Recent Releases of fedsa_public
fedsa_public - davidalejandromiranda/fedsa_public: Exploring the breast cancer risk detection by dynamic light scattering
This project showcases the data processing techniques employed in Field Effect Detection by Spectral Analysis (FEDSA). The experimental data obtained from particles in suspension is treated as dynamic light scattering data to estimate particle sizes. By analyzing the power spectra data in different frequency bands, we investigate the potential of predicting the risk of cancer. The Jupyter Notebook titled data_processing.ipynb allows users to visualize the experimental data and explore the multivariate analysis and logistic regression modeling of power spectrum bands. This repository complements the associated research paper and provides a comprehensive overview of the methodology and findings of the study.
- Jupyter Notebook
Published by davidalejandromiranda over 2 years ago
fedsa_public - Exploring the breast cancer risk detection by dynamic light scattering
This project showcases the data processing techniques employed in Field Effect Detection by Spectral Analysis (FEDSA). The experimental data obtained from particles in suspension is treated as dynamic light scattering data to estimate particle sizes. By analyzing the power spectra data in different frequency bands, we investigate the potential of predicting the risk of cancer. The Jupyter Notebook titled data_processing.ipynb allows users to visualize the experimental data and explore the multivariate analysis and logistic regression modeling of power spectrum bands. This repository complements the associated research paper and provides a comprehensive overview of the methodology and findings of the study.
- Jupyter Notebook
Published by davidalejandromiranda over 2 years ago
fedsa_public - Exploring the breast cancer risk detection by dynamic light scattering
This project showcases the data processing techniques employed in Field Effect Detection by Spectral Analysis (FEDSA). The experimental data obtained from particles in suspension is treated as dynamic light scattering data to estimate particle sizes. By analyzing the power spectra data in different frequency bands, we investigate the potential of predicting the risk of cancer. The Jupyter Notebook titled data_processing.ipynb allows users to visualize the experimental data and explore the multivariate analysis and logistic regression modeling of power spectrum bands. This repository complements the associated research paper and provides a comprehensive overview of the methodology and findings of the study.
- Jupyter Notebook
Published by davidalejandromiranda over 2 years ago