Machine learning methods for estimating bent photonic crystal fiber based SPR sensor properties.

Cem Kalyoncu, Ahmet Yasli, Huseyin Ademgil

Open access · goldAbstract read

In one paragraph

Article in Heliyon, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it

0cells of the map it votes in

3citing papers in PubMed

2.3field-weighted citation impact, top 11% 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

3 citing papers in PubMed, 31 citations in OpenAlex.

Ultra-sensitive graphene-gold hybrid dual core photonic crystal fiber sensor based on surface plasmon resonance for bio-analyte detection.Scientific reports · 2026
Article
Machine learning-driven optimization of monolithic gold plasmonic sensors: Achieving ultrahigh sensitivity with interpretable linear models.PloS one · 2026
Article
ResNet14Attention network for identifying the titration end-point of potassium dichromate.Heliyon · 2023
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

3 authors at 1 institution in 1 country.

Cem KalyoncuEuropean University of Lefke, Faculty of Engineering, Computer Engineering Department, Lefke, TR-10, Turkey.

Ahmet YasliEuropean University of Lefke, Faculty of Engineering, Electrical and Electronics Engineering Department, Lefke, TR-10, Turkey.

Huseyin AdemgilEuropean University of Lefke, Faculty of Engineering, Computer Engineering Department, Lefke, TR-10, Turkey.

European University of Lefke · TR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the recent years, the use of machine learning approaches in optical devices and fibers is increasing. However, most methods concentrate on the use of Artificial Neural Network (ANN) methods due to the ability of automatically fitting to the problem. In this work, a classical non-linear regression method, namely k-Nearest Neighbor Regression (KNNR) is proposed for determining the loss characteristics of a photonic crystal fiber (PCF) based surface plasmon resonance (SPR) sensor in the presence of a bend in either

Indexed as

k-nearest neighbor regressionMachine learningOptical sensingPhotonic crystal fiber sensorSurface plasmon resonance

Identifiers

PMID36406686

DOI10.1016/j.heliyon.2022.e11582

PMCPMC9667262

OpenAlexW4308661038

What OpenQuestion holds

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LicenceCC BY

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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.