Hospital-Based Medicine

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Latest AI and machine learning research in intensivists for healthcare professionals.

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Showing 1471-1491 of 6,177 articles
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 ...

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 ...

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...

A graph-based approach to multi-source heterogeneous information fusion in stock market.

The stock market is an important part of the capital market, and the research on the price fluctuati...

A benchmark study of deep learning-based multi-omics data fusion methods for cancer.

BACKGROUND: A fused method using a combination of multi-omics data enables a comprehensive study of ...

A novel approach for COVID-19 Infection forecasting based on multi-source deep transfer learning.

COVID-19 is a contagious disease; so, predicting its future infections in a provincial region requir...

I-TASSER-MTD: a deep-learning-based platform for multi-domain protein structure and function prediction.

Most proteins in cells are composed of multiple folding units (or domains) to perform complex functi...

Explainable Artificial Intelligence Helps in Understanding the Effect of Fibronectin on Survival of Sepsis.

Fibronectin (FN) plays an essential role in the host's response to infection. In previous studies, a...

Comparative Convolutional Dynamic Multi-Attention Recommendation Model.

Recently, an attention mechanism has been used to help recommender systems grasp user interests more...

Predictions on multi-class terminal ballistics datasets using conditional Generative Adversarial Networks.

Ballistic impacts are a primary risk in both civil and military defence applications, where successf...

A new fuzzy rule based multi-objective optimization method for cross-scale injection molding of protein electrophoresis microfluidic chips.

Injection molding is one of the most promising technologies for the large-scale production and appli...

Prediction algorithm for ICU mortality and length of stay using machine learning.

Machine learning can predict outcomes and determine variables contributing to precise prediction, an...

A Two-Stream Graph Convolutional Network Based on Brain Connectivity for Anesthetized States Analysis.

Investigating neural mechanisms of anesthesia process and developing efficient anesthetized state de...

Evaluating Ensemble Learning Methods for Multi-Modal Emotion Recognition Using Sensor Data Fusion.

Automatic recognition of human emotions is not a trivial process. There are many factors affecting e...

Towards in vivo ground truth susceptibility for single-orientation deep learning QSM: A multi-orientation gradient-echo MRI dataset.

Recently, deep neural networks have shown great potential for solving dipole inversion of quantitati...

An Efficient Multi-Scale Convolutional Neural Network Based Multi-Class Brain MRI Classification for SaMD.

A brain tumor is the growth of abnormal cells in certain brain tissues with a high mortality rate; t...

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