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Nov 10, 2020 · In this work, we use machine learning algorithms such as Random Forest (RF), IBK, Bagging, J48 and MLP on WISDM Smartphone and Smartwatch Activity and ...
To detect an emergency, Human Activity Recognition (HAR) can play an important role. Human activities such as shouting, running here and there, crying, ...
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The purpose of this study was to design an effective, exploratory model using machine learning (ML) technology to predict LoS for patients presenting with ...
Conclusion These early results show that a Machine Learning model based on neural networks has potential to improve recognition of OHCA. The results are thought ...
This manuscript describes a novel approach to predict, detect, and intervene vulnerable older adults at risk of ADE using machine learning. Toxicologists' ...
In this paper, we present a detection model developed with machine learning that can help to make a quick diagnosis of respiratory complications and reduce ...
A Novel Analysis of Performance and Inference Time of Machine Learning Models to Detect Cardiovascular Emergency Situation of Rescue Patients. November 2022.
To investigate predictors and performance of machine learning algorithms designed to predict acute hospitalizations in elderly recipients of home care services.
The authors propose a binary classifier approach developed from a machine learning ensemble method to filter and dump malicious traffic.
We introduce RIMD, a novel deep learning model that consists of a decay mechanism, modular recurrent networks, and a custom loss function that learns minor ...