Critical Care

Latest AI and machine learning research in critical care for healthcare professionals.

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Research on Named Entity Recognition Based on Multi-Task Learning and Biaffine Mechanism.

Commonly used nested entity recognition methods are span-based entity recognition methods, which foc...

MSFR-Net: Multi-modality and single-modality feature recalibration network for brain tumor segmentation.

BACKGROUND: Accurate and automated brain tumor segmentation from multi-modality MR images plays a si...

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 cond...

Integration of Multi-Head Self-Attention and Convolution for Person Re-Identification.

Person re-identification is essential to intelligent video analytics, whose results affect downstrea...

Swin-MFA: A Multi-Modal Fusion Attention Network Based on Swin-Transformer for Low-Light Image Human Segmentation.

In recent years, image segmentation based on deep learning has been widely used in medical imaging, ...

Detecting and Classifying Nuclei Using Multi-Scale Fully Convolutional Network.

The detection and classification of nuclei play an important role in the histopathological analysis....

A Deep Q-Network-Based Algorithm for Multi-Connectivity Optimization in Heterogeneous Cellular-Networks.

The use of multi-connectivity has become a useful tool to manage the traffic in heterogeneous cellul...

Device-Free Multi-Location Human Activity Recognition Using Deep Complex Network.

Wi-Fi-based human activity recognition has attracted broad attention for its advantages, which inclu...

Simultaneously exploring multi-scale and asymmetric EEG features for emotion recognition.

In recent years, emotion recognition based on electroencephalography (EEG) has received growing inte...

Interstitial lung disease detection using template matching combined sparse coding and blended multi class support vector machine.

Interstitial lung disease (ILD), representing a collection of disorders, is considered to be the dea...

Exploring machine learning for audio-based respiratory condition screening: A concise review of databases, methods, and open issues.

Auscultation plays an important role in the clinic, and the research community has been exploring ma...

RIS-Assisted Multi-Antenna AmBC Signal Detection Using Deep Reinforcement Learning.

Signal detection is one of the most critical and challenging issues in ambient backscatter communica...

Multi-Model Running Latency Optimization in an Edge Computing Paradigm.

Recent advances in both lightweight deep learning algorithms and edge computing increasingly enable ...

Application of Machine Learning in Hospitalized Patients with Severe COVID-19 Treated with Tocilizumab.

Among the IL-6 inhibitors, tocilizumab is the most widely used therapeutic option in patients with S...

Acoustic scene classification based on three-dimensional multi-channel feature-correlated deep learning networks.

As an effective approach to perceive environments, acoustic scene classification (ASC) has received ...

A systematic review of deep learning methods for modeling electrocardiograms during sleep.

Sleep is one of the most important human physiological activities, and plays an essential role in hu...

Multi-label classification of fundus images with graph convolutional network and LightGBM.

Early detection and treatment of retinal disorders are critical for avoiding irreversible visual imp...

The role of deep learning in urban water management: A critical review.

Deep learning techniques and algorithms are emerging as a disruptive technology with the potential t...

Dynamic Sepsis Prediction for Intensive Care Unit Patients Using XGBoost-Based Model With Novel Time-Dependent Features.

Sepsis is a systemic inflammatory response caused by pathogens such as bacteria. Because its pathoge...

Adaptive Multi-Modal Fusion Framework for Activity Monitoring of People With Mobility Disability.

The development of activity recognition based on multi-modal data makes it possible to reduce human ...

Multi-Scale Convolutional Neural Network Ensemble for Multi-Class Arrhythmia Classification.

The automated analysis of electrocardiogram (ECG) signals plays a crucial role in the early diagnosi...

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