Výsledky vyhledávání - "Jimenez, Ana"

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STAL: Spike Threshold Adaptive Learning Encoder for Classification of Pain-Related Biosignal Data

Autor: Hens, Freek, Dehshibi, Mohammad Mahdi, Bagheriye, Leila, Shahsavari, Mahyar, Tajadura-Jiménez, Ana

This paper presents the first application of spiking neural networks (SNNs) for the classification of chronic lower back pain (CLBP) using the EmoPain dataset. Our work has two main contributions. We introduce Spike Threshold Adaptive Learning (STAL)

Externí odkaz: http://arxiv.org/abs/2407.08362

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Artificial Inductive Bias for Synthetic Tabular Data Generation in Data-Scarce Scenarios

Autor: Apellániz, Patricia A., Jiménez, Ana, Galende, Borja Arroyo, Parras, Juan, Zazo, Santiago

While synthetic tabular data generation using Deep Generative Models (DGMs) offers a compelling solution to data scarcity and privacy concerns, their effectiveness relies on substantial training data, often unavailable in real-world applications. Thi

Externí odkaz: http://arxiv.org/abs/2407.03080

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L-SFAN: Lightweight Spatially-focused Attention Network for Pain Behavior Detection

Autor: Ortigoso-Narro, Jorge, Diaz-de-Maria, Fernando, Dehshibi, Mohammad Mahdi, Tajadura-Jiménez, Ana

Chronic Low Back Pain (CLBP) afflicts millions globally, significantly impacting individuals' well-being and imposing economic burdens on healthcare systems. While artificial intelligence (AI) and deep learning offer promising avenues for analyzing p

Externí odkaz: http://arxiv.org/abs/2406.16913

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Synthetic Tabular Data Validation: A Divergence-Based Approach

Autor: Apellániz, Patricia A., Jiménez, Ana, Galende, Borja Arroyo, Parras, Juan, Zazo, Santiago

The ever-increasing use of generative models in various fields where tabular data is used highlights the need for robust and standardized validation metrics to assess the similarity between real and synthetic data. Current methods lack a unified fram

Externí odkaz: http://arxiv.org/abs/2405.07822

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Pain level and pain-related behaviour classification using GRU-based sparsely-connected RNNs

Autor: Dehshibi, Mohammad Mahdi, Olugbade, Temitayo, Diaz-de-Maria, Fernando, Bianchi-Berthouze, Nadia, Tajadura-Jiménez, Ana

Publikováno v: IEEE Journal of Selected Topics in Signal Processing, 17 (3), 2023, 677-688

There is a growing body of studies on applying deep learning to biometrics analysis. Certain circumstances, however, could impair the objective measures and accuracy of the proposed biometric data analysis methods. For instance, people with chronic p

Externí odkaz: http://arxiv.org/abs/2212.14806

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