords–Euclidean combinatorial optimization problem; iJ (1)
optimization problem on arrangements; stochastic s
combinatorial optimization; branch and bound method.
where A,B are independent random variables,
I. INTRODUCTION ,R1, Z,  0.
i i
Actual trend of the modern theory of optimization is
We say that random variables X,Y are
to study the problems of combinatorial nature under
different types of uncertainty ([1–7] and others). In H-equivalent if and only if HXHY [12]. The
particular stochastic optimization problems attract the
equivalence class of an element X is called the H-class
attention of researchers recently (for example [8–16]).
One approach for formalization of optimization problems and is denoted by [X] .
H
under uncertainty is based on the introduction of the order If HX HY (  is a symbol of
relation on the set of corresponding variables. Earlier the l l
authors [11, 12] proposed two ways of ordering of lexicographical order) for any X X , Y Y
random variables. The first one is based on comparison of H H
mathematical expectation, dispersion, values and then we say that classes [X] ,[Y] are in order of
H H
probabilities of discrete random variables. According to
non-decreasing and write [X] [Y] .
the second way a given set of independent random k k
variables is partitioned by equivalence, which based on Using introduced linear order, let us order the
the comparison of their numerical characteristics; the elements of quotient set / H :
linear order is defined on the quotient set. Some
[X ] [X ] ... [X ] . [X ] X is the
properties of this linear orders and corresponding 1 H 2 H m H m H s
optimization problems were discussed in [11–16]. In this maximum class and [X ] is the minimum one. The
1 H
article we propose branch and bound method for solving
definition of the minimum and maximum allows setting
of stochastic combinatorial optimization problems on
the optimization problem for finding the extreme
arrangements.
elements in the given conditions. Such problems are
II. STATEMENT OF H-PROBLEM called H-problems of stochastic combinatorial
optimization problems.
Let some of the initial data in the optimization
Let X (X  ,X  ,...,X  ) ,
problem be discrete random variables. The last will be H 1 H 2 H k H
denoted by Latin capital letters ( A,B,...). Let  be a k
set of independent discrete random variables, XH cj Xj where cj R1 , Xj 
J 1,2,...,n. We say that j1 H
n jJ . Linear H-problem of stochastic combinatorial
k
8
optimization on a sphere kcan be formulated as Suppose elements of multiset G are in order of
follow: find extremum and extremal of X  increasing:
H 0 g  g ... g . (7)
subject to X . 1 2 
H Consider the following deterministic problem: find a
Further we discuss linear H-problem of stochastic pair  x*,x* such that
combinatorial optimization where feasible set is a subset 1
of common set of arrangements Ek  from elements  x* min k c x , (8)
of multiset  G1H ,G2H ,...,GH (main x1* argmxinEkkcjx1 ,j j (9)
concepts of Euclidean combinatorial optimization are j j
xEk j1
used from [17]). Suppose G1,...,G are independent k
where  xc x , c h C  jJ .
random variables, h G 0 iJ ; coefficients 1 j j j 1 j k
1 i  j1
of linear function X  are positive real numbers. As it follows from the lemma 1 [16], the minimal of
H function  x on Ek  is a permutation of
Thus linear H-problem of stochastic combinatorial 1 
optimization on arrangements can be expressed as elements of multiset g , ,g . Consequently
  1 k
follow: find a pair  X* ,X* such that if point x is not an element of a common set of
H H
permutations E  then  x* x. Since
X*  min k c X  , (2) k 1 1
 H XHEk j1 j  jH  xk c x k h C x 
1 j j 1 j j ,
X* arg min k c X  , (3) j1 j1
 H XHEk j1 j  jH h k C x h  x
1 j j 1 1
subject to  j1 
X (X  ,X  ,...,X  ), (4) we have  x*  x xE .
H 1 H 2 H k H 1 1 k
where k. Therefore minimal in a solution of problem (5)–(6) is
Further we will consider only H-classes. Therefore a permutation of elements of multiset , i.e. minimum
instead [X]H we will write X . of objective function is L* k C g where g ,
1 j i i
j j
I. PROPERTIES OF H -PROBLEM j1
D
i i i ,i J , j,tJ . Thus problem (5)–(6)
k j t j t k k
Consider function  xC x where
1 j j can be expressed as follow: find a pair Y*,Y*
j1
coefficients C jJ are H-classes ( C  such that
j k j
jJ ), x x ,...,x Rk .Linear H -problem Y* min k gY , (10)
k 1 k d YEkj1 j j
of stochastic combinatorial optimization is the problem k
Y*  argming Y , (11)
that can be expressed as follow: find a pair j j
 x*,x* such that k YEk j1
1 where FYg Y , multiset  C ,...,C  .
k j j 1 k
 x* min C x , (5) j1
1 xEkG j1 j j Suppose elements of multiset  are indexed so that
k satisfy the condition
x* argminC x , (6)
xEkG j1 j j HC1l HC2l ...l HCk.(12)
where Ek  is a common set of arrangements from From theorem 4 [15] it follows that the arrangement,

