Journal of
Systemics, Cybernetics and Informatics

ISSN: 1690-4524 (Online)

Indexed by
EBSCO, Cabell, DOAJ (Directory of Open Access Journals), Academic Journals Database, and Google Scholar

Listed in
Cabell Directory of Publishing Opportunities and in Ulrich’s Periodical Directory

Re-Published in
(A Community of about 10.000.000 Academics)

Editorial Advisory Board's Chair
William Lesso

Nagib C. Callaos

Sponsored by
The International Institute of
Informatics and Systemics


Editorial Advisory Board

Journal's Reviewers

Description and Aims

Submission of Articles

Areas and Subareas

Information to Contributors

Editorial Peer Review Methodology

Influence of the Training Methods in the Diagnosis of Multiple Sclerosis Using Radial Basis Functions Artificial Neural Networks
Ángel Gutiérrez
(pages: 1-6)

Improving Real Time Motor Skills in Physical Education by Virtual Computerized Technology Training. A Successful Attempt at Teaching Novice Computer Users.
Esther Zaretsky
(pages: 7-13)

Cyber Safety Education in Developing Countries
Rossouw Von Solms, Suné Von Solms
(pages: 14-19)

CRIS: A Rule-Based Approach for Customized Academic Advising
Chung-Wei Yeh
(pages: 20-24)

Competition-Stability Relationship in the Banking Sector
Jelena Titko, Konstantins Kozlovskis, Gulbakhyt Kaliyeva
(pages: 25-31)

Effectiveness and Utility of a Case-Based Model for Delivering Engineering Ethics Professional Development Units
Heidi Ann Hahn
(pages: 32-37)

New Organic Semiconductor Materials Applied in Organic Photovoltaic and Optical Devices
Andre F. S. Guedes, Vilmar P. Guedes, Simone Tartari, Mônica L. Souza, Idaulo J. Cunha
(pages: 38-40)

Implementation of Massive Agent Model Using Repast HPC and GPU
Shinsaku Segawa, Shofuku Kin, Hidenori Kawamura, Keiji Suzuki
(pages: 41-45)

The IT - Information Technology Governance and points to be considered in its practical implementation in Corporations
Altino José Mentzingen de Moraes
(pages: 46-50)

Price System for Water Supply and its Economic Impact Analysis
Jing Zhao, Hongzhen Ni, Genfa Chen, Jifeng Li, Shujun Bao
(pages: 51-57)

The Decision Support in Electricity Generation: A Model of Integrated Parameters and Indicators
Renata Maria Abrantes Baracho, Rogério Amaral Bonatti, Francisco Ricardo A. C. Baracho, Christiano Pessanha, Marina M. Starling Rezende, Cláudio Homero Ferreira Silva
(pages: 58-63)

Network Complexity Measures. An Information-Theoretic Approach.
Matthias Dehmer, Stefan Pickl
(pages: 64-67)

Participation in Information Markets Research: A New Conceptualization and Measurement
Khalid N. Alhayyan
(pages: 68-76)

Urban Stage 2014: Navigating Relationships during a Collaboration between Local Businesses, Nonprofits, a Large University, and a Mid-Sized City
David A. Driskill, Timothy J. Elliot
(pages: 77-83)

The Application of Karl Popper’s Three Worlds Schema to Questions about Information in the Fields of Complexity, Cybernetics, and Informatics
Paul D. Nugent, Richard Montague, Emilio Collar Jr.
(pages: 84-88)

Developing a GIS for Rural School Transportation in Minas Gerais, Brazil
Marcelo Franco Porto, Stanislas Thiéry, Nilson Tadeu Ramos Nunes, Izabela Ribas Vianna de Carvalho, João Fernando Machry Sarubbi, Cristiano Maciel da Silva
(pages: 89-94)





Neural Network for Principal Component Analysis with Applications in Image Compression

Luminita State, Catalina Lucia Cocianu, Vlamos Panayiotis

Classical feature extraction and data projection methods have been extensively investigated in the pattern recognition and exploratory data analysis literature. Feature extraction and multivariate data projection allow avoiding the “curse of dimensionality”, improve the generalization ability of classifiers and significantly reduce the computational requirements of pattern classifiers. During the past decade a large number of artificial neural networks and learning algorithms have been proposed for solving feature extraction problems, most of them being adaptive in nature and well-suited for many real environments where adaptive approach is required. Principal Component Analysis, also called Karhunen-Loeve transform is a well-known statistical method for feature extraction, data compression and multivariate data projection and so far it has been broadly used in a large series of signal and image processing, pattern recognition and data analysis applications.

Full Text