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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.01854 |
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| _version_ | 1866916272915087360 |
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| author | Bouzouita, Manel Zayer, Fakhreddine Tzouvadaki, Ioulia Carrara, Sandro Belgacem, Hamdi |
| author_facet | Bouzouita, Manel Zayer, Fakhreddine Tzouvadaki, Ioulia Carrara, Sandro Belgacem, Hamdi |
| contents | This paper presents a novel methodology for modeling memristive biosensing within COMSOL Multiphysics, focusing on critical performance metrics such as antigen-antibody binding concentration and output resistive states. By studying the impact of increasing inlet concentrations, insights into binding concentration curve and output resistance variations are uncovered. The resultant simulation data effectively trains a support vector machine classifier (SVMC), achieving a remarkable accuracy rate of 97%. The incorporation of artificial intelligence (AI) through SVM demonstrates promising strides in advancing AI-based memristive biosensing modeling, potentially elevating their performance standards and applicability across diverse domains. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_01854 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring Memristive Biosensing Dynamics: A COMSOL Multiphysics Approach Bouzouita, Manel Zayer, Fakhreddine Tzouvadaki, Ioulia Carrara, Sandro Belgacem, Hamdi Applied Physics This paper presents a novel methodology for modeling memristive biosensing within COMSOL Multiphysics, focusing on critical performance metrics such as antigen-antibody binding concentration and output resistive states. By studying the impact of increasing inlet concentrations, insights into binding concentration curve and output resistance variations are uncovered. The resultant simulation data effectively trains a support vector machine classifier (SVMC), achieving a remarkable accuracy rate of 97%. The incorporation of artificial intelligence (AI) through SVM demonstrates promising strides in advancing AI-based memristive biosensing modeling, potentially elevating their performance standards and applicability across diverse domains. |
| title | Exploring Memristive Biosensing Dynamics: A COMSOL Multiphysics Approach |
| topic | Applied Physics |
| url | https://arxiv.org/abs/2406.01854 |