Ali Maina Bukar

TL;DR: A supervised appearance model (sAM) is proposed that improves on AAM by replacing PCA with partial least-squares regression and is used for the problems of age and gender classification.

TL;DR: It is concluded that it is feasible to have a robust diagnostic machine learning model for burns assessment that can be deployed to remote locations faced with access to specialized burns specialists, thereby aiding in decision-making as quick as possible.

TL;DR: This work uses a pretrained deep residual neural network to extract features, and then utilises a sparse partial least-squares regression approach to estimate ages from the side-view of face images.

TL;DR: This work analyzes neuroscience publications affiliated with African institutions between 1996 and 2017 to provide insights into the current state of African neuroscience research in a global context.

TL;DR: The proposed pipeline achieved a state-of-the-art prediction accuracy and interestingly indicates that decision can be made in less than a minute whether the injury requires surgical intervention such as skin grafting or not.