MFC-GAN: class-imbalanced dataset classification using multiple fake class generative adversarial network.
- Submitting institution
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Robert Gordon University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- Elyan_1
- Type
- D - Journal article
- DOI
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10.1016/j.neucom.2019.06.043
- Title of journal
- Neurocomputing
- Article number
- -
- First page
- 212
- Volume
- 361
- Issue
- -
- ISSN
- 1872-8286
- Open access status
- Compliant
- Month of publication
- July
- Year of publication
- 2019
- URL
-
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- Supplementary information
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- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
-
- Research group(s)
-
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- Citation count
- 10
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The methods developed proved to be very useful for a wide range of real-world applications (e.g. classifying symbols in engineering drawings, face image classification and others) and formed the underpinning research in the KTP with Mintra Group on developing a remote invigilation system (£173,000, grant https://info.ktponline.org.uk/action/details/partnership.aspx?id=11526).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -