Journal of
Systemics, Cybernetics and Informatics

ISSN: 1690-4524 (Online)

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Editorial Advisory Board's Chair
William Lesso

Nagib C. Callaos

Sponsored by
The International Institute of
Informatics and Systemics

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No Warranty Express or Implied: Why Do We Have So Many Problems With the Computer Systems That Pervade Our Lives?
John W. Coffey
(pages: 1-6)

Can You Hear Me Now? An Innovative Approach to Assess and Build Connections with Online Learner’s
Tina M. Serafini, Risa Blair
(pages: 7-11)

End-to-end Security with Translation
Kevin E. Foltz
(pages: 12-17)

(Assistive) Technology at the Point of Instruction: Barriers and Possibilities
Lorayne Robertson
(pages: 18-24)

Supplementing Multiple Modalities and Universal Design in Learning with Goal-Setting
Russell Jay Hendel
(pages: 25-30)

Experts Informing Experts
Robert Hammond
(pages: 31-35)

Internet of Things – A New Epistemic Object
Rolf Dornberger, Terry Inglese, Safak Korkut
(pages: 36-44)

An Experiment in Interdisciplinary STEM Education: Insights from the Catholic Intellectual Tradition
Fr. Joseph R. Laracy, Thomas Marlowe, Fr. Gerald J. Buonopane
(pages: 45-53)

Big History Understanding of Complexity, Informatics and Cybernetics
John L. Motloch
(pages: 54-60)

Flourishing Organizations
Maria Jakubik
(pages: 61-72)

Pros & Cons of Smart ICT in Some Governmental Applications
Dusan Soltes
(pages: 73-75)

Information Exchange in Vehicles Ad-Hock Networks
Tomas Zelinka
(pages: 76-80)

Living in a Digital World: Improving Skills to Meet the Challenges of Digital Transformation Through Authentic and Game-Based Learning
Margit Scholl, Frauke Fuhrmann
(pages: 81-86)

Psychotherapy via the Internet as a Novel Tool for Clinical Use
Ulrich Sprick
(pages: 87-94)

Technology Intercepts for Cyber Security Applied to Critical Infrastructures
Mario La Manna
(pages: 95-100)

“And Then a Miracle Occurs …” – Engaging the Challenge of Operationalizing Theories of Success in Digital Transformation
Michael Von Kutzschenbach
(pages: 101-105)

Multidisciplinary Learning Extends Communication Skill, and Helps Cross Cultural Understandings: Biomedical Engineering
Shigehiro Hashimoto
(pages: 106-112)

Integrating Teaching, Research and Problem Solving: An Experience in Progress in the Mucuri Valley Region (Brazil)
Leônidas Conceição Barroso
(pages: 113-118)

Meeting Learning Challenges in Product Design Education with and through Additive Manufacturing
William Lavatelli Kempton, Steinar Killi, Andrew Morrison
(pages: 119-129)

Creating and Using Symbolic Mental Structures via Piaget’s Constructivism and Popper’s Three Worlds View with Falsifiability to Achieve Critical Thinking by Students in the Physical Sciences
Matthew E. Edwards
(pages: 130-134)

Creativity in Higher Education: Comparative Genetic Analyses on the Dopaminergic System in Relation to Creativity, Addiction, Schizophrenia in Humans and Non-Human Primates
Bernard Wallner, Sonja Windhager, Katrin Schaefer, Martin Fieder
(pages: 135-142)





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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