Methods and Instruments of Artificial Intelligence

210 Pages·2016·4.43 MB·English

by  Setlak G.

Galina Setlak, Krassimir Markov
(editors)

Methods and Instruments
of
Artificial Intelligence

I T H E A®
R Z E S Z O V – S O F I A
2010
Galina Setlak, Krassimir Markov (ed.)
Methods and Instruments of Artificial Intelligence
ITHEA®
Rzeszow, Poland – Sofia, Bulgaria, 2010
ISBN 978-954-16-0049-8

First edition
Recommended for publication by The Scientific Council of the Institute of Information Theories and Applications FOI ITHEA
ITHEA IBS ISC: 20.

This book maintains articles on actual problems of research and application of information technologies,
especially the new approaches, models, algorithms and methods for Hybrid Intelligent Systems; Intelligent Agents
and Multi-Agent Systems; Software Engineering and Development; Knowledge Representation and Management;
Intelligent Robots; Intelligent Manufacturing Systems; Business Intelligence Systems; Intelligent Medical and
Diagnostic Systems; Computer Engineering, Mechanical Engineering.
It is represented that book articles will be interesting for experts in the field of information technologies as well as
for practical users.

General Sponsor: Consortium FOI Bulgaria (www.foibg.com).

Printed in Poland

Copyright © 2010 All rights reserved
© 2010 ITHEA® – Publisher; Sofia, 1000, P.O.B. 775, Bulgaria. www.ithea.org ; e-mail: [email protected]
© 2010 Galina Setlak, Krassimir Markov – Editors
© 2010 For all authors in the book.

® ITHEA is a registered trade mark of FOI-COMMERCE Co., Bulgaria

ISBN 978-954-16-0049-8
C\o Jusautor, Sofia, 2010
I T H E A ® 3

PREFACE

ITHEA® International Scientific Society (ITHEA® ISS) is aimed to support growing collaboration between scientists
from all over the world.
ITHEA® Publishing House is the official publisher of the works of the ITHEA® ISS.
The scope of the books of the ITHEA® ISS covers the area of Informatics and Computer Science.
ITHEA® ISS welcomes scientific papers and books connected with any information theory or its application.
ITHEA® ISS rules for preparing the manuscripts are compulsory.
Responsibility for papers and books published by ITHEA® belongs to authors.

This book maintains articles on actual problems of research and application of information technologies,
especially the new approaches, models, algorithms and methods for
 Hybrid Intelligent Systems;
 Intelligent Agents and Multi-Agent Systems;
 Software Engineering and Development;
 Knowledge Representation and Management;
 Intelligent Robots;
 Intelligent Manufacturing Systems;
 Business Intelligence Systems;
 Intelligent Medical and Diagnostic Systems;
 Computer Engineering and Mechanical Engineering.
It is represented that book articles will be interesting for experts in the field of information technologies as well as
for practical users.

We express our thanks to all authors, editors and collaborators as well as to the General Sponsor.
The great success of ITHEA International Journals, International Books and International Conferences belongs to
the whole of the ITHEA International Scientific Society.

Rzeszow – Sofia
September 2010
G. Setlak, K. Markov
4 Methods and Instruments of Artificial Intelligence

Papers in this book are selected from the ITHEA® ISS Joint International Events of Informatics "ITA 2010”:
CFDM Second International Conference "Classification, Forecasting, Data Mining"
i-i-i International Conference "Information - Interaction – Intellect"
i.Tech Eight International Conference "Information Research and Applications"
ISK V-th International Conference “Informatics in the Scientific Knowledge”
MeL V-th International Conference "Modern (e-) Learning"
KDS XVI-th International Conference "Knowledge - Dialogue – Solution"
CML XII-th International Conference “Cognitive Modeling in Linguistics”
INFOS Thirth International Conference "Intelligent Information and Engineering Systems"
NIT International Conference “Natural Information Technologies”
GIT Eight International Workshop on General Information Theory
ISSI Forth International Summer School on Informatics

