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Dataset in support of the Southampton doctoral thesis 'Machine learning for optimal unexploded ordnance mitigation'

Dataset in support of the Southampton doctoral thesis 'Machine learning for optimal unexploded ordnance mitigation'
Dataset in support of the Southampton doctoral thesis 'Machine learning for optimal unexploded ordnance mitigation'
This dataset contains :Python code for Magnetic Modeling and Machine Learning of Spheroids The code is sub-divided into 3 folders: Custom Packages: These are packages which will be called by the other scripts. Forward Modelling: This folder contains code for three stages. - 01_Forward_Model_Perfect_Data: Use the parameter input file to determine the range of parameters to create in the dataset -02_Realistic_Data_Conversion.py: Converts the newly created perfect dataset into a realistic dataset. The user can define multiple parameters within this script. - 03_Feature_Extraction.py: This will extract features from the gridded data to be used in the machine learning. Machine Learning: This folder contains code set up to train the Random Forest and Deep Learning examples models of both regression and classification.
University of Southampton
Underhay, Sara Lise Macdonald
f7c29f8f-6aab-4d06-814e-def0d25bcf98
Underhay, Sara Lise Macdonald
f7c29f8f-6aab-4d06-814e-def0d25bcf98

Underhay, Sara Lise Macdonald (2024) Dataset in support of the Southampton doctoral thesis 'Machine learning for optimal unexploded ordnance mitigation'. University of Southampton doi:10.5258/SOTON/D2786 [Dataset]

Record type: Dataset

Abstract

This dataset contains :Python code for Magnetic Modeling and Machine Learning of Spheroids The code is sub-divided into 3 folders: Custom Packages: These are packages which will be called by the other scripts. Forward Modelling: This folder contains code for three stages. - 01_Forward_Model_Perfect_Data: Use the parameter input file to determine the range of parameters to create in the dataset -02_Realistic_Data_Conversion.py: Converts the newly created perfect dataset into a realistic dataset. The user can define multiple parameters within this script. - 03_Feature_Extraction.py: This will extract features from the gridded data to be used in the machine learning. Machine Learning: This folder contains code set up to train the Random Forest and Deep Learning examples models of both regression and classification.

Archive
Underhay_PythonCode.zip - Software
Restricted to System admin until 12 September 2024.
Available under License Creative Commons Attribution.
Text
thesis_readme.txt - Dataset
Available under License Creative Commons Attribution.
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More information

Published date: 12 September 2024
Related URLs:
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Identifiers

Local EPrints ID: 489057
URI: http://eprints.soton.ac.uk/id/eprint/489057
DOI: doi:10.5258/SOTON/D2786
PURE UUID: d4d7248d-7a2b-4a3a-b9ba-fdba9a499091
ORCID for Sara Lise Macdonald Underhay: ORCID iD orcid.org/0000-0002-2833-4366

Catalogue record

Date deposited: 11 Apr 2024 17:21
Last modified: 11 Apr 2024 17:22

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Contributors

Creator: Sara Lise Macdonald Underhay ORCID iD

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