Project: To better understand the impact of demographics on mental illness by understanding the relationship between the demographic variables, the disease, and the connectomic data.
Outcome: Fellows Joseph and Anisie were able build a model which successfully identified key features, such as age and sex which contribute to the predictions of their model.
Project: Victoire Djimna worked to provide insights on how Simprint’s fingerprint technology was being used and its effectiveness as a biometric tool for identification.
Outcome: The analysis that Victoire delivered enabled Simprints to evaluate how their technology was performing, to make improvements to project operations on the ground and also to influence the design of future projects.
Project: Faith Benson and Cyrille Feudjio developed an unsupervised machine learning model to predict when parts are likely to fail within a hydroelectric power plant.
Outcome: Identify anomalies at certain times and also identify abnormal patterns which are likely to be the cause of the anomalies detected.
Project: Fellows Bright Aboh and Aimable Ishimwe Manzi developed a system to remotely identify tobacco fields using satellite imagery data aiming to improve the socio-economic wellbeing of the farmers by suggesting best practices.
Outcome: Model that could identify tobacco leaves and therefore tobacco farms and also measure the land surface which the crop was being grown on.
Project: Sylvera Massawe and Aurelie Jodelle Kemme designed a data-driven system that could create targeted recommendations of suitable products to tails.com customers, aiming to introduce new products to customers.
Outcome: Developed a recommender system targeting products to pre-existing tails.com customers.