Sorry, you need to enable JavaScript to visit this website.

Department of Archaeology

 
Read more at: iMapNut: Machine Learning to Map and Address Causal Factors of Child Malnutrition in Low- and- Middle- Income Countries

iMapNut: Machine Learning to Map and Address Causal Factors of Child Malnutrition in Low- and- Middle- Income Countries

This project aims to improve the poor integration of localized data linking various WASH dimensions (infrastructure, access, practices) and children nutritional status at the population level as well as the poor involvement of policy makers concerned with WASH in local and country level nutritional programs. Our network aims include:
1.

Read more at: Transitions in early stone tool technologies: a computer vision and machine learning approach

Transitions in early stone tool technologies: a computer vision and machine learning approach

The transition from Oldowan to Acheulean technologies are hypothesised to be concomitant with advances in cognition and behaviour. However, the nature of these shifts, and their cultural and evolutionary implications are poorly defined and understood. While extensive literature exists on these technologies, significant differences in research methods and traditions make comparative and comprehensive analyses problematic.