Evolving Ensemble Models for Image Segmentation Using Enhanced Particle Swarm Optimization
- Submitting institution
-
University of Northumbria at Newcastle
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 22062113
- Type
- D - Journal article
- DOI
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10.1109/ACCESS.2019.2903015
- Title of journal
- IEEE Access
- Article number
- -
- First page
- 34004
- Volume
- 7
- Issue
- -
- ISSN
- 2169-3536
- Open access status
- Compliant
- Month of publication
- March
- 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
- No
- Number of additional authors
-
5
- Research group(s)
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C - Digital Learning Laboratory (DLL)
- Citation count
- 21
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The conclusion of this research is the generation of diverse evolving deep neural networks using a novel evolutionary algorithm to tackle different image segmentation problems with diverse noisy and unstructured data distributions. The findings led to an invitation to organize a special session on Deep Processing of Unstructured Data for the IEEE-sponsored International Conference on Machine Learning and Cybernetics, 2019. This work was supported partly by the European Union (EU) sponsored (Erasmus Mundus) cLINK (Centre of excellence for Learning, Innovation, Networking and Knowledge) Project EU under Grant 2645 totalling €2.5 million, in collaboration with 6 EU and 8 Asian partners.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -