activity
20192025
most citedAutomatic Assessment of Alzheimer's Disease Diagnosis Based on Deep Learning Techniques

169 citations · 510 across the 15 of their papers we have counts for

collaborators

15 papers

cs.CV2025

Enhancing Orthopox Image Classification Using Hybrid Machine Learning and Deep Learning Models

Alejandro Puente-Castro, Enrique Fernandez-Blanco, Daniel Rivero +1

Orthopoxvirus infections must be accurately classified from medical pictures for an easy and early diagnosis and epidemic prevention. The necessity for automated and scalable solut…

cs.RO2024★ 1 cited

Genetic Algorithm Based System for Path Planning with Unmanned Aerial Vehicles Swarms in Cell-Grid Environments

Alejandro Puente-Castro, Enrique Fernandez-Blanco, Daniel Rivero

Path Planning methods for autonomously controlling swarms of unmanned aerial vehicles (UAVs) are gaining momentum due to their operational advantages. An increasing number of scena…

cs.LG2024

Harmful algal bloom forecasting. A comparison between stream and batch learning

Andres Molares-Ulloa, Elisabet Rocruz, Daniel Rivero +4

Diarrhetic Shellfish Poisoning (DSP) is a global health threat arising from shellfish contaminated with toxins produced by dinoflagellates. The condition, with its widespread incid…

cs.LG2024★ 15 cited

Hybrid Machine Learning techniques in the management of harmful algal blooms impact

Andres Molares-Ulloa, Daniel Rivero, Jesus Gil Ruiz +2

Harmful algal blooms (HABs) are episodes of high concentrations of algae that are potentially toxic for human consumption. Mollusc farming can be affected by HABs because, as filte…

cs.AI2024★ 6 cited

Machine Learning in management of precautionary closures caused by lipophilic biotoxins

Andres Molares-Ulloa, Enrique Fernandez-Blanco, Alejandro Pazos +1

Mussel farming is one of the most important aquaculture industries. The main risk to mussel farming is harmful algal blooms (HABs), which pose a risk to human consumption. In Galic…

cs.AI2023★ 88 cited

Q-Learning based system for path planning with unmanned aerial vehicles swarms in obstacle environments

Alejandro Puente-Castro, Daniel Rivero, Eurico Pedrosa +3

Path Planning methods for autonomous control of Unmanned Aerial Vehicle (UAV) swarms are on the rise because of all the advantages they bring. There are more and more scenarios whe…