Authors:
Boutheina Amina Aoun
1
;
Hend Bouziri
1
and
Zouhour Neji Ben Salem
2
Affiliations:
1
Higher Institute of Management University, Tunisia
;
2
National School of Computer Sciences, Tunisia
Keyword(s):
Microscopic Evacuation, Evacuee Characteristics, Itinerary, Artificial Neural Networks, Hopfield Networks.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Manipulation
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Intelligent Artificial Perception and Neural Sensors
;
Methodologies and Methods
;
Neural Computation Issues in Social Behaviour Emergence
;
Neural Network Software and Applications
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
The problem of evacuation raised a lot of interest as its objective of saving lives is of an extreme importance. In this context, many researches supplied solutions allowing to plan the process of evacuation in case of disaster. Certain solutions took into account the behavior of the crowd, while others treated the evacuees in an independent way. For that purpose, we dedicate our study to this last type of evacuation, namely the microscopic evacuation. Our approach is based on the artificial neural networks which we considered capable of generating
a human behavior thanks to their neuronal aspect. We proposed a solution capable of planning a microscopic evacuation of building by having recourse to Hopfield neural networks. We supplied an experimental study on the real cases of two hospitals. This study also brought a comparison of our model with another neuronal model for evacuation which is the self organizing map.