Artificial intelligence and internet of things (AI-IoT) technologies in response to COVID-19 pandemic: A systematic review
The origin of the COVID-19 pandemic has given overture to redirection, as well as
innovation to many digital technologies. Even after the progression of vaccination efforts …
innovation to many digital technologies. Even after the progression of vaccination efforts …
AI-Based human audio processing for COVID-19: A comprehensive overview
Abstract The Coronavirus (COVID-19) pandemic impelled several research efforts, from
collecting COVID-19 patients' data to screening them for virus detection. Some COVID-19 …
collecting COVID-19 patients' data to screening them for virus detection. Some COVID-19 …
COVID-19 cough classification using machine learning and global smartphone recordings
We present a machine learning based COVID-19 cough classifier which can discriminate
COVID-19 positive coughs from both COVID-19 negative and healthy coughs recorded on a …
COVID-19 positive coughs from both COVID-19 negative and healthy coughs recorded on a …
COVID-19 detection in cough, breath and speech using deep transfer learning and bottleneck features
We present an experimental investigation into the effectiveness of transfer learning and
bottleneck feature extraction in detecting COVID-19 from audio recordings of cough, breath …
bottleneck feature extraction in detecting COVID-19 from audio recordings of cough, breath …
The voice of COVID-19: Breath and cough recording classification with temporal decision trees and random forests
Symbolic learning is the logic-based approach to machine learning, and its mission is to
provide algorithms and methodologies to extract logical information from data and express it …
provide algorithms and methodologies to extract logical information from data and express it …
Self-supervised contrastive learning for medical time series: A systematic review
Medical time series are sequential data collected over time that measures health-related
signals, such as electroencephalography (EEG), electrocardiography (ECG), and intensive …
signals, such as electroencephalography (EEG), electrocardiography (ECG), and intensive …
Coswara: A respiratory sounds and symptoms dataset for remote screening of SARS-CoV-2 infection
This paper presents the Coswara dataset, a dataset containing diverse set of respiratory
sounds and rich meta-data, recorded between April-2020 and February-2022 from 2635 …
sounds and rich meta-data, recorded between April-2020 and February-2022 from 2635 …
A deep ensemble neural network with attention mechanisms for lung abnormality classification using audio inputs
Medical audio classification for lung abnormality diagnosis is a challenging problem owing
to comparatively unstructured audio signals present in the respiratory sound clips. To tackle …
to comparatively unstructured audio signals present in the respiratory sound clips. To tackle …
Deep learning and machine learning-based voice analysis for the detection of COVID-19: A proposal and comparison of architectures
G Costantini, C Robotti, M Benazzo… - Knowledge-Based …, 2022 - Elsevier
Alongside the currently used nasal swab testing, the COVID-19 pandemic situation would
gain noticeable advantages from low-cost tests that are available at any-time, anywhere, at a …
gain noticeable advantages from low-cost tests that are available at any-time, anywhere, at a …
Significance of voiced and unvoiced speech segments for the detection of common cold
This work investigates the significance of the voiced and unvoiced region for detecting
common cold from the speech signal. In literature, the entire speech signal is processed to …
common cold from the speech signal. In literature, the entire speech signal is processed to …