Journal of
Systemics, Cybernetics and Informatics
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ISSN: 1690-4524 (Online)


Peer Reviewed Journal via three different mandatory reviewing processes, since 2006, and, from September 2020, a fourth mandatory peer-editing has been added.

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Published by
The International Institute of Informatics and Cybernetics


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Academia.edu
(A Community of about 40.000.000 Academics)


Honorary Editorial Advisory Board's Chair
William Lesso (1931-2015)

Editor-in-Chief
Nagib C. Callaos


Sponsored by
The International Institute of
Informatics and Systemics

www.iiis.org
 

Editorial Advisory Board

Quality Assurance

Editors

Journal's Reviewers
Call for Special Articles
 

Description and Aims

Submission of Articles

Areas and Subareas

Information to Contributors

Editorial Peer Review Methodology

Integrating Reviewing Processes


Smart Cities: Challenges and Opportunities
Mohammad Ilyas
(pages: 1-6)

Bridging the Gap: Communicating to Increase the Visibility and Impact of Your Academic Work
Erin Ryan
(pages: 7-12)

Cross-Cultural Online Networking Based on Biomedical Engineering to Motivate Transdisciplinary Communication Skills
Shigehiro Hashimoto
(pages: 13-17)

Interdisciplinary Approaches to Learning Informatics
Masaaki Kunigami
(pages: 18-22)

The Impact of Artificial Intelligence and the Importance of Transdisciplinary Research
R. Cherinka, J. Prezzama, P. O'Leary
(pages: 23-28)

Emotional Communication as Complex Phenomenon in Musical Interpretation – Proposal for a Systemic Model That Promotes a Transdisciplinary Process of Self-Formation and Reflection Around Expressiveness as a Lived Experience
Fuensanta Fernández de Velazco, Eduardo Carpinteyro-Lara, Saúl Rodríguez-Luna
(pages: 29-33)

A Multi-Disciplinary Cybernetic Approach to Pedagogic Excellence
Russell Jay Hendel
(pages: 34-41)

The Ethics of Artificial Intelligence in the Era of Generative AI
Vassilka D. Kirova, Cyril S. Ku, Joseph R. Laracy, Thomas J. Marlowe
(pages: 42-50)

Trans-Disciplinary Communication: Context and Semantics
Maurício Vieira Kritz
(pages: 51-57)

A Brave New World: AI as a Nascent Regime?
Jasmin Cowin, Birgit Oberer, Cristo Leon
(pages: 58-66)

The Role of Art and Science – Relational Dynamics in Human Ecology
Giorgio Pizziolo, Rita Micarelli
(pages: 67-75)

Advancing Entrepreneurship Education: An Integrated Approach to Empowering Future Innovators
Birgit Oberer, Alptekin Erkollar
(pages: 76-81)

Harmonizing Horizons: The Symphony of Human-Machine Collaboration in the Age of AI
Birgit Oberer, Alptekin Erkollar
(pages: 82-86)

How Do Students Learn Artificial Intelligence in Interdisciplinary Field of Biomedical Engineering?
Shigehiro Hashimoto
(pages: 87-91)

What is ChatGPT and its Present and Future for Artificial Intelligence in Trans-Disciplinary Communications?
Richard Segall
(pages: 92-98)


 

Abstracts

 


ABSTRACT


Influence of the Training Methods in the Diagnosis of Multiple Sclerosis Using Radial Basis Functions Artificial Neural Networks

Ángel Gutiérrez


The data available in the average clinical study of a disease is very often small. This is one of the main obstacles in the application of neural networks to the classification of biological signals used for diagnosing diseases. A rule of thumb states that the number of parameters (weights) that can be used for training a neural network should be around 15% of the available data, to avoid overlearning. This condition puts a limit on the dimension of the input space.

Different authors have used different approaches to solve this problem, like eliminating redundancy in the data, preprocessing the data to find centers for the radial basis functions, or extracting a small number of features that were used as inputs. It is clear that the classification would be better the more features we could feed into the network.

The approach utilized in this paper is incrementing the number of training elements with randomly expanding training sets. This way the number of original signals does not constraint the dimension of the input set in the radial basis network. Then we train the network using the method that minimizes the error function using the gradient descent algorithm and the method that uses the particle swarm optimization technique.

A comparison between the two methods showed that for the same number of iterations on both methods, the particle swarm optimization was faster, it was learning to recognize only the sick people. On the other hand, the gradient method was not as good in general better at identifying those people.

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