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Predicting Parkinson's disease evolution using deep learning
Parkinson's disease is a neurological condition that occurs in nearly 1% of the world's population.
Coevolution of Neural Architectures and Features for Stock Market Forecasting: A Multi-objective Decision Perspective
In a multi objective setting, a portfolio manager's highly consequential decisions can benefit from assessing alternative forecasting models of stock index movement.
Mixing Deep Learning and Multiple Criteria Optimization: An Application to Distributed Learning with Multiple Datasets
In the case of labeled data and supervised learning, machine training consists in minimizing the loss function subject to different constraints.
Federated Deep Learning in Electricity Forecasting: An MCDM Approach
In fact, Deep Learning models require plenty of resources, and distributed training is needed.
A Multi-criteria Approach to Evolve Sparse Neural Architectures for Stock Market Forecasting
This study proposes a new framework to evolve efficacious yet parsimonious neural architectures for the movement prediction of stock market indices using technical indicators as inputs.
