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Comparing machine learning and deep learning regression frameworks for accurate prediction of dielectrophoretic force
An intelligent sensing framework using Machine Learning (ML) and Deep Learning (DL) architectures to precisely quantify dielectrophoretic force invoked on microparticles in a textile electrode-based DEP sensing device is reported. The prediction accuracy and generalization ability of the framework w...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9279499/ https://www.ncbi.nlm.nih.gov/pubmed/35831342 http://dx.doi.org/10.1038/s41598-022-16114-5 |
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author | Ajala, Sunday Muraleedharan Jalajamony, Harikrishnan Nair, Midhun Marimuthu, Pradeep Fernandez, Renny Edwin |
author_facet | Ajala, Sunday Muraleedharan Jalajamony, Harikrishnan Nair, Midhun Marimuthu, Pradeep Fernandez, Renny Edwin |
author_sort | Ajala, Sunday |
collection | PubMed |
description | An intelligent sensing framework using Machine Learning (ML) and Deep Learning (DL) architectures to precisely quantify dielectrophoretic force invoked on microparticles in a textile electrode-based DEP sensing device is reported. The prediction accuracy and generalization ability of the framework was validated using experimental results. Images of pearl chain alignment at varying input voltages were used to build deep regression models using modified ML and CNN architectures that can correlate pearl chain alignment patterns of Saccharomyces cerevisiae(yeast) cells and polystyrene microbeads to DEP force. Various ML models such as K-Nearest Neighbor, Support Vector Machine, Random Forest, Neural Networks, and Linear Regression along with DL models such as Convolutional Neural Network (CNN) architectures of AlexNet, ResNet-50, MobileNetV2, and GoogLeNet have been analyzed in order to build an effective regression framework to estimate the force induced on yeast cells and microbeads. The efficiencies of the models were evaluated using Mean Absolute Error, Mean Absolute Relative, Mean Squared Error, R-squared, and Root Mean Square Error (RMSE) as evaluation metrics. ResNet-50 with RMSPROP gave the best performance, with a validation RMSE of 0.0918 on yeast cells while AlexNet with ADAM optimizer gave the best performance, with a validation RMSE of 0.1745 on microbeads. This provides a baseline for further studies in the application of deep learning in DEP aided Lab-on-Chip devices. |
format | Online Article Text |
id | pubmed-9279499 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-92794992022-07-14 Comparing machine learning and deep learning regression frameworks for accurate prediction of dielectrophoretic force Ajala, Sunday Muraleedharan Jalajamony, Harikrishnan Nair, Midhun Marimuthu, Pradeep Fernandez, Renny Edwin Sci Rep Article An intelligent sensing framework using Machine Learning (ML) and Deep Learning (DL) architectures to precisely quantify dielectrophoretic force invoked on microparticles in a textile electrode-based DEP sensing device is reported. The prediction accuracy and generalization ability of the framework was validated using experimental results. Images of pearl chain alignment at varying input voltages were used to build deep regression models using modified ML and CNN architectures that can correlate pearl chain alignment patterns of Saccharomyces cerevisiae(yeast) cells and polystyrene microbeads to DEP force. Various ML models such as K-Nearest Neighbor, Support Vector Machine, Random Forest, Neural Networks, and Linear Regression along with DL models such as Convolutional Neural Network (CNN) architectures of AlexNet, ResNet-50, MobileNetV2, and GoogLeNet have been analyzed in order to build an effective regression framework to estimate the force induced on yeast cells and microbeads. The efficiencies of the models were evaluated using Mean Absolute Error, Mean Absolute Relative, Mean Squared Error, R-squared, and Root Mean Square Error (RMSE) as evaluation metrics. ResNet-50 with RMSPROP gave the best performance, with a validation RMSE of 0.0918 on yeast cells while AlexNet with ADAM optimizer gave the best performance, with a validation RMSE of 0.1745 on microbeads. This provides a baseline for further studies in the application of deep learning in DEP aided Lab-on-Chip devices. Nature Publishing Group UK 2022-07-13 /pmc/articles/PMC9279499/ /pubmed/35831342 http://dx.doi.org/10.1038/s41598-022-16114-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Ajala, Sunday Muraleedharan Jalajamony, Harikrishnan Nair, Midhun Marimuthu, Pradeep Fernandez, Renny Edwin Comparing machine learning and deep learning regression frameworks for accurate prediction of dielectrophoretic force |
title | Comparing machine learning and deep learning regression frameworks for accurate prediction of dielectrophoretic force |
title_full | Comparing machine learning and deep learning regression frameworks for accurate prediction of dielectrophoretic force |
title_fullStr | Comparing machine learning and deep learning regression frameworks for accurate prediction of dielectrophoretic force |
title_full_unstemmed | Comparing machine learning and deep learning regression frameworks for accurate prediction of dielectrophoretic force |
title_short | Comparing machine learning and deep learning regression frameworks for accurate prediction of dielectrophoretic force |
title_sort | comparing machine learning and deep learning regression frameworks for accurate prediction of dielectrophoretic force |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9279499/ https://www.ncbi.nlm.nih.gov/pubmed/35831342 http://dx.doi.org/10.1038/s41598-022-16114-5 |
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