Code and data supporting 'A Comprehensive Analysis of Machine Learning and Deep Learning Models for Identifying Pilots' Mental States from Imbalanced Physiological Data'

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dc.contributor.authorAlreshidi, Ibrahim
dc.contributor.authorMoulitsas, Irene
dc.contributor.authorJenkins, Karl
dc.contributor.authorYadav, Satendra
dc.date.accessioned2024-06-03T06:46:22Z
dc.date.available2024-06-03T06:46:22Z
dc.date.issued2023-09-18 16:40
dc.description.abstractData: This folder contains: - A dataset called combined_df4, which contains the power spectral density features after employing SMOTE. - A dataset called combined_df5, which contains the power spectral density features after employing SMOTE and cosine similarity. Source code: This folder contains: - A jupyter notebook called AdaBoost.ipynb which was used to generate the results for the AdaBoost algorithm. - A jupyter notebook called CNN.ipynb which was used to generate the results for the CNN algorithm. - A jupyter notebook called CNN+LSTM.ipynb which was used to generate the results for the CNN+LSTMalgorithm. - A jupyter notebook called LSTM.ipynb which was used to generate the results for the LSTMalgorithm. - A jupyter notebook called FNN.ipynb which was used to generate the results for the FNN algorithm. - A jupyter notebook called Random_Forest.ipynb which was used to generate the results for the Random Forest algorithm. - A jupyter notebook called XGBoost.ipynb which was used to generate the results for the XGBoost algorithm.
dc.identifier.citationAlreshidi, Ibrahim; Moulitsas, Irene; Jenkins, Karl; Yadav, Satendra (2023). Code and data supporting 'A Comprehensive Analysis of Machine Learning and Deep Learning Models for Identifying Pilots' Mental States from Imbalanced Physiological Data'. Cranfield Online Research Data (CORD). Software. https://doi.org/10.17862/cranfield.rd.24156345
dc.identifier.doi10.17862/cranfield.rd.24156345
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/21793
dc.publisherCranfield University
dc.relation.supplementshttps://doi.org/10.2514/6.2023-4529'
dc.rightsMIT
dc.rights.urihttps://opensource.org/licenses/MIT
dc.subjectMachine Learning'
dc.subject'Pilot Deficiencies'
dc.subject'Mental State Classifications'
dc.titleCode and data supporting 'A Comprehensive Analysis of Machine Learning and Deep Learning Models for Identifying Pilots' Mental States from Imbalanced Physiological Data'
dc.typeSoftware

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