Please use this identifier to cite or link to this item:
|Title: ||An Assessment to Toxicological Risk of Pesticide Exposure|
|Authors: ||Coelho, Cristina|
Martins, M. Rosário
|Editors: ||Li, Hongxiu|
|Keywords: ||Pesticide Exposure|
Knowledge Representation and Reasoning
Artificial Neuronal Networks
|Issue Date: ||2016|
|Publisher: ||Springer International Publishing|
|Citation: ||Coelho, C., Martins, M.R., Lima, N., Vicente, H. & Neves, J., An Assessment to Toxicological Risk of Pesticide Exposure. In H. Li, P.
Nykänen, R. Suomi, N. Wickramasinghe, G. Widén & M. Zhan, Eds., Building Sustainable Health Eco-systems, Communications in Computer and Information Science, Vol. 636, pp. 139-150. Springer International Publishing, Cham, Switzerland, 2016.|
|Abstract: ||On the one hand, pesticides may be absorbed into the body orally, dermally, ocularly and by inhalation and the human exposure may be dietary, recreational and/or occupational where toxicity could be acute or chronic. On the other hand, the environmental fate and toxicity of the pesticide is contingent on the physico-chemical characteristics of pesticide, the soil composition and adsorption. Human toxicity is also dependent on the exposure time and individual’s susceptibility. Therefore, this work will focus on the development of an Artificial Intelligence based diagnosis support system to assess the pesticide toxicological risk to humanoid, built under a formal framework based on Logic Programming to knowledge representation and reasoning, complemented with an approach to computing grounded on Artificial Neural Networks. The proposed solution is unique in itself, once it caters for the explicit treatment of incomplete, unknown, or even self-contradictory information, either in terms of a qualitative or quantitative setting.|
|Appears in Collections:||HERCULES - Publicações - Capítulos de Livros|
QUI - Publicações - Capítulos de Livros
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.