ITA 2010 took place in Bulgaria, Croatia, Poland, Spain and Ukraine. It has been organized by
 ITHEA International Scientific Society
in collaboration with:
 ITHEA International Journal "Information Theories and Applications"
 ITHEA International Journal "Information Technologies and Knowledge"
 Institute of Information Theories and Applications FOI ITHEA
 Rzeszow University of Technology (Poland)
 Universidad Politecnica de Madrid (Spain)
 V.M.Glushkov Institute of Cybernetics of National Academy of Sciences of Ukraine
 Taras Shevchenko National University of Kiev (Ukraine)
 University of Calgary (Canada)
 BenGurion University (Israel)
 University of Hasselt (Belgium)
 Dorodnicyn Computing Centre of the Russian Academy of Sciences (Russia)
 Institute of Linguistics, Russian Academy of Sciences (Russia)
 Association of Developers and Users of Intelligent Systems (Ukraine)
 Institute of Mathematics and Informatics, BAS (Bulgaria)
 Institute of Mathematics of SD RAN (Russia)
 New Bulgarian University (Bulgaria)
 The University of Zadar (Croatia)
 Kharkiv National University of Radio Electronics (Ukraine)
 Kazan State University (Russia)
 Alexandru Ioan Cuza University (Romania)
 Moscow State Linguistic University (Russia)
 Astrakhan State University (Russia)
as well as many other scientific organizations. For more information: www.ithea.org .
I T H E A ® 5

TABLE OF CONTENTS

Preface .................................................................................................................................................... 3 
Table of Contents ................................................................................................................................... 5 
Index of Authors .................................................................................................................................... 7 
HYBRID INTELLIGENT SYSTEMS 
Fuzzy ARTMAP Neural Networks for Computer Aided Diagnosis 
Anatoli Nachev ......................................................................................................................................................... 9 
Hybrid Systems of Computational Intelligence Evolved From Self-learning Spiking Neural
Network 
Yevgeniy Bodyanskiy, Artem Dolotov .................................................................................................................... 17 
Conceptual model of Decision Support System in a Manufacturing Enterprise 
Monika Piróg-Mazur, Galina Setlak ....................................................................................................................... 25 
INTELLIGENT AGENTS AND MULTI-AGENT SYSTEMS 
Toward the Reference Model for Agent-Based Simulation of Extended Enterprises 
Jakieła Jacek, Litwin Paweł, Olech Marcin ............................................................................................................ 34 
SOFTWARE ENGINEERING AND DEVELOPMENT 
Software Development Process Dynamics Modeling as State Machine 
Leonid Lyubchyk, Vasyl Soloshchuk ..................................................................................................................... 67 
System of Software Production Management 
Galina Setlak, Sławomir Pieczonka ....................................................................................................................... 75 
KNOWLEDGE REPRESENTATION AND MANAGEMENT 
Selection of Knowledge Management Models and Use of the Introduction of Tele Working Model
of Disabled 
Tatjana Bilevičienė, Eglė Bilevičiūtė ...................................................................................................................... 82 
Advanced Decision Making Functions and Some Methods of Information Processing for Socio
Economic Clusters 
Anatolij Krissilov, Viacheslav Chumachenko ......................................................................................................... 89 
Computer-Aided Design of Experiments in the Field of Knowledge-Based Economy 
Dorota Dejniak, Monika Piróg-Mazur ..................................................................................................................... 97 
Realization of Interdisciplinary Links Between Disciplines Connected to Information
Technologies 
Marina Solovey ..................................................................................................................................................... 103 
Presentation of Production Oriented Data and Knowledge 
Arkadiusz Rzucidło ............................................................................................................................................... 106
6 Methods and Instruments of Artificial Intelligence

INTELLIGENT ROBOTS 
Teleoperation and Semiautonomy Movement Modes of IBIS Robot 
Piotr Bigaj, Maciej T. Trojnacki, Jakub Bartoszek ............................................................................................... 111 
INTELLIGENT MANUFACTURING SYSTEMS 
The Intelligent Principle of Virtual Laboratories for Computer Aided Design of Smart Sensor
Systems 
Iliya Mitov, Anatolij Krissilov, Oleksandr Palagin, Peter Stanchev, Vitalii Velychko, Volodymyr Romanov,
Igor Galelyuka, Krassimira Ivanova, Krassimir Markov ....................................................................................... 119 
Application of Artificial Intelligence Tools to the Classification of Structural Modules 
Galina Setlak, Tomasz Kożak .............................................................................................................................. 125 
Visualization Options of Assembly Procedure 
Jana Jedináková, Marek Kočiško, Slavomír Gajdarik ....................................................................................... 131 
VHDL-Modeling of a Gas Laser’s Gas Discharge Circuit 
Nataliya Golian, Vera Golian, Olga Kalynychenko .............................................................................................. 136 
BUSINESS INTELLIGENCE SYSTEMS 
Business Intelligence as a Decision Support System 
Justyna Stasieńko ................................................................................................................................................ 141 
Computer Modeling for Modern Business 
Elena Serova ........................................................................................................................................................ 149 
Analysis and Synthesis of Goals of Complex Industrial Systems 
Lyudmila Lukyanova ............................................................................................................................................ 156 
INTELLIGENT MEDICAL AND DIAGNOSTIC SYSTEMS 
Synthesis of Static Medical Images with Using Machine Learning Methods 
Łukasz Piątek, Grzegorz Owsiany ....................................................................................................................... 161 
A Concept of Design Process of Intelligent System Supporting Diabetes Diagnostics 
Wioletta Szajnar, Galina Setlak ........................................................................................................................... 168 
COMPUTER ENGINEERING, MECHANICAL ENGINEERING 
Surface Profile Analysis Package 
Paweł Dobrzański, Magdalena Dobrzańska, Wiesław Żelasko .......................................................................... 179 
Method of Conglomerates Recognition and Their Separation Into the Parts 
Volodymyr Hrytsyk, Viktor Vlakh .......................................................................................................................... 184 
Failure Prediction in Complex Computer Systems 
Paweł Janczarek .................................................................................................................................................. 193 
The Automation of Parameter Ppq Identification Process for Profiles with Functional Properties 
Wiesław Graboń ................................................................................................................................................... 198 
Simulation of the effect of stylus tip radius on the results of surface topography measurement 
Sławomir Górka, Paweł Pawlus ........................................................................................................................... 203 
ITHEA International Scientific Society .............................................................................................. 210
I T H E A ® 7

