Neural Networks
Symbolic and Neural Learning Algorithms: An Experimental Comparison
Machine Learning
Advanced Methods in Neural Computing
Advanced Methods in Neural Computing
Computers and Operations Research - Special issue: Emerging economics
Gold Price, Neural Networks and Genetic Algorithm
Computational Economics
International Journal of Intelligent Systems in Accounting and Finance Management
Forecasting Economic Data with Neural Networks
Computational Economics
RAILWAY PASSENGER TRAFFIC VOLUME PREDICTION BASED ON NEURAL NETWORK
Applied Artificial Intelligence
Computational Statistics & Data Analysis
The use of data mining and neural networks for forecasting stock market returns
Expert Systems with Applications: An International Journal
IEEE Transactions on Neural Networks
A general regression neural network
IEEE Transactions on Neural Networks
Hi-index | 0.03 |
The per capita ecological footprint (EF) is one of the most-widely recognized measures of environmental sustainability. It seeks to quantify the Earth's biological capacity required to support human activity. This study uses three neuro-computational methodologies: multi-layer perceptron neural network (MLP), probabilistic neural network (PNN) and generalized regression neural network (GRNN) to predict and classify the EF of 140 nations. Accuracy indices are used to assess the prediction and classification accuracy of the three methodologies. The study shows that neuro-computational models outperform traditional statistical techniques such as regression analysis and discriminant analysis in predicting and classifying per capita EF due to their robustness and flexibility of modeling algorithms.