ArticleBiomedical optics express2026
Simultaneous dual-analyte detection biosensor through a stacking-based ensemble machine learning approach: design and optimization.
Article in Biomedical optics express, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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Who cites it
2 citing papers in PubMed.
- Multimodal Optical Biosensing for Precision Medicine and Healthcare: Introduction to the feature issue.Biomedical optics express · 2026Article
- Simultaneous dual-analyte detection biosensor through a stacking-based ensemble machine learning approach: design and optimization.Biomedical optics express · 2026Article
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Authors and funding
5 authors.
Funding
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Abstract
In this paper, a photonic crystal fiber (PCF) surface plasmon resonance (SPR) sensor capable of detecting a wide range of refractive indices (RI) from 1.15 to 1.41 is designed and optimized using a stacking-based ensemble machine learning (ML) model. The stacking model is constructed using three regression models, including the random forest regressor (RFR), the gradient boosting regressor (GBR), and the decision tree regressor (DTR). To analyze the detection capabilities and different characteristics of the sensor, the finite element method (FEM)-based perfectly matched layer (PML) is applied as a boundary condition. A chemically stable noble plasmonic material, gold (AU), with a thickness of 40 nm, is used to create the SPR effect and is placed on the outer layer of the fiber to make the sensor practically implementable. The numerical data from the FEM simulation were used to train and validate the proposed ML model. The proposed ML model provided an MSE value of 0.3584, an MAE of 0.2151, and an R
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Registered trials
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.