Mohammad Majid al-Rifaie

Mohammad Majid al-Rifaie, Tim Blackwell

The use of ray projections to reconstruct images is a common technique in medical imaging. Dealing with incomplete data is particularly important when a patient is vulnerable to potentially damaging radiation or is unable to cope with the long scanning time. This paper utilises the reformulation of the problem into an optimisation tasks, followed by using a swarm-based reconstruction from highly undersampled data where particles move in image space in an attempt to minimise the reconstruction error. The process is prone to noise and, in addition to the recently introduced search space expansion technique, a further smoothing process, total variation regularisation, is adapted and investigated. The proposed method is shown to produce lower reproduction errors compared to standard tomographic reconstruction toolbox algorithms as well as one of the leading high-dimensional optimisers on the clinically important Shepp-Logan phantom.

Mohammad Majid al-Rifaie, Marc Cavazza

Customisation in food properties is a challenging task involving optimisation of the production process with the demand to support computational creativity which is geared towards ensuring the presence of alternatives. This paper addresses the personalisation of beer properties in the specific case of craft beers where the production process is more flexible. We investigate the problem by using three swarm intelligence and evolutionary computation techniques that enable brewers to map physico-chemical properties to target organoleptic properties to design a specific brew. While there are several tools, using the original mathematical and chemistry formulas, or machine learning models that deal with the process of determining beer properties based on the pre-determined quantities of ingredients, the next step is to investigate an automated quantitative ingredient selection approach. The process is illustrated by a number of experiments designing craft beers where the results are investigated by "cloning" popular commercial brands based on their known properties. Algorithms performance is evaluated using accuracy, efficiency, reliability, population-diversity, iteration-based improvements and solution diversity. The proposed approach allows for the discovery of new recipes, personalisation and alternative high-fidelity reproduction of existing ones.