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Identifying extra-terrestrial intelligence using machine learning

Gutowska, Małgorzata, Scriney, Michael orcid logoORCID: 0000-0001-6813-2630 and McCarren, Andrew orcid logoORCID: 0000-0002-7297-0984 (2020) Identifying extra-terrestrial intelligence using machine learning. In: 27th AIAI Irish Conference on Artificial Intelligence and Cognitive Science, 5 - 6 Dec 2019, Galway, Ireland.

Abstract
Since the date of establishment of the SETI Institute, its scientists have used various approaches in their search for extra-terrestrial intelligence (SETI). A novel idea involved image categorisation techniques in classifying radio signals represented by 2D spectrograms. The dataset of simulated radio signals, created for classification purposes have been used in this work to train models based on neural network architectures. It is shown in this paper that combining three different models, trained on features obtained by various techniques, has a positive impact on model accuracy and performance. Features learned by a convolutional neural network (CNN), bottleneck features from existing models and manually extracted features from the spectrograms comprised the three feature sets used as training data for the combined model. It was also shown that combining different methods of spectrogram generation resulted in improving the accuracy of the final model.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Convolutional Neural Network; Image Processing; Spectrograms
Subjects:Computer Science > Image processing
Computer Science > Machine learning
Physical Sciences > Astronomy
Physical Sciences > Spectrum analysis
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Published in: Proceedings for the 27th AIAI Irish Conference on Artificial Intelligence and Cognitive Science. 2563. CEUR-WS.
Publisher:CEUR-WS
Official URL:http://ceur-ws.org/Vol-2563/aics_28.pdf
Copyright Information:© 2019 The Authors. CC-BY 4.0
ID Code:24020
Deposited On:07 Oct 2020 12:43 by Malgorzata Gutowska . Last Modified 07 Oct 2020 12:43
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