About: E-Quarantine: A Smart Health System for Monitoring Coronavirus Patients for Remotely Quarantine

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: E-Quarantine: A Smart Health System for Monitoring Coronavirus Patients for Remotely Quarantine Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	E-Quarantine: A Smart Health System for Monitoring Coronavirus Patients for Remotely Quarantine
Creator	Hassanein, Aboul Hassanien, Ehab Hussein, Walaa Mohey El-Din, Doaa
Source	ArXiv
abstract	Coronavirus becomes officially a global pandemic due to the speed spreading off in various countries. An increasing number of infected with this disease causes the Inability problem to fully care in hospitals and afflict many doctors and nurses inside the hospitals. This paper proposes a smart health system that monitors the patients holding the Coronavirus remotely. Due to protect the lives of the health services members (like physicians and nurses) from infection. This smart system observes the people with this disease based on putting many sensors to record many features of their patients in every second. These parameters include measuring the patient's temperature, respiratory rate, pulse rate, blood pressure, and time. The proposed system saves lives and improves making decisions in dangerous cases. It proposes using artificial intelligence and Internet-of-things to make remotely quarantine and develop decisions in various situations. It provides monitoring patients remotely and guarantees giving patients medicines and getting complete health care without anyone getting sick with this disease. It targets two people's slides the most serious medical conditions and infection and the lowest serious medical conditions in their houses. Observing in hospitals for the most serious medical cases that cause infection in thousands of healthcare members so there is a big need to uses it. Other less serious patients slide, this system enables physicians to monitor patients and get the healthcare from patient's houses to save places for the critical cases in hospitals.
has issue date	2020-05-05(xsd:dateTime)
has license	arxiv
sha1sum (hex)	4f613f2e7af41cfe3b352cc8dadbf1a76daceb68
resource representing a document's title	E-Quarantine: A Smart Health System for Monitoring Coronavirus Patients for Remotely Quarantine
resource representing a document's body	covid:4f613f2e7af41cfe3b352cc8dadbf1a76daceb68#body_text
is schema:about of	named entity 'coronavirus' named entity 'Coronavirus' named entity 'making decisions' named entity 'respiratory rate' named entity 'pulse rate' named entity 'data sources' named entity 'anomaly detection' named entity 'neurons' named entity 'missing data' named entity 'machine learning algorithms' named entity 'infection' named entity 'blood pressure' named entity 'natural language processing' named entity 'hidden units' named entity 'big data' named entity 'infection' named entity 'heart rate' named entity 'feature learning' named entity 'Big Data analytics' named entity 'gradient flow' named entity 'predictive analysis' named entity 'recurrent neural network' named entity 'Long Short-Term Memory' named entity 'time-series' named entity 'big data analysis' named entity 'time domain' named entity 'RNN' named entity 'combining rule' named entity 'feature extraction' named entity 'infection' named entity 'backpropagation' named entity 'algorithm' named entity 'COVID 19' named entity 'recurrent neural network' named entity 'DNN' named entity 'LSTM' named entity 'time domain' named entity 'LSTM' named entity 'deep learning' named entity 'respiratory rate' named entity 'learning rate' named entity 'flowchart' named entity 'deep learning' named entity 'training data' named entity 'training dataset' named entity 'Deep learning' named entity 'data set' named entity 'data type' named entity 'time-series data' named entity 'RNN' named entity 'classifiers' named entity 'Data visualization' named entity 'blood pressure' named entity '6, 7, 8' named entity 'convolutional neural network' named entity 'DNN' named entity 'high-risk' named entity 'feature vectors' named entity 'Heart rate' named entity 'time-series data' named entity 'respiratory rate' named entity 'training data' named entity 'internet-of-things' named entity 'PH level' named entity 'CNN' named entity 'neural network' named entity 'evolution' named entity 'respiratory rate' named entity 'RNN' named entity 'sensor' named entity 'LSTM' named entity 'LSTM' named entity 'artificial neural networks' named entity 'data augmentation' named entity 'Dempster-Shafer' named entity 'skewness' named entity 'virus' named entity 'LSTM' named entity 'feature extraction' named entity 'Algorithm' named entity 'IoT' named entity 'immune defenses' named entity 'deep learning' named entity 'LSTM'

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software