681 citations · 772 across the 8 of their papers we have counts for
9 papers
REVEL Framework to measure Local Linear Explanations for black-box models: Deep Learning Image Classification case of study
Iván Sevillano-García, Julián Luengo-Martín, Francisco Herrera
Explainable artificial intelligence is proposed to provide explanations for reasoning performed by an Artificial Intelligence. There is no consensus on how to evaluate the quality…
A robust approach for deep neural networks in presence of label noise: relabelling and filtering instances during training
Anabel Gómez-Ríos, Julián Luengo, Francisco Herrera
Deep learning has outperformed other machine learning algorithms in a variety of tasks, and as a result, it is widely used. However, like other machine learning algorithms, deep le…
MULTICAST: MULTI Confirmation-level Alarm SysTem based on CNN and LSTM to mitigate false alarms for handgun detection in video-surveillance
Roberto Olmos, Siham Tabik, Francisco Perez-Hernandez +2
Despite the constant advances in computer vision, integrating modern single-image detectors in real-time handgun alarm systems in video-surveillance is still debatable. Using such…
CI-dataset and DetDSCI methodology for detecting too small and too large critical infrastructures in satellite images: Airports and electrical substations as case study
Francisco Pérez-Hernández, José Rodríguez-Ortega, Yassir Benhammou +2
The detection of critical infrastructures in large territories represented by aerial and satellite images is of high importance in several fields such as in security, anomaly detec…
EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage use case
Natalia Díaz-Rodríguez, Alberto Lamas, Jules Sanchez +7
The latest Deep Learning (DL) models for detection and classification have achieved an unprecedented performance over classical machine learning algorithms. However, DL models are…
MNIST-NET10: A heterogeneous deep networks fusion based on the degree of certainty to reach 0.1 error rate. Ensembles overview and proposal
S. Tabik, R. F. Alvear-Sandoval, M. M. Ruiz +3
Ensemble methods have been widely used for improving the results of the best single classificationmodel. A large body of works have achieved better performance mainly by applying o…
Deep Learning in Video Multi-Object Tracking: A Survey
Gioele Ciaparrone, Francisco Luque Sánchez, Siham Tabik +3
The problem of Multiple Object Tracking (MOT) consists in following the trajectory of different objects in a sequence, usually a video. In recent years, with the rise of Deep Learn…
Deep-Learning Convolutional Neural Networks for scattered shrub detection with Google Earth Imagery
Emilio Guirado, Siham Tabik, Domingo Alcaraz-Segura +2
There is a growing demand for accurate high-resolution land cover maps in many fields, e.g., in land-use planning and biodiversity conservation. Developing such maps has been perfo…
Automatic Handgun Detection Alarm in Videos Using Deep Learning
Roberto Olmos, Siham Tabik, Francisco Herrera
Current surveillance and control systems still require human supervision and intervention. This work presents a novel automatic handgun detection system in videos appropriate for b…