Adaptive melanoma diagnosis using evolving clustering, ensemble and deep neural networks
- Submitting institution
-
University of Northumbria at Newcastle
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 25207991
- Type
- D - Journal article
- DOI
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10.1016/j.knosys.2019.06.015
- Title of journal
- Knowledge-Based Systems
- Article number
- 104807
- First page
- -
- Volume
- 187
- Issue
- -
- ISSN
- 0950-7051
- Open access status
- Compliant
- Month of publication
- June
- 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
-
2
- Research group(s)
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D - Computer Vision and Natural Computing (CVNC)
- Citation count
- 8
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The novel idea of evolving deep and ensemble neural networks was recognized by a keynote speech on “Machine Learning for Signals Analytics: Principles and Applications” in International Conference on Pattern Recognition and Machine Learning, 11-13/07/2020-China, an invited speech on “Data Modelling and Analytics: A Computational Intelligence Approach” in International Conference on Mathematical Modelling and Computational Methods in Science and Engineering, 22-24/Jan/2020-India, and guest editorialship for special issue on “IoT and AI Solutions for Smart City” in Elsevier Internet of Things (https://www.journals.elsevier.com/internet-of-things/call-for-papers/special-issue-on-iot-and-artificial-intelligence). It underpinned the award of Australian Research Council project (USD308K, 2019-2021), entitled “Quantification, optimisation, and application of deep uncertainty”.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -