Simulating the Poverty Risk Threshold in France

Constantin Ilie

ISBN 10: 6139947170 / ISBN 13: 9786139947171

Published by LAP LAMBERT Academic Publishing Jan 2019, 2019

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -This book aims at researching the possibility of predicting the poverty risk threshold in France, considering the sustainable development indicators defined and registered by the European Union, and using a technique from the artificial intelligence field, i.e. artificial neural networks (ANNs). The main objective is to determine a more efficient economic method, which can be easily applied, in order to simulate the influence of sustainable development indicators on the poverty risk threshold, and to explore the possibility of predicting the latter. Thus, we could assess the poverty risk and establish policies and strategies in order to lower this risk, through actions that eliminate the causes of those sustainable development indicators which negatively influence the poverty risk threshold. The authors' research and contributions demonstrated the possibility of using the ANNs for modeling and simulation, in order to predict the poverty risk threshold in France, with results that can be used and applied in risk management. 52 pp. Englisch.

Seller Inventory # 9786139947171

Bibliographic Details

• Title: Simulating the Poverty Risk Threshold in France
• Publisher: LAP LAMBERT Academic Publishing Jan 2019
• Publication Date: 2019
• Language: English
• ISBN 10: 6139947170
• ISBN 13: 9786139947171
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 4D millimeters 220 millimeters width by 150 millimeters height by 4 millimeters depth
• Weight: 96 grams

About this title

Synopsis

This book aims at researching the possibility of predicting the poverty risk threshold in France, considering the sustainable development indicators defined and registered by the European Union, and using a technique from the artificial intelligence field, i.e. artificial neural networks (ANNs). The main objective is to determine a more efficient economic method, which can be easily applied, in order to simulate the influence of sustainable development indicators on the poverty risk threshold, and to explore the possibility of predicting the latter. Thus, we could assess the poverty risk and establish policies and strategies in order to lower this risk, through actions that eliminate the causes of those sustainable development indicators which negatively influence the poverty risk threshold. The authors’ research and contributions demonstrated the possibility of using the ANNs for modeling and simulation, in order to predict the poverty risk threshold in France, with results that can be used and applied in risk management.