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Crowding

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Modular hierarchical reinforcement learning for multi-destination navigation in hybrid crowds.

Neural networks : the official journal of the International Neural Network Society
Real-world robot applications usually require navigating agents to face multiple destinations. Besides, the real-world crowded environments usually contain dynamic and static crowds that implicitly interact with each other during navigation. To addre...

Managing low-acuity patients in an Emergency Department through simulation-based multiobjective optimization using a neural network metamodel.

Health care management science
This paper deals with Emergency Department (ED) fast-tracks for low-acuity patients, a strategy often adopted to reduce ED overcrowding. We focus on optimizing resource allocation in minor injuries units, which are theĀ ED units that can treat low-acu...

A study on deep learning model based on global-local structure for crowd flow prediction.

Scientific reports
Crowd flow prediction has been studied for a variety of purposes, ranging from the private sector such as location selection of stores according to the characteristics of commercial districts and customer-tailored marketing to the public sector for s...

Exploring Hospital Overcrowding with an Explainable Time-to-Event Machine Learning Approach.

Studies in health technology and informatics
Emergency department (ED) overcrowding is a complex problem that is intricately linked with the operations of other hospital departments. Leveraging ED real-world production data provides a unique opportunity to comprehend this multifaceted problem h...

Optimized deep maxout for crowd anomaly detection: A hybrid optimization-based model.

Network (Bristol, England)
Monitoring Surveillance video is really time-consuming, and the complexity of typical crowd behaviour in crowded situations makes this even more challenging. This has sparked a curiosity about computer vision-based anomaly detection. This study intro...

Crowd Counting Using Meta-Test-Time Adaptation.

International journal of neural systems
Machine learning algorithms are commonly used for quickly and efficiently counting people from a crowd. Test-time adaptation methods for crowd counting adjust model parameters and employ additional data augmentation to better adapt the model to the s...

Enhanced forecasting of emergency department patient arrivals using feature engineering approach and machine learning.

BMC medical informatics and decision making
BACKGROUND: Emergency department (ED) overcrowding is an important problem in many countries. Accurate predictions of ED patient arrivals can help management to better allocate staff and medical resources. In this study, we investigate the use of cal...

Predicting emergency department admissions using a machine-learning algorithm: a proof of concept with retrospective study.

BMC emergency medicine
INTRODUCTION: Overcrowding in emergency departments (ED) is a major public health issue, leading to increased workload and exhaustion for the teams, resulting poor outcomes. It seems interesting to be able to predict the admissions of patients in the...

Does surface completion fail to support uncrowding?

Journal of vision
In crowding, perception of a target deteriorates in the presence of nearby elements. As the entire stimulus configuration across large parts of the visual field influences crowding and not just nearby elements, low-level explanations, such as local p...