Reduction in sensor response time using long short-term memory network forecasting

Simon J. Ward; Sharon M. Weiss

doi:10.1117/12.2676836

4 October 2023 Reduction in sensor response time using long short-term memory network forecasting

Simon J. Ward, Sharon M. Weiss

Proceedings Volume 12675, Applications of Machine Learning 2023; 126750E (2023) https://doi.org/10.1117/12.2676836
Event: SPIE Optical Engineering + Applications, 2023, San Diego, California, United States

Abstract

We report a generalizable computational approach to dramatically reduce biomolecular and chemicalsensor response time for applications including medical diagnostics. Comparing the performance of different models, we use experimental data to train ensembles of both traditional recurrent neural networks (RNN) and long short-term memory (LSTM) networks, to accurately predict equilibrium sensor response from data measured over a short time span. This approach is particularly advantageous for sensor platforms with long response times due to poor mass transport, including porous silicon optical biosensors, which we use to validate this methodology through exposure to various concentrations of protein solution and subsequent analysis.

Conference Presentation

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Simon J. Ward and Sharon M. Weiss "Reduction in sensor response time using long short-term memory network forecasting", Proc. SPIE 12675, Applications of Machine Learning 2023, 126750E (4 October 2023); https://doi.org/10.1117/12.2676836

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