Evidence map›Paper›PMID 37928011›Full record

ArticleHeliyon2023

Hybrid deep learning model based smart IOT based monitoring system for Covid-19.

Liping Yu, M M Vijay, J Sunil, V G Anisha Gnana Vincy, Vediyappan Govindan, M Ijaz Khan, Shahid Ali, Nissren Tamam, Barno Sayfutdinovna Abdullaeva

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.9field-weighted citation impact, top 24% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 4 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 7 institutions in 6 countries.

Liping YuSchool of Computer Science and Technology, Shandong Technology and Business University, Yantai, 264005, China.
M M VijaySCAD College of Engineering and Technology, Tirunelveli, India.
J SunilDepartment of Computer Science and Engineering, Annai Vailankanni College of Engineering, Kanyakumari, India.
V G Anisha Gnana VincyDepartment of Computer Science and Engineering, Danish Ahmed College of Engineering, India.
Vediyappan GovindanDepartment of Mathematics, Hindustan Institute of Technology and Science (Deemed to be University), Padur, Kelambakkam, 603103, India.
M Ijaz KhanDepartment of Mechanical Engineering, Lebanese American University, Kraytem, Beirut, 1102-2801, Lebanon.
Shahid AliSchool of Electronics Engineering Peking University, Beijing, China.
Nissren TamamDepartment of Physics, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
Barno Sayfutdinovna AbdullaevaPedagogical Sciences, Vice-Rector for Scientific Affairs, Tashkent State Pedagogical University, Tashkent, Uzbekistan.
Hindustan Institute of Technology and Science · INNational Pedagogical University of Uzbekistan · UZPeking University · CNPrincess Nourah bint Abdulrahman University · SARiphah International University · PKShandong Institute of Business and Technology · CNSocial Change and Development · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recently, COVID-19 becomes a hot topic and explicitly made people follow social distancing and quarantine practices all over the world. Meanwhile, it is arduous to visit medical professionals intermittently by the patients for fear of spreading the disease. This IoT-based healthcare monitoring system is utilized by many professionals, can be accessed remotely, and provides treatment accordingly. In context with this, we designed an IoT-based healthcare monitoring system that sophisticatedly measures and monitors the parameters of patients such as oxygen level, blood pressure, temperature, and heart rate. This system can be widely used in rural areas that are linked to the nearest city hospitals to monitor the patients. The collected data from the monitoring system are stored in the cloud-based data storage and for the classification our approach proposes an innovative Recurrent Convolutional Neural Network (RCNN) based Puzzle optimization algorithm (PO). Based on the outcome further treatments are made with the assistance of physicians. Experimental analyses are made and analyzed the performance with state-of-art works. The availability of more data storage capacity in the cloud can make physicians access the previous data effortlessly.

Indexed as

Healthcare monitoring systemIoT monitoring systemOxygen levelPuzzle optimization algorithmQuarantine practices

Identifiers

PMID37928011
PMCPMC10623272
OpenAlexW4387747871

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.