Classifying Periodic Astrophysical Phenomena from non-survey optimized variable-cadence observational data
- Submitting institution
-
Liverpool John Moores University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 951
- Type
- D - Journal article
- DOI
-
10.1016/j.eswa.2019.04.035
- Title of journal
- Expert Systems with Applications
- Article number
- -
- First page
- 94
- Volume
- 131
- Issue
- -
- ISSN
- 0957-4174
- Open access status
- Compliant
- Month of publication
- April
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
4
- Research group(s)
-
-
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This output represents the culmination of research supported by the Royal Academy of Engineering Newton Research Collaboration Programme (NRCP1617/5/67, £6000k, 2016-2017) in collaboration with Pontifícia Universidade Católica do Rio de Janeiro, Brazil. It demonstrates the power of a feature extraction method compared to feature engineering performed by previous studies, and led to keynote addresses at two international meetings - the Second International Conference on Internet of Things, Data and Cloud Computing (ICC 2017), University of Cambridge, United Kingdom and the Sixth International Conference on Digital Information Processing and Communications (ICDIPC 2016), Beirut, Lebanon.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
