Evidence map›Paper›PMID 34664014›Full record

ArticleJournal of signal processing systems2022

iBlock: An Intelligent Decentralised Blockchain-based Pandemic Detection and Assisting System.

Bhaskara S Egala, Ashok K Pradhan, Venkataramana Badarla, Saraju P Mohanty

Abstract read
In one paragraph

Article in Journal of signal processing systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Review
  2. Article
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

4 authors.

Bhaskara S EgalaDepartment of Computer Science & Engineering, School of Engineering and Applied Science at SRM University, Amaravati, AP India.
Ashok K PradhanDepartment of Computer Science & Engineering, School of Engineering and Applied Science at SRM University, Amaravati, AP India.
Venkataramana BadarlaDepartment of Computer Science & Engineering, Indian Institute of Technology (IIT), Tirupati, AP India.
Saraju P MohantyDepartment of Computer Science & Engineering, University of North Texas, Denton, TX USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The recent COVID-19 outbreak highlighted the requirement for a more sophisticated healthcare system and real-time data analytics in the pandemic mitigation process. Moreover, real-time data plays a crucial role in the detection and alerting process. Combining smart healthcare systems with accurate real-time information about medical service availability, vaccination, and how the pandemic is spreading can directly affect the quality of life and economy. The existing architecture models are become inadequate in handling the pandemic mitigation process using real-time data. The present models are server-centric and controlled by a single party, where the management of confidentiality, integrity, and availability (CIA) of data is doubtful. Therefore, a decentralised user-centric model is necessary, where the CIA of user data is assured. In this paper, we have suggested a decentralized blockchain-based pandemic detection and assistance system (iBlock). The iBlock uses robust technologies like hybrid computing and IPFS to support system functionality. A pseudo-anonymous personal identity is introduced using H-PCS and cryptography for anonymous data sharing. The distributed data management module guarantees data CIA, security, and privacy using cryptography mechanisms. Furthermore, it delivers useful intelligent information in the form of suggestions and alerts to assist the users. Finally, the iBlock reduces stress on healthcare infrastructure and workers by providing accurate predictions and early warnings using AI/ML.

Indexed as

Artificial Intelligence (AI) / Machine Learning (ML)BlockchainCollaborated Medical Database (CMD)Fog ComputingHealth Cyber-Physical Systems (H-CPS)Pandemic Detection and Control

Identifiers

PMID34664014
PMCPMC8515159

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.