INDEX OF AUTHORS

Anatoli Nachev 9 Nataliya Golian 136
Anatolij Krissilov 89, 119 Olech Marcin 34
Arkadiusz Rzucidło 106 Oleksandr Palagin 119
Artem Dolotov 17 Olga Kalynychenko 136
Dorota Dejniak 97 Paweł Dobrzański 179
Eglė Bilevičiūtė 82 Paweł Janczarek 193
Elena Serova 149 Paweł Pawlus 203
Galina Setlak 25, 75, 125, 168 Peter Stanchev 119
Grzegorz Owsiany 161 Piotr Bigaj 111
Igor Galelyuka 119 Slavomír Gajdarik 131
Iliya Mitov 119 Sławomir Górka 203
Jakieła Jacek 34 Sławomir Pieczonka 75
Jakub Bartoszek 111 Tatjana Bilevičienė 82
Jana Jedináková 131 Tomasz Kożak 125
Justyna Stasieńko 141 Vasyl Soloshchuk 67
Krassimir Markov 119 Vera Golian 136
Krassimira Ivanova 119 Viacheslav Chumachenko 89
Leonid Lyubchyk 67 Viktor Vlakh 184
Litwin Paweł 34 Vitalii Velychko 119
Łukasz Piątek 161 Volodymyr Hrytsyk 184
Lyudmila Lukyanova 156 Volodymyr Romanov 119
Maciej T. Trojnacki 111 Wiesław Graboń 198
Magdalena Dobrzańska 179 Wiesław Żelasko 179
Marek Kočiško 131 Wioletta Szajnar 168
Marina Solovey 103 Yevgeniy Bodyanskiy 17
Monika Piróg-Mazur 25, 97
8 Methods and Instruments of Artificial Intelligence
I T H E A ® 9

