Publications associées à Matrice de confusion

Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium

Kamiar Aminian, Anisoara Ionescu, Abolfazl Soltani, Francesca Salis

Background: Although digital mobility outcomes (DMOs) can be readily calculated from real-world data collected with wearable devices and ad-hoc algorithms, technical validation is still required. The aim of this paper is to comparatively assess and validat ...

BMC2023

The progressive loss of brain network fingerprints in Amyotrophic Lateral Sclerosis predicts clinical impairment

Enrico Amico, Antonella Romano

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterised by functional connectivity alterations in both motor and extra-motor brain regions. Within the framework of network analysis, fingerprinting represents a reliable approach to ...

ELSEVIER SCI LTD2022

Estimating The Degree of Sleepiness by Integrating Articulatory Feature Knowledge In Raw Waveform Based CNNs

Julian David Fritsch, Subrahmanya Pavankumar Dubagunta

Speech-based degree of sleepiness estimation is an emerging research problem. In the literature, this problem has been mainly addressed through modeling of low level of descriptors. This paper investigates an end-to-end approach, where given raw waveform a ...

Idiap2019

Chemical machine learning with kernels: The impact of loss functions

Volkan Cevher, Quoc Tran Dinh, Sandip De, Junhong Lin

Machine learning promises to accelerate materials discovery by allowing computational efficient property predictions from a small number of reference calculations. As a result, the literature has spent a considerable effort in designing representations tha ...

2019

Chemical machine learning with kernels: The key impact of loss functions

Volkan Cevher, Sandip De, Junhong Lin, Van Quang Nguyen

Machine learning promises to accelerate materials discovery by allowing computational efficient property predictions from a small number of reference calculations. As a result, the literature spent a considerable effort in designing representations that ca ...

2018

A Non-linear Filter to Detect Atrial Activation from Intracardiac Electrograms

Jean-Marc Vesin, Sasan Yazdani, Andréa Buttu, Etienne Pruvot

Atrial activation (AA) detection during atrial fibrillation (AF) can be considered as a first step in estimating AF characteristics such as the AF cycle length. The detection of AA from intracardiac electrograms (EGM) remains challenging due to the constan ...

2017

SmarPer: Context-Aware and Automatic Runtime-Permissions for Mobile Devices

Jean-Pierre Hubaux, Kévin Clément Huguenin, Italo Ivan Dacosta Petrocelli, Mohammad Emtiyaz Khan, Joana Catarina Soares Machado, Katarzyna Lucja Olejnik

Permission systems are the main defense that mobile platforms, such as Android and iOS, offer to users to protect their private data from prying apps. However, due to the tension between usability and control, such systems have several limitations that oft ...

IEEE2017

Wearable barometric pressure sensor to improve postural transition recognition of mobility-impaired stroke patients

Kamiar Aminian, Anisoara Ionescu, Fabien Massé

Sit-to-stand and Stand-to-sit transfers (STS) provide relevant information regarding the functional limitation of mobility- impaired patients. The characterization of STS pattern using a single trunk fixed inertial sensor has been proposed as an objective ...

Institute of Electrical and Electronics Engineers2016

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Matrice de confusion

GANDALF: Graph-based transformer and Data Augmentation Active Learning Framework with interpretable features for multi-label chest Xray classification

Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium

The progressive loss of brain network fingerprints in Amyotrophic Lateral Sclerosis predicts clinical impairment

Estimating The Degree of Sleepiness by Integrating Articulatory Feature Knowledge In Raw Waveform Based CNNs

Chemical machine learning with kernels: The impact of loss functions

Chemical machine learning with kernels: The key impact of loss functions

The complexity of error metrics

A Non-linear Filter to Detect Atrial Activation from Intracardiac Electrograms

SmarPer: Context-Aware and Automatic Runtime-Permissions for Mobile Devices

Wearable barometric pressure sensor to improve postural transition recognition of mobility-impaired stroke patients

Graph Chatbot

Chattez avec Graph Search

Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium

Wearable barometric pressure sensor to improve postural transition recognition of mobility-impaired stroke patients

Estimating The Degree of Sleepiness by Integrating Articulatory Feature Knowledge In Raw Waveform Based CNNs

Chemical machine learning with kernels: The key impact of loss functions

The complexity of error metrics

SmarPer: Context-Aware and Automatic Runtime-Permissions for Mobile Devices

GANDALF: Graph-based transformer and Data Augmentation Active Learning Framework with interpretable features for multi-label chest Xray classification

The progressive loss of brain network fingerprints in Amyotrophic Lateral Sclerosis predicts clinical impairment

Chemical machine learning with kernels: The impact of loss functions

A Non-linear Filter to Detect Atrial Activation from Intracardiac Electrograms