elements of multiset  g ,g ,...,g  , g R1 which satisfies the conditions Yj* Cj jJk , is
1 2  j minimal in a solution of the problem (10)–(11). Hence
jJ . k k
 g Y g C for any permutation of elements of
j j j j
j1 j1
9
multiset C1,C2,...,Ck . Then also Suppose X cX . Since elements of multiset
i i
k C x k g C for any permutation of elements of i1
j j j j  are in order of non-decreasing we obtain
j1 j1
     
multiset g , ,g , i.e. x* g , ,g  is H G1 l H G2 l ...l H Gp .
1 k 1 k
minimal in a solution of the problem (5)–(6). We prove At the same time coefficient of objective function are
the follow theorem. in order of non-increasing. From theorem 4 [13] it
Theorem 1. Suppose coefficients of objective function follows that the arrangement X*, which satisfies the
satisfy the condition (7) and elements of multiset satisfy condition X* G iJ , is a minimal of function
the condition (12). Then the point x*such that x*  g i i 
j j X on the set E . Thus X* X
p
jJ is minimal in a solution of problem (5)–(6).
k X E.
p
II. BRANCH AND BOUND METHOD FOR SOLVING OF
Since eigenvector satisfies the condition (1) we see
H- AND HD-PROBLEMS
that for any X  the correlation
Now we consider branch and bound method for
    
solving linear H-problem of stochastic combinatorial HXHcjXjHciXi
optimization and linear H -problem. jI  i1 
d
Let us first consider H-problem (2)–(4). We propose Hc G HX Hc G 
branch on common set of arrangements Ek  instead jI j rj l jI j rj

feasible set Ek  . When an arrangements is HX*HjIcjGrj Hi1ciGi
obtained we check if this arrangement belong to set . Q
Set  Ek , which is obtained on certain level of is true.

branching, is determined as follow Therefore  X for all X  .
Xj Gr , jI (13) Theorem is proved.
j
Consider now linear H -problem: find a pair (5)–(6)
where I is a set of indexes I  J , I t. d
k subject to
We say that in the set  values of variables X , x(x ,x , ,x ) Rk. (15)
j 1 2 k
jI are fixed. The rest variables are denoted As above, set Ek  is determined as follow

X1,X2,...,X ( k t ). Let also xj  gr , jI . (16)
    j
  c jI ,  c jI . Suppose variables Assume that
B j j

Xi (iJ) are indexed so that elements of multiset jICjgrj i1Cigi (17)
c ,c ,...,c  satisfy c c ...c . Let also (notation is similar given above at the solution of H-
1 2  1 2 
problem).
 
  G jI ,   , G ( iJ , From theorem 1 it follows that point
B rj B i p x* g ,...,g  is a minimal of function
1 k
p   t ) are elements of multiset  ; we

 xC x on the set E . Therefore
assume that G satisfy G G ... G . 1 i i p
i 1 2 p i1
Theorem 2. Let  Ek  satisfies (4). A bound  x*  x xE. Further, similar to
 1 1 p
of X on  may be calculated as proof of theorem 2 we obtain that  x .
 Hence we prove the follow theorem.
c G cG . (14)
j rj i i Theorem 3. Let  Ek  satisfies (16). A
jI i1 
Proof. Clearly, for any X  values of variables bound of  x on  may be calculated according
1
X1,X2,...,X we have X1,X2,...,XEp. to (17).
10