Journal of
Systemics, Cybernetics and Informatics
 



ISSN: 1690-4524 (Online)


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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
 

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Behavior of Cell in Uniform Shear Flow Field between Rotating Cone and Stationary Plate
Shigehiro Hashimoto, Hiromi Sugimoto, Haruka Hino
(pages: 1-7)

Teachers Continuing Professional Development: Trends in European Countries. Towards Teachers' Professionalism
Liliana Budkowska, Pawel Poszytek
(pages: 8-12)

Play, Connect and Learn: Using Mobile Phones to Improve Early Grade Reading Skills at Home
Ira Joshi
(pages: 13-16)

May Parental Reading Behavior Explain the Gender Differences in Subteeners’ Reading Attitude?
Aniko Joó, Erzsébet Dani
(pages: 17-22)

Leadership and Literacy Processes in School Improvement Creating and Supporting a Community of Success: A Case Study Examining the Principal’s Role in the Reconstitution of a Campus to Transform Literacy and Learning
W. Todd Duncan, Lisa E. Colvin
(pages: 23-28)

Rwandan Collaborative Model for Educator Capacity Building
Andrew Moore, Vincentie Nyangoma, Jaco Du Toit, Peter Wallet, Pascal Rukundo
(pages: 29-35)

Do You Know Where Your Students Are? Digital Supervision and Digital Privacy in Schools
Lorayne Robertson, Laurie Corrigan
(pages: 36-42)

A General Case Study of Complexity Science: Analytical and Logical Interconnection Between Soft and Hard Sciences (Invited Paper)
Jack Jia-Sheng Huang, Yu-Heng Jan
(pages: 43-48)

Novel Application of Immobilized Bacillus Cells for Biotreatment of Furfural-Laden Wastewater
Haneen A. Khudhair, Zainab Z. Ismail
(pages: 49-54)

Reliable Sub-Nanosecond Switching of a Perpendicular SOT-MRAM Cell without External Magnetic Field
Viktor Sverdlov, Alexander Makarov, Siegfried Selberherr
(pages: 55-59)

The Methodology and Implementation of Unique Technology Focused Entrepreneurship/Intrepreneurship Programs
Stephen A. Szygenda, Diana M. Easton
(pages: 60-66)

Intelligent Fault Pattern Recognition of Aerial Photovoltaic Module Images Based on Deep Learning Technique
Xiaoxia Li, Qiang Yang, Wenjun Yan, Zhebo Chen
(pages: 67-71)

Real-Time Implementation of Model Predictive Control in a Low-Cost Embedded Device
John Espinoza, Jorge Buele, Esteban X. Castellanos, Marco Pilatásig, Paulina Ayala, Marcelo V. García
(pages: 72-77)

Real-Time Sentimental Polarity Classification on Live Social-Media
Khalid N. Alhayyan, Imran Ahmad
(pages: 78-84)

Information Modeling and Information Retrieval for the Internet of things (IoT) in Buildings
Renata Baracho, Izabella Cunha, Mário Lúcio Pereira Junior
(pages: 85-91)


 

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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