Evidence map›Paper›PMID 33426424›Full record

ArticleInternational journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management2021

Newer post-COVID perspective: Teledental encryption by de-multiplexed perceptrons.

Joydeep Dey, Arindam Sarkar, Sunil Karforma

Abstract read
In one paragraph

Article in International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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

3 authors.

Joydeep DeyDepartment of Computer Science, M.U.C. Women's College, Burdwan, WB India.
Arindam SarkarDepartment of Computer Science and Electronics, R.K.M. Vidyamandira, Belur, WB India.
Sunil KarformaDepartment of Computer Science, The University of Burdwan, Burdwan, WB India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper presents an efficient mechanism for secured encryption of intraoral information in the emerging field of Teledental. Due to global rapid surge in the (Coronavirus Disease) COVID patients, the services of Teledental are best suited in the newer post-COVID era. A devised perceptron has been intelligently embedded with de-multiplexing ability to transmit data to the dentists has been proposed. Exact session key has been developed through learning rules applied on the perceptrons by both the patient and dentist. For simplicity, gingivitis data is highly recommended to transmit in a highly secured manner with patients' data integrity. Gingivitis is an important dental disease which is primarily caused by the bacterial colonization. It shows gum bleeding and inflammations in the gingiva. Encrypted transmission is required to the Dentist for early diagnosis and treatment in Teledental system in this pandemic context. Gingivitis data are then broken into parts by the demultiplexer followed by individual proposed header generation. It is predominantly done to confuse the intruders about the originality of the intraoral data. Chi-square, Avalanche, Strict Avalanche, etc. were carried on the proposed partial shares to generate good outcomes when compared to classical algorithms. To confuse the intruders, character frequency, floating frequency, and autocorrelation were tested extensively. It is a newer approach to avail the secured Teledental features in post-COVID time.

Indexed as

COVIDDemultiplexingGingivitisPerceptronTeledental

Identifiers

PMID33426424
PMCPMC7776307

What OpenQuestion holds

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Registered trials

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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.