Projects per year
Personal profile
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
Education/Academic qualification
Doctor of Philosophy, The flare necessities: machine learning tools for solar flare data analysis, University of Glasgow
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Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
Projects
- 1 Finished
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From Pixels to Particles: A Computational Toolbox for Predicting Pharmaceutical Powder Characteristics
Salehian, M. (Principal Investigator), Armstrong, J. (Principal Investigator) & Boyle, C. (Principal Investigator)
1/09/23 → 1/09/24
Project: Internally funded project
Research output
- 2 Article
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Flexible modelling of the dissolution performance of directly compressed tablets
Maclean, N., Armstrong, J. A., Carroll, M. A., Salehian, M., Mann, J., Reynolds, G., Johnston, B. & Markl, D., 10 May 2024, In: International Journal of Pharmaceutics. 656, 10 p., 124084.Research output: Contribution to journal › Article › peer-review
Open AccessFile4 Citations (Scopus)75 Downloads (Pure) -
Predicting pharmaceutical powder flow from microscopy images using deep learning
Wilkinson, M. R., Pereira Diaz, L., Vassileiou, A. D., Armstrong, J. A., Brown, C. J., Castro-Dominguez, B. & Florence, A. J., 1 Apr 2023, In: Digital Discovery. 2, 2, p. 459-470 12 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile2 Citations (Scopus)63 Downloads (Pure)
Datasets
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Data for: "Flexible Modelling of the Dissolution Performance of Directly Compressed Tablets"
Maclean, N. (Creator), Armstrong, J. (Creator), Carroll, M. (Contributor), Salehian, M. (Contributor), Mann, J. (Contributor), Reynolds, G. (Contributor), Johnston, B. (Supervisor) & Markl, D. (Supervisor), University of Strathclyde, 5 Apr 2024
DOI: 10.15129/0de30626-a898-42a5-9548-342a748062bc
Dataset
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Data for: "Deep Learning approaches for the prediction of powder flow of pharmaceutical materials"
Pereira-Diaz, L. (Creator), Brown, C. (Supervisor), Florence, A. (Supervisor), Vassileiou, A. (Contributor) & Armstrong, J. (Contributor), University of Strathclyde, 27 Sept 2022
DOI: 10.15129/a45be5c3-4f8b-4d43-9b29-c4f4222a7716
Dataset