Journal of
Systemics, Cybernetics and Informatics
HOME  |   CURRENT ISSUE   |   PAST ISSUES   |   RELATED PUBLICATIONS     IIIS   |   SEARCH     CONTACT US
 


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
Academia.edu
(A Community of about 40.000.000 Academics)


Editorial Advisory Board's Chair
William Lesso

Editor-in-Chief
Nagib C. Callaos


Sponsored by
The International Institute of
Informatics and Systemics

www.iiis.org

 

Editorial Advisory Board

Journal's Reviewers
 

Description and Aims

Submission of Articles

Areas and Subareas

Information to Contributors

Editorial Peer Review Methodology


Culture Propels the Intersection of Ethos, Pathos, and Logos with Innovation and Entrepreneurship
Marta Szabo White
(pages: 1-6)

INTUITEL and the Hypercube Model - Developing Adaptive Learning Environments
Kevin Fuchs, Peter A. Henning, Mutfried Hartmann
(pages: 7-11)

Baseline Study: Online Oil, Gas and Safety Technology Education for Ohio Residents of a Community Based Corrections Facility
George Ash, Alexis Benner
(pages: 12-13)

Causal Bayes Model of Mathematical Competence in Kindergarten
Božidar Tepeš, Gordana Lešin, Ana Hrkac, Krunoslav Tepeš
(pages: 14-17)

Change Requires Change! Information Technology, Student Preparedness and Industry Collaboration: Supporting the Bridging Process between Education and Training with Innovative Solutions
Jill Anne O’Sullivan
(pages: 18-21)

Virtual Learning Environment for Entrepreneurship: A Conceptual Model
Douglas Sparkes, Karin Schmidlin, Mark Hsu
(pages: 22-24)

Student Teachers’ Modeling of Acceleration Using a Video-Based Laboratory in Physics Education: A Multimodal Case Study
Louis Trudel, Abdeljalil Métioui, Gilbert Arbez
(pages: 25-30)

Effectiveness of a Constructivistic Multimedia-Learning Package on Shaping and Guiding Students’ Attitudes Toward Physics
Divya C. Senan, Matthew E. Edwards, Salam Khan, Asha J. Vilasini
(pages: 31-37)

A Preliminary Study of Pelletized Ecuadorian Cocoa Pod Husk for its Use as a Source of Renewable Energy
Luis Velázquez-Araque, José Cárdenas
(pages: 38-42)

Combination of Bayesian and Latent Semantic Analysis with Domain Specific Knowledge
Shen Lu, Richard S. Segall
(pages: 43-50)

Impact of Optimization and Parallelism on Factorization Speed of SIQS
Dominik Breitenbacher, Ivan Homoliak, Jiri Jaros, Petr Hanacek
(pages: 51-58)

Searching the Web for Earth Science Data: Semiotics to Cybernetics and Back
Bruce R. Barkstrom
(pages: 59-63)

Hybrid Optical Devices: The Case of the Unification of the Electrochromic Device and the Organic Solar Cell
Andre F. S. Guedes, Vilmar P. Guedes, Simone Tartari, Mônica L. Souza, Idaulo J. Cunha
(pages: 64-67)

An Organizational-Technical Concept to Deal with Open Source Software License Terms
Sergius Dyck, Daniel Haferkorn, Jennifer Sander
(pages: 68-73)

Implementing a Hybrid Graduate Program: Lessons Learned One Year Later
Ronda Sturgill, Jacob Wilson, J. C. Andersen
(pages: 74-76)

Creating the Economy of Virtuality: Systemic Aspects and Educational Considerations
Julio Rezende
(pages: 77-83)

Educational Software for the Teaching and Learning of Quadrilaterals Generated from a Programming Language and the Dabeja Method (Invited Paper)
Daniel Bejarano Segura, Piedad Chica Sosa
(pages: 84-89)


 

Abstracts

 


ABSTRACT


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