Hybrid Intelligent Systems
FUZZY ARTMAP NEURAL NETWORKS FOR COMPUTER AIDED DIAGNOSIS
Anatoli Nachev
Abstract: The economic and social values of breast cancer diagnosis are very high. This study explores the
predictive abilities of Fuzzy ARTMAP neural networks for breast cancer diagnosis. The data used is
a combination of 39 mammographic, sonographic, and other descriptors, which is novel for the field. By using
feature selection techniques we propose a subset of 21 descriptors that outperform the full feature set and
outperforms the prediction model based on the most popular MLP neural networks. We also explored the model
performance by ROC analysis and used metrics, such as max accuracy, area under the ROC curve, and area
under the convex hull. Due to lack of specificity, many diagnosis tools entail unnecessary surgical biopsies, which
motivated us to explore the clinically relevant metrics partial area under the ROC curve where sensitivity is above
90% and specificity at 98% sensitivity. In conclusion we find that the Fuzzy ARTMAP neural network is
a promising prediction tool for breast cancer diagnosis. To the best of our knowledge, the Fuzzy ARTMAP neural
networks have not been studied in that area until now.
Keywords: data mining, neural networks, Fuzzy ARTMAP, heterogeneous data; breast cancer diagnosis,
computer aided diagnosis
ACM Classification Keywords: I.5.1- Computing Methodologies - Pattern Recognition – Models - Neural Nets
Introduction
Breast cancer ranks first in the causes of cancer deaths among women in developed countries and is second in
developing countries [Parker, 1997], [Lacey et al., 2002]. The best way to reduce deaths due to breast cancer is
to treat the disease at an earlier stage. Earlier treatment requires early diagnosis, and early diagnosis requires an
accurate and reliable diagnostic procedure that allows physicians to differentiate benign from malignant lesions.
The economic and social values of breast cancer diagnosis are very high. Some studies show that only a third of
suspicious masses are determined to be malignant and many surgical biopsies are unnecessary [Jemal et al.,
2005]. Breast cancer diagnosis is a typical machine learning problem for many years. It has been dealt with
using various machine learning algorithms and computer aided detection/diagnosis (CAD) tools. The problem is
nontrivial and difficult to solve as the data set is noisy and relatively small. Techniques which rely on a large
training data set would not work well for this problem.
A considerable amount of research in the area has been done based on rich variety of modalities and sources of
medical information, such as: digitized screen-film mammograms, sonograms, magnetic resonance imaging
(MRI) images, and gene expression profiles, etc. [Jesneck et al. 2006]. Current computerized breast cancer
diagnosis tend to use only one information source, usually mammographic data in the form of descriptors defined
by the Breast Imaging Reporting and Data System (BI-RADS) lexicon [BI-RADS, 2003]. Initially, BI-RADS was
developed for standardization of mammographic descriptors only, but recently the American College of Radiology
included a breast sonography extension, which standardizes various sonographic descriptors of lesions. Jesneck
et al. [2007] have used a specific combination of BI-RADS mammographic and sonographic descriptors and
10 Methods and Instruments of Artificial Intelligence

some proposed by Stavros et al. [1995] to build a predictive model. Their study was pioneering in using such
a combination and they report that predictive abilities of the linear discriminant analysis (LDA) and multi-layer
preceptrons (MPL) are similar in the context of using either all 39 descriptors or a suggested subset of
14 descriptors. MLP have been largely applied to breast cancer diagnosis applications, but they have
a drawback: the model assumes predefined network architecture, including connectivity and node activation
functions, and training algorithm to learn to predict. The issue of designing a near optimal network architecture
can be formulated as a search problem and still remains open.
This paper describes an alternative approach based on Fuzzy ARTMAP neural networks which feature well
established architecture and fast one-pass online learning. To the best of our knowledge Fuzzy ARTMAP neural
networks has not been used to date for breast cancer diagnosis with a combination of mammographic and
sonographic descriptors.
The paper is organized as follows: Section 2 provides an overview of the Fuzzy ARTMAP neural network
architecture used build a predictive model; Section 3 introduces the dataset used in the study, its features, and
preprocessing of data; Section 4 presents and discuses the experimental results; and Section 5 gives the
conclusions.
Fuzzy ARTMAP Neural Networks
ARTMAP systems are based on the Adaptive Resonance Theory (ART) for neural network modeling [Grossberg,
1976]. ARTMAP architectures are neural networks that develop stable recognition codes in real time in response
to arbitrary sequences of input patterns. They were designed to solve the stability-plasticity dilemma that every
intelligent machine learning system has to face: how to keep learning from new events without forgetting
previously learned information. Fuzzy ARTMAP networks were designed to accept real-valued input patterns
[Carpenter et al., 1992]. They learn by either simultaneously establishing suitable categories in both input and
output space (tasks carried out within the so-called ARTa and ARTb modules respectively) or linking input and
output categories according to joint occurrence and predictive success (the linkages being stored in a special unit
called the map field or ARTab (see Figure 1). Modules are made of fields, which consist of neurons (nodes). Each
ART module a comparison layer (F), and a recognition layer (F) with m and n neurons, respectively. All
1 2
categorization and learning are achieved by sequentially modifying the connection weights, between the layers
(black circles in the figure). In Fuzzy ARTMAP systems data can be processed with either natural or complement
coding [Carpenter et al., 1992]: if natural coding is used, a data item is processed as it is, otherwise, it is
augmented with its complement to 1. Thus, if a data sample d is an n-dimensional vector, system actually works
on a 2n-dimensional vector.
The numbers of weights in the ARTa and ARTb modules are system parameters determining the number and
dimension of weights in the ARTab module. During training, a sample and the category label are provided as
input to the ARTa and ARTb modules, which causes an activation to flow from the excited neurons (categories) in
ARTa and ARTb into ARTab and then eventually back to ARTa. During testing a given input vector activates
(predicts) a single category in the ARTb and ARTab modules. Whenever a pattern A activates a layer F1,
it propagates through weighted connections w to layer F2. Activation of each node j in the F2 layer is determined
ij
by the function:
(1)