Vision-based detection of aircrafts and UAVs

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Detecting digital face manipulation in images and video has attracted extensive attention due to the potential risk to public trust. To counteract the malicious usage of such techniques, deep learning-based deepfake detection methods have been employed and ...

This thesis addresses key challenges in developing adaptive lower-limb exoskeletons to enhance balance, mobility, and user autonomy during daily living activities. It presents a comprehensive exploration of three critical aspects: push recovery during stan ...

Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

Rigid body interactions are fundamental to numerous scientific disciplines, but remain challenging to simulate due to their abrupt nonlinear nature and sensitivity to complex, often unknown environmental factors. These challenges call for adaptable learnin ...

We present a finite elements-neural network approach for the numerical approximation of parametric partial differential equations. The algorithm generates training data from finite element simulations, and uses a data -driven (supervised) feedforward neura ...

This paper introduces MNE-RT, a Python package designed for real-time neural feature extraction from magne-toencephalography (MEG) and electroencephalography (EEG) signals in Brain-Computer Interface (BCI) systems. The package incorporates efficient algori ...

Digital twins are virtual models of physical objects or systems that enable real-time monitoring and analysis. In the ﬁeld of stone masonry buildings, digital twins can be used to assess damage, predict maintenance needs, and opti- mize building performanc ...

Significant damages caused by lightning can be avoided by predicting lightning and taking precautionary measures. Here, a machine learning model is developed to nowcast lightning flashes using dew point temperature, precipitation, wind speed, wind directio ...

In the realm of point cloud scene understanding, particularly in indoor scenes, objects are arranged following human habits, resulting in objects of certain semantics being closely positioned and displaying notable inter-object correlations. This can creat ...

This study investigates Quantum Extreme Learning Machines (QELMs), which enhance classical Extreme Learning Machines (ELMs) by incorporating quantum features. ...

The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) context, one is interested in obtaining real -time and many-query evaluations of parametric ...

Background: Computer-aided detection and diagnosis (CAD) systems can enhance the objectivity of visual inspection with acetic acid (VIA), which is widely used in low- and middle-income countries (LMICs) for cervical cancer detection. VIA's reliance on subj ...

Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing mach ...

Computer vision has made remarkable strides in recent decades, becoming a cornerstone of modern technology with applications ranging from autonomous driving to medical imaging. However, despite these advancements, several core challenges remain, especially ...

The remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...

Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collective behaviour, this study introduces a deep learning model to assess social in ...

Vision-based detection of aircrafts and UAVs

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Assessment framework for deepfake detection in real-world situations

Partial Assistance with Lower-Limb Exoskeletons to Enhance Gait and Balance in Daily Living Activities

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Integrating Physics and Topology in Neural Networks for Learning Rigid Body Dynamics

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Advances on Real Time M/EEG Neural Feature Extraction

Digital Twins of Stone Masonry Buildings for Damage Assessment

Exploring the portability of ML-based lightning nowcasting models

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Harnessing Photon Indistinguishability in Quantum Extreme Learning Machines

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

Barriers and Facilitators to the Preadoption of a Computer-Aided Diagnosis Tool for Cervical Cancer: Qualitative Study on Health Care Providers' Perspectives in Western Cameroon

Machine learning-aided generative molecular design

Improving Human Pose & Shape Estimation with Explicit and Implicit Priors

Towards Trustworthy Deep Learning for Image Reconstruction

Predicting the long-term collective behaviour of fish pairs with deep learning