Sensitivity Analysis of Convolutional Neural Network
Is this project an undergraduate, graduate, or faculty project?
Undergraduate
Project Type
individual
Campus
Daytona Beach
Authors' Class Standing
Annaelise Swanson, Junior
Lead Presenter's Name
Annaelise Swanson
Lead Presenter's College
DB College of Arts and Sciences
Faculty Mentor Name
Mihhail Berezovski
Abstract
This study aims to develop a Convolutional Neural Network (CNN) that is capable of accurately identifying the spectra value of materials in photographs. Due to their capacity for capturing spatial features and patterns, CNNs are very useful for the analysis of image data. By introducing noise to the images, we will be able to gauge how well the CNN performs by evaluating its sensitivity and analyzing its error loss consistency. Insights on the CNN's efficiency in evaluating picture data and its potential for use in noisy image data in the real world will be provided by the study's findings. This study could help strengthen CNNs so they can handle noisy picture data more successfully. Overall, this study highlights the importance of measuring the sensitivity of CNNs to evaluate their performance in image data analysis, particularly when dealing with noisy image data.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
Yes, SURF
Sensitivity Analysis of Convolutional Neural Network
This study aims to develop a Convolutional Neural Network (CNN) that is capable of accurately identifying the spectra value of materials in photographs. Due to their capacity for capturing spatial features and patterns, CNNs are very useful for the analysis of image data. By introducing noise to the images, we will be able to gauge how well the CNN performs by evaluating its sensitivity and analyzing its error loss consistency. Insights on the CNN's efficiency in evaluating picture data and its potential for use in noisy image data in the real world will be provided by the study's findings. This study could help strengthen CNNs so they can handle noisy picture data more successfully. Overall, this study highlights the importance of measuring the sensitivity of CNNs to evaluate their performance in image data analysis, particularly when dealing with noisy image data.