Recent advancements and applications of deep learning in heart failure: Α systematic review
G Petmezas, VE Papageorgiou, V Vassilikos… - Computers in Biology …, 2024 - Elsevier
Background Heart failure (HF), a global health challenge, requires innovative diagnostic and
management approaches. The rapid evolution of deep learning (DL) in healthcare …
management approaches. The rapid evolution of deep learning (DL) in healthcare …
Hierarchical multi-class classification of voice disorders using self-supervised models and glottal features
Previous studies on the automatic classification of voice disorders have mostly investigated
the binary classification task, which aims to distinguish pathological voice from healthy …
the binary classification task, which aims to distinguish pathological voice from healthy …
[PDF][PDF] Dual memory fusion for multimodal speech emotion recognition
Deep learning has been widely used in multi-modal Speech Emotion Recognition (SER) to
learn sentiment-related features by aggregating representations from multiple modes …
learn sentiment-related features by aggregating representations from multiple modes …
Remembering What Is Important: A Factorised Multi-Head Retrieval and Auxiliary Memory Stabilisation Scheme for Human Motion Prediction
Humans exhibit complex motions that vary depending on the activity they are performing, the
interactions they engage in, as well as subject-specific preferences. Therefore, forecasting a …
interactions they engage in, as well as subject-specific preferences. Therefore, forecasting a …
Machine Learning-Enabled Hypertension Screening Through Acoustical Speech Analysis: Model Development and Validation
Hypertension, referred to as the “silent killer” by the World Health Organization, affects over
35% of the global population. Early diagnosis and behavioural interventions have been …
35% of the global population. Early diagnosis and behavioural interventions have been …
[PDF][PDF] 2.8 Paper G: Dual Memory Fusion for Multimodal Speech Emotion Recognition
Deep learning has been widely used in multi-modal Speech Emotion Recognition (SER) to
learn sentiment-related features by aggregating representations from multiple modes …
learn sentiment-related features by aggregating representations from multiple modes …