Combining deep residual network features with supervised machine learning algorithms to classify diverse food image datasets
- Submitting institution
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University of Ulster
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 76465673
- Type
- D - Journal article
- DOI
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10.1016/j.compbiomed.2018.02.008
- Title of journal
- Computers in Biology and Medicine
- Article number
- -
- First page
- 217
- Volume
- 95
- Issue
- -
- ISSN
- 0010-4825
- Open access status
- Compliant
- Month of publication
- February
- Year of publication
- 2018
- URL
-
-
- 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
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3
- Research group(s)
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C - Pervasive Computing Research Centre
- Citation count
- 18
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- <28> This work was used to help Ulster in their award of a successfully funded Horizon2020 MSCA-RISE project named the STOP Obesity Platform (Grant No. 823978, 2019-2023, http://stopproject.eu). The EU project involves partners from 4 EU countries. The first author (McAllister, then a DfE funded PhD student) of the paper went on to become a KTP associate (Verbal Arts Centre - www.theverbal.co, James Kerr, KTP011070, 2018-2020) in the area of digital health and the KTP achieved the highest grade possible from InnovateUK (‘outstanding’).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -