This page gathers all Data Science and Deep Learning applications developed at IFCA by the Advanced Computing and e-Science Group.
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AI4EOSC (Artificial Intelligence for the European Open Science Cloud) delivers an enhanced set of advanced [...]
The goal is to perform a comprehensive study of data anonymization techniques. We present the implementation [...]
FACE (FAIR Computational Epidemiology) is a consortium of the Spanish National Research Council (CSIC), participated by [...]
Federated learning is a data decentralization privacy-preserving technique used to perform machine or deep learning in [...]
This project closely related to the AI4EOSC project. The goal is to support Machine Learning applications for [...]
Project INMUGEN is using deep learning to identify the individual risk of developing severe COVID19 [...]
Proyect BRANYAS is using deep learning to infer profile risks associated to COVID19 for people living [...]
We are using deep learning techniques for image analysis to look for radiomic markers in thoracic [...]
We trained an deep learning model to try to predict COVID incidence using current incidence [...]
We want to understand the relation between wine consumption and the risk of developing alzheimer. For [...]
This usecase intends to link large-scale atmospheric variables with the local variables of interest relying on [...]
This project intends to improve classical Intrusion Detection Systems (IDS) using Deep Learning tools. The idea is [...]
We use use a deep learning classifier to accurately predict water depth (bathymetry) from Sentinel-2 images. The [...]
We use a deep learning classifier to accurately predict water quality (algae) from Sentinel-2 images. The training [...]
The project analyzes the complete corpus of news about COVID19 published by The Conversation (in Spanish) [...]
We train an image classifier to classify events of the DAMIC-M (DArk Matter In CCDs) [...]
We use use a deep learning classifier to accurately detect and monitor forest fires from [...]
We use use a machine learning models to infer water quality variables (nutrients) from surrogate variables [...]
The goal of the project is to prepare a new generation of e-infrastructures that harness latest [...]
We use Deep Learning to upscale the multispectral images of several satellites (Sentinel 2, Landsat 8, [...]
We trained an audio classifier to identify bird species from bird sounds. We used the Xenocanto [...]
The aim of this project is to improve classification speed and accuracy of chest X-ray [...]
We trained an image classifier to identify conus marine snails at species level. The training dataset [...]
We trained an image classifier to identify phytoplankton in collaboration with the Vlaams Instituut voor de [...]
We trained an image classifier to identify seed images in collaboration with Spanish Royal Botanical Garden. As [...]
We used an autoencoder to learn in an unsupervised manner the distribution of species in a [...]
We trained an image classifier to identify zooplankton in collaboration with the Lifewatch project. [...]
We trained an image classifier to identify plants species. In a first iteration we used the [...]
The application of deep learning techniques using convolutional neural networks to the classification of particle collisions [...]