Public Health

A novel multimodal fusion framework for early diagnosis and accurate classification of COVID-19 patients using X-ray images and speech signal processing techniques.

BACKGROUND AND OBJECTIVE: COVID-19 outbreak has become one of the most challenging problems for human being. It is a communicable disease caused by a new coronavirus strain, which infected over 375 million people already and caused almost 6 million deaths. This paper aims to develop and design a framework for early diagnosis and fast classification of COVID-19 symptoms using multimodal Deep Learni...

Cooperative Downloading for LEO Satellite Networks: A DRL-Based Approach.

In low earth orbit (LEO) satellite-based applications (e.g., remote sensing and surveillance), it is important to efficiently transmit collected data to ground stations (GS). However, LEO satellites' high mobility and resultant insufficient time for downloading make this challenging. In this paper, we propose a deep-reinforcement-learning (DRL)-based cooperative downloading scheme, which utilizes ...

Where Nanosensors Meet Machine Learning: Prospects and Challenges in Detecting Disease X.

Disease X is a hypothetical unknown disease that has the potential to cause an epidemic or pandemic outbreak in the future. Nanosensors are attractive...

Climate change and health in Southeast Asia - defining research priorities and the role of the Wellcome Trust Africa Asia Programmes.

This article summarises a recent virtual meeting organised by the Oxford University Clinical Research Unit in Vietnam on the topic of climate change a...

Anomaly Detection in Traffic Surveillance Videos Using Deep Learning.

In the recent past, a huge number of cameras have been placed in a variety of public and private areas for the purposes of surveillance, the monitorin...

A Novel Method for COVID-19 Detection Based on DCNNs and Hierarchical Structure.

The worldwide outbreak of the new coronavirus disease (COVID-19) has been declared a pandemic by the World Health Organization (WHO). It has a devasta...

A deep learning-based model predicts survival for patients with laryngeal squamous cell carcinoma: a large population-based study.

OBJECTIVES: To assess the performance of DeepSurv, a deep learning-based model in the survival prediction of laryngeal squamous cell carcinoma (LSCC) ...

Using Machine Learning Methods Incorporating Individual Reader Annotations to Classify Paediatric Chest Radiographs in Epidemiological Studies.

: Epidemiological studies that involve interpretation of chest radiographs (CXRs) suffer from inter-reader and intra-reader variability. Inter-reader ...

Deep Convolutional Neural Network Mechanism Assessment of COVID-19 Severity.

As an epidemic, COVID-19's core test instrument still has serious flaws. To improve the present condition, all capabilities and tools available in thi...

Automated COVID-19 Classification Using Heap-Based Optimization with the Deep Transfer Learning Model.

The outbreak of the COVID-19 pandemic necessitates prompt identification of affected persons to restrict the spread of the COVID-19 epidemic. Radiolog...

Prediction of Potential Commercially Available Inhibitors against SARS-CoV-2 by Multi-Task Deep Learning Model.

The outbreak of COVID-19 caused millions of deaths worldwide, and the number of total infections is still rising. It is necessary to identify some pot...

VISOR-NET: Visibility Estimation Based on Deep Ordinal Relative Learning under Discrete-Level Labels.

This paper proposes a novel end-to-end pipeline that uses the ordinal information and relative relation of images for visibility estimation (VISOR-NET...

Identification of missing hierarchical relations in the vaccine ontology using acquired term pairs.

BACKGROUND: The Vaccine Ontology (VO) is a biomedical ontology that standardizes vaccine annotation. Errors in VO will affect a multitude of applicati...

Analysis on COVID-19 Infection Spread Rate during Relief Schemes Using Graph Theory and Deep Learning.

The novel coronavirus 2019 (COVID-19) disease is a pandemic which affects thousands of people throughout the world. It has rapidly spread throughout I...

Food traceability 4.0 as part of the fourth industrial revolution: key enabling technologies.

Food Traceability 4.0 (FT 4.0) is about tracing foods in the era of the fourth industrial revolution (Industry 4.0) with techniques and technologies r...

An End-to-End Human Abnormal Behavior Recognition Framework for Crowds With Mentally Disordered Individuals.

Abnormal or violent behavior by people with mental disorders is common. When individuals with mental disorders exhibit abnormal behavior in public pla...

DAFLNet: Dual Asymmetric Feature Learning Network for COVID-19 Disease Diagnosis in X-Rays.

COVID-19 has become the largest public health event worldwide since its outbreak, and early detection is a prerequisite for effective treatment. Chest...

Natural Language Processing to Improve Prediction of Incident Atrial Fibrillation Using Electronic Health Records.

Background Models predicting atrial fibrillation (AF) risk, such as Cohorts for Heart and Aging Research in Genomic Epidemiology AF (CHARGE-AF), have ...

Novel Coronavirus and Common Pneumonia Detection from CT Scans Using Deep Learning-Based Extracted Features.

COVID-19 which was announced as a pandemic on 11 March 2020, is still infecting millions to date as the vaccines that have been developed do not preve...

An Empirical Analysis of an Optimized Pretrained Deep Learning Model for COVID-19 Diagnosis.

As a result of the COVID-19 outbreak, which has put the world in an unprecedented predicament, thousands of people have died. Data from structured and...