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Found 10 papers, 2 papers with code
Harmful algal bloom forecasting. A comparison between stream and batch learning
High biomass of toxin-producing algae such as DSP are known as Harmful Algal Blooms (HABs).
Machine Learning in management of precautionary closures caused by lipophilic biotoxins
The main risk to mussel farming is harmful algal blooms (HABs), which pose a risk to human consumption.
Hybrid Machine Learning techniques in the management of harmful algal blooms impact
Harmful algal blooms (HABs) are episodes of high concentrations of algae that are potentially toxic for human consumption.
Q-Learning based system for path planning with unmanned aerial vehicles swarms in obstacle environments
Regardless of the size of the map or the number of UAVs in the swarm, the goal of these paths is to ensure complete coverage of an area with fixed obstacles for tasks, like field prospecting.
Ensemble of Convolution Neural Networks on Heterogeneous Signals for Sleep Stage Scoring
Over the years, several approaches have tried to tackle the problem of performing an automatic scoring of the sleeping stages.
Convolutional Neural Networks for Sleep Stage Scoring on a Two-Channel EEG Signal
This process is carried out manually, which can be highly time-consuming and very prone to annotation errors.
A New Deterministic Technique for Symbolic Regression
This paper describes a new method for Symbolic Regression that allows to find mathematical expressions from a dataset.
Classical Music Prediction and Composition by means of Variational Autoencoders
However, results show that the system is able to return accurate representations and predictions in unseen data.
Classification of Two-channel Signals by Means of Genetic Programming
Traditionally, signal classification is a process in which previous knowledge of the signals is needed.
A Hybrid Evolutionary System for Automated Artificial Neural Networks Generation and Simplification in Biomedical Applications
One of the main problems in the development of this technique is the slow performance of the full process.
