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Caillault, E. (2005) Architecture and Learning of a Neuro-Markovian Hybrid System for Handwriting Recognition. Signal and Image Processing, 2005, University of Nantes.

has been cited by the following article:

• TITLE: Improving Resilience Models of Health Systems before COVID-19 Pandemic in Côte d’Ivoire

  AUTHORS: Gérard B. N’Guessan, Ida Brou Assie, Jean S. Inkpé Haudie

  KEYWORDS: Resilience, Corona Virus, Bayesian Network, Neural Network

  JOURNAL NAME: Journal of Computer and Communications, Vol.11 No.2, February 16, 2023

  ABSTRACT: The resilience of health systems is a major issue and challenge in patients’ safety. This security refers to the ability to adapt, approach, anticipate or change after a shock. These shocks generally concern large-scale situations such as the COVID-19 pandemic. To cope with these shocks, certain risk management tools have appeared in healthcare centres. Among these new tools are those called “resilience engineering”. However, failure to master some of them sometimes generates erroneous results that lead to even more serious consequences in decision-making. This article proposes an Ivorian resilience model for managing the coronavirus pandemic. The model is the combination of Bayesian and neural techniques with multilayer perceptron in order to best assist Ivorian specialists in their decision-making.