  In order to efficiently analyse the vast amount of data generated by solar
space missions and ground-base instruments, modern machine learning techniques
such as decision trees, support vector machines (SVMs) and neural networks can
be very useful. In this paper we present initial results from using a
convolutional neural network (CNN) to analyse observations from the Atmospheric
Imaging Assembly (AIA) in the 1600A wavelength. The data is pre-processed to
locate flaring regions where flare ribbons are visible in the observations. The
CNN is created and trained to automatically analyse the shape and position of
the flare ribbons, by identifying whether each image belongs into one of four
classes: two-ribbon flare, compact/circular ribbon flare, limb flare or quiet
Sun, with the final class acting as a control for any data included in the
training or test sets where flaring regions are not present. The network
created can classify flare ribbon observations into any of the four classes
with a final accuracy of 94%. Initial results show that most of the images are
correctly classified with the compact flare class being the only class where
accuracy drops below 90% ad some observations are wrongly classified as
belonging to the limb class.
