DIGITAL LIBRARY
ON QUANTIFIED EVALUATION OF NOISY DATA IMPACT ON CHILDREN'S MENTAL DEVELOPMENT USING ARTIFICIAL NEURAL NETWORKS
1 Al-Baha University (SAUDI ARABIA)
2 Otto-von-Guericke-University (GERMANY)
3 Telecommunication & Technology Company, Cairo (EGYPT)
About this paper:
Appears in: ICERI2013 Proceedings
Publication year: 2013
Pages: 4069-4077
ISBN: 978-84-616-3847-5
ISSN: 2340-1095
Conference name: 6th International Conference of Education, Research and Innovation
Dates: 18-20 November, 2013
Location: Seville, Spain
Abstract:
The effects of the physical environment includes: noise level, overcrowding, and housing and neighborhood quality. These components of physical environment are very significant for children’s mental development. Furthermore, the physical environment profoundly influences developmental outcomes including academic achievement, cognitive, social and emotional development.

Herein, this piece of research specifically addresses the study of an interdisciplinary challenging problem faced by teachers' accessible classroom activity. Namely, noisy data which considered as main cause of environmental annoyance and it negatively affects the quality of life of a large proportion of the population. In addition, health and cognitive effects, although modest, may be of importance given the number of people increasingly exposed to environmental noise and the chronic nature of exposure. Furthermore, the presented work is motivated by “Evans’ research reveals significant reading delays for children living near airports and exposed to airport noise".

Briefly, the aim of this article is to alert school administrators to the effects of noise on children's cognition, while development of reading skills. The presented study adopts realistic simulation of noisy data phenomenon by using Artificial Neural Networks (ANNs). Accordingly, obtained simulation results quantified the observed hazardous impact on children's mental abilities due to their exposure to noises. By more details, the effect of signal to noise(S/N) ratio is considered to measure quantitatively the improvement of learning performance . In other words, as cleared data provided at children's classrooms , that maximizes S/N ratio. Consequently, such better noisy learning environment results in better mental children's mental development. Interestingly, the analysis of obtained simulation results revealed the relation between environmental learning S/N ratio and learning rate parameter of Artificial neural network.
Keywords:
Artificial neural network modeling, Noisy learning environment, Signal to noise ratio, Children's